Category: Corona Virus

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The unequal effects of the healtheconomy trade-off during the … – Nature.com

November 17, 2023

We build a data-driven, granular ABM of the New YorkNewarkJersey City, NYNJPA metro area. The main agents of the model are the 416,442 individuals of a synthetic population that is representative of the real population across multiple socioeconomic characteristics, including household composition, age, income, occupation and possibility to work from home (WFH) (for a schematic representation, see Fig. 1, and for a detailed description of the model, see Methods and Supplementary Information).

The economic module depicts the flow of goods and services between industries and from industries to final consumers (inputoutput network), while the epidemic module tracks pathogen exposure at workplaces, community/consumption venues, schools and households (contact network). Agents display high heterogeneity across various socioeconomic characteristics (see box). The economic and epidemic modules are closely linked: the epidemic module impacts economic outcomes by reducing consumption due to infection fear, while the economic model influences epidemic spread by altering workplace and community contacts through employment changes in different industries.

The epidemic module of the ABM is built on the contact network that connects synthetic individuals. This network has multiple layers, where each layer captures interactions occurring (1) in the household, (2) in school, (3) in the workplace and (4) in the community (during on-site consumption, such as in shops, restaurants or movie theatres). Epidemic propagation occurs on these networks, built via anonymized, privacy-enhanced mobility data from opted-in users, which inform workplace and community interactions. These data, collected through a General Data Protection Regulation (GDPR)-compliant framework by Cuebiq, provide daily workplace visitation patterns and estimates for colocation probabilities in community spaces, based on a Foursquare dataset. The ABM employs a stochastic, discrete-time disease transmission model on the contact network and synthetic population, with individuals transitioning between epidemic states based on key time-to-event intervals (for example, incubation period, generation time and so on) derived from SARS-CoV-2 transmission data.

From an economic point of view, individuals play a role both as workers and consumers. They work in one of multiple industries, producing goods and services that are either sold to other industries as intermediate products or sold to final consumers as consumption products. The economic module specifically emphasizes employment and consumption. In particular, hiring and firing decisions are driven by industry workforce requirements, closures of economic activities and possibility of remote work. Consumption patterns vary among agents based on age and income, and dynamically adjust in response to the evolving state of the pandemic. Specifically, households tend to curtail their demand for services from customer-facing industries because of the fear of the disease (customer-facing industries are entertainment, accommodationfood, other services, retail, transportation, health and education; Supplementary Section 3.1.3). The model also considers the inputoutput network of intermediates that industries use to produce final goods and services18, leading to the propagation of COVID-19 shocks to the entire economy.

We calibrate the models key parameters, including the parameter regulating the behavioural changes, dubbed fear of infection, to fit crucial epidemic and economic statistics from the first wave in the NY metro area (Supplementary Section 4). Epidemiological parameters are adjusted to fit ancestral SARS-CoV-2 lineages (Supplementary Table 5). Simulations start on 12 February 2020, protective measures are imposed on 16 March and relaxed on 15 May 15, and simulations end on 30 June. As protective measures, we close schools, mandate WFH and shut down all non-essential economic activities, such as entertainment and most of the accommodationfood industry, but also large parts of manufacturing and construction. We use the official NY regulations to estimate the degree to which a given industry is essential (Supplementary Section 3.2.1) and assume that workers who can WFH are not directly affected by these closures2. We name this set of assumptions the empirical scenario.

Our model accurately matches the six official economic statistics we calibrated it on (Fig. 2a). It correctly reproduces the fact that employment declined more strongly than gross domestic product (GDP) (this is because industries most affected by shutdown orders produce less output per worker). It also correctly reproduces the fact that consumption of goods and services produced by customer-facing industries declined more strongly than consumption of goods and services produced by industries that are not customer facing, ranging from manufacturing products to utilities and financial services (Supplementary Fig. 15).

a,b, Statistics that were directly targeted in the parameter calibration. a, The percentage change from OctoberDecember 2019 (2019Q4) to AprilMay 2020 (2020Q2) across six official economic statistics, in the model and in the data (Supplementary Section 4). Here, and throughout the paper, we report the mean and error bars (2.597.5 percentile range) across simulation runs that differ by stochastic factors (Methods). b, A comparison between model and data for the number of COVID-19 deaths, which is the key epidemic statistic that we targeted. ce, Validation results for statistics that were not directly targeted. c, Employment in April 2020 as a percentage of employment in February 2020, across the main two-digit NAICS industries, in the model and in the data. The circle size is proportional to employment in February 2020. d, Employment and consumption, in the model and in the data3, among low-income and high-income households (low income <$27,000 and high income >$60,000; these bands are chosen for comparison to real data; Supplementary Section 5.1.1). e, The ratio between community contacts with infectious individuals (Supplementary Section 5.2) before and after the imposition of protective measures, in the model and in the data, for the seven customer-facing industries (Supplementary Section 3.1.3). The circle size is proportional to the share of pre-pandemic contacts.

Our model can also be validated against empirical properties that were not directly targeted in the parameter calibration procedure (Fig. 2c,d). First, thanks to our estimate of pandemic shocks2 and shock propagation model17, we are able to recover industry-specific changes in employment induced by the pandemic (Fig. 2c), with a Pearson correlation coefficient of 0.82 (P value 2104) between model and data. This accuracy is in large part due to our models ability to take industry-specific estimates as inputs, but also to the propagation mechanisms embedded in the model. Indeed, the estimates by supply shocks alone have only a Pearson correlation of 0.69 (P value 4103, Supplementary Fig. 14). Second, thanks to our granular and data-driven characterization of employment and consumption patterns (Supplementary Figs. 8 and 12), we reproduce a key fact: low-income individuals were more likely to become unemployed but reduced consumption less than high-income individuals3,19 (Fig. 2d). This happens because low-income individuals are more likely to work in the occupations most affected by closures, such as food preparation and serving, building and grounds cleaning and personal care and service (Supplementary Fig. 16), but they spend a larger share of their income on essential goods and services such as housing and utilities (we do not consider here the effect of the stimulus programme, which would further increase the spending of low-income individuals).

On the epidemic side, our model correctly matches the death count data on which it has been calibrated, correctly replicating the spike in the number of reported deaths in April 2020 and the strong reduction in June (Fig. 2d and Supplementary Fig. 18). It also correctly estimates the changes in contact patterns that occurred after protective measures were implemented, although these data were not used for parameter calibration (Fig. 2e). Both in the model and in the data, community contacts substantially reduced (Pearson 0.75 and P value 0.05), more in mostly non-essential industries such as entertainment and restaurants than in mostly essential industries such as retail and health. We also accurately estimate the reduction in workplace contacts across industries (Supplementary Fig. 20; Pearson 0.88 and P value 5106), the temporal profile of reduction in contacts (Supplementary Fig. 19) and the increase in prevalence over time (Supplementary Fig. 18). Finally, the model makes a number of estimates about how many infections happen across each layer and industry over time, as well as which occupation, income and age groups are most affected (Supplementary Figs. 2123). While we are not able to find data to quantitatively evaluate these estimates, our literature review provides some support to these findings (Supplementary Section 5.2.1).

In our analysis, we quantitatively explore the effects of three key factors that shaped the behavioural and policy response to the first COVID-19 wave. In the following, we use baseline to refer to estimated parameters calibrated with empirical data.

As a first set of counterfactual scenarios, we explore the effect of adjusting the magnitude of the fear of infection parameter that regulates behaviour change. Generally, we treat this parameter as uniform across individuals, per survey evidence20. However, we also consider an age-specific fear counterfactual. Our baseline calibration yields a fear of infection parameter distribution (Supplementary Fig. 13), implying a 14% consumption demand reduction in customer-facing industries due to infection fear at the epidemic peak. This calibrated value, which merges NPI effects with behaviour change, cannot be causally interpreted. Absence of NPIs would necessitate a steeper consumption drop for the model to explain observed behavioural changes, thus estimating higher infection fear. But the lack of real-world data without NPIs hampers this estimate. Instead, we explore two counterfactuals: low (0.1 times baseline) and high (10 times baseline), translating to a 1% and 77% consumption reduction due to fear at the peak, respectively. These scenarios facilitate comparing stronger fear of infection effects with stricter NPIs.

Next, we vary two policy-related factors. First, we experiment with different economic activity closures. Besides the baseline scenario with all non-essential industries closed, we consider two milder closure scenarios: (1) only non-essential customer-facing industries are closed and (2) no closures, with all economic activities open. Second, we simulate protective measures starting either 4weeks earlier (17 February 2020) or 2weeks later (30 March 2020). Additional counterfactuals, including partial closure of customer-facing industries and no WFH or school closures, are explored in Supplementary Information (Supplementary Figs. 24 and 25).

Aggregate economic and epidemic results are shown in Fig. 3, while results disaggregated by income, geography and industry are shown in Fig. 4 (Supplementary Figs. 2733). Figure 3a conveys our first main result: stricter closure of economic activities and higher fear of infection both lead to increased unemployment and fewer COVID-19 deaths. To illustrate this, consider a scenario with baseline fear of infection and all economic activities open, represented by the light-coloured circle. If we maintain the fear of infection at the baseline level, but close all non-essential economic activities (as in the baseline scenario), unemployment surges by 64%, while the number of deaths drops by 35%. Likewise, if we instead keep the closure level at the empirical baseline but increase fear of infection (represented by the dark triangle), we see a 40% rise in unemployment and a 50% decrease in deaths relative to the empirical scenario. Similar trends are observed in other scenarios. Although the total death count and average unemployment can vary substantially across simulation runs, the relative impacts of different policies remain robust (Supplementary Fig. 26).

a,b, Deaths and unemployment across scenarios. For each scenario, we show the aggregate unemployment rate and the cumulative number of deaths, as averaged throughout the simulation period and the simulation runs (Supplementary Fig. 26 shows the variability across simulation runs and discusses its interpretation). The empirical scenario is highlighted to serve as a benchmark. Scenarios are distinguished by the strength of behaviour change, as exemplified by the fear of infection parameter (square: low; circle: baseline; triangle: high). a, Scenarios are further distinguished by the specific closure of economic activities (all non-essential industries, as occurred empirically, only customer-facing industries and no closures), keeping the start of protective measures fixed at the baseline, empirically observed date. b, Scenarios are further distinguished by the start of protective measures (baseline: 16 March 2020, as empirically; early: 17 February 2020 and late: 30 March 2020), keeping closures fixed at all non-essential industries. c, For the specific combination of high fear of infection and three different starts of protective measures, a time series of unemployment and deaths corresponding to the three scenarios is shown.

a, Workplace infections and unemployment across income classes. Top: we vary the level of closures, keeping fear of infection and the start of protective measures to their baseline values (so the case with all non-essential activities closed is the empirical scenario). Bottom: we vary fear of infection keeping all economic activities open and starting protective measures on the baseline date. b, Maps of unemployment across census tracts in New York City, corresponding to two scenarios in a, including the empirical scenario (asterisk) and the counterfactual with no closures and low fear (hash). c, Infections and reduction in consumption across five selected industries and three levels of closures, for baseline fear and start of protective measures.

Both higher fear of infection and stricter closures lead to saving lives at the expense of jobs, for low and high income workers alike (Fig. 4a). However, for low income workers, higher fear of infection or stricter closures have a larger effect, leading to more lives saved and more jobs lost, compared with high-income workers. As we will show later, outside the household setting, most infections occur in customer-facing industries, where most low-income workers are concentrated. Thus, mandated closure or spontaneous avoidance of these industries leads to both more unemployment and fewer workplace infections among low-income workers.

The unequal economic outcomes of the empirical scenario also lead to geographical disparities. Figure 4b shows two maps of unemployment in Manhattan in the empirical scenario (asterisk) and in a counterfactual with low fear and no closures (hash). We see that in the counterfactual, the unemployment rate is very evenly spatially distributed, while in the empirical scenario, low-income areas such as the Queens and the Bronx have a high unemployment rate of more than 20%, compared with high-income areas such as Manhattan, with unemployment rates around 15%.

Overall, these results contribute to the ongoing debate on the relative effectiveness of behavioural change versus NPIs in preserving both public health and the economy. While it is intuitive to expect stricter mandated NPIs to increase unemployment and decrease COVID-19 deaths, it is less apparent that heightened behavioural adaptation would yield similar results (Discussion). Our findings highlight a qualitative parallel between substantial behavioural change and stringent economic activity closures. Spontaneous avoidance of services offered by customer-facing industries, akin to their mandated closure, results in increased unemployment but fewer fatalities. This trend is particularly pronounced among low-income individuals.

Our model also evaluates the efficacy of closing all non-essential economic activities, including large segments of manufacturing and construction, compared with exclusively closing customer-facing industries. We find that the mandated closure of all non-essential activities only marginally decreases deaths compared with solely closing customer-facing industries, but it drastically increases unemployment. In comparison with the baseline scenario, a counterfactual that only closes customer-facing industries results in a slightly higher death rate (4% higher), but substantially mitigates unemployment, reducing it by 36%. To explain these results, consider Fig. 4c. Most infections occur in customer-facing sectors such as entertainment and accommodationfood. Their closure curbs infections considerably but also consumption and employment. Conversely, closing manufacturing or construction marginally impacts infections but drastically reduces consumption. Professional services remains largely unaffected also because of WFH adaptability.

Methodologically, these industry-specific results were obtained because we associated each consumption venue from mobility data to an economic activity, allowing the quantification of industry-specific contacts. This granular, data-driven approach provides insights that more aggregate, qualitative models might overlook.

Another counterfactual exploration concerns the effectiveness of starting epidemic mitigation and control earlier (4weeks before) or later (2weeks later) than in the empirical scenario. As we show in Fig. 3b, delaying these measures marginally reduces unemployment by 2% but causes a notable 50% rise in deaths. In a high fear-of-infection scenario, late measures result in both a 46% increase in deaths and a 12% rise in unemployment. The mechanism for these results is suggested in Fig. 3c, where we show time series across the three counterfactuals with high fear of infection. We see that an early start of mitigation measures prevents an epidemic wave, leading to no further increase in unemployment due to fear of infection. Conversely, with a baseline or late start, substantial behaviour change leads to reduced consumption, and this, in turn, leads industries to fire their employees, increasing unemployment. Thus, starting mitigation measures early is crucial to improve epidemic outcomes, and possibly economic outcomes too. Our preliminary investigation (Supplementary Section 6.4) also shows that with an early start of protective measures, it is possible to avoid an excessive burden on the healthcare system, as measured by a usage of more than 50% of the nominal capacity of intensive care units.

In the empirical scenario, the parameterization of the fear of infection is uniformly applied across individuals, as suggested by survey evidence20. However, we also examined a counterfactual in which young individuals adopt less behavioural changes (low fear of the disease) than older individuals (high fear of the disease), considering this might be a more optimal situation for pandemic control through behavioural change. Here, at-risk older individuals would internalize the infection risk more, while younger individuals, less likely to suffer severe consequences, could maintain higher consumption and contribute to herd immunity. We explore how this scenario plays out quantitatively in the data-driven, granular agent-based model.

To explore the effects of heterogeneous behavioural changes, we group all households into three classes based on the age of their household head (034, 3564 and 65+years). We assume that fear of infection in each class is proportional to the risk of death in that class (Supplementary Section 6.5). We also normalize the fear of infection parameter across age bracket so that the mean takes the same baseline value that we considered in the empirical scenario. This normalization ensures that results are driven by a different distribution of fear across age groups, rather than by changes in overall fear. At the end of this procedure, the fear level among households aged 034years is 0.02 times the baseline, households aged 3564years have a fear level of 0.48 times the baseline and households aged 65+years have a considerably higher fear level of 4.91 times the baseline. We compare the age-specific scenario with the scenario in which all agents have uniform baseline fear (as in the empirical scenario).

We report aggregate results in Fig. 5a, which reproduces the same scenarios as Fig. 3 for uniform fear, next to the new results for age-specific fear. Adjusting for age-specific fear, while keeping other factors constant, marginally reduces both unemployment and deaths compared with uniform fear. In the all open scenario, where the effect is most pronounced, age-specific fear reduces deaths by 6% and unemployment by 5%. For comparison, closing customer-facing industries cuts deaths by 28% but increases unemployment by 22%.

a, Aggregate deaths and unemployment across scenarios. The general interpretation is the same as Fig. 3; here, uniform fear is represented by circles and age-specific fear is represented by plus symbols. b, For the scenario all openearly start, time series of the level of workplace and community contacts and consumption demand of customer-facing industries, disaggregated by type of fear (solid lines, age-specific fear and dashed lines, uniform fear) and by age groups with heterogeneous fear. These time series show how fear of infection reduces contacts and consumption demand. c, For the same scenario as b, the time series of infections per 1,000 individuals disaggregated by age groups and by whether they occurred outside or within households, and actual consumption demand (relative to the pre-pandemic level) disaggregated by industry and by whether industries are customer facing or not.

In Fig. 5b,c, we examine industry- and age-specific effects, focusing on ages 034years and 65+years. First, in Fig. 5b, we show how fear of infection reduces contacts and consumption demand. As expected, uniform fear leads to equal reductions across all ages by construction, while age-specific fear instead leads to the least reduction in young agents and the most in older agents. Total consumption decreases less than contacts as it may not require direct contact, such as ordering takeaway food (Methods).

In Fig. 5c, we first consider infections (left plots). We distinguish infections occurring inside households from those happening outside (community or workplace contacts). Outside the household, infections among older agents decrease with age-specific fear compared with uniform fear, especially around the epidemic peak where they are 30% lower. However, in the waning phase of the epidemic, infections are comparable in both scenarios. In contrast, we see an opposite trend among young households, where a large number of infections happen later due to their very low fear of infection. Within households, the differences between age-specific and uniform scenarios are less pronounced.

The relatively small decrease in deaths with age-specific fear can be explained in two ways. On the one hand, the time series of reductions in contacts and infections show that older individuals drastically reduce their contacts only after the epidemic peak. This delay results from the lag between infection and death reporting; behaviour change intensifies when individuals become aware of the number of COVID-19 deaths. On the other hand, older individuals cannot avoid infections within their own households.

As we can also see in Fig. 5c (right plots), age-dependent fear of infection alters consumption demand across industries. Consumption demand decreases more in health, a customer-facing industry on which old agents spend a disproportionate amount of income, and less in accommodationfood, on which young agents spend a higher share of their income. At the same time, consumption demand increases towards industries that are not customer facing because households reallocate part of their consumption budget to those industries. As individuals in older age groups decrease consumption more and thus have more budget to reallocate because of higher fear of infection, this results in higher consumption demand towards industries that have high consumption share among old individuals, such as finance. By contrast, because younger individuals spend a large fraction of their income on real estate and, with low fear, they do not reallocate much, the increase in demand for real estate services is lower than if fear of infection was uniform across ages.

In summary, these findings show that even when individuals adjust their behaviours in response to their personal risk levels during a pandemic, it only modestly affects health and economic outcomes. Moreover, our results quantify the complex ripple effects across various sociodemographic groups.

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The unequal effects of the healtheconomy trade-off during the ... - Nature.com

Senolytics show promise in combating brain aging and COVID-19 … – News-Medical.Net

November 17, 2023

In a recent study published in the journal Nature Aging, an international team of researchers observed that senolytics can alleviate physiologic brain aging and coronavirus disease 2019 (COVID-19) neuropathology.Senolytics are a class of drugs that selectively target and eliminate senescent cells, which are cells that have stopped dividing and contribute to aging and age-related diseases.

Most COVID-19 patients often experience diverse neurologic complications. Further, autopsied brain tissue transcriptomic data suggest associations between severe COVID patients' cognitive decline and brain aging signatures. While recent reports implicate senescent cells in neurodegeneration and cognitive decline in aged mice and in vivo neuropathology models, their contribution to human brain tissue aging and COVID-19 pathology in the central nervous system remains unknown.

Study: Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology. Image Credit:Jose Luis Calvo/ Shutterstock

In the present study, researchers tested the effects of senolytics on physiological brain aging and COVID-19 neuropathology. First, they generated human brain organoids (BOs) from embryonic stem cells and physiologically aged them for eight months. Subsequently, the BOs were treated with two doses of senolytics, such as the dasatinib-quercetin (D+Q) combination, ABT-737, and navitoclax, for one month at a two-week interval.

Senolytic interventions significantly reduced senescence-associated -galactosidase (SA--gal) activity, indicating the elimination of senescent cells. This was further confirmed by significantly higher levels of lamin B1 (a nuclear marker downregulated in senescence) in treated BOs. Next, the team investigated the cell types involved in senescence phenotypes by co-immunolabeling with a senescence marker (p16).

Over three-fourths of p16-positive cells coimmunostained with astrocytes (positive for glial fibrillary acidic protein), while approximately 15% co-localized with mature neurons (positive for neuronal nuclei antigen). These two brain cell populations represented a majority (> 90%) of p16-positive cells. The team found a significant reduction in senescent astrocyte populations following treatment, with the D+Q combination being the most potent. However, the effect of senolytics in reducing senescent neurons was less apparent.

RNA sequencing revealed the upregulation of lamin B1 messenger RNA (mRNA) levels across all senolytic treatments. Additionally, 81 senescence-related mRNAs were consistently suppressed with senolytic treatments. Further, aging clock predictions were performed based on whole-transcriptome sequencing. D+Q treatment of nine-month-old organoids returned their gene expression age to levels of eight-month-old organoids.

This phenotype was not observed with other tested senolytic interventions. D+Q treatment-induced changes in gene expression correlated with mammalian signatures of pro-longevity interventions, such as rapamycin administration and caloric restriction. Next, the team estimated the prevalence of senescent cells in the autopsied frontal cortex from the brains of age-matched patients who died due to severe COVID-19 or non-neurologic and non-infectious causes.

Brains of COVID-19 decedents showed over seven-fold increase in p16-positive cells than those from non-COVID-19 controls. Next, human BOs were exposed to different viral pathogens to examine how (neurotropic) viruses contribute to aging-induced neuropathology. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was mainly detected in neurons, microglia, and neural progenitors.

Seven SARS-CoV-2 variants were also tested, and their senescence phenotypes were ranked by SA--gal activity. Most variants significantly increased SA--gal, but the Delta variant exhibited the most potent induction. Moreover, there was a distinctive colocalization of viral spike and SA--gal in Delta-infected BOs. Besides, a statistically higher induction of senescence was evident between organoids infected for five and 10 days.

Increased senescence observed at 10 days post-infection (dpi) relative to 5 dpi suggested that SARS-CoV-2 infection may have triggered secondary senescence. Interestingly, non-infected senescent cells were enriched within 150 m of infected senescent cells, supporting the putative bystander effect of infected cells in triggering secondary senescence. Senescence was also triggered by infection of BOs with Japanese encephalitis virus (JEV), Zika virus (ZIKV), or Rocio virus (ROCV).

The researchers examined the associations of transcriptomic changes between COVID-19 patients and SARS-CoV-2-infected BOs. Among nearly 1,600 genes with differential expression between infected and non-infected BOs, there were 485 differentially expressed genes in COVID-19 patients' brain samples. Notably, known senescence and aging pathways were enriched in this common gene set.

Long-term senolytic treatment prevents selective accumulation of senescent cells in physiologically aged human BOs.af, BOs were generated and grown in vitro for 8months and subsequently exposed to two doses (one every 2weeks) of either navitoclax (2.5M), ABT-737 (10M) or D+Q (D, 10M; Q, 25M) within the following month, after which organoids (n=814) were collected for in situ analysis.a, SA--gal assay was performed on organoid sections. Each data point in the bar graph represents a single organoid analyzed. Data presented as means.d.; at least eight individual organoids were analyzed per condition; one-way analysis of variance (ANOVA) with Tukeys multiple-comparison post hoc corrections.b, Lamin B1 staining was performed on organoid sections. Each data point in the scatter plot represents the integrated intensity of each cell within organoid sections. At least eight individual organoids were analyzed per condition; one-way ANOVA with Tukeys multiple-comparison post hoc corrections.c,d, Representative images from quantifications shown ina,b, respectively. Scale bar, 0.3mm.e, Representative immunofluorescent images of regions from organoids treated with the indicated senolytics and vehicle control. Samples were individually immunolabeled with antibodies against GFAP, Sox2 and NeuN and co-stained for p16. Arrows indicate coimmunoreactivity of NeuN and p16. Scale bar, 50m.f, Bar graphs showing colocalization quantification performed on organoid sections. Data presented as means.d.; three individual organoids were analyzed per condition; one-way ANOVA with Tukeys multiple-comparison post hoc corrections. a.u., arbitrary units.

Next, the team evaluated the impact of the selective elimination of senescent cells with senolytic interventions. Senolytics significantly reduced the number of BO cells with SA--gal activity five days after SARS-CoV-2 infection. Of note, the impact of senolytics was more prominent in BOs infected with the Delta variant, and senolytics reverted Delta variant-induced lamin B1 loss and p21 upregulation.

Pretreating BOs with senolytics before infection resulted in a significant reduction of virus-induced senescence. Layer 6 corticothalamic neurons and gamma-aminobutyric acid (GABA)ergic ganglionic eminence neurons were the two populations with significantly higher incidence of senescence following infection, and senolytic interventions prevented cellular senescence in these populations.

Finally, the researchers infected K18-hACE2 mice (that express the human angiotensin-converting enzyme 2 [hACE2] under the regulation of keratin 18 [K18] promoter) with SARS-CoV-2 Delta. Senolytics with blood-brain barrier permeability, such as D+Q, navitoclax, and fisetin, were administered 24 hours post-infection, with subsequent treatments every two days. Infected mice had shortened life spans, with a median survival of five days.

However, fisetin or D+Q treatment significantly improved survival. All control animals died by 10 dpi, whereas 13% of navitoclax-, 38% of D+Q-, and 22% of fisetin-treated mice were alive at 12 dpi (experimental endpoint). Senolytics, especially D+Q, caused a profound decrease in COVID-19-related features and significantly reduced viral gene expression in mice brains.

The brains of infected mice exhibited an increase in inflammatory senescence-associated secretory phenotype (SASP) and p16 senescence markers, and (all) senolytic treatments normalized senescence and SASP gene expression of infected animals to the levels observed in non-infected brains. Delta infection also caused a loss of dopaminergic neurons in the brainstem, with a concomitant increase in astrogliosis. However, recurrent administration of senolytics partially prevented the loss of dopaminergic neurons and abrogated the onset of astrogliosis.

Taken together, the study demonstrated that senescent cells accumulate in physiologically aged human BOs, with long-term senolytic intervention(s) substantially reducing cellular senescence and inflammation. Further, D+Q treatment uniquely induced anti-aging and pro-longevity gene expression changes in BOs.

Besides, COVID-19 patients' brains exhibit rapid accumulation of cellular senescence relative to age-matched controls. Neurotropic viruses (ROCV, JEV, and ZIKV) and SARS-CoV-2 can infect BOs and induce cellular senescence, and the SARS-CoV-2 Delta variant triggers the most potent induction of senescence. Short-term senolytic interventions could reduce SARS-CoV-2 gene expression in infected BOs and prevent senescence of GABAergic and corticothalamic neurons.

Notably, senolytics ameliorated COVID-19 neuropathology in infected K18-hACE2 mice, improved their survival and clinical score, and reduced SASP, senescence, and viral gene expression. Overall, the findings highlight the vital role of cellular senescence in brain aging, COVID-19, neuropathology, and the therapeutic impact of senolytics.

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Senolytics show promise in combating brain aging and COVID-19 ... - News-Medical.Net

Coffee and COVID: Study finds coffee inhibits SARS-CoV-2, offers … – News-Medical.Net

November 17, 2023

In a recent study published in the journal Cell and Bioscience, researchers investigated whether coffee has (inhibitory) effects against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Several variants of SARS-CoV-2 have emerged throughout the coronavirus disease 2019 (COVID-19) pandemic. Further, the protection conferred by vaccines wanes over time, prompting the introduction of vaccine boosters. Besides, diet can influence SARS-CoV-2 infection efficiency. Recent reports suggest that a polyphenol-rich diet and physical activity could trigger an active immune response and reduce the risk of severe disease.

Coffee is one of the most consumed beverages and contains polyphenols, such as caffeic acid and chlorogenic acid (CGA), and antioxidants like trigonelline, melanoidins, and cafestol. One study revealed that coffee consumption (at least one cup per day) was associated with a 10% lower COVID-19 risk among the United Kingdom (UK) Biobank participants. Nevertheless, the underlying mechanisms remain unclear.

Study:Coffee as a dietary strategy to prevent SARS-CoV-2 infection

In the present study, researchers examined the effectiveness of coffee against SARS-CoV-2. They used a SARS-CoV-2 pseudovirus assay to assess the effects of coffee on viral entry in a human embryonic kidney cell line (293T) expressing angiotensin-converting enzyme 2 (ACE2). Ground coffee (6 mg/ml) showed a dose-dependent reduction in viral entry. Next, they tested the effects of several commercial instant coffee products.

Instant coffee products (1 mg/ml) consistently inhibited the entry of wild-type SARS-CoV-2 and variants (Alpha, Delta, and Omicron). Next, the team evaluated how additives in coffee, such as cream, low fat, milk, and sugar, affect its potency. This showed that additives had no impact on the inhibitory effects of coffee. The researchers performed an enzyme-linked immunosorbent assay (ELISA) and observed that ground and instant coffee interrupted spike-ACE2 interactions.

In addition, the team observed that ground and instant coffee inhibited the activity of transmembrane protease serine 2 (TMPRSS2). Next, they tested whether ACE2 and TMPRSS2 expression could be regulated. To this end, hepatocellular carcinoma (HCC) HepG2 cells and Huh7 cells, which express high levels of ACE2 and TMPRSS2, respectively, were treated with varying concentrations of coffee. This significantly reduced ACE2 and TMPRSS2 transcript and protein levels.

Besides, the activity of cathepsin L (CTSL), which facilitates SARS-CoV-2 entry, was also affected upon coffee treatment. Next, ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) was performed to identify compounds in coffee responsible for observed effects. This analysis revealed seven peaks at 274 nm; these fractions were collected and evaluated separately.

The sixth (F6) and seventh (F7) fractions exhibited potent inhibitory effects against SARS-CoV-2 entry. CGA and caffeine were detected in F6, whereas luteolin, methyl ferulic acid, isocholorogenic acid A (isoCGA-A), isoCGA-B, and isoCGA-C were present in F7. Next, the inhibitory effect of a mixture containing the five F7 compounds was only half that observed with F7, suggesting that some undetected compounds in the F7 fraction contributed to inhibition.

Additionally, the compounds detected in F6 and F7 were separately tested. IsoCGAs, CGA, and caffeine inhibited the entry of wild-type SARS-CoV-2, and isoCGA-A was the most potent compound. Luteolin and methyl ferulic acid lacked inhibitory effects. Furthermore, isoCGAs, especially isoCGA-A, effectively inhibited the entry of the Alpha, Delta, and Omicron variants. Further analyses indicated that isoCGAs and CGA can inhibit the interactions between ACE2 and viral spike.

IsoCGAs were the top candidates in docking analyses based on binding energy scores, followed by CGA and caffeine. Next, the effects of isoCGAs, CGA, and caffeine on TMPRSS2 activity were evaluated. Consistently, isoCGAs achieved better inhibition against TMPRSS2 than CGA or caffeine. Furthermore, decaffeinated coffee was found to reduce spike-ACE2 interactions and TMPRSS2 activity.

Finally, 64 healthy Taiwanese individuals aged 21-40 were randomized to consume regular coffee (high- or low-dose), decaffeinated coffee (high- or low-dose), or water (control) for two days. Sera were collected before and after the intervention. Samples from most individuals in the regular coffee groups inhibited wild-type SARS-CoV-2 and the Omicron variant. Likewise, sera from decaffeinated coffee consumers, especially the high-dose group, also inhibited SARS-CoV-2.

The findings suggest that coffee can limit SARS-CoV-2 infection by inhibiting spike-ACE2 interactions, TMPRSS2, and CTSL. Coffee also reduced protein levels of ACE2 and TMPRSS2. Bioactive compounds in coffee, such as CGA, isoCGAs, and caffeine, showed inhibitory effects. The human trial showed that sera from regular and decaffeinated coffee consumers can suppress SARS-CoV-2, including the Omicron variant. Overall, the authors suggest that coffee intake could be a potential dietary strategy to prevent infection in the post-COVID era.

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Proposed methodology

The architecture of the proposed model as shown in Fig.1 considered CT scan images as the input to detect COVID-19 or non-COVID-19 images. The CT scan image datasets were collected and merged from three publicly available datasets. Since the dataset images were not of the same size, they were resized and merged. The images were then converted to grayscale from RGB. A modified region-based clustering method was proposed to segment the CT scan grayscale images. Furthermore, the model deliberated two feature extraction techniques including contourlet transform and CNN. Firstly, the contourlet transform method and secondly, the CNN feature extraction technique extracted feature vectors. These two vectors were fused in one feature vector, which was used as the input to train the classification model. The fused feature vector considered a large number of features that helped to accurately identify the COVID-19 or normal images. The system also proposed an authentic feature selection technique that extracted meta-heuristic features by using BDE. This optimized vector was subsequently used to recognize COVID-19 CT scan test pictures using an ensemble classifier.

Proposed methodology for detecting COVID-19.

The most important step in designing a computer-aided diagnostic (CAD) system for detecting COVID-19 at an early stage is the CT scan image segmentation22. In order to diagnose unusual disorders, segmentation is widely used in the area of medical images. Manual segmentation of the same medical images is possible. Image segmentation utilizing segmentation algorithms has a higher accuracy compared to manual segmentation. The original fuzzy c-means (FCM) algorithm23 works well for segmenting noise-free images, however, it fails to accurately segment the images with noise, outliers, or other imaging artifacts. The modified region-based clustering technique was used in this work to segment the CT images. The objective of the modified region-based clustering algorithm was updated to reduce the intensity of homogeneities by including spatial neighborhood information and altering the membership weighting of each cluster. The proposed segmentation algorithm has the following advantages: (a) propagates more homogeneous regions than other old fuzzy c-means algorithms, (b) manages noisy spots and (c) it is comparatively less sensitive to noise. These techniques have produced excellent output images with the simplest approach to isolate the objects from the background.

A chest CT scan is a useful medical imaging tool for accurately diagnosing COVID-19 cases24. As the open repository had a limited quantity of CT scan images, thus the images from all three databases were integrated to form a new database for this work. A total of 11,407 CT images with 7397 images from COVID-19 class and 4010 images from non-COVID19 class. The training and testing phases included images of COVID-19 and non-COVID-19.

The SARS-CoV-2 CT-scan dataset19 has 2482 CT scan images from 120 patients, including 1252 CT scans of 60 patients infected with SARS-CoV-2 from men (32) and females (28), and 1230 CT scan images of 60 patients who were not infected with SARS-CoV-2 but had other pulmonary disorders. The data of CT scan images was gathered from hospitals in Sao Paulo, Brazil. The CT scan images in this dataset are digital scans of printed CT tests, and there is no criterion for image size. The smallest CT scan images in the dataset are 324412 pixels, while the largest CT scans are 484456 pixels. In this dataset, the number of training and testing images are 1842 and 640 respectively.

The original CT scans image of 377 people are included in this COVID-19 CT image dataset20. There are 1558 and 4826 CT scan images, respectively, belonging to 95 affected COVID-19 people and 282 normal people. The Negin Medical Center in Sari, Iran, provided this dataset. All the CT image sizes are 2562563. In this dataset, the number of training and testing images are 5594 and 790 respectively.

These publicly available datasets are collected from authentic website21. This dataset contains a total of 2541 CT scan images with 1200 COVID-19 and 1341 non-COVID-19. In this dataset, a total of 1726 and 815 images are considered for the training and validation.

As the open repository had a limited quantity of CT scan images, the images from all three databases were integrated to form a new database for this work. A total of 11,407 CT images with 7397 images from the COVID-19 class and 4010 images from the non-COVID-19 class. Figure2 demonstrates sample CT scan images from each dataset. The training and testing phases included images of COVID-19 and non-COVID-19.

Sample CT scan images from three datasets.

Image pre-processing is a key step in medical image processing to obtain meaningful information and appropriate classification by eliminating noisy or distorted pixels from each CT scan image. In this stage, the images were first resized to 256256 pixels and transformed from RGB to grayscale images using the MATLAB function as the input for the model development. Color has no significance in detecting COVID-19 from the CT scan images hence grayscale images were employed during building the models to avoid any false classification and complexity. Grayscale images are simpler and easier to process than color images because they contain only one-color channel, which represents the intensity of the color for each pixel. Figure3 displays the preprocessing steps employed in this work.

Preprocessing steps applied to the COVID-19 and non-COVID-19 images.

Histogram equalization, an image processing technique that is frequently used on CT scan images to improve image quality in black and white color scales. The input images and its contrast-enhanced (after histogram equalization) images are shown in Fig.3 with the related histograms. Histogram equalization was achieved by efficiently spreading out the most frequent intensity values, extending the image intensity range. The adoption of a spatially variable histogram equalization technique seems to improve the visibility of anatomic structures in various clinical scenarios25. However, the technique increased the amount of noise and artifacts in the presented image.

The region-based clustering was employed to simplify the COVID-19 image region, which ensured less computational complexity and relatively accurate analysis. K-means, C-means, thresholding, morphology-based, edge-based, watershed, region-growing, and cluster-based approaches are among the various segmentation algorithms26. The authors of this paper proposed a cluster-based algorithm that segmented the image effectively and provided a better performance in terms of measuring evaluation matrices SSIM (structural similarity index), PSNR (peak signal to noise ratio) and RMSE (root mean square error) scores.

The proposed segmentation method partitioned the COVID-19 image into four clusters (C1 to C4) as gray matter (GM), cerebra-spinal fluid (CSF), white matter (WM), the necrotic focus of glioblastoma multiforme (GBM). The proposed segmentation technique employs an iterative process to locate the cluster region. In each iteration, the clusters centroid is modified to reduce the distance between pixels and the centroid. The mean brightness of all pixels within a cluster and the distance are obtained by using Eqs.(1) and (2) respectively. The COVID-19 segmentation process is depicted in Algorithm 1.

$${mu }_{k}= {C}_{k} sum_{i=0}^{N}frac{{Z}_{i}}{N},$$

(1)

$$r= left|{mu }_{k}-{x}_{i}right|,$$

(2)

where ({mu }_{k}) is the clusters mean intensity, and r means pixels distance from a clusters centroid. The intensity of the ith pixel within a cluster is ({Z}_{i}), ({C}_{k}) is the center of the kth cluster, and ({x}_{i}) is the intensity of the ith pixel. The number of pixels in a cluster is denoted by N. The COVID-19 segmentation process is depicted in Algorithm 1. Figure4 illustrates the grouping of COVID-19 image data step by step.

Applied modified region based clustering method for COVID19 and non-COVID19 image segmentation.

Algorithm 1: Proposed segmentation algorithm.

The contourlet transform tries to capture curves rather than points and includes anisotropy and directionality. The CT was created to solve the wavelet transforms limitations such as poor directionality, shift sensitivity and lack of phase information27. At each scale, it allows for a variable and elastic number of directions while obtaining virtually critical sampling. The contourlet transform28 is accomplished based on two steps including Laplacian pyramid decomposition and directional filter banks (DFB). At every level of the Laplacian pyramid, a down-sampled lowpass version of the source image is generated, as well as the difference between the source image and the down-sample lowpass image, resulting in a high-pass image. The next level Laplacian pyramid builds an iterative structure linking with the down-sampled lowpass version of the original signal. DFBs are used to create high-frequency sub-bands with a variety of directions. The contourlet transform acts on two-dimensional CT scan images. This work generated sixteen different multi-directional multiscale images using four-level CT with the 9-7 filter and computed thirteen various image features, including entropy, homogeneity, energy, correlation, and others from the segmented images, by enumerating the gray level co-occurrence matrix (GLCM) of each image. Figure5 presents the contourlet transformed images considering edges, lines, textures and contours in contrast to the wavelet transform.

Overall structure of contourlet transform feature extraction method.

For feature extraction, the proposed system employed the benchmark VGG19 CNN model, which outperformed the other CNN models such as AlexNet, GoogleNet, and ResNet50. A 19-layer version of VGGNet29 was used to create this network. Figure6 shows the VGG19 architecture, which includes sixteen convolution layers and three fully connected (dense) layers. For each convolution layers output, a non-linear ReLU was employed as an activation function. The entire convolution sections were divided into five sub-regions by five consecutive max-pooling layers. Two convolution layers were employed with depth dimensions of 64 and 128 respectively. Each of the other three sub-regions was made up of four consecutive convolution layers with depth sizes of 256, 512, and 512 in each sub-region. In this case, a convolutional kernel of size of 33 was chosen. The last layer of the proposed VGG19 models was replaced by a softmax classification layer. Two fully connected layers with neurons 1024 and 4096 were installed before the output layer. As a result, the fully connected layer yields 4096 features for classification.

Architecture of VGG19 for feature extraction from CT scan images.

A fusion-feature vector was created by combining the extracted features from the contourlet transform and CNN. Overlapping, redundancy, and dimensional expansion are regular occurrences in all fusion-based techniques, therefore dimension reduction, as well as redundancy minimization or the elimination of irrelevant features, is required to obtain the optimum features. Many researchers obtain optimized features using Principal Component Analysis (PCA)30 and minimum RedundancyMaximum Relevance (mRMR)31 but the BDE feature optimization method provides better performance than the others. For the dataset used in this study, three feature optimization approaches were tested and BED performed best.

In the mRMR feature selection algorithm, the mutual dependencies of x and y variable can be determined using Eq.(3) where p(x), p(y) and p(x,y) are the probability density functions.

$$Ileft(x,yright)= iint pleft(x,yright){text{log}}frac{p(x,y)}{p(x)p(y)}dxdy.$$

(3)

Equation(4) approximates the maximal relevance D(S,c), where xi is the mean of all mutual dependencies and c is the class. As a result, the function R(S), is represented by Eq.(5) that can be used to add minimal redundancies. S is the feature combination.

$${text{max}}Dleft(S,cright)= frac{1}{left|Sright|}sum_{{x}_{iin S, }}Ileft({x}_{i, }cright),$$

(4)

$${text{max}}Rleft(Sright)= frac{1}{{|S|}^{2}}sum_{{x}_{i}{{x}_{j}}_{in S, }}I({x}_{i, }{x}_{j, }).$$

(5)

In the PCA algorithm, the covariance of features is determined to take uncorrelated features. PCA uses Eq.(6) to combine the correlated features.

$$rho = frac{sum_{i=1}^{N}left({X}_{i}-overline{X }right)({Y}_{i}-overline{Y })}{n-1}.$$

(6)

The BDE feature selection technique is a heuristic evolutionary strategy for reducing the successive problem. The notion of advanced binary differential evolution (ABDE) is expanded to include feature selection difficulties. Three random vectors ({P}_{u1}), ({P}_{u2}), and ({P}_{u3}) are chosen for vector pk for the mutation operation, so that u1 (ne) u2 (ne) u3 (ne) k, where k is a population vector arrangement. The dth characteristic of the difference vector (Eq.(7)) is zero if the dth dimensions of the vectors ({P}_{u1}) and ({P}_{u2}) are equal; otherwise, it has the same value as the vector ({P}_{u1}):

$${difference, vector}_{k}^{d}= left{begin{array}{l}0, {P}_{u1}^{d}= {P}_{u2}^{d} \ {P}_{u1, other}end{array}right}.$$

(7)

Following that, the mutation and crossover processes are carried out, as illustrated by the Eqs.(8) and (9).

$${mute, vector}_{k}^{d}= left{begin{array}{l}1, {if, different ,vector}_{k}^{d}= 1 \ {{P}_{u3}^{d}}_{, other}end{array}right},$$

(8)

$${W}_{k}^{d}= left{begin{array}{l} {mute, vector}_{k}^{d} , if y le CR left|dright| d={d}_{random} \ {{P}_{k}^{d}}_{, other}end{array}right}.$$

(9)

Here, W denotes the try vector, ({CR}_{epsilon })(0, 1), a crossover amount, and ({gamma }_{varepsilon })(0, 1) denotes the mutation amount. If the try vector ({W}_{k}) has a higher fitness value than the current vector ({P}_{k}), then it will be replaced in the selection phase. In a different way, the current vector ({P}_{k}) is saved for the next generation. Finally, this fused method achieved 1300 accurate optimized features.

Figure7 illustrates the steps in obtaining the optimized features in a single vector by fusing the features vectors extracted by the contourlet transform and CNN. The size of this feature vector is 4109. BDE based feature selection method was then employed to get 1300 most discriminating features.

Block diagram of optimised feature selection process.

The authors suggested a novel, straightforward hybrid selective mean filter (HSMF) technique32 to calculate the average value selectively, unlike the traditional mean filter (MF) method, which calculates the average pixel utilizing all pixels in a given kernel region. A threshold value was used to define pixel selection (h). Noise was not considered in the noise reduction procedure if an adjacent pixel in a kernel was higher or smaller than the threshold value from the value of the core pixel. The pixel selection was performed with the following Eq.(10).

$${I}^{prime}left(x+i,y+jright)= left{begin{array}{l}Ileft(x+i,y+jright), quad if left|Ileft(x,yright)-I(x+i,y+j)right|le h\ 0, quad if left|Ileft(x,yright)-I(x+i,y+j)right|>hend{array}right..$$

(10)

If (left|Ileft(x,yright)-I(x+i,y+j)right|le h, for every i and j) then ({N}^{{{prime}}}left(x,yright)=N-1.) The noise image reduction is then calculated using Eq.(11).

$${I}_{SMF} left(x,yright)= frac{{sum }_{i=-frac{n-1}{2},j=-frac{m-1}{2}}^{+frac{n-1}{2},+frac{m-1}{2}}I^{prime}(x+i,y+j)}{N^{prime}(x,y)}.$$

(11)

In the Eqs.(10) and (11), the disparities between all nearby pixel values and the central pixel value are likely to exceed h in the edge areas. The pixel value ({I}_{SMF})(x, y) is equal to I in this situation (x, y). In contrast, in the homogenous regions, the disparities between all nearby pixel values and the central pixel value are likely to be smaller than h. The pixel value ({I}_{SMF}) (x, y) is equivalent to ({I}_{MF}) in such situations (x, y). Figure8 depicts the noise reduction process of the HSMF method. The mean pixel value at the central pixel in a position (x, y) was calculated only from the black area where the differences in pixel values from the value of the central pixel were less than the threshold value, not from all the pixels in a particular square kernel (i.e., union of black and red areas). The pixels outside of the black region, as well as those still inside the kernel of interest with pixel values higher than the threshold value, were not included in the calculation.

An illustration of picking neighboring pixels for noise reduction in the hybrid selective mean filter (HSMF) method.

The threshold (h) was calculated using the magnitude of the standard deviation (SD) of the pixel values inside an image, which is a measure of noise33. To cover the majority of the image noise in this study, a 3 SD threshold was utilized. An approach proposed in Ref.34 was used to determine the SD automatically. This selects the minimum value of the standard deviation map automatically (SDM) as defined by Eq.(12).

$$SD=mathrm{min}left(SDMright).$$

(12)

The HSMF was supposed to reduce the noise dramatically while maintaining good spatial resolution. The technique is computationally light and fast as it is based on MF, making it easier to employ in clinical imaging than the BF (bilateral filter). Figure9 displays the filtered image by using the HSMF method.

Filtered CT scan images using hybrid selective mean filter method.

To determine the COVID-19, a ML/DL based ensemble classifier was employed35. Four ensemble models are commonly used to create the predictive classifier such as boosting, bagging, stacking, and voting36. The bagging approach of the ensemble methods like a bootstrap aggregation was used in this experiment. To compare the classification performance utilizing the optimized feature vector, three distinct types of classifiers including Long Short-Term Memory (LSTM), ResNet50 and Support Vector Machine (SVM) were employed. These three base classifiers were chosen as they typically outperform other ML/DL techniques. The categorization of any new instance by ensemble approaches is based on the classification votes of the basic classifiers. The output of each base classifier is regarded as a vote, with v=1 for the COVID-19 class and v=0 for the non-COVID-19 class.

The ensemble decision class is one that receives majority of the votes from the base classifiers that means (left(if {sum }_{i=1}^{n}v>frac{n}{2}right)) as indicated in Eq.(13).

$$Ensemble, Class= sumlimits _{i=1}^{n}v,$$

(13)

where the total number of base classifiers is n.

Figure10 represents the ensemble classifier-based bagging approaches where C1, C2, and C3 depict the LSTM, ResNet50, and SVM base classifiers, respectively. Similarly, P1, P2, and P3 signify the votes they represent. The final classification result combines the votes P1, P2, and P3 using Eq.(13) to yield the anticipated class based on the majority votes. To train the base classifiers, the training dataset set was divided into three subsets, D1, D2, and D3, then the testing was performed after training.

The bagging approach in the ensemble classifier.

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Epstein-Barr virus reactivation is not causative for post-COVID-19 ... - BMC Infectious Diseases

Utah researchers put airborne viral transmission risks under the … – @theU

November 17, 2023

See the original post from the College of Engineering here.

As the COVID pandemic began to unfold in late 2019, researchers around the world scrambled to learn as much as possible about the novel virus responsible for the disease. Until more was known about how this microorganism jumped from person to person, the only surefire mitigation strategy involved strict lockdowns and quarantines. And even as more of the picture came into focus, healthcare experts, policymakers and the general public clashed over the remaining uncertainty.

While a better understanding of the coronavirus has enabled most of public life to return to pre-lockdown routines, one critical setting still struggles with this uncertainty: hospitals. Working with or around infected patients means that coming into contact with virus-laden aerosols is unavoidable, but the riskiness of any given interaction is difficult to assess.

Now, researchers at the University of Utah are conducting a study that aims to quantify these risks in a more fundamental way than ever before. Rather than relying on intuition or guesswork, hospitals will have real data on how infectious viruses remain after common aerosol-generating procedures, from performing CPR to changing a patients bedsheets.

The study is led byKerry Kelly, associate professor of chemical engineering in the University of Utahs John and Marcia Price College of Engineering. She is collaborating with researchers at the Us Spencer Fox Eccles School of Medicine, includingDarrah Sleeth, associate professor in the Division of Occupational & Environmental Health and at theRocky Mountain Center for Occupational and Environmental Health, Catherine Loc-Carrillo, adjunct assistant professor in the Division of Epidemiology, andKristi Warren, research assistant professor in the Division of Pulmonary Medicine, as well asRachael Jonesat the UCLA Fielding School of Public Health.

PHOTO CREDIT: Dan Hixson/University of Utah College of Engineering

Chemical engineering professor Kerry Kelly.

When working with patients known to have a contagious disease, healthcare providers and other hospital workers follow a litany of procedures to protect themselves, as well as other patients, from infection. These procedures are tailored to the organism in question and the risk entailed by the specific interactions the patient requires.

Coronavirus presents a particular challenge for infection control given how quickly and easily it spreads. With viruses hitching a ride on the moisture of every exhale, even the most basic interactions with infected patients could be considered high risk.

Previous attempts to quantify this exposure risk have measured how much viral genetic material aerosols contain, but this data is limited when it comes to a key element: just because viral DNA or RNA is present in the sample does not mean that it was part of a functioning virus when it was captured.

When you pull air through a solid filter, you can catch virus-carrying aerosols, but then they quickly dry out and die, Kelly said. By capturing them in a liquid, well be able to tell whether the aerosols emitted by these procedures contained enough viable virus to actually cause an infection.

Kelly has been working with this technology as part of her research on particulate-based air pollution. When the pandemic hit and the risk of various activities became a fiercely debated topic, she immediately began brainstorming how to apply her expertise to the problem.

There are many activities that take place in a hospital that could be considered aerosol-generating procedures, Sleeth said. Although it seems obvious that some are riskier than others, there still isnt a good way of comparing them. That means decisions are currently being made with incomplete information, and that can have real consequences for both patients and healthcare workers.

Supported by a 3-year $2.3 million grant from the National Institutes of Health, the Utah and UCLA researchers will collect aerosol samples from real hospital interactions with both influenza and COVID patients. The potential aerosol-generating procedures studied will include medical procedures with obvious risks of encountering aerosols, such as intubating a patient or measuring their pulmonary strength, as well as everyday interactions, such as changing bed linens.

Once the samples are captured, the researchers will associate particle sizes with viral load and virus viability, with a long-term goal of developing appropriate protective measures. Correlating an aerosols diameter to its likelihood of containing functional viruses, for example, could directly inform infection control procedures, such as what kinds of personal protective equipment are necessary for a procedure.

The best ways to protect healthcare personnel from infectious aerosols remains quite controversial among some stakeholders, but it is critical to the health of workers and patients that we build an evidence base that enables robust decision making, Jones said.

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Utah researchers put airborne viral transmission risks under the ... - @theU

Fear of COVID-19 in a Population of Pregnant Women in Spain … – Dove Medical Press

November 17, 2023

Introduction

The coronavirus disease pandemic of 2019 (COVID-19) became a global health emergency resulting in the need to take measures to prevent the spread of the virus such as containment, quarantine, and border closures. These measures have produced profound changes in lifestyle, and the fear of getting infected with the disease and its possible consequences at a personal, family, or social level have generated high levels of anxiety.13

Data related to the COVID-19 pandemic has been evolving since the year 2020, where SARS-CoV-2 struck the entire world. The pandemic has not only resulted in physical disturbances, but has also led to psychological, economic, and social alterations. COVID-19 develops as a result of a respiratory tract infection caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).

This disease, which has affected the entire population to a greater or lesser extent, caused greater concern in certain vulnerable groups such as the elderly, the immunocompromised, or pregnant women, among others, due to previous illnesses or vital situations of special risk.4,5

COVID-19 has effects on the physical health, causing severe pneumonia, acute respiratory distress syndrome (ARDS), bacterial superinfection, cardiac alterations, shock, sepsis, thrombotic complications, pulmonary fibrosis, etc., and on mental health, especially anxiety and depression in the general population6 o it is associated with an increased risk of ICU admissions, hospitalisation,79 and although the existence of vertical transmission is extremely rare,10,11 there does appear to be evidence of an increased risk for developing pre-eclampsia, for threatened preterm birth, or low birth weight.12,13

During the pandemic, the impact this has had on the psychological health of the general population has been studied.14,15 However, there are few studies related to vulnerable population groups such as pregnant women.16,17 In addition, psychological reactions secondary to health crises seem to persist over time18 and in this sense, the presence of fear and anxiety on an ongoing basis and in the prenatal period constitutes one of the precursors of postpartum depression.1921 Although the measures taken over the last three years have been fundamental in minimising the effects of the disease, this may have had a high psychological cost for certain population groups that needs to be assessed.

Previous studies have found that public health emergencies (eg 2003 SARS-CoV) triggered a range of emotional stress responses that involved increased levels of anxiety, fear, and other negative emotions.8,22,23

Fear is defined as a response of the organism that is triggered by a situation of physical or psychological threat or danger, the purpose of which is to provide the organism with energy to overcome or counteract it by means of a response that has an obvious survival purpose.24

However, it is one of the reactions that produces the greatest number of mental, behavioural, emotional, and psychosomatic disorders.25

Fear levels in pregnancy have been studied by many researchers in the pre-pandemic period, as the pregnant woman is considered a high-risk vulnerable group compared to the non-pregnant population due to both physical and psychological changes that occur during pregnancy. According to a systematic review, the prevalence of fear during pregnancy was 14% before the pandemic,26,27 and other research correlates low and high levels of fear of birth with prolonged labour, caesarean section, increased use of epidural analgesia, and prenatal and postpartum depression or anxiety.2832

Some authors suggest that the psychological effects of uncertainty generated by health crises are more likely to persist over time,33 and that chronic fear and anxiety are precursors to psychological disorders such as depression or stress.34 These are negative emotions that are maintained over time and that, when identified as permanent in the individual, may predispose to physical illnesses and/or generate or aggravate previous mental disorders.35

On the other hand, some research has pointed to the relationship between maternal prenatal stress and anxiety levels with compromised optimal development of the hypothalamic-pituitary-adrenal axis (HPA), limbic system, and prefrontal cortex.36,37 Fear and concern about the disease affect the behaviour of individuals. Although there is no evidence of vertical transmission of SARS-CoV-2, uncertainty affects the most susceptible expectant mothers, as no one wants to be infected with a virus that presents a high risk of morbidity.38

Research on the psychological effect of the COVID-19 pandemic on pregnant women as a group of special vulnerability is still scarce in the Spanish population.23,24 Therefore, there is a need to assess levels of anxiety and fear as attitudinal factors that could be relevant in the adoption of preventive measures against new and future health threats that may require measures to restrict our freedom or health measures such as isolation or confinement.

In this context, for the assessment of the presence of fear and anxiety of COVID-19, a psychometric validation and development of the AMICO_Pregnant scale was carried out, a scale adapted and validated for specific use in the population of pregnant women in Spain. AMICO_Pregnant was designed by a panel of 10 experts using the Delphi technique and it was validated in previous studies, with a two-factor structure and 16 items that explained 78.936% of the variance. The version agreed by the panel of experts was piloted on a set of 1013 pregnant women with a mean age of 33.38 years and a standard deviation of 5.2 years, who completed a total of 978 surveys over a period of 4 months. In relation to the current pregnancy, 3.4% of the pregnant women were in the first trimester of pregnancy, 13.6% in the second one, and the majority were full-term (83%) (median=37 weeks). Over 58.8% were primigravidae and 98.7% had a singleton pregnancy. Pregnancy control was mainly low risk (52.1%).

The mean of the AMICO_Pregnant scale was 5.04 points (SD=2.36), with a range of scores from 1 to 10. The study of percentiles and quartiles allowed for a three-level relationship for the scale, ie low level with scores from 0 to 3.06, intermediate level from 3.07 to 6.53, and high level with a score above 6.54. The reliability indices measured by both Cronbachs Alpha and McDonalds Omega were 0.95 and 0.94, respectively. The bivariate analysis showed statistically significant relationships between the AMICO_Pregnant scale and some of the assessed obstetric variables.39

The aim of the study was to assess the levels of fear and anxiety of COVID-19 in the particularly vulnerable population group of women during pregnancy.

There are significant differences between the level of anxiety and fear of COVID-19 and the different categories of the studied variables.

Descriptive cross-sectional study based on questionnaires disseminated between 24 March and 26 July 2022.

The study by Gomez-Salgado et al40 developed the Anxiety and Fear of COVID-19 Assessment Scale (AMICO), based on the original 10-item version of the FCV-19 scale and incorporating 8 new items assessing the specific presence of anxiety due to COVID-19. The research confirmed a two-dimensional structure of 16 items, as well as two factors that explained 64.8% of the variance.41 The reliability study gave a total Cronbachs value of 0.92 for factor 1 (Anxiety) and of 0.90 for factor 2 (Fear). The AMICO scale has been validated in different population groups: the elderly,42 nurses,43 the general adult population,44 or the general adult population in United Kingdom.45

In this context, the scale for assessing fear and anxiety of COVID-19 in pregnant women (AMICO_Pregnant) was designed and validated in a previous study based on the Spanish version of the AMICO scale. The adaptation of AMICO to the population of pregnant women was carried out using the Delphi technique by a panel of 10 experts with an academic level of Doctor or Official Masters Degree and whose areas of knowledge were obstetrics, public health, or psychology; one of the experts was also pregnant at 28 weeks of gestation.

Exploratory factor analysis obtained a two-dimensional structure of 16 items (anxiety and fear) that could explain 78.935% of the variance (KMO test=0.962; Bartlett's test X2=19,100.292; p<0.000).

The reliability study was carried out with both Cronbachs Alpha and McDonalds Omega coefficient estimates, giving values of 0.95 and 0.94, respectively.

The questionnaire contains 16 items with a Likert-type response scale from 1 to 10, where 1 expresses a very low level of fear and anxiety and 10 a very high level. The total score of the scale is obtained by calculating the mean score from the self-reported responses, and the value ranges from 1 to 10 points. A mean total score 3.06 indicates a low level of fear and anxiety; a mean total score between 3.07 and 6.53 is indicative of intermediate levels of fear and anxiety; and a mean total score 6.54 would suggest a clinically relevant level of fear and anxiety. The MannWhitney U statistic confirmed the significant differences between the different levels, with values of p=0.001.39

The number of births registered in Spain during 2021 amounted to 337,380 births.46 The required sample size was calculated considering a confidence level of 95%, for a maximum sampling error of 5%, and it amounted to 385 participants.47 A probabilistic snowball sampling was carried out. The research team carried out a process of identification of professional associations and health centres which were asked to collaborate in the dissemination, guaranteeing the confidentiality of the data. However, the final sample obtained was 978 pregnant women.

Sociodemographic variables included age, place of residence, marital and cohabitation status, level of education, occupation and employment sector, personal history, obstetric history, relationship with the disease, feeling of danger, and vaccination status against COVID-19.

Finally, the last section of the questionnaire included the AMICO_Pregnant scale with the aim of measuring fear and anxiety of COVID-19 disease. The final survey consisted of a total of 33 items.

The data collection tool was designed using the GoogleFoms software. The questionnaire was disseminated by mass mailing to residents throughout Spain by a free national magazine with content and newsletters related to pregnancy and childcare (Mi Beb y Yo), and a link and QR code redirecting to the aforementioned GoogleForms form was distributed to subscribers who had agreed to participate in the research.

In turn, different organisations related to perinatal care, professional associations, obstetrics and gynaecology nurse training centres, primary care centres, as well as public and private hospitals in the national territory also collaborated in the dissemination of the survey. The researchers contacted the different organisations that were asked to collaborate in the dissemination. Some professionals belonging to the different contacted organisations collaborated in the dissemination of the questionnaire when the pregnant woman went to her prenatal check-up. The nurse specialist in obstetrics and gynaecology offered her the possibility to participate in the study by means of a QR code that redirected her to the online questionnaire.

Once the respondent accessed the form, information was displayed on the legal conditions, consent, and confidentiality of data, which they had to accept in order to access the questionnaire; the form also included a contact e-mail address to provide a reference for consultation and exercise of rights and duties in terms of data confidentiality, and to make enquiries about the study.

Univariate and bivariate data analysis was performed using the Statistical Program for Social Sciences (SPSS) version 28.0 package. First, an analysis of the normality of the distribution of the AMICO_Pregnant score data was performed by calculating the KolmogorovSmirnov test, showing a significance level of p=0.000, which indicated an absence of normality.

For the bivariate analysis, different non-parametric tests were used based on the specific characteristics of the variables studied: Wilcoxon signed-rank test, KruskalWallis test, Mann-Whitney U-test, and Spearmans Rho, also estimated to analyse the correlation between two quantitative variables.

The study was conducted in accordance with the Declaration of Helsinki 2013 by the World Medical Association and the European Unions Good Clinical Practice Directive (Directive 2005/28/EC). The research protocol was presented and approved by the Biomedical Research Ethics Committee of the province. The research protocol was also approved.48 All participants were informed about the procedure and objectives of the study, and they gave their informed consent prior to the start of data collection.

The questionnaire was answered by a total of 1013 pregnant women over 18 years of age and with a mean age of 33.38 years (SD=5.28). Of the total sample, 35 (3.5%) women completed the questionnaire partially, resulting in a complete sample of 978. 3.4% were in the first trimester of pregnancy, 13.6% in the second trimester, and 83% in the third trimester (M=37; SD=3.44). 30% were recruited at the antenatal clinic of the University Regional Hospital of Malaga, Maternity Hospital.

Responses were also obtained from all the Spanish provinces. 60.9% of the responses were from the province of Malaga, being the province with the highest response rate, followed by 5.7% from Madrid and 4.1% from Barcelona. The provinces of Seville and Valencia had participation rates between 3% and 5%. The rest of the 47 provinces in Spain showed a participation rate of less than 3% (Table 1).

Table 1 Description of the Sample

In terms of marital status, 89.1% declared themselves to be in a relationship and only 10.9% were single; 47.5% said they were married, 41.1% were in a stable relationship, and 10.4%, 0.7%, and 0.4% said they were single, divorced or widowed, respectively. In relation to the employment sector, 33.6% belonged to the service sector, 22.8% to auxiliary, technological, or financial activities, 20.8% to the health sector, 13.9% to education, and the remaining 6.9% were engaged in other activities. 2% were unemployed.

The highest level of education was university (35.3%), followed by upper secondary school or vocational training (28.7%), and Masters degree or doctorate (16.1%). Primary and secondary studies were reported by 3.4% and 15.7%, respectively, and only 0.8% had no studies.

Obstetric history showed a representative sample in terms of nulliparity or multiparity, with 58.8% being pregnant for the first time and 41.2% having been pregnant two or more times. 98.7% were singleton pregnancies and 68.7% were spontaneous conception and wanted pregnancies. Personal history showed 73.8% of pregnant women with no history of clinical interest and the main complaints were respiratory diseases, bronchial asthma, or previous pneumonia in 7.4%, followed by autoimmune diseases in 4.2% or coagulation problems in 3.1%.

In relation to contact with COVID-19 disease, 48.7% of the pregnant women had had contact with the disease, 9.1% stated that a member of their family had had contact with COVID-19, and the remaining 42.3% stated that they had not had contact with the disease or only outside their family circle. In this regard, 93.9% reported having received at least one dose of the COVID-19 vaccine. In relation to the influence of the disease on their birth plans, 19.6% acknowledged having suffered changes in their birth plans due to the pandemic situation.

In relation to the self-perception of the pandemic danger situation with respect to the previous year (2020) in a range of 1 to 10, the mean was 4.89 points (SD=2.07), and in relation to the self-perception of the levels of fear and anxiety with respect to the previous year, in a range of 1 to 10 the mean was 4.75 points (SD=2.49).

The mean of the AMICO_Pregnant scale was 5.04 points (SD=2.36). The bivariate analysis showed statistically significant differences in the mean score of the scale and the following variables: weeks of gestation, contact with the disease, vaccination schedule, and changes in the birth plan (Table 1). Pregnant women with pregnancies closer to term showed lower levels of fear and anxiety than those at lower gestational weeks (37 wk; =5.04 p=0.000). In addition, pregnant women who had experienced the disease showed lower levels of fear and anxiety (=4.66) than those who had not had contact with the disease (=5.22) or the contact had been outside their family circle (=5.23). On the other hand, pregnant women who had received any dose of the COVID-19 vaccine showed similar levels of fear and anxiety, with no significant differences between the number of doses received (1 dose=5.26; 2 doses=4.90; 3 doses=5.11). However, those who had not received any doses showed significantly lower levels (=3.09 sig.=0.000). In relation to changes in their birth plan, pregnant women who had not experienced changes in their birth plan or who had no birth or delivery plan showed lower levels of fear and anxiety (=4.33) than those whose birth plans had been modified (=6.04).

The bivariate analysis of the correlation between quantitative variables showed statistically significant results. On the one hand, between the levels of fear and anxiety according to the AMICO_Pregnant scale and the self-perception of the level of danger of the pandemic situation with that of a year earlier using the Tau B correlation coefficient (0.556 p=0.000), ie, pregnant women who reported a low level of danger showed lower levels of fear and anxiety. On the other hand, self-reported levels of fear and anxiety in relation to the pandemic situation one year earlier showed a strong relationship with levels of fear and anxiety assessed with the AMICO_Pregnant scale according to the Tau B correlation coefficient (0.727 p=0.000), ie pregnant women who self-reported a decrease in levels of fear and anxiety compared to one year earlier showed lower levels of fear and anxiety on the scale. Finally, there was no significance between anxiety and fear levels and the age of the pregnant woman.

A categorical regression analysis was performed with the mean total score of the AMICO_Pregnant scale as the dependent variable and the categorical variables that had shown significant differences in the bivariate analysis, showing an adequate adjustment; 28% of the variance of the data was explained by this regression model, which made it possible to identify the variables that predicted high levels of fear and anxiety: administered vaccinations, contact with COVID-19 patients, and changes in the birth plan obtained p-values <0.05 (Table 2).

Table 2 Categorical Regression Model

In this sense, the results showed that women who had received only one dose of vaccination had higher levels of fear and anxiety compared to those who had received a complete vaccination or even those who had not received any dose. Pregnant women who had not had contact with the disease within the family had higher levels of fear and anxiety, and finally, the existence of a birth plan that had undergone changes or the absence of any birth or delivery planning were shown to be events that increased the levels of fear and anxiety in pregnant women.

This study attempts to indicate the effect that the COVID-19 pandemic has had on the levels of fear and anxiety in pregnant women as mediators of psychological well-being. This situation of special vulnerability with the presence of specific expectations and attitudes may have an impact on the ongoing pregnancy or become a determinant of the psycho-affective perception of the pregnant woman. However, when it comes to mental health in relation to the pandemic, it is difficult to find research that focuses exclusively on pregnant women despite being a particularly vulnerable group. Some authors have reported moderate levels of fear of the disease in pregnant women one year after the start of the pandemic.17

Fear during pregnancy may represent a relevant risk factor in the normal development of the current pregnancy; some authors point out an incidence between 45% and 77%.2,49,50 In this regard, there is research on the consequences that fear can have on infant psychophysiological development.23 Moreover, anxiety is an additional risk factor that intervenes in neonatal development in a number of ways.37,5153

This research has shown the multiple circumstances that can have an impact on the levels of fear and anxiety of women with a pregnancy in progress, becoming determinants of their state of mind.

More specifically, the findings show that pregnant women show an intermediate level of fear and anxiety, in agreement with research carried out two years earlier, such as that by

Allande-Cuss et al,54 which assessed levels of fear and anxiety in the general population in Spain at the start of the pandemic, showing a psychological impact considered moderate. However, the work by Morgado-Toscano et al45 in the UK general population showed lower levels of psychological impact during the months of April to June 2021 in relation to both the studies by Allande-Cuss et al35 and the findings of the present study.

The results may have been influenced by several factors: first, the timing of data collection and the pandemic situation; during the data collection period of this research, vaccination coverage in Spain was above 92% in the population over 12 years of age, more than 92% of those over 60 years of age had received a booster dose, and the main indicators for monitoring the pandemic were at a low risk level in most parts of Spain.28

On the other hand, sex, especially if we compare a female-only population group with a group of both sexes, should be taken into account. It seems to be a determinant in the levels of fear and anxiety, and it may have contributed to intermediate levels despite the low risk situation of the pandemic at the time. Indeed, different studies have always shown higher levels in women compared to men.29

Uncertainty in relation to pregnancy or delivery plans, clinical variability, uncertainty in relation to contagiousness in their environment, the lack of a complete vaccination schedule and, consequently, uncertainty in relation to the disease and its effects on both mother and newborn have shown to be factors underlying these increases in the levels of fear and anxiety in pregnant women during the pandemic, according to the data collected. The group of women who had not been infected with COVID-19 showed higher levels of fear and anxiety when compared to the group of women who had been infected with COVD-19 or whose contact with the disease was limited to outside their family circle. These results support the data obtained in other research on the general population or on vulnerable population groups such as the elderly.42,54

A closer examination of the findings and conclusions of the authors cited above suggests that the uncertainty caused by an unexpected circumstance considered a threat to health, in this case a pandemic situation, can generate emotions of fear and anxiety in pregnant women5557 as they do not know how to deal with a circumstance that alters their expectations and doubt their ability to adapt to the new circumstance, thus producing fear and mistrust towards the near future.58 On the other hand, the findings of this research in relation to the variables related to individual health do not show significant differences in the evaluation of the levels of fear and anxiety in relation to personal history, type of conception, type of prenatal control, number of embryos or number of previous pregnancies. In this sense, in agreement with other research, despite the situation of special vulnerability on a physiological level due to the changes that pregnancy produces in the woman and despite the fact that the health of the woman is fundamental for a state of well-being of the foetus, the woman prioritises the health of the pregnancy or of the baby over her own; this is a psychological approach linked to an innate attitude of protection of the offspring.27,37,59 However, the pandemic situation may have exacerbated this protective attitude and generated a response by increasing levels of fear and anxiety in women.33 In addition, multiparity has been considered by some authors as a protective factor against levels of anxiety and fear in relation to pregnancy or childbirth,34 probably due to the experience of having had a previous birth. However, the findings do not show significant differences between primigravidae and multigestation, which may be explained by the specificity of the questions asked in the data collection on fear and anxiety related to COVID-19 disease.

In relation to age or marital support as influential factors in the levels of fear and anxiety, the descriptive analysis used in the interpretation of the data shows a homogeneous sample in terms of age, which justifies the absence of significant differences, and on the other hand, a sample with 89.1% in a couple situation justifies the absence of significant differences in this sense.

In addition, social variables such as employment sector or level of education might have been particularly affected by the timing of the research, which was carried out with hardly any employment restrictions, which would justify the absence of significant differences.

Finally, pregnant womens perceptions of danger may vary depending on multiple factors such as the information they receive, their experiences within their family circle, their social or work environment, and the way they feel about their pregnancy.23,49 Although the pandemic has brought about changes in lifestyle, in relationships with others, or with the healthcare system,60 when analysing the results obtained in this study, pregnant women who understood the pandemic situation to be better than in the past, which may suggest that they had adapted to living with the new normality, showed greater emotional resilience in the face of the present situation, reporting lower levels of fear and anxiety.

Therefore, the findings are consistent with current thinking on the emotional consequences of a pandemic.1,2,49,50,61 The pregnant woman, at first, tends to show negative feelings about the potential threat posed by the pandemic during pregnancy, with positive emotions emerging secondary to circumstances such as the vaccination schedule, weeks of gestation, maintenance of her birth plans, or previous experiences with the disease. This evolves into a state of ambiguity that is common in pregnant women due to feelings of vulnerability against which they do not feel prepared, but nevertheless, they face the threat thanks to their coping mechanisms and the support of their social circle.

Regarding the limitations of this research, it should be noted that 60.9% of the sample was collected from the city of Malaga. However, this province constitutes 3.6% of the countrys population and the sample collection process was not probabilistic. On the other hand, a high proportion of educated women participated, which is common in research, as educated women are the ones who participate the most in research.35 In this sense, therefore, the results obtained are not entirely extrapolable to the entire Spanish population despite the sample size, and besides, the pandemic situation has evolved differently in each geographical area. Another limitation is that none of the participants informed of mental health disorders, which may be attributed to the participants fear of reporting them.

Furthermore, the results obtained are in the context of the COVID-19 disease. However, despite the above limitations, the research has generated interesting results for the nursing clinical practice that may be applied in the context of other respiratory infectious diseases affecting a particularly vulnerable group such as pregnant women, as well as for the scientific community interested in the field of mental health.

The results of this research show certain characteristics of pregnant women who, despite a pandemic situation in a state of remission, reported intermediate levels of fear and anxiety. Pregnant women who were close to term, without previous close contact with the disease, without a complete vaccination schedule, and who had undergone changes in their pregnancy or birth expectations were more likely to report negative experiences in terms of levels of fear and anxiety. Given the potential consequences of untreated high levels of fear and anxiety during gestation,32,57,62 identifying women who are more immersed in their expectations of pregnancy and childbirth by working proactively to help manage greater flexibility towards the different deviations that may occur in pregnancy and childbirth can help moderate fear and anxiety if pregnancy and childbirth do not proceed according to their expectations. Mental health prevention and treatment in the antenatal period helps to mitigate the effects of high levels of fear and anxiety at a time of particular mental health vulnerability.

This research has not received any public or private funding.

Francisco Javier Muoz-Vela and Regina Allande-Cuss are co-first authors for this study. The authors of this research have no conflicts of interest, and the research findings are the product of the analysis of the results obtained.

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37. Jelii L, Sovilj M, Bogavac I, et al. The Impact of Maternal Anxiety on Early Child Development During the COVID-19 Pandemic. Front Psychol. 2021;12. doi:10.3389/fpsyg.2021.792053

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Covid Vaccine Mandates Banned in Texas – Undark Magazine

November 15, 2023

A sweeping ban on Covid-19 vaccine requirements for all private businesses, including hospitals, is the latest blow to medically vulnerable Texans who rely on others immunization to shield themselves from highly transmissible viruses.

Tamer coronavirus variants and a soft vaccine booster rollout have contributed to a lessened sense of urgency around the virus. But the new measure, which Gov. Greg Abbott signed into law on Friday, could risk the health of groups like organ transplant recipients, cancer patients, and those with underlying conditions as common as severe asthma.

These risks led to some bipartisan dissent during original Senate discussions of the bill, especially from state Sens. Borris Miles, D-Houston and Kelly Hancock, R-Fort Worth, who both take immunosuppressants for their respective kidney transplants.

I live a pretty normal life and am not fearful, but it does make you think about others, Hancock said. Theres just a balance we have to keep in mind just try to always think of others and the positions they may be in.

For one, vaccines are less effective in some of these patients because their conditions prevent their bodies from manufacturing the white blood cells that can recognize and fight off viruses. But even with protection, the virus can exacerbate underlying conditions and lead to long-term symptoms of the virus, known as long Covid.

Scientists and health experts agree that the vaccine is safe and effective for most people with functioning immune systems, in reducing both transmission and severity of the virus.

Everybodys going to be different, so its not automatic that a compromised individual will end up in the hospital or in the ICU, said Jimmy Widmer, an internal medicine specialist. But what we do know throughout the past three and a half years of Covid, is that time and time again, study after study has shown that those who are immunocompromised are hospitalized at a higher rate.

For all of Undarks coverage of the global Covid-19 pandemic, please visit our extensive coronavirus archive.

In the past, state lawmakers efforts to stymie vaccine mandates have excluded hospitals and other medical facilities partially because under federal emergency rules, the U.S. Centers for Medicare and Medicaid Services required vaccinations among employees.

The regulation was withdrawn over the summer, and since then, many facilities have differed on their rules. A vast majority of them did not even have a blanket mandate at this point, said Carrie Kroll, an advocacy leader for the Texas Hospital Association.

Were very hopeful that the worst of the Covid pandemic is behind us, Kroll said. But we know with infectious disease, what may rule today may not rule in six months, in terms of disease levels and what this disease morphs into.

In the end, lawmakers included a provision that would allow hospitals to require unvaccinated employees to wear personal protective equipment despite advocates fighting for a complete exclusion from the bill.

Alice Barton, a retired infectious disease doctor living in Austin, said its impossible to imagine this measure will be enforced. Barton, 70, has severe asthma and an autoimmune disease, and said she just received the triple vaccine for the flu, Covid, and RSV.

Im the only person still who wears a mask to the doctors office. Im one of two people in my church who wears a mask, Barton said. One becomes lonely. Its not just being physically isolated from other people. Its that other people arent thinking about us anymore.

In the end, lawmakers included a provision that would allow hospitals to require unvaccinated employees to wear personal protective equipment.

Barton is one of many people worried that state lawmakers will continue further down the warpath against vaccine requirements, onto other immunizations like those for polio or measles.

But, with the law now in place, advocates hope to transform the idea that people have to get the vaccine into an idea that they should to protect their peers.

Chase Bearden, a leader at the Coalition of Texans with Disabilities, said now that theres less external pressure on Texans to make this decision, he hopes they realize its one they can make of their own accord.

What can we all do on a personal level to keep everyone safe, especially those who may not have the great health that the rest of us do? Bearden said. Theres so many family members that are going through cancer treatment or living with a chronic health condition. And yes, youre a healthy person. You dont think you need it. But if you get it, you easily pass that on to the next person who takes it home.

Stephanie Duke, an attorney who helps handle disaster management at Disability Rights Texas, said the state should be doing everything it can to promote public health, and that should include people with disabilities.

Alice Barton is one of many people worried that state lawmakers will continue further down the warpath against vaccine requirements, onto other immunizations like those for polio or measles.

People go in to get health care, and you would expect your health care provider to be doing everything they can to make that safe, Duke said.

Duke said government officials often forget to include disabled people in disaster preparedness, and the global pandemic was no different than a hurricane evacuation. Several policies issued during the height of the public health emergency have hurt those who dont have functioning immune systems or with chronic illnesses.

For instance, when the pandemic began, disabled people werent a specific category included in the demographic data that states began collecting on the virus, she said.

Shit is going to happen. Lights are going to go out, were going to have viruses again. This is the world that we live in, Duke said. But how we plan for it, is how we give people choices to promote their safety, autonomy and independence after an event and build that resilience.

Neelam Bohra is a 2023-24 New York Times disability reporting fellow, based at The Texas Tribune through a partnership with The New York Times and the National Center on Disability and Journalism.

Disclosure: Coalition of Texans with Disabilities, Texas Hospital Association, and The New York Times have been financial supporters of The Texas Tribune, a nonprofit, nonpartisan news organization that is funded in part by donations from members, foundations, and corporate sponsors. Financial supporters play no role in the Tribunes journalism. Find a complete list of them here.

This article was first published by The Texas Tribune, a nonprofit, nonpartisan media organization that informs Texans and engages with them about public policy, politics, government, and statewide issues.

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Covid strain that killed 8000 cats found in UK. The symptoms to look out for – The Independent

November 15, 2023

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A deadly and highly infectious strain of coronavirus that killed 8,000 cats in Cyprus has made its way to the UK, scientists have said.

A cat that was brought to the UK from the Mediterranean island was found to be infected, after it developed symptoms and was sent for tests and treatment by its owner.

The strain has been identified as a new hybrid of existing feline coronavirus and canine coronavirus and is called F-CoV-23, while it is not linked to Covid-19.

Symptoms for the coronavirus include mild diarrheoa and lethargy, yet in the majority of cases cats display no symptoms at all, which makes it difficult to diagnose and treat.

However, one in 10 cases mutates into the virus feline infection peritonitis (FIP), which is often deadly and causes a loss of appetite, jaundice and anemia.

There is no evidence this disease can spread to humans and dogs (Nick Ansell/PA)

(PA Archive)

It is estimated that this new virus was responsible for an outbreak in Cyprus, although reports suggest that the number of killed cats could be more than 300,000.

In a desperate attempt to curb the outbreak, Cypriot officials authorised human Covid treatments on cats to be used in August.

However, scientists from the Royal Veterinary College, the University of Edinburgh and the Cypriot government found that the British case had the same genetic fingerprint as 91 of those in Cyprus.

In the study, which has been published before it has been peer-reviewed, the scientists warn there is a significant risk of the outbreak spreading further.

This is exemplified by the recent confirmation of a first UK-imported case with further investigations into other cases ongoing, they add.

It also found that the combination of canine and feline coronaviruses which includes the cat virus gaining the dog pathogens spike protein has led it to become more infectious.

Cats diagnosed with feline infection peritonitis, which is caused by the coronavirus, become lethargic, and commonly suffer from a fever, a swollen abdomen and inflammation.

It is almost always fatal unless treated, while a veterinary drug called GS-441524 can treat FIP effectively if given early but is currently expensive.

While they are effective, it is currently illegal for vets to use human Covid drugs, such as remdesivir and molnupiravir, to treat a cat with FIP in the UK.

Experts have said there is no evidence that dogs or humans can be infected, while there is no reason for worried cat owners to keep their pets inside and away from other animals at present.

Dr Alexandros Chardas, Lecturer in Veterinary Anatomic Pathology, and Dr Sarah Tayler, Lecturer in Small Animal Internal Medicine, both at the Royal Veterinary College, told The Independent: If the cat has not travelled to Cyprus or been in contact with other cats that have visited Cyprus, the risk is minimal.

Given the low density of stray cats in the UK, the likelihood of FCoV-23 spreading is considered to be low. However, catteries, rehoming centres, pet hotels, and veterinary practices should remain vigilant and informed about this emerging virus.

The cats develop the classic signs of FIP with enlarged abdomen and can also be off their food. Occasionally, they may display neurological clinical signs or experience difficulty breathing. In the presence of suspected clinical signs, owners are advised to promptly contact their veterinarian for assessment and guidance.

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Covid strain that killed 8000 cats found in UK. The symptoms to look out for - The Independent

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