Category: Covid-19

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Measuring job stress of dental workers in China during the COVID-19 pandemic: reliability and validity of the hospital … – BMC Psychiatry

April 2, 2024

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Measuring job stress of dental workers in China during the COVID-19 pandemic: reliability and validity of the hospital ... - BMC Psychiatry

Incident allergic diseases in post-COVID-19 condition: multinational cohort studies from South Korea, Japan and the UK – Nature.com

April 2, 2024

Data source

The Kyung Hee University (KHUH 2022-06-042), the Korea Disease Control and Prevention Agency (KDCA), the National Health Insurance Service (NHIS; KDCA-NHIS-2022-1-632) of South Korea, JMDC (PHP-00002201-04), and UKB (94075) approved the study protocol.

Written informed consent was obtained from all participants at enrollment. We used three large-scale, nationwide and population-based cohort designs in this study: a South Korean nationwide cohort (K-COV-N cohort [main cohort]; total n=10,027,506), a Japanese claims-based cohort (JMDC cohort [replication cohort A]; total n=12,218,680) and a UK prospective cohort from the UK Biobank (UKB cohort [replication cohort B]; total n=468,617). Both the K-COV-N and JMDC cohorts employ a universal health insurance system. The UKB, meanwhile, is a dataset comprised of voluntary participation, including biomedical samples and health information. Detailed explanations of the JMDC and UKB cohorts can be found in supplemental material section.

The K-COV-N cohort is a large-scale, nationwide, general population-based cohort in South Korea, covering 98% of the South Korean population34. The cohort was developed and provided by the NHIS of South Korea and KDCA focused on individuals aged 20 years between January 1, 2018, and December 31, 2021. It contained information on COVID-19 vaccination, SARS-CoV-2 test results, COVID-19-related outcomes, results of national health examination, death records, and health insurance data including outpatient and inpatient information. The following characteristics of the Korean database enable us to construct a well-designed cohort: (1) A comprehensive healthcare system, implemented by the Korean government, covers people who have been infected with SARS-CoV-2; (2) all information was anonymized by the Korean government34; (3) It includes SARS-CoV-2 test results, vaccination status, and COVID-19-related hospital records; and (4) the overall predictive value for diagnostic records of the NHIS was 82% according to a previous study6,35,36.

We included all individuals aged 20 years with COVID-19 and non-infected participants from 2020 to 2021 (total n=10,027,506). We precluded those who meet the following criteria: (1) insufficient socioeconomic information or died before; and (2) history of allergic diseases in the pre-observation period, defined as two years (n=4,335,150). Eventually, 5,692,356 individuals were included from South Korea in this study.

The exposure was SARS-CoV-2 infection, which was defined if the participants tested positive for COVID-19 either by real-time reverse transcriptase polymerase chain reaction or rapid antigen testing of nasopharyngeal swabs. We considered the original SARS-CoV-2 if the initial infection was before July 31, 2021, and the delta variant was from August 1, 202137. Patients who were admitted to an intensive care unit and those who required oxygen therapy, extracorporeal membrane oxygenation, renal replacement, or cardio resuscitation were perceived as having moderate to severe COVID-1938. The others were considered having mild COVID-19. The COVID-19 vaccination status was categorized according to dosage (unvaccinated, 1, and 2 times). Individuals who were vaccinated with the Johnson & Johnson/Janssen vaccine were considered twice vaccinated after the single dose.

The primary outcome was the onset of allergic diseases, including: asthma, AR, AD, and FA7. Also, the term allergic diseases refers to a diagnosis of any of the following condition: asthma, AR, AD, or FA39,40. Allergic asthma was identified as asthma combined with an additional allergic disorder (AR, AD, or FA), while non-allergic asthma was classified as asthma occurring in the absence of any allergic diseases7. We defined patients with allergic diseases as those having at least two claims during the observation period and were taking relevant medications. We provided a list of the ICD-10 codes and medications used to define each disease in this study (TableS1).

The demographic characteristics of the participants were obtained from the health insurance database as followings: sex, age (2039, 4059, and 60 years), household income (low [039 percentile], middle [4079 percentile], and high [80100 percentile]), and region of residence (urban and rural)34. The information on body mass index (underweight [<18.5kg/m2], normal [18.523.0kg/m2], overweight [23.025.0kg/m2], obese [25.0kg/m2], and unknown), blood pressure (systolic blood pressure <140mmHg and diastolic blood pressure <90mmHg, systolic blood pressure 140mmHg or diastolic blood pressure 90mmHg, and unknown), fasting blood glucose (<100, 100mg/dL, and unknown), serum total cholesterol (<200, 200240, 240mg/dL, and unknown) and glomerular filtration rate (<60, 6090, 90mL/min/1.73m2, and unknown) were included from the fasting serum samples of national health examination41. The CCI, history of cardiovascular disease, chronic kidney disease, and chronic obstructive pulmonary disease, history of medication use for diabetes, hyperlipidemia, and hypertension, smoking status (non-, ex-, and current smoker), alcoholic drinks (<1, 12, 34, 5 days per week, and unknown), and aerobic physical activity (sufficient [600 Metabolic Equivalent Task scores], insufficient, and unknown) were collected based on ICD-10 code and/or results of national health examination12,42. Additionally, to minimize bias related to missing data, we focused on the missing indicator method, generating missing indicator variables and incorporating them into the adjustment variables43.

We executed 1:5 exposure-driven propensity score matching to balance the distribution of covariates in the two groups. We used a greedy nearest-neighbor algorithm with random selection without replacement within caliper widths of 0.001 standard deviations44,45. We assessed the adequacy of matching by comparing SMDs. A SMD<0.1 indicated no major imbalance in the two groups44,45. We constructed the following covariates as matching variables for South Korea: age, sex, household income, region of residence, CCI, body mass index, blood pressure, fasting blood glucose, serum total cholesterol, glomerular filtration rate, smoking status, alcoholic drinks, aerobic physical activity, and history of medication use for diabetes mellitus, dyslipidemia, and hypertension. For the replication cohorts of Japan and the UK, we also used similar covariates as matching variables (Supplement Material). All covariates were regarded as adjustment variables in further statistical models. After propensity score matching, a total of 836,164 individuals were included in the study (FigureS1 and Table1).

The same ICD-10 codes, definition of exposures and outcomes, observation period, and propensity score matching were utilized for the JMDC and the UKB cohorts as well (Supplement Material). Due to the absence of SARS-CoV-2 vaccination data41, the JMDC and the UKB cohort were used only to validate the main findings of the K-COV-N cohort. After propensity score matching, the JMDC and the UKB cohorts consisted of 2,541,021 and 325,843 individuals, respectively (Figs.S2 and S3).

As aforementioned, SARS-CoV-2 infection was defined as primary exposure and the incident allergic diseases after at least 30 days of infection was defined as the primary outcome in the general population-based cohorts of South Korea, Japan and the UK (TablesS2S3). To overcome immortal time bias, the date of the first diagnosis of SARS-CoV-2 was perceived as the individual index date. We considered 20182019 the pre-observation period to observe the history of medical diagnosis. The observation period of the Korean cohort was between January 1, 2020, and December 31, 2021. The follow-up ended on December 31, 2021, or upon the death of the subject (Fig.S4).

We performed 1:5 exposure-driven propensity matching in the nationwide cohorts of South Korea, Japan, and the UK (Table1 andS4, S5). A Cox proportional hazard regression model with estimates of HRs and 95% CIs was used to explore incident overall and four subtypes (asthma, AR, AD, and FA) of allergic diseases associated with post-COVID-19 conditions45. We further assessed the time attenuation effect of allergic diseases following SARS-CoV-2 infection (<3, 36, and 6 months) to reduce reverse causation. This refers to the duration it took for patients infected with COVID-19 to be diagnosed with allergic diseases, and includes individuals who had not been diagnosed during the pre-observation period. We performed several subgroup analyses to the following parameters: severity of COVID-19 (mild and moderate to severe), strain type (original and delta), and dosage of SARS-CoV-2 vaccination (0, 1, and 2 times). In addition, we executed stratification analyses according to sex, age, household income, CCI, body mass index, alcohol drinking status, aerobic physical activity and strain type of SARS-CoV-2 (TablesS11S20). We used SAS (version 9.4; SAS Institute Inc., Cary, NC, USA) to perform all statistical analyses in this study. A two-sided p-value less than 0.05 was considered statistically significant (TablesS23S25).

We conducted sensitivity analyses to assess the reliability of the findings from our primary analyses. First, to validate the study results and identify detection bias, we included tympanic membrane perforation disease as a negative control in our analyses for both the main and replication cohorts (TableS26)46. Second, to reduce misclassification bias due to dyspnea, we performed an analysis excluding symptoms of dyspnea in asthma cases. (TableS27). Third, we established a strict diagnostic criterion for asthma in the main cohort (TableS28). We conducted analyses on cases diagnosed with asthma, considering those with a history of emergency department visits or hospitalization47. Fourth, allergic asthma and non-allergic asthma were compared as distinct groups due to differences in the asthma phenotype (TableS29). Fifth, in order to examine the impact of COVID-19 severity on allergic diseases, the mild group and the moderate to severe group were analyzed as two separate cohorts (TablesS21 and S22). Sixth, we analyzed the onset of allergic diseases in relation to SARS-CoV-2 infection status among individuals with the same number of vaccine doses, for understanding the long-term immune protection provided by the COVID-19 vaccine and its effectiveness extent (TableS30). In the same context, we conducted a time attenuation analysis to identify potential impacts, including the decrease in immunity over time (TableS31).

In the case of the main cohort and replication cohort A, the outcome measures were determined independently, without any involvement from the participants. In contrast, for replication cohort B, the participants were directly involved in determining the outcome measures through a process of voluntary reporting. The study design and implementation were conducted without consultation. However, we plan to disseminate the results of this study to all study participants and wider relevant communities upon request.

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Incident allergic diseases in post-COVID-19 condition: multinational cohort studies from South Korea, Japan and the UK - Nature.com

Senator O’Mara’s weekly column ‘From the Capitol’ — for the week of April 1, 2024 — ‘NY’s COVID-19 response still … – The New York State Senate

April 2, 2024

Senator O'Mara offers his weekly perspective on many of the key challenges and issues facing the Legislature, as well as on legislative actions, local initiatives, state programs and policies, and more. Stop back every Monday for Senator O'Mara's latest column...

This week, "NY's COVID-19 response still clouded in secrecy"

On the fourth anniversary of ex-Governor Andrew Cuomos now infamous March 25, 2020 executive order forcing New Yorks nursing homes to accept COVID-positive patients back into their facilities -- a move that many believe directly contributed to many COVID-related deaths in long-term care facilities -- I joined legislative colleagues and Voices for Seniors advocates at the Capitol last week to remember one of the most terrible chapters in this states history.

The remembering remains necessary because Governor Kathy Hochul and the state Legislatures Democrat leaders seem determined to forget.

The ongoing, unexplainable lack of urgency on a comprehensive, top-to-bottom, independent examination of New Yorks COVID-19 response -- including its costs, what New York did right and, more importantly, where things went wrong -- remains unacceptable, but thats where things stand. A desperately needed reassessment and reexamination has never been a frontline priority for Albany Democrats, even though its critical, unfinished work.

Exhibit A is the fact that Governor Hochul has shown no interest whatsoever in getting to the bottom of New Yorks tragic decision to pressure nursing homes into accepting COVID hospital patients. As I said, it will forever be one of the saddest chapters in this states history. We cannot allow it to be ignored and forgotten. New York States COVID response needs to be independently examined for the sake of justice for the families who lost loved ones in nursing homes and to ensure that what went wrong, on many levels, never happens again.

Yes, two years ago, the Hochul administration announced a contract with a Virginia-based consulting firm to delve into the states COVID-related policies and actions beginning in March 2020. Set aside the troubling fact that the release of this reviews findings, despite costing taxpayers at least $4.3 billion, has been repeatedly delayed, even worse is that, from the beginning, these hired investigators have reported directly to the governor and her top aides.

In other words, Governor Hochul essentially chose to follow the playbook of her predecessor, disgraced ex-Governor Andrew Cuomo, by conducting an in-house review of New Yorks COVID response instead of convening an independent investigatory panel.

Recall what took place in that first in-house review conducted by the Cuomo-led state Health Department. The report tried to conclude that the March 25th directive "could not be the driver" of COVID cases or COVID-related deaths in nursing homes. However, a later investigation found that the report had been "substantially revised by the Executive Chamber and largely intended to combat criticisms" about the directive. It was later uncovered that former Governor Cuomo and his inner circle misreported the number of COVID-19 deaths in nursing homes with the state attorney general finding, in early 2021, that the Cuomo administration had undercounted COVID deaths in New York State by as much as 50%. Reports have revealed that the Cuomo response was replete with lies, misinformation, stonewalling, whitewashing, and ultimately, bald-faced personal gain for the former governor with a $5.1 million book deal.

Now, Governor Hochul wants to call her hand-picked reviewer an outside, independent investigation but thats far from the case. Many of us remain troubled that its the only reexamination underway and its one that will wind up being just another in-house, multi-million-dollar whitewashing of the truth -- another stonewalling effort to cover up and conceal bad decisions, especially on nursing homes.

The best way to ensure New York is better prepared in the future, is to openly and honestly assess the mistakes of the past. Thousands of families continue to mourn the loss of their loved ones in nursing homes due to the disastrous March 25th directive from former Governor Cuomo. They deserve the thorough, transparent investigation that was promised, not more inaction from their state government," Senate Republican Leader Rob Ortt said at our news conference last week.

Assembly Minority Leader Will Barclay added, Four years of unanswered questions, four years of families grieving, four years of zero transparency. Governor Kathy Hochul promised families an independent review of the states policies during the early phases of the COVID-19 pandemic. New York families are still awaiting that review. Making matters worse, the Reimagining Long-Term Care Task Force, which was designed to study deep-rooted issues in New Yorks long-term care systems and nursing homes, has never met.

From the earliest days of the pandemic, when I first began serving as the Ranking Member on the Senate Investigations Committee, Senate and Assembly Republicans have repeatedly requested legislative hearings equipped with subpoena power to seek answers and provide accountability for the families who lost loved ones due to the previous administrations mishandling of the pandemic.

Of course, since then, many abuses of power at the highest levels of New York government have come to light. Yet, for some reason, Albany Democrats have tried to keep any meaningful, independent reviews at bay. In fact, since the Democrats obtained complete one-party control six years ago, there has been no exercise of checks and balances between the branches of government, certainly no legislative oversight of the executive.

Remarkably, what has continually defined the post-COVID Hochul administration is a glaring lack of urgency to reexamine the pandemic response, absolutely no urgency from the Democrat supermajorities in both houses of the State Legislature for a review of all of it, from the beginning until now -- its costs, shortcomings, outright failures, what worked and what didnt, what actions should remain in place going forward and what needs to be scrapped immediately.

The longer the states one-party control avoids an honest and independent reassessment of the most devastating public health crisis this state ever faced, the more transparency gets clouded, the more credibility is eroded, and the more the effectiveness of New Yorks future responses is jeopardized and weakened. ###

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Senator O'Mara's weekly column 'From the Capitol' -- for the week of April 1, 2024 -- 'NY's COVID-19 response still ... - The New York State Senate

COVID-19-Associated Rhino-Orbito-Cerebral Mucormycosis: A Single Tertiary Care Center Experience of Imaging … – Cureus

April 2, 2024

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COVID-19-Associated Rhino-Orbito-Cerebral Mucormycosis: A Single Tertiary Care Center Experience of Imaging ... - Cureus

Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections | Communications … – Nature.com

April 2, 2024

Using a volumetric microfluidic-based DBS device that collects precisely 10l of whole blood, a protocol was tailored to analyze 276 proteins by proximity extension assays (PEA) (Fig.1). After benchmarking the procedure in a pilot study against paired EDTA plasma samples, DBS collected in Stockholm during the spring of 2020 and in Stockholm and Gothenburg during May of 2021 were analyzed for proteins associated with SARS-CoV-2 seropositivity. The studies revealed proteins relevant to COVID-19 pathogenesis and immune response.

Home-sampling devices were mailed to random individuals in metropolitan Stockholm and Gothenburg. Dried blood spots (DBS) were collected by finger pricking and mailed back to our laboratory for analysis. We eluted proteins from the DBS discs to first determine antibodies against SARS-CoV-2. Three studies were designed with donors stratified by serostatus and matched on self-reported information: Study 1 from 2020 compared antibody-negative (IgMIgG) with antibody-positive subjects (IgM+IgG+); Study 2 from 2020 compared IgM-positive (IgM+IgG) with IgG-positive donors (IgMIgG+); Study 3 from 2021 investigated vaccination-nave donors who were either antibody-negative (IgG) or antibody-positive (IgG+). Proximity extension assays (PEA) were applied to measure the levels of 276 proteins and evaluate their association with the different immune response groups.

To assess the suitability of the DBS preparation for proteomics analyses, protein profiles of 92 circulating proteins related to cardiovascular diseases were investigated (Fig.2ac). The levels, correlations, and interquartile range (IQR) between proteins were compared between EDTA plasma collected by venous blood draw and corresponding DBS samples collected at the same visit by finger-pricking from 12 donors (Supplementary Data1). It was found that 91 out of 92 proteins were detected in >90% of the sample types, respectively, the investigated proteins could be measured in DBS and paired plasma samples.

a The volcano plot displays the difference in relative protein levels between dried blood spot (DBS) and EDTA plasma obtained from 12 donors. The differences in the abundance of 92 proteins, reported as normalized protein expression (NPX), are categorized by FDR<0.01 (horizontal dotted line) and NPXof1 (vertical dotted lines). Blue dots represent proteins with the most significant differences, orange dots show those with noticeable differences, and green dots represent proteins for which no differences were observed. b Frequency of Spearman correlation coefficients for the 92 proteins. The vertical dotted line indicates rs=0. c Differences in protein IQR between DBS and plasma. The vertical dotted lines have been added for orientation at IQR= 0 and of0.5.

As shown in Fig.2a, paired t-tests showed that proteins with elevated NPX abundance levels in DBS (FDR P<0.01) were the platelet glycoprotein VI (GP6, expressed in skin or macrophages), the bleomycin hydrolase (BLMH, expressed in skin keratinocytes), azurocidin 1 (AZU1, expressed in neutrophils), as well as caspase 3 (CASP3, expressed in granulocytes). Likewise, collagen type I alpha 1 chain (COL1A1, expressed in fibroblasts) was more abundant in the plasma samples (Supplementary Data1). The examples suggest that finger-prick DBS samples can offer improved detectability for skin and blood cell-related proteins for the PEA and possibly other assays.

We also correlated the protein profiles to compare the ranking of the paired samples (rs=0.67 [0.61, 0.99]); see Fig.2b. In general, 62% (57/92) of the protein profiles correlate between plasma and DBS (rs>0.7). Profiles of cellular proteins such as previously mentioned CASP3, proteinase 3 (PRTN3, expressed in neutrophils), F11 receptor (JAMA, expressed in epithelial cells), and selectin P (SELP, expressed on fibroblasts), were the most discordant (rs<0). On the other hand, secreted proteins such as NPPB, IGFBP1, CD163, CPB1, and proteins known to leak into blood, such as EPCAM, LDLR, and SELE, were highly concordant (rs>0.95). Profiles of proteins elevated in plasma agreed with DBS profiles (rs=0.81 [0.45, 0.99]). The observed discordance between the two specimens was primarily found for proteins with higher levels in DBS samples. In addition, we examined the IQRs of the 92 proteins in the paired DBS and plasma. The IQR of the endothelial coagulation protein VWF was noticeably higher in plasma (alongside AZU1 and CASP3). The proteins MCP1 and RETN, both secreted by hematopoietic blood cells, revealed higher IQRs in DBS (Fig.2c). Considering all targets, the protein IQRs were not significantly different between DBS and plasma (P=0.44). Testing how the different ranges of detected proteins varied within a given sample type showed that the sample IQRs for DBS were significantly larger than those for plasma IQRs (P<1.8108).

Finally, we investigated the sample types concerning the blood cell expression and protein secretion using differences in NPX (NPX) and correlation (rs) values. With data from the Human Protein Atlas, we annotated the 92 proteins for their RNA expression in tissue29 and blood cells30 and the locations of protein secretion31; see Supplementary Data1. We found that 30% of the proteins were not expressed in blood cells. The levels of these proteins were similar between DBS and plasma (NPX=0.0) and correlated well (rs=0.80); see Supplementary Table1. The remaining 70% contained proteins expressed by different blood cell types. The NPX levels of these proteins were generally higher in DBS than in plasma (NPX=1.1 [0.4, 2.0]), and the correlation was lower (rs=0.59 [0.45, 0.72]). As shown in Supplementary Table2, the proteins secreted primarily into blood were more similar between DBS and plasma (NPX=0.3; rs=0.71; N=20) than proteins secreted to other locations (NPX=1.3; rs=0.61; N=20), or the cellular proteins (NPX=0.8; rs=0.61; N=20). This analysis suggests that protein leakage from blood cells contributed to the differences between the two sample types. Proteins secreted into the circulation by other organs than blood were more similar between the sample types.

The comparative analysis of paired DBS and plasma samples, exemplified here by 92 proteins, revealed differences and commonalities between the sample types. This points to the opportunity to uncover novel associations with DBS and suggests being cautious when aiming to validate findings with the other sample type.

In April 2020, we sent 2000 home sampling kits to the Stockholm population to measure antibodies against SARS-CoV-2 in dried blood22. The levels of IgM or IgG were determined using multiplexed bead-based assays that included multiple proteins representing the viral antigens. A population-based density cut-off of the antibody levels detected for the coronavirus spike and nucleocapsid proteins was used to classify the serostatus of each sample. Since not all individuals were diagnosed by PCR or experienced symptoms from the infection, we had only self-reported information about a diagnosed infection in one of the studies. For the other, we used only IgM and IgG to group participants into phases post-infection, as suggested by others32. In May 2021, a few months after vaccines against COVID-19 became available, we repeated the sample collection by sending a second set of 2000 home-sampling kits to populations in Stockholm and Gothenburg to determine the serostatus during the second year of the pandemic. Using their serostatuses, we selected representative subsets from our collections (N=228) to perform protein profiling by PEA.

The first study (study 1) from April 2020 was collected during the pandemics first wave. It consisted of 83 DBS donors, among which 44 participants were selected based on their serological immune response (IgM+IgG+). These seropositive participants presented the peak of the immune response, which we determined by detecting IgG and IgM against multiple SARS-CoV-2 antigens. The group was matched with 37 seronegative individuals (IgMIgG) based on demographic traits and reported symptoms. There were no significant differences in self-reported symptoms, and only three subjects in the seropositive group reported severe symptoms (Table1). The seropositive subjects of study 1 had only been exposed to the wild-type variant.

The second study (study 2), also collected in April 2020, included 66 participants representing the different phases of the serological immune response against the viral infection. The stratification was based on antibodies detected against the S proteins of SARS-CoV-2. We selected 26 individuals with signs of an acute immune response against the virus by being IgM seropositive only (IgM+IgG). This group was compared with 40 individuals without detectable IgM levels but being seropositive for IgG (IgMIgG+). The IgG+ group, annotated as having already passed the acute phase, was slightly older, but otherwise, there were no significant differences between the demographics and the reported symptoms (Table2). The subjects in study 2 had only been exposed to the wild-type variant.

The third study (study 3) was conducted in late spring 2021 and included 80 unvaccinated participants who donated DBS samples more than a year into the pandemic. We stratified these as seropositive (IgG+) or negative (IgG) based on antibodies detected against the S and N proteins of SARS-CoV-2. On average, the 37 seropositive individuals reported being infected five months before DBS sampling. Compared to the previous studies conducted during the dominance of the wild-type variant, study 3 represents a set of individuals with much longer possible exposure to different viral strains before the Omicron wave. The groups were matched for sex and age. There was a slight difference in age distribution between the groups, with the seropositive slightly older. The frequency of self-reported symptoms differed, with about a third of asymptomatic seropositive donors (Table3). The infections of seropositive participants in study 3 could have been caused by different SARS-CoV-2 variants.

In the following, we provide a general overview of the data and then discuss the details and analyses conducted for the three studies. We first evaluated the data globally, searched for possible outliers, and studied the variance of the circulating protein levels in each set to judge the quality and similarity between the data sets. We then determined the common associations of the DBS proteomes with the self-reported traits of age and sex. Lastly, we applied multivariate analysis to identify combinations of proteins to differentiate the serostatus groups, and univariate analysis for associations with symptoms, serostatus, and antibody levels. In each study set, we profiled 276 proteins associated with cardiovascular and metabolic processes such as angiogenesis, blood vessel morphogenesis, inflammation, and cell adhesion.

To begin with, we investigated the general properties of the proteomics data without considering the serostatus categories. Our analysis of the DBS eluates revealed that 260 proteins (94.2%) could be detected in >90% of the samples from all three study sets. For the downstream analysis, we included 264 proteins (95.6%) above the detection limit for at least 50% of the samples in all three study sets. Replicated analysis of five unique DBS eluates revealed a high reproducibility of the protein measurements, with >90% of the proteins reporting a coefficient of variation (CV)<10% (Supplementary Data2). Global and unsupervised data analyses were performed to determine the integrity of the data and identify any patterns or biases due to seropositivity. The median NPX and IQR values were used to systematically identify possible outliers by setting the threshold to 3 SDs from the mean for each variable. We considered it unlikely that age, sex, symptoms, or serostatus would alter the protein content of samples for the analyzed targets to the degree that identifying a sample as an outlier would have a physiological reason. To account for non-biological differences between DBS samples provided by untrained individuals, we apply the antibody-specific probabilistic quotient normalization (AbsPQN), which we previously developed for affinity proteomic studies of plasma samples27. Applying AbsPQN to the three panels used in the three study sets decreased the percent variance explained by the first principal component (PC1) from 40.8% 15.8% to 15.0% 1.2%. AbsPQN reduced the differences in the average and distribution of NPX levels. Consequently, AbsPQN-processed data was used to reidentify outliers and for all the downstream analyses. We found eight samples that deviated (Supplementary Fig.1), thus resulting in their exclusion from the summary tables (Tables13). Out of 236 donors, the proteomics data from 228 samples (97%) qualified for the investigations.

Next, we evaluated the general variation in protein levels to identify stable and highly variable ones. As illustrated in Fig.3, all data sets presented a similar distribution of IQR values. There was a very good agreement of the IQR values between the three sets (rs>0.86, CV=15%; see Supplementary Data2). To highlight a few, the most dispersed levels (IQR>1.5) were found for primarily secreted proteins IGFBP1, MBL2, MEP1B, and SSC4D. Interestingly, MBL2, a protein involved in complement activation, has been previously associated with COVID-19 severity and mortality in intensive care patients25,33,34. Among the least variable proteins (IQR<0.15) were the intracellular proteins CRKL, SOD1, and BLMH, all expressed by various organs. BLMH, a protein highly expressed by the skin tissue29 and one of the proteins most differentially abundant when comparing DBS with plasma (see above). The observed concordance in IQR values of independent sample sets supported the quality and utility of the data for further detailed analyses of the COVID-19-related phenotypes.

Distribution of protein level variance across dried blood spot (DBS) samples from the population (a) study 1 (N=81), (b) study 2 (N=63) and (c) study 3 (N=77). Each dot represents the interquartile ranges (IQR) of one protein, ranked by the dispersion of normalized protein expression (NPX) values. Proteins with narrow distributions are ranked to the left, and proteins with varying levels are ranked on the right.

To learn more about the general structure of the data, we conducted unsupervised correlation analyses of 264 protein levels within each of the four serostatus groups. As depicted in the heatmaps presented in the Fig.4ac, the overall relationships between the protein correlations differed between the serostatus groups. The distributions of the correlation values centered around zero (Supplementary Fig.2). A stability analysis of the clusters was performed to prioritize the most stable clusters and choose representative protein correlations across all groups. Cluster #4 of the IgG+ in study 3 was deemed the most stable cluster with a mean Jaccard index (MJI)=0.54. The cluster contained 20 proteins originating from different PEA panels. Twelve of the 20 proteins (60%) also clustered in the other five sample sets, and the included ANXA1, PGLYRP1, ITGAM, PLAUR, RETN, TNFRSF10C, NADK, CHI3L1, LCN2, S100P, DEFA1, and PAG1. Interestingly, these twelve proteins originated from the hematopoietic system, including the bone marrow, neutrophils, eosinophils, monocytes, or lymphoid tissue. Despite belonging to different PEA panels, proteins such as LCN2, S100P, PAG1, and PLAUR were shown to correlate highly (rs>0.8) in all six sample sets. Further details about cluster assignment and protein-protein corrections across study sets can be found in Supplementary Data3. The cluster analysis suggests that proteome profiling of DBS samples can provide insights into coordinated cellular regulations of the humoral and inflammatory immune response.

The heat maps reveal the inter-protein correlations obtained from hierarchical clustering for the four serostatus groups from (a) study 1, (b) study 2, and (c) study 3. The green circles indicate the clusters containing twelve proteins that grouped together in all sample sets. The number of branches was selected based on Gap statistics.

The current knowledge about DBS-derived protein-trait associations is still sparse. Before investigating the relationships between protein levels and SARS-CoV-2 infections, we studied their association with age and sex. The two basic demographic parameters are tested for in nearly all biomedical studies, are known to influence the circulating protein levels in serum or plasma samples, and were collected from all participants in our studies. Consequently, replicating the protein-age and protein-sex associations in three study sets would indicate the datas utility. As we have shown when comparing plasma and DBS, however, the differences between the sample types could influence the outcome of the association comparison. We also note that the age distribution of the three sample sets was slightly different, see Tables13. Using a linear model, we determined the protein-trait associations and performed a meta-analysis to rank the proteins by the combined p-values (Supplementary Data4). Several proteins were associated with age or sex in all three studies with concordant directions of association (Supplementary Fig.3). This included the well-known sex-specific protein MMP3 (combined P=3.21011), a protease involved in collagen degeneration. MMP3 has been associated with coronary heart disease and acute respiratory distress syndrome35and was studied in COVID-1925. In addition (combined P<105), sex influences the proteins ALCAM and SSC4D, expressed by the parathyroid glands; CNTN1, found in the brain and sex-specific organs; and IGFBP6, a protein highly expressed in the female sex organs. RNA expression studies support the observed associations with sex29. For age, we found strong associations with GDF-15 (combined P=11017), a frequently discussed biomarker for aging36, across all three studies. In addition (combined P<1010), meta-analysis identified age-associated proteins in all datasets for the secreted neuronal protein MEPE, the lymphoid protein SELL, the endothelial proteins t-PA or the B-cell receptor CR2. The consistency of age and sex associations across all study sets confirms the data quality and supports its utility in analyzing these in the context of COVID-19.

In the following, we highlight the outcomes of investigating changes in protein levels related to SARS-CoV-2 infections. A LASSO regression analysis was used to identify a combination of proteins that differ between the serostatus groups in each of the three studies. Summary statistics and the group-specific protein values (z-scores) can be found in Supplementary Data4.

For study 1 (Fig.5a), 19 proteins were selected, of which 17 (90%) had higher levels in the seropositive group. Ranked by their importance score (Fig.5b), annexin A11 (ANXA11), found in muscle cells and granulocytes, and the low-affinity immunoglobulin gamma Fc region receptor II-a (FCGR2A), also known as CD32A or FcRII, were most informative. Both proteins had a reduced abundance in the COVID-19 seropositive group. Interestingly, FCGR2A has been described to trigger a cellular response against pathogens and is involved in phagocytosis, and a recent report suggested that these receptors can mediate the infection of monocytes with the virus37. Detecting lower levels of FCGR2A could either indicate an increased SARS-CoV-2-induced clearing of immune cells or reflect reduced access to the receptors epitopes while internalizing antibody-bound pathogens. In addition, significant differences were observed for the previously introduced MBL2 and MMP3 and proteins related to different physiological mechanisms. These included proteins secreted by the liver during the stress response and angiogenesis (ANG), a brain- and B-cell-derived neurogenic protein (CHL1), a protease secreted by the pancreas (CPB1), a platelet-derived glycoprotein involved in coagulation (GP1BA) as well as a cytokine receptor related to T-cell immunity (IL2RA). These processes have also been described in studies using venous blood draws23,38. SDC4, a cell adhesion protein found in the extracellular matrix of the liver, lung, kidney, and T-cells, has been suggested to act like ACE2 and linked to the cellular uptake of the SARS-CoV-2 virus39 and revealed anti-inflammatory functions in patients with acute pneumonia40. As shown in the network in Fig.5c, physiological relationships between some of the proteins have been suggested for acute phase processes and innate immunity, platelet activation, coagulation, and cellular adhesion. Correlation analysis of protein and IgG or IgM levels revealed only moderate relationships (rs<0.5, P<0.001; see Supplementary Fig.4). We observed the strongest correlation between circulating CHL1 and IgM levels reported for anti-RBD (rs=0.46; P=0.00002) and anti-S (rs=0.38; P=0.001). Noteworthy were the negative correlations of FCGR2A with anti-RBD (rs=0.38; P=0.0005) and S (rs=0.32; P=0.004). This is supported by studies suggesting that FCGRs mediate the uptake of the antibody-coated virus into monocytes, causing the cells to undergo lytic programmed cell death and reduce levels of circulating FCGR2A37. MMP3 and IgG levels correlated with anti-S (rs=0.37; P=0.0006) and anti-RBD (rs=0.35; P=0.003) in the opposite direction. Similar trends and relationships were determined for MBL2, VWF, GP1BA, and ANG. Univariate logistic regression for serostatus ranked MBL2, ANG, and FCGR2A on top (P<0.01). Finally, we compared the variances of protein levels between the two groups and found that the distribution of SELL levels (P=0.009) was unequal.

a Least absolute shrinkage and selection operator (LASSO) analysis shortlisted proteins differentiating seropositive (N=37) and seronegative (N=44) subjects in study 1. The y-axis represents the centered and scaled data provided as normalized protein expression (NPX) values. b Ranked importance score of selected proteins. c Using the STRING database, we identified interactions between the selected features and obtained a network centered around acute phase processes and innate immunity, host-virus interactions, coagulation, and cellular adhesion. d LASSO selected proteins from study 2 comparing donors representing the early (N=22) and late infection phases (N=41), and (e) corresponding importance scores. f LASSO selected proteins from study 3, comparing seropositive (N=40) and seronegative donors (N=37). The boxplots show the 25% and 75% quantiles (lower and upper hinges) with the median in the center, the whiskers extending to hinges 1.5 interquartile ranges (IQR), and visible data points outside these ranges.

For study 2, LASSO selected five proteins, of which LILBR1 and FAM3C were elevated in the group representing the early phase of the infection (Fig.5d, e). STRING analysis revealed no known interactions between the proteins; however, syndecan 4 (SDC4) overlapped with the proteins selected in study 1. Elevated levels of SDC4 were found for the later phase group and seropositive in study 1. With LILRB1, an immunoglobulin-like receptor found on monocytes, the metabolism-regulating protein FAM3C, the coagulation factor 11 (F11), and the lung protein cathepsin H (CTSH), a variety of biological processes were represented. Interestingly, SDC4, LILRB1, and CTSH share expression in lung tissues. Correlation analysis revealed negative coefficients between IgM levels detected for the S antigen and the levels of the proteins CTSH and SDC4 (rs>0.37; P<0.003). Using univariate logistic regression, the five proteins were weakly significantly associated with serostatus (P<0.03). When comparing the variance of protein levels in each group, the levels of CCL5 were most unequally distributed (P=0.001).

For study 3 (Fig.5f), only one protein was selected by LASSO: the complement C3d receptor 2 (CR2), also known as CD21. Found primarily in the lymphatic system and on B-cells, elevated levels of CR2 were associated with prior infection with SARS-CoV-2. Interestingly, CR2 has been described as a human receptor for the Epstein-Barr virus (EBV), representing an additional element of innate immunity and host-virus interactions41. There was a positive correlation between levels of CR2 and anti-S antibodies (rs=0.38; P=0.0006), and when using univariate logistic regression, a more significant association with serostatus than for the markers shortlisted above (P=0.0004). It is worth noting that, compared to study 1, infections of the seropositive participants in study 3 were not limited to the few months at the start of the pandemic. When comparing the protein level variances, the macrophage protein of CCL24 was most unequally distributed (P=0.009).

Finally, we used common health-related information to perform a meta-analysis of the self-reported symptoms. As shown in Tables13, we asked the participants in all three studies for COVID-related symptoms such as fever, breathing difficulties, or loss of taste and smell. Top-ranked (combined P<0.01) were two previously described age-associated proteins (GDF15, SELL) and COL18A1, an extracellular adhesion collagen expressed primarily by the liver and involved in endothelial cell migration, as well as C1QTNF1, a secreted multifunctional adipokine found in smooth muscles and adipose tissue. These associations were less significant and overlapped with those observed for age or sex. No interactions have yet been reported in STRING to suggest a direct connection between their physiological function.

In subjects representing two waves of the pandemic and pre- and post-infection phases, profiling proteins related to cardiometabolic processes in DBS samples revealed insights observed in studies performed in serum or plasma collected in the clinic. Our investigation confirmed coordinated co-regulations of protein levels in immune response, cell adhesion, and cellular virus entry processes.

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Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections | Communications ... - Nature.com

Utilizing Text Mining on Electronic Health Records for COVID-19 Outbreak Surveillance – Physician’s Weekly

April 2, 2024

The following is a summary of COVID-19 outbreaks surveillance through text mining applied to electronic health records, published in the March 2024 issue of Infectious Disease by Rocha et al.

The COVID-19 pandemic spurred a surge in tech solutions, but more technologies are needed to be helpful in low-resource settings for disease monitoring.

Researchers conducted a retrospective study to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect potential new outbreaks in real-time.

They developed an epidemiological indicator that served as a proxy for suspected COVID-19 cases, utilizing health records from Emergency Care Units (ECUs) and employing text mining methods. The dataset consisted of 2,760,862 medical records from nine ECUs, each containing patient age, reported symptoms, and admission timestamps. A dataset of 1,026,804 officially confirmed COVID-19 cases was utilized, covering records from January 2020 to May 2022. Models were assessed using sample cross-correlation between two finite stochastic time series.

The results showed that for patients aged 18 years, the time lag () was 72 days with a cross-correlation () of approximately ~0.82 for the first wave, 25 days with a cross-correlation () of around ~0.93 for the second wave, and 17 days with a cross-correlation () of about ~0.88 for the third wave.

Investigators concluded that the model effectively detects signs of potential COVID-19 outbreaks weeks ahead of traditional methods, allowing for earlier public health interventions.

Source: bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-024-09250-y

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Utilizing Text Mining on Electronic Health Records for COVID-19 Outbreak Surveillance - Physician's Weekly

An effective treatment for COVID-19 is underused – UKNow

April 2, 2024

The University of Kentucky Public Relations & Strategic Communications Office provides a weekly health column available for use and reprint by news media. This week's column is by Nicholas Van Sickels, M.D., the director of infection prevention and control at UK HealthCare.

LEXINGTON, Ky. (April 1, 2024) A safe and effective medication designed to prevent mild-to-moderate COVID-19 infections from becoming more dangerous has been available for almost two years. But recent studies have shown many patients eligible for the drug Paxlovid havent been prescribed it.

In a clinical trial, the orally taken medication reduced the risk of hospitalization and death by 86%. Paxlovid was fully approved by the Food and Drug Administration in May 2023 but has been available through an emergency use authorization since 2022.

A pre-print study of over a million COVID-19 patients found that less than 10% of eligible patients were prescribed Paxlovid, also known as nirmatrelvir-ritonavir. The reasons behind the treatments slow uptake could include confusion over whos eligible or even unfamiliarity with the drug.

How does it work?

The antiviral drug works to stop the replication of the virus which causes COVID-19 at the cellular level. It comes as a dose pack; two of the pills are the active medicine, nirmatrelvir, and one is a booster medicine, ritonavir, which helps the nirmatrelvir maintain effective levels in the bloodstream.

Paxlovid is most effective when taken within five days of developing symptoms.

Whos eligible?

The treatment is designed for high-risk patients who have a mild or moderate case of COVID-19 but hope to avoid more severe consequences. You might be considered high-risk if youre:

A full list of medical conditions that may leave you at high-risk for a severe case of COVID-19 are listed on the CDCs website here.

When should I ask my doctor about Paxlovid?

If you are at high-risk for complications from COVID-19, its best not to wait before asking your doctor about Paxlovid. The treatment should be taken within five days of the onset of symptoms.

A study from the University of Hong Kong, which analyzed data from over 87,000 patients who took nirmatrelvir-ritonavir, found that earlier treatment even within that five-day windowleads to better outcomes.

The treatment will be free for Medicare or Medicaid beneficiaries through the end of 2024 via the U.S. governments Patient Assistance Program. Those with private insurance can enroll in the Paxcess program to help lower out-of-pocket costs.

When you ask your health care provider about Paxlovid, make sure to update them on all other medications you are taking. While most medications are generally safe to take with Paxlovid, there are some which need dose adjustments or should be held during and for a short while after treatment. On very rare occasions, treatments other than Paxlovid are needed for people at high-risk for complications from COVID-19 who take certain medications.

How can I best protect myself from COVID-19?

Improvements in how COVID-19 is prevented and treated have led to a relative relaxation in guidance on how to avoid the virus. In early March, the CDC began to group its COVID-19 guidelines with other diseases caused by respiratory viruses such as flu and respiratory syncytial virus (RSV).

The basic guidelines around avoiding respiratory viruses include:

If you get sick with a respiratory virus, its best to stay home and away from others.

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An effective treatment for COVID-19 is underused - UKNow

Lessons (not) learned: A critical reflection on the COVID-19 pandemic – Books – The Jakarta Post

April 2, 2024

he pandemicis over. It has been more than four years since Indonesia officially announced its first cases of COVID-19. Yet, questions linger about whether we as a community have become more prepared to deal with a crippling global health crisis.

The "lucky ones or the more privileged among us may look back into the first two years of the pandemic through our social media feeds with a sense of bittersweet nostalgia. How can we forget the first days of remote working and studying from home, or that moment when we could only wave at our relatives during religious holidays?

But for others, particularly those on the margins, the pandemic was a traumatic moment that turned lives upside down. Some lost their loved ones to the virus while others lost their jobs and were thrown into precarious financial positions. It was the moment when they realized how the prevailing social, political and economic order failed to protect them during one of the most challenging times in history.

A collection of scholarly articles released by the University of Indonesias Asia Research Center (ARC) titledMediatisasi Politik pada Masa Pandemi di Indonesia yang Neoliberal(The Mediatization of Politics during Pandemics in Neoliberal Indonesia) is an attempt from Indonesian scholars from different fieldsto provide a critical evaluation of what went wrong and what went right during that critical period. It does so by examining how the politics of pandemics or social practices conducted by the elite and the grassroots during the crisis was mediatized in digital settings.

The book, published by Kepustakaan Populer Gramedia (KPG), is worth a read not only for academics, but for anyone that wishes to understand the structural and material factors that not only deeply influenced state policies to address the crisis, but also shaped how people perceived and responded to the policies.

Pandemics and neoliberalism

Writing from the tradition of critical social sciences, which consider power relationships an important factor in scholarly analysis, ARC deputy director Diatyka Widya Pertama Yasih, who coedited the book, asserts that the authors aim to critically evaluate the impacts of neoliberal policies on Indonesian society during the health crisis. Neoliberalism here refers to a set of political economic practices that are based on the idea that our well-being could only be improved within an institutional framework characterized by strong private property rights, free market and free trade (Harvey, 2005, p. 2).

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Lessons (not) learned: A critical reflection on the COVID-19 pandemic - Books - The Jakarta Post

COVID-19 or Coronavirus like pandemic may happen again? Here’s what experts claim – The Economic Times

March 26, 2024

COVID-19 or the Coronavirus became pandemic after World Health Organization declared it so on March 11, 2020. Subsequently, human lives around the world changed forever. While the impact of Covid has subsided, experts have claimed that next pandemic could strike anytime, as per a report Sky News.

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In addition to deaths from the virus, long COVID -- which scientists still don't understand -- has afflicted many people.

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Q2. What is another name of Covid-19? A2. Another name of Covid-19 is Coronavirus

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COVID-19 or Coronavirus like pandemic may happen again? Here's what experts claim - The Economic Times

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