Category: Corona Virus

TUESDAY, Nov. 28, 2023 (HealthDay News) -- For patients with systemic autoimmune rheumatic diseases using disease-modifying antirheumatic drugs (DMARDs), receiving a fourth COVID-19 mRNA vaccine reduces the risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, according to a study published online Nov. 15 in The Lancet Rheumatology.

Jennifer S. Hanberg, M.D., from Brigham and Women's Hospital in Boston, and colleagues conducted an emulated target trial using observational data to compare receiving versus not receiving a fourth mRNA vaccine dose among patients with systemic autoimmune rheumatic diseases who were prescribed DMARDs. Data were included for 4,305 patients: 3,126 received a fourth dose, and 1,179 did not. After emulation of the time-sequential once-per-week trials and overlap propensity score weighting, both groups included 2,563 adults.

Of the 2,563 participants, 54.3 percent had rheumatoid arthritis; the most frequent treatments used were conventional synthetic DMARDs and biological DMARDs (58.1 and 39.3 percent, respectively). The researchers found that the risk for SARS-CoV-2 was lower among patients receiving versus not receiving a fourth vaccine dose (hazard ratio [HR], 0.59). The risk for admission to hospital or death within 3 to +14 days of SARS-CoV-2 infection was also lower with receipt of a fourth vaccine dose (HR, 0.35).

"Patients with systemic autoimmune rheumatic diseases should be encouraged to receive at least four doses of mRNA vaccines," the authors write.

Several authors disclosed ties to the biopharmaceutical industry.

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Fourth COVID-19 Shot Beneficial in Patients With Autoimmune ... - HealthDay

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Psychological Impact of COVID-19 and Its Influence on Parental ... - Cureus

Respiratory virus season is ramping up across the United States, and the US Centers for Disease Control and Prevention is warning that low vaccination rates are leaving many at risk.

About 15% of adults and 5% of children have gotten the latest Covid-19 vaccine, according to CDC data through mid-November.

Heres the bottom line: COVID-19 vaccine uptake is lower than wed like to see,and most people will be without the added protection that can reduce the severity of COVID-19, the agency wrote in anupdateon its website.

Covid-19 hospitalizations are on the rise again, with more than 16,000 new admissions during the week ending November 11. Hospitalization rates are lower than they were at this time last year but nearly three times higher than they were during this summers record low.

There are a number of reasons why its important for people to get the latest Covid-19 vaccine, CDC Director Dr. Mandy Cohen said.

This Covid virus has changed, and you want the most updated Covid vaccine to protect you as we go into this winter season, she said. Second, we know that the protection that you get from either having Covid before or being vaccinated before decreases over time. Vaccination can also help reduce the risk for long Covid, she said.

Flu and RSV levels are also increasing, but most respiratory virus hospitalizations this season have been among people with Covid-19, CDC data shows. About a third of adults and children have gotten their flu shot this season, and about 14% of older adults have gotten the new RSV vaccine.

COVID-19 isstill an important cause of hospitalization and death, especially for older adults and people with certain underlying medical conditions, the CDC wrote in the latest online update. COVID-19 vaccines dont prevent every infection thats true of lots of vaccines butthey can reduce illness severityin people who get vaccinated but still get sick, helping to save lives, reduce hospitalizations, and prevent trips to the doctor.

This winters respiratory virus season is just beginning, and now is a great time to get vaccinated, Cohen said. The sooner the better. It does take about two weeks for your body to build up the maximum amount of antibodies, but its never too late.

The current Covid-19 vaccination rate is significantly higher among seniors, but more than two-thirds of this high-risk population has not gotten the latest vaccine, according to the CDC.

Because older people are much more likely toget hospitalizedanddie from COVID-19, it is critical that this population get vaccinated to protect themselves against severe outcomes from COVID-19, the CDC wrote.

The CDC also found racial and ethnic disparities in Covid-19 vaccination rates, with uptake about half as high among Black and Hispanic adults compared with White adults.

However, separatesurvey datafrom KFF found that Black and Hispanic adults are much more likely than White adults to say they have gotten the new vaccine or plan to do so.

Overall, the KFF survey found that most adults in the US are not worried about getting sick with Covid-19 or spreading it over the holidays. Only about half say they plan to get the latest vaccine, and theres a similar split around plans to take other precautions, such as masking or avoiding crowded places and travel.

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Covid-19 vaccination rate is 'lower than we'd like to see,' CDC says - KADN

TUESDAY, Nov. 28, 2023 (HealthDay News) -- Fluvoxamine does not reduce duration of COVID-19 symptoms in patients with mild or moderate COVID-19, according to a study published online Nov. 17 in theJournal of the American Medical Association.

Thomas G. Stewart, Ph.D., from the University of Virginia in Charlottesville, and colleagues from the Accelerating COVID-19 Therapeutic Interventions and Vaccines-6 Study Group assessed the effectiveness of fluvoxamine versus placebo for treating mild to moderate COVID-19. The analysis included 1,208 participants randomly assigned to receive fluvoxamine (50 mg twice daily on day 1 followed by 100 mg twice daily for 12 additional days; 601 patients) or placebo (607 patients).

The researchers found no differences in time to sustained recovery between the two groups (adjusted hazard ratio, 0.99; 95 percent credible interval, 0.89 to 1.09; P for efficacy = 0.40). Similarly, in both groups, unadjusted median time to sustained recovery was 10 days. No deaths were reported, but 35 participants reported health care use events (defined as death, hospitalization, or emergency department/urgent care visit), including 14 in the fluvoxamine group and 21 in the placebo group (hazard ratio, 0.69; 95 percent credible interval, 0.27 to 1.21; P for efficacy = 0.86). Seven serious adverse events occurred in six participants (two with fluvoxamine and four with placebo).

"Although one-third fewer health care use events occurred in the fluvoxamine intervention group, the difference did not meet prespecified decision thresholds for concluding a treatment effect," the authors write.

Several authors disclosed ties to the pharmaceutical industry.

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Fluvoxamine Does Not Shorten Duration of COVID-19 Symptoms - HealthDay

This systematic review and meta-analysis provides a comprehensive and in-depth examination of findings from studies on long-term cardiac complications of COVID-19. Up to July 2023, at least 150 studies examined 49 different long-term cardiac complications of COVID-19. Chest pain and arrhythmia were the two most widely reported complications. These studies varied substantially in different aspects of study design, and only a quarter of them were of high quality based on our quality assessment. Meta-analysis identified high heterogeneity across studies for almost all cardiac complications, and subgroup analyses showed systematic differences in reported prevalence by the quality and characteristics of included studies. Most strikingly, we observed that studies of high quality reported much lower prevalence of different cardiac complications compared to studies of medium and low quality. To our knowledge, this is the first meta-analysis that quantitatively examined how reported findings of studies on long-term cardiac complications of COVID-19 differ by study quality and characteristics.

It is evident that many COVID-19 survivors have experienced lasting cardiac complications, even those who did not have previous cardiovascular diseases or comorbidities, and who had a low risk of cardiovascular diseases before the pandemic. To date, multiple reviews have examined the long-term cardiac complications of COVID-19 (Additional file 1: Table S6) [4, 5, 9,10,11,12,13,14,15, 28,29,30,31,32]. For example, several previous systematic reviews and meta-analyses also quantified chest pain and arrhythmia as two of the most common long-term cardiac complications, with prevalence estimates for chest pain ranging from 5 to 16% and prevalence estimates for arrhythmia ranging from 10 to 11% [9, 29, 30, 32]. Our prevalence estimates of these two complications broadly agree with these previous findings. A probable source of minor differences in prevalence estimates comparing our study to previous studies is that different systematic reviews and meta-analyses used different inclusion criteria to select studies.

Besides being more updated and comprehensive in terms of article search, our study examined whether there are systematic differences in reported findings by the quality and characteristics of included studies. Meta-analysis stratified by these factors shows that studies of low quality, small sample size, unsystematic sampling method, and cross-sectional design are more likely to report higher prevalence estimates of long-term cardiac complications. For example, the prevalence of chest pain among studies of low quality (22.17%) was five times higher than that among studies of low quality (3.89%). Such patterns were also observed for the prevalence of arrhythmia (studies of low quality vs. high: 24.09% vs. 2.68%) and other less examined long-term cardiac complications. This observation shows how sensitive reported findings can be depending on the quality and characteristics of studies on cardiac complications of COVID-19. Therefore, it is important to take these factors into account and better interpret the findings from these studies.

In our quality assessment, we determined that around 25% of 150 included studies were of high quality and the remaining 75% were of medium or low quality. The small number of high-quality studies demonstrates the urgent need to improve the quality of studies investigating the long-term cardiac complications of COVID-19. Due to the large number of studies included in this systematic review and meta-analysis, we did not enumerate their references. A list of these studies ordered by total quality assessment score can be accessed in Additional file 1: Table S2. A key feature among included studies of medium or low quality was that they were predominantly based on clinical or hospital samples. These studies usually had small sample sizes and had no or only one point of follow-up. While relatively small clinical or hospital-based studies, which are often easier to conduct in shorter periods, can be useful to establish preliminary evidence of an association, especially in an emergent situation such as a pandemic, they often lack population representativeness and statistical power to make broader conclusions about the hypothesized relationships. Furthermore, cross-sectional studies cannot establish the temporality required to infer any causal relationship and prohibit examinations of changes in complications over time.

Interestingly, we observed that studies published in later years (2021 to 2023) had a higher quality assessment score than those earliest studies published in 2020. This shows a general trend of improvement in study quality over time. However, similar quality scores were observed for studies published in 2022 and 2023 (average quality score 9.9 vs. 9.8), which may indicate a recent stagnation in the improvement of study quality on this topic. It is interesting to examine the trend of study quality as more related studies are coming out.

Based on the above findings, we formulated some recommendations for the design and analysis of future studies on long-term cardiac complications of COVID-19 (Table 2). Many studies adopted convenience sampling schemes, which hinders the interpretability and generalizability of their findings. Therefore, it is important to conduct systematic sampling, which can facilitate a continuing and meaningful exploration of the data collected and underpin clinical research. The majority of included studies only assessed long-term complications at a single time point. It is therefore challenging to examine how the long-term complications may change over time. Many studies did not distinguish between long-term complications following COVID and pre-COVID complications at baseline level. Most COVID-19 studies on other types of long-term complications have used similar data sources and analytical methods and will have similar methodological problems as we discussed above. Our study for the first time quantified how reported findings can differ by study quality and selected characteristics. This demonstrates the importance of addressing these methodological problems for COVID-19 studies reporting on long-term cardiac complications and other complications as well.

Although pathophysiological mechanisms underlying COVID-19 cardiac complications remain unclear, studies suggest that the chronic inflammatory response may be hyperactivated by persistent viral reservoirs in the initial acute phase, which may lead to post-acute COVID-19 cardiovascular sequelae [4, 5, 13]. Studies have shown that over 20% of patients with acute COVID-19 had evidence of cardiac injury, even if they did not have underlying cardiovascular diseases or pre-existing comorbidities [33,34,35,36]. It is hypothesized that viral invasion through binding angiotensin-converting enzyme-2 (ACE-2) causes a cytokine storm and triggers systemic hyper-inflammation, which can affect multiple organ systems and induce cardiac injury as one of the severe complications [4, 15]. Persistent chest pain and arrhythmia may be indicative of underlying cardiac abnormalities and damage resulting from systematic hyper-inflammation and/or viral myocarditis affecting the cardiac conduction system. It is critical for clinicians to thoroughly examine patients with long-term cardiac complications of COVID-19, especially for survivors with pre-existing cardiac conditions and other high-risk comorbidities.

Our systematic review and meta-analysis have multiple strengths. First, to our knowledge, this is the most comprehensive systematic review focusing on long-term cardiac complications of COVID-19. It included preprints and articles published in different languages and the global network. Second, in an effort to ensure our results were up to date, we regularly updated our search to capture articles published from the early phases of the pandemic to the most recently published studies. Our review will serve as an invaluable resource for updating researchers and clinicians on key discoveries around long-term cardiac complications of COVID-19. Third, we assessed the quality of included articles from the perspective of study design and epidemiologic principles and provided detailed recommendations on future long-COVID epidemiologic research. The NOS tool assessed the quality of each included study and potential risk of bias, and the GRADE approach determined the level of evidence. Fourth, we performed meta-analysis and subgroup analyses to examine patterns of reported findings, and we observed systematic patterns of reported findings of existing studies.

Our study also has several limitations. First, studies included in our systematic review and meta-analysis are highly heterogeneous. We, therefore, performed subgroup analyses by multiple characteristics, and we believe that existing heterogeneity across studies makes it difficult to generalize our results to the general population. Second, we were unable to stratify our meta-analysis by the length of follow-up because of widely varying follow-up times and different index dates of follow-up across studies. We intended to report the meta-analysis results by hospitalization status; however, most studies have a mixed cohort of inpatients and outpatients, and some studies did not report this information. Such variations in design and lack of detailed data made the stratified results hard to interpret. Finally, we could not stratify our analyses based on prior comorbidities, history of cardiovascular diseases, treatment or medication use for cardiac complications, or COVID-19 vaccination status due to limited reporting of such information, particularly in the studies published during the initial stages of the pandemic. This is because much of the related information was not clearly given in most existing studies. We plan to conduct these analyses once more data on these factors becomes available.

As the pandemic comes to an end worldwide, we may live together with COVID-19 in the coming years, and the epidemiology of long-term cardiac manifestations of COVID-19 might change over time. We think that multiple factors may strongly influence the prevalence or rate of long-term cardiac complications of COVID-19, including a shift in the demographic affected from primarily older people with comorbidities at the beginning to the general population, the availability of vaccination, treatment, and in-home testing, and the emergence of new COVID-19 variants [5, 37]. In future studies, how these factors may influence long-term cardiac complications of COVID-19 should be carefully examined.

In conclusion, we found there were diverse manifestations of cardiac complications, and many can last for months and even years. There is substantial heterogeneity in terms of study design and systematic differences in the reported prevalence of complications by study quality and characteristics. Specifically, we found that studies with low-quality, small sample size, unsystematic sampling method, or cross-sectional design were most likely to report a higher prevalence of complications among individuals who survived COVID-19. We believe that a deeper understanding of long COVID is currently prevented by the limitations of the published literature. Our study underscores the need to conduct high-quality studies on long COVID and the importance of long-term cardiac surveillance of COVID-19 survivors.

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Datasets

Due to its retrospective nature, informed consent was waived, and all data were anonymized. This project was approved by the human research ethics committee of the Chulabhorn Research Institute (research project code 167/2564) and complied with the Declaration of Helsinki. These COVID-19 patients were confirmed by RT-PCR acquired from Chulabhorn Hospital who underwent non-contrast enhanced axial chest CT as a part of routine clinical care throughout the pandemic.

In this study, we randomly selected 124 cases from the database. The selection contained 28 cases without lung lesions and 96 cases with lung lesions. According to TSS, experienced radiologists classified the cases with lung lesions as mild, moderate, and severe. We divided the selection into 3 groups, i.e., training set, test set 1, and test set 2. The training set was used in model training and validation for lung segmentation and lesion segmentation; test set 1 was for segmentation performance evaluation; and test set 2 was for TSS prediction evaluation. We also randomly selected these cases for each group. In addition, for the training set and test set 1, the numbers of cases across different severity types were set to be equal to prevent class imbalance in the training set (the class imbalance causing a potential bias in the trained model) and for a fair comparison in test set 1. The number of CT slices in these cases ranged from 92 to 208. This information was described in Table 1.

The lung CT data were saved in JPEG format with a resolution of 512512 pixels and labeled by a program called LabelME41 (version 4.5.12). The resulting labels were in JavaScript Object Notation (JSON) format. All labeled data were validated by four radiologists and then converted into matrices for model training and evaluation.

In the data preprocessing phase, CT scan images (JPEG format) and labeled data (JSON format) were resized using the cv2.resize function from 512512 pixels to 256256 pixels to minimize the required memory resources (RAM). The interpolation parameter was set to INTER_AREA for the CT scan images and INTER_NEAREST" for the label data because this solution prevented any alteration of the values specified in each pixel. In addition, our model input shape was fixed at a size of 128256256. The CT volumes were adjusted to 128 images per patient according to the following three conditions (Fig.1).

The first condition, if the CT volume comprised 128 or fewer images, a 256256 zero-padding matrix was added to increase the volume to 128 images.

The second condition, if the CT volume had between 129 and 175 CT images, 128 images from the CT volume's middle range were selected to train the model because both the lung parenchyma and lesions appear in this range.

The third condition, if the CT volume contained more than 175 CT images, we skipped the CT slice by selecting only odd-numbered images and adding a 256256 zero-padding matrix to reach a total of 128 images.

Overall pipeline of the data preprocessing.

A color adjustment method was applied to improve image contrast by using the contrast-limited adaptive histogram equalization (CLAHE) technique42, which is available in the OpenCV library43. The CLAHE parameters were set to a clipLimit of 3 and a tileGridSize of (8, 8). Models were trained/tested in two experiments: the first with original images (no color adjustment) and the second with CLAHE-adjusted images.

A 256-slice dual-energy CT scanner (Revolution CT with Gemstone Spectral Imaging (GSI) Xtream, GE Healthcare) at Chulabhorn Hospital was used in this study. An axial chest CT scan without contrast agent was applied. The protocol started with a scout view from lung apices to lung bases in anteriorposterior (AP) and lateral views, and followed by an axial chest scan covering lung apices through bases from inferior to superior. The parameters were quiet breath inspiration, 1.25mm thickness, 0.28s/rotation, 0.992 pitch, GSI calculated kVp, 190mA, lung window of (1550, 700), soft tissue window of (400, 40), and postprocessing multiplanar reconstruction. The scan time was less than 1.6s.

Two models were used in this study: (1) a lung lobe segmentation model and (2) a lesion segmentation model. Training set: 32 cases were split into 24 cases (75%) for model training and 8 cases (25%) for validation, where the dataset was divided equally at each severity type to prevent overfitting. According to related studies, a model that combines a 3D-UNet structure with a DenseNet or ResNet is effective in segmenting parts of the image precisely. Therefore, pre-trained DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 models were obtained through a segmentation-models-3D package from Solovyev et al.44.

Lung lobe segmentation. A multiclass semantic segmentation model was used to segment the five lung lobes. Annotated labels consisted of six categories: 0, 1, 2, 3, 4, and 5, which indicate the background, right upper lobe (RUL), right lower lobe (RLL), right middle lobe (RML), left upper lobe (LUL), and left lower lobe (LLL), respectively.

Lesion segmentation. The lesion model was developed from a binary semantic segmentation model that outputs the value 1 for lesion areas and 0 for background areas. Images without extrapulmonary regions are preferred for lesion model training. The dataset used for model training was preprocessed as described in the data preparation section.

In the model training process, lung lobe and lesions segmentation models were trained on servers equipped with an Intel(R) Xeon(R) Gold 6126 CPU at 2.60GHz, 40GB of RAM, and an NVIDIA Tesla V100 SXM2 GPU. Figure2 shows the overall workflow. The models output is the predicted classfor each pixel, which is then used to compute the percentage area of lesions in each lung lobe for the CT score. This score is then used to calculate the TSS value for diagnosing the severity of the current pathology. For both models, Adam optimization was used, the loss function was a hybrid loss function (focal loss+Dice loss), the learning rate was set to 0.0001, a regularizer that applies L2 regularization was used with a value of 0.01, the batch size was set to 1, and the maximum number of epochs was 200. The lesion model activation function was set to sigmoid with a dropout rate of 0.4, whereas the pulmonary lobe model activation function was set to SoftMax with a dropout rate of 0.2. The hybrid loss technique45, which combined focal loss and Dice loss, was used to improve model performance.

Segmentation Model Workflow and Total Severity Score Calculation Protocol for Lung CT Scans.

The PI in each lung lobe was calculated by dividing the number of predicted lesion pixels by the total number of lung lobe pixels in the CT volume. The predicted lesion pixels were obtained from the output of the lesion segmentation model, whereas the predicted lung lobe pixels were derived from the output of the lung lobe segmentation model, in which the value of each pixel identifies the lobar type in the lung CT image. Therefore, the PI was calculated by performing the following equation.

$$Percentage, of{, Infection}_{lobe}= frac{Lesion, Area, (pixels)}{Lung ,Lobe, Area, (pixels)}times 100$$

The TSS proposed by Chung et al.20 was calculated from the sum of the five-lobe CT score, which was calculated from PI based on the criteria listed in Table 2. The severity of COVID-19 patients can be classified from the TSS value based on the severity criteria in Table 3 and the following equation:

$$TSS =CT ,Scor{e}_{RUL}+CT ,Scor{e}_{RML}+CT ,Scor{e}_{RLL}+CT, Scor{e}_{LUL}+CT, Scor{e}_{LLL}$$

The most commonly utilized measurement to evaluate image segmentation is the Dice similarity coefficient (DSC). The DSC calculated the relative overlap between the predicted area and ground truth, and it was used to choose the most appropriate model. The DSC was defined as follows:

$$DSC = frac{2TP}{2TP + FP + FN}$$

where the term true positive (TP) refers to an outcome such that the model correctly predicts the positive class, false positive (FP) is an outcome such that the model incorrectly predicts the positive class, and false negative (FN) is an outcome such that the model incorrectly predicts the negative class.

Hausdorff distance was proposed by Felix Hausdorff in 191446,47. The measure was applied to evaluate the models performance by measuring the distance between two images in pixels. The distance is defined as

$$Hleft(A,Bright)=mathrm{max}left(hleft(A,Bright), hleft(B,Aright)right)$$

$$hleft(A,Bright)={mathrm{max}}_{mathit{aepsilon A}}{mathrm{min}}_{bin B}||a-b||,$$

where A is a set containing p points (pixels on image A): ({{a}_{1},{a}_{2},dots ,{a}_{p}}) and B is a set containing q points (pixels on image B): ({{b}_{1},{b}_{2},dots ,{b}_{q}}). For implementation, we applied the function implemented in SciPy package48. Since the images used in this research were 256256 pixels, the Hausdorff distance range was ([0, 256sqrt{2}]).

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Segmentation of lung lobes and lesions in chest CT for the ... - Nature.com

U.S. households are now eligible to order an additional four at-home Covid-19 tests free of cost through the government.

Residential households in the U.S. can now submit an order throughCovidtests.govfor four individual rapid antigen Covid-19 tests.

Orders started to ship on November 27, according toUSPS. People without an internet connection can call 1-800-232-0233 (TTY 1-888-720-7489) to request tests.

The U.S. government had suspended the rapid test distribution program earlier in May, then reopened it in September. Residents who havent placed an order since the program reopened can place two orders, which will provide eight tests in total, according to USPS.

Covid-19 rapid tests can be taken at home and can be used regardless of whether someone has symptoms. The tests should work through the end of the year; some of the dates on the tests may show that they are expired, but the US Food and Drug Administration hasextended those dates.

The U.S. Centers for Disease Control and Preventionrecommends people testif they have any Covid-19-like symptoms including a sore throat, runny nose, loss of smell or taste, or a fever. People may also want to test before they are going to be a part of a large event, like a concert or a conference, particularly if people are not up-to-date on their vaccines. Antivirals are available to treat Covid-19 and flu, and testing can help determine which medication is needed.

Covid-19 hospital admissions and emergency department visits are once again on the rise after a few weeks of downturn, according to theCDC. For the week ending November 11, the percentage of Covid-related emergency department visits was 1.4%, or just over 16,200 people similar to rates seen throughout this month and last month.

Overall, outpatient visits for flu-like illness are lower than they were at this time last year but higher than in the previous four seasons. CDC forecasting suggests that this respiratory virus season will result in about the same number of hospitalizations as last season.

Seasonal flu activity is alsoincreasingin most parts of the country with flu-like activity labeled as high in New Mexico, Florida, Alabama, Mississippi, Georgia and South Carolina, according to the CDC. There have been at least 780,000 illnesses, 8,000 hospitalizations, and 490 deaths from flu so far this season, the agency estimates.

More than a third of adults and nearly a third of children have gotten their flu shot this year, CDC data shows. About 14% of adults and 5% of children have gotten the new Covid-19 vaccine while 14% of older adults ages 60 and up have gotten the new RSV vaccine.

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Even more free home Covid-19 tests are available for order from the ... - KADN

FOR many of us, the covid-19 pandemic is fading into memory. But for millions of people, that isnt possible as they are still unwell. An illness that is often brief and mild is, for some, the start of a rollercoaster of symptoms that can last years. Today, around 65 million people may have long covid.

That is the bad news. But around four years since the first cases emerged, evidence of the causes of long covid is rapidly accumulating, paving the way for treatments. Multiple trials of therapies are under way and several have already shown promising results. It is now also clear that people experience wide differences in their long covid symptoms, so treating this condition is an exercise in personalised medicine: no single approach will work for everyone.

Many questions remain, however. Can the plummeting levels of certain hormones explain the fatigue and brain fog, and is the persistence of the virus really key to understanding what is going on? And what should we do and not do to avoid developing long covid in the first place?

The SARS-CoV-2 coronavirus started spreading around the world in early 2020. Within months, reports began emerging that some people were experiencing lingering symptoms. The term long covid was coined in May 2020 and widely adopted. The most common symptoms include headaches, brain fog and fatigue, or post-exertional malaise, in which even small amounts of activity cause exhaustion. Altogether, more than 200 symptoms have been reported, ranging from depression to gastrointestinal problems.

Since that time

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Long covid: What we now know about its causes and possible treatments - New Scientist

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