Category: Covid-19

FILE - The exterior of the Internal Revenue Service (IRS) building is seen in Washington, on March 22, 2013. Congress is racing to wind down a tax break meant to encourage businesses to keep workers on the payroll during the COVID-19 pandemic. What was expected to cost the government $55 billion has instead cost it nearly five times that amount as of July. Meanwhile, new claims pour into the IRS each week. (AP Photo/Susan Walsh, File)

WASHINGTON When IRS Commissioner Danny Werfel met privately with senators recently, the chairman of the Senate Finance Committee asked for his assessment of a startling report: A whistleblower estimated that 95% of claims now being made by businesses for a COVID-era tax break werefraudulent.

He looked at his shoes and he basically said, Yeah, recalled the lawmaker who posed that question, Sen. Ron Wyden, D-Ore.

The answer explains why Congress is racing to wind down what is known as theemployee retention tax credit. Congress established the program during the coronavirus pandemic as an incentive for businesses to keep workers on the payroll.

Demand for the credit soared as Congress extended the tax break and made it available to more companies.Aggressive marketersdangled the prospect of enormous refunds to business owners if they would just apply. As a result, what was expected to cost the federal government $55 billion has instead ballooned to nearly five times that amount as of July. Meanwhile, new claims are still pouring into the IRS each week, ensuring a growing price tag that lawmakers are anxious to cap.

Lawmakers across the political spectrum who rarely agree on little else from liberal Sen. Elizabeth Warren, D-Mass., to conservative Sen. Ron Johnson, R-Wis. agree its time to close down the program.

I dont have the exact number, but its like almost universal fraud in the program. It should be ended, Johnson said. I dont see how anybody could support it.

Warren added: The standards were too loose and the oversight was too thin.

The Joint Committee on Taxation estimates that winding down the program more quickly and increasing penalties for those companies promoting improper claims would generate about $79 billion over 10 years.

Lawmakers aim to use the savings to offset the cost of three business tax breaks and a more generous child tax credit for many low-income families. Households benefiting from the changes in the child tax credit would see an average tax cut of $680 in the first year, according to an estimate from the nonpartisan Tax Policy Center.

That tax credit is $2,000 per child, but only $1,600 is refundable, which makes it available to parents who owe little to nothing in federal income taxes. An agreement reached earlier this month by congressional tax-writers would increase the maximum refundable child tax credit to $1,800 for 2023 tax returns, $1,900 for the following year and $2,000 for 2025 tax returns. The Center on Budget and Policy Priorities, a liberal think tank and advocacy group, projected that about 16 million children in low-income families would benefit from the child tax credit expansion.

The packagewas overwhelmingly approved by a House committee last week, 40-3, showing it has broad, bipartisan support.

But passage through Congress is not assured because many key senators have concerns about aspects of the bill. Wyden said a strong vote in the House could spur the Senate into quicker action. Still, passing major legislation in an election year is generally a heavy lift.

Under current law, taxpayers have until April 15, 2025, to claim the employee retention credit. The bill would bars new claims after Jan. 31 of this year. It also would impose stiff penalties on those who are promoting the employer retention tax credit if they know or have reason to know their advice will lead to an underreporting of tax liabilities.

When Congress created the tax break for employers at the pandemics onset, it proved so popular that lawmakers extended and amended the program three times. The credit, worth up to $26,000 per employee, can be claimed on wages paid through 2021.

To qualify, generally businesses must show that a local or state government order related to the COVID-19 pandemic resulted in their business having to close or partially suspend operations. Or the businesses must show they experienced a significant decline in revenues.

Larry Gray, a certified public accountant from Rolla, Missouri, said he had concerns early on about how the program could be abused.

There was no documentation really to speak and the IRS just sent out the checks, Gray said. They just started printing the checks and I believe Congress was wanting them to print the checks.

His hunch has proven correct, judging by the filings that he has reviewed. He has even lost clients who didnt want to hear that they did not qualify when others were telling them they did. Generally, he said, the businesses that dont qualify are failing to cite the government order that resulted in their closure or partial suspension. They are also routinely citing reasons for reimbursement that dont meet the programs criteria. For example, one company said it was struggling to find employees and had to raise wages as a justification for qualifying.

If I go through the narratives on the filings that Im looking at, every business in America qualifies, Gray said.

The IRSpaused accepting claimsfor the tax credit in September last year, until 2024 due to rising concerns that an influx of applications are fraudulent. At that point, it had received 3.6 million claims.

Some fraud has been prolific. For instance, a New Jersey tax preparerwas arrested in July on chargesrelated to fraudulently seeking over $124 million from the IRS when he filed more than 1,000 tax returns claiming the employment tax credits.

In an update issued Thursday about the program, the IRS said that it has thousands of audit in the pipeline and that as of Dec. 31, it has initiated 352 criminal investigations involving more than $2.9 billion in potentially fraudulent claims. Separately, it has opened nine civil investigations of marketers that potentially misled employers on eligibility to file claims.

Werfelbriefed the Senate Finance Committeerecently on the measures that have been put into place to address the fraud, including developing a special withdrawal program for those with unprocessed claims and a voluntary disclosure program for those who believed they were improperly paid. Since then, the IRS has seen an immediate 40% decline in average weekly claims, he said.

Lawmakers emphasize that cutting down on the fraudulent claims should also help the IRS more quickly resolve the legitimate claims that businesses have filed and are still awaiting resolution. In early December, the IRS had a backlog of about 1 million claims.

Congress routinely has difficulty finding offsets to pay for new spending or tax cuts. But in this case, the employee retention tax credit appears to have few friends left on Capitol Hill.

Well-intentioned, but boy oh boy, said Sen. Mark Warner, D-Va., in summing up the program.

Originally posted here:

A COVID-era program is awash in fraud. Congress aims to wind it down and expand the child tax credit - Anchorage Daily News

Network based word-embedding (mpDisNet)

The OMIM database was used to collect 394 disease types to be used in mpDisNet models. Results from the reproduced model show that, majority of the high-scoring disorders are cancer-related phrases, as can be observed in the reproduction score distribution (Fig.1). The distribution indicates that, MpDisNet scores are highly biased towards the cancer related terms. (Supplementary doc: Similarity mpdisnet.xlsx), We discovered 10,563 disease-disease associations with a score higher than 0.9, which is computed using vector cosine similarities. 6838 out of 10,593 disease similarities contain at least one cancer related term, which constitutes nearly %65 of the scores higher than 0.9.

Score distribution of the mpDisNet (reproduction model) that represents the effect of the cancer-term dominance in the disease interaction scores. (a) Reproduced model score distribution of all disease scores from the mpDisNet trial. (b) Score distributions of cancer-terms in the range between 0.9 and 0.95. (c) Score distributions of the remaining (non-cancer) disease interactions.

Scores of cancer-related phrases, as shown in Fig.1a, are likewise the main reason for the higher score accumulation between 0.95 and 1. This has a significant impact on the overall distribution of scores for diseases other than cancer. Because of the large amount of cancer-related research and the disease's complications, cancer is strongly linked to all other diseases, resulting in higher comorbidity ratings. Removal of the cancer-related elements from the disease similarity scores reduces the score accumulation on the high score range, as seen in Fig.1c.

When cancer terms and their linked miRNAs are removed from the training data, significant changes in the score distribution is observed. This change in the distribution also indicates that, the number of highly connected elements such as cancer terms also leads to a reduction in the occurrence of other disease representations in the model. Since multiple pathway dysfunctions emerge in cancers, a larger number of related miRNAs were reported in literature. Indeed, as it shown in Table 2, number of discovered miRNAs for cancer terms are large in comparison to median number of miRNAs (Fig.2) per disease used in this study. Cancer related miRNAs constitute the outlier points in Fig.3a and lead to high number of occurrences of cancers in training data as shown in Fig.3bd. The imbalance in number of miRNAs in cancer and non-cancer diseases lead to dominant occurrence of cancer terms over other diseases, which increases the possibility of random selection of cancers in different sub-networks in the corpus and causes over-training of their vectors.

Comparison of the medians of the number of miRNAs are related with cancer-type diseases and the rest of the OMIM disease dataset.

Effects of variations of miRNA counts in diseases and the disease frequencies in the training data. (a) Boxplot of the number of the miRNAs of each diseases indicate a narrow IQR range (box) and high number of outliers (circles). (b) Occurrence frequencies of each disease in training data in non-modified version. c Scatter plot of mean score of each disease and its frequency in the training data. (d) Positive correlation between the disease frequencies and number of miRNAs.

Further, unbalanced occurrence of words (diseases) causes instabilities such as vector update rate disparities between high and low frequency words (Fig.3). The degree of learning for each condition will eventually be affected by differences in the number of updates of the individual diseasevectors17. As a result, there will be differences between reliability of disease interaction scores for relatively rare disorders and high frequency disorders. In NLP models, this property can be used to classify the words by their semantic information importance. However, in disease representations, there is no difference between the diseases in terms of information values i.e., diseases cannot be classified as more important or less important in our context as in other NLP problems. This is the main difference between the real words and word-like representations. By removing highly connected diseases, we would like to increase the score reliability of the rest of the diseases and consequently increase the prediction performance.

Prior to applying the approach to the COVID-19 disease to find possible comorbidities, we aimed to increase the disease interaction identification performance. Use of heterogeneous miRNA-gene-disease network approach is mostly conserved in our analysis, which is based on data from miR2Disease and HMDD miRNA-disease interactions9,18. The random walk method based on meta-paths has also been preserved. However, changes have been made to increase the accuracy of the network method. In contrast to the original architecture, we used a TF-TF interaction network rather than the PPI to be able to represent the regulatory mechanisms in a more precise manner. The cosine similarity of the disease vectors, which is one of the distance metrics used for quantifying the word similarities in NLP applications, was utilized to analyze disease similarities (comorbidities) for performance evaluation of the method.

miRNA expression profiles of SARS-CoV-2 infected cells have been collected from Wyler dataset19. 24-h mock-infected samples were used as control samples. Infected Calu3 cell samples have been analyzed for identification of differentially expressed miRNAs (Calu3 4h12h24h). 39 significant and differentially expressed miRNAs have been identified (adj.p-value<0.05) which includes hsa-mir-4485, hsa-mir-483 and has-mir-155.

The score distribution in the reproduced version and in our version with improvements in the disease list and transcription-factor implementation has been shown as a heatmap for all disease scores (Fig.4).

Heatmaps representing the similarity scores in reproduction and TF integrated network. All diseases are placed into x and y axis and they are colored based on their similarity scores as green (1) and blue (0). (a) Heatmap of the reproduction scores. (b) Scores after cancer-associated diseases are removed (sub-frame of part A that matches with diseases in part E). (c) Scores of Transcription-factor integrated model instead of Human PPI with cancer terms. (d) TF model without cancer terms (sub-frame of part B that matches with diseases in part F). (e) Updated training without cancer terms, with Human PPI, and (f) updated training with TF interaction network without cancer terms. Black color represents the zero score that indicates that no association between diseases was found. The region between the black ticks on the x and y axes in (a) and (c) indicate the cancer (right) and non-cancer (left) diseases.

Excluding cancer terms and their related miRNAs from the heatmap data resulted in higher scores on non-cancer disease relationships. Figure4a depicts the reproduction score distribution, with cancer-related phrases gathered in the top-right side of the graph, which alsohas the highest scores. It was transformed into Fig.4b by deleting cancer-related rows and columns from the heatmap, resulting in a clear distinction between the effect of cancer-related terms on the score distribution. Figure4e, on the other hand, was produced by retraining disease pair scores after removing cancer diseases and their corresponding miRNA set from the training dataset of themodel, it can be seen that the overall score profile for non-cancer diseases has improved. Figure4c demonstrates the distribution of disease similarity scores including cancer terms when TF-TF interaction data is utilized instead of Human-PPI. In this case, some disease relationships were lost, and the majority of disease scores were reduced. However, scores of the some of the rare disease interactions were increased that may be of significance. Figure4f demonstrates that when cancer terms are omitted from the TF-TF included trials, the effect on the scores is similar with the upper row, again demonstrating the cancer domination in models where cancer interactions were included. Score distribution differences between the TF-TF regulatory map implementation and PPI can be seen when Fig.4c and f are compared. Although the scores of the TF network models are lower than the PPI network models, the training time of the models has been greatly decreased due to the smaller vocabulary when TF network is used.

Several diseases were found to have no comorbidity with the rest of the diseases (Table 3). All of these diseases have quite a limited number of miRNA connections in the disease-miRNA data in the network. In the Reproduction and Cancerless models, most of them only have one miRNA interaction. When the TF-TF interaction data was used, the non-comorbid disease list was expanded to include some of two miRNA-connected disorders that are not linked to the transcription factor interactions in the network.

ROC (Receiver Operator Characteristic) curves are often used to evaluate the performances of prediction algorithms by presenting true positive rate and false positive rate of predictions as a curve. For this evaluation, information on true positives and true negatives is needed. Compilation of True Positives (comorbidities) from literature is relatively easy despite the scarcity of verified disease-disease interactions in the literature. However, finding the True Negatives is far more challenging as there is no literature data that directly reports non-comorbid pairs of diseases. One way is designating disease pairs with a low RR score or no interaction information as non-comorbid. This technique classifies comorbidities not yet reported in the literature as False Positives (FP) in ROC curve calculations. As a result, True-positive (TP) scores are hampered when each disease has a small number of known comorbidities. To address this negative bias on AUROC, more comorbidity data is needed to increase the TP/FP ratio. We expanded the amount of clinical data in the validation set to be used in mpDisNet., as a result, the AUROC performance was greatly improved over the original implementation.

The performance of original implementation of mpDisNet in terms of AUROC (Area Under ROC) was 0.65, which was higher when compared to the AUROC of the overlap method (0.58), a simpler methodology that finds comorbidities by comparing shared miRNA ratios between two disorders, The key drawback of the ROC analysis in the original implementation is the high number of predicted Disease-Disease interactions, which is around 90.000 [n*(n1)], compared to a small number of known disease interactions which is 81. To be able to expand this list, the disease pairs with RR higher than 1.5 in MediCare dataset and the comorbid disease list of 81 pairs were merged, after all disease names were converted to ICD-9. Generated final Disease-Disease scores (Supplementary file: rev_over_15.xlsx) were converted to pivot table by using pandas python package20. The compiled data visualized as heatmap (Fig.5) with matplotlib v3.4 seaborn python package21. Disease similarity scores were used to calculate TP and FP rates when compared to compiled list of comorbidities and ROC curves were drawn for all cases (Fig.6). The main objective of this improvement is to maximize TPR to better understand the algorithm's true discovery performance. However, since the algorithm's False Discovery Rate cannot be changed, as previously stated, and all disease interactions that have not yet been documented in the literature ought to be labeled as 0, leading to false positives.

Diseases that have at least 1.5 Relative-Risk (RR) score from US Medicare data visualized as heatmap with matplotlib seaborn python package v3.4. Full sized heatmap can be found in Supplementary Fig. 1 (disease_heatmap.pdf) and full list of disease comorbidity scores from MediCare data can be found in material rev_over_15.xlsx).

Receiver-Operator Curve (ROC) curve of all models. (a) ROC score for reproduced model with same setup in the original MpDisNet implementation. (b) ROC curve of cancer removed model scored compared to limited known disease interactions (81 pairs). (c) TF-TF substituted model scores compared to limited disease data. (d) Reproduction of the original model with extended known disease pairs. (e) Cancer removed model with extended disease pairs. (f) Cancer removed and TF integrated model with extended disease pairs.

To determine whether the implementation of the TF-TF regulation mechanism has a beneficial effect on the discovery of comorbidities, a comparison between the PPI network and the transcription factor-implemented network was made.

Figure6a presents the reproduction of the original implementation, and the same AUROC is reproduced as expected. The modifications on the model (removal of cancer terms and using TF-TF instead of PPI) did not improve the results as seen in Fig.6b and c, when they were evaluated using limited clinical data. However, in the second row of Fig.6, use of extended clinical data significantly improves the AUROC when compared to its counterpart on the first row.

We further hypothesized that correlation between scores of disease pairs may be a more accurate measure of similarity or comorbidity between them. A high positive correlation of scores indicates that the pair of diseases have similar scores with other diseases, hence has a common profile of similarities in their mechanisms. This approach also mitigates the impact of low-frequency disorders having low scores due to lack of miRNA connections.

We tested two alternative correlation metrics; Pearson and Spearman correlations are calculated between similarity scores of each disease pair using our mpDisNet results as shown in Fig.7. Although both metrics produced similar results, to reduce the effect of possible methodological bias, both Pearson and Spearman correlation score-based performances were kept in the ROC curves. When correlations are used for evaluation of performances, we find that cancer-term included model scores also have slightly improved AUROC performance. There is more obvious improvement in Cancerless model than other models. We could say that the similarity between diseases is more prominent when we keep PPI and remove cancer terms as seen in Fig.7b. In addition, concordance of the Spearman and Person correlations in Cancerless model could be evidence of improved score reliability when compared to the other models. But in order to keep taking into consideration the non-normal distribution of the similarity scores between disease pairs, the non-parametric Spearman correlation coefficients may be more appropriate to keep. Therefore, only Spearman correlation of vector similarity scores were used to determine possible COVID comorbidities in the following section.

Updated ROC representations of three main approaches (a) Reproduction data with No correction (blue), Pearson correlated scores (gold), Spearman correlation (green), (b) cancer removed, and correlation implemented. (c) Cancer removed and TF integrated network with correlation tunings.

Although our modifications on the algorithm reduced the scores in general, we have observed that low scores of some disease pairs in reproduction model are increased in the modified models. For example, there is an increase in Rheumatoid Arthritis and Depression comorbidity score from 0.79 to 0.90, which is one of the known comorbidities in the literature22. Another elevated score is between epilepsy and chronic hepatitis, clinical evidence suggests that these two disorders are strongly comorbid and should be further examined23. The algorithm cannot provide any direction information between disease comorbidities, therefore it is not possible to assume causality such as one disease being the cause of another disease, since the direction of the comorbidities cannot be implemented into the network structure yet. However, these findings could indicate that, patients with one disease could have a higher genetic and regulatory inclination to another disease which have high similarity score to the first disease24.

We have used our results to investigate comorbidity of COVID-19 with other diseases as a case study. Highly scored diseases that are potentially comorbid with COVID-19 have been retrieved from cancer-removed network training results with Spearman correlation of scores. The threshold has been chosen as 0.9 since it was found as the optimum threshold for the Cancerless model in the ROC curve performance analysis. The algorithm found 156 diseases (Supplementary table: COVID_comorbs.xlsx) with a similarity score of more than 0.9 and correlation of more than 0.95, indicating a strong link to COVID-19 with associated genes and miRNAs. There are also 57 disorders with a score of 0.8 to 0.9, it can be suggested that a moderate link between thesediseases and COVID-19 exists. We have identified high-scored associations with disorders that had clinical evidence of increased risk with COVID-19 on the CDC website, such as Diabetes (0.996), Heart Diseases (0.989), Schizophrenia (0.994), and Hypertension (0.994) (https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/underlying-evidence-table.html) (Supplementary Dat: CDC_Diseases.xlsx). When the Spearman correlation is applied to the result file, the number of probable COVID disease interactions (Scores over 0.95) increases (From 97 to 210). Also, the overall score of CDC diseases increased from 0.92 (stdev0.054) to 0.98 (stdev0.012).

Encouragingly, we have further found that there are strong links between immune response and infection in diseases such as Hepatitis, Infectious Disorders, and several lung-related diseases. High-scores were also found for vessel and artery-related disorders, such as coronary artery disease, aortic aneurysm, and renal-related diseases. Additionally, various neurological and psychological disorders, such as Alzheimers, Parkinsons, Depression, and Schizophrenia, may raise the impact of COVID-19 according to our results. Indeed, recently this link was shown for Parkinson's Disease in the literature25.

While application of disease similarity networks to the NLP models is a promising approach, there are some challenges that should be tackled. The first of them is biases in data, as stated in the first part of the Results and Discussion, over-representation of diseases such as cancer and subtypes can substantially skew the disease representations as shown in Fig.1. The choice of network also has an impact on the outcome. Since the final goal is to trace back the disease similarities and identify the potential genes/metabolic activities that mediate the similarities, it is important to keep only the interactions that are mechanistically meaningful. The original reliance on human PPI may not have offered the mechanistic precision that TF-TF interaction network could. While the benefits of integrating TF-TF were not immediately obvious, exploring specific subtypes of regulatory mechanisms in future models could augment performance further. A critical limitation in the initial approach was the scant validation data, confining the model's evaluative robustness. Diseases, influenced by factors like genetics and environment, require a model that captures this complexity. Word2vec and similar embeddings, while powerful, have the risk of oversimplifying these complexities. A holistic view, potentially achievable by assimilating diverse data sources like clinical records and genomic databases, is desirable. While the introduction of correlation metrics illuminates aspects of disease similarity, it is paramount to distinguish between mere correlation and actual causation. Lastly, presented model could provide a quick and broad perspective on disease comorbidities by offering easy implementation. However, while this quick glimpse is valuable in such cases as pandemics, a deeper dive into the underlying causes and intricacies of these disease connections is essential. As we forge ahead, it becomes evident that continuous refinement and validation are not just beneficial but crucial on these applications.

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Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19 ... - Nature.com

The commercials make it sound so simple: If its COVID, Paxlovid.

But the slogan, catchy though it may be, belies a harsher reality that some public health and elected officials have long acknowledged and worked to rectify: For many, getting access to the therapeutic should be much easier than it has been.

The issue is not one of scarcity, as the antiviral is widely abundant. Nor is pricing a major barrier, as Paxlovid is cheap or even free for many. Nor even is it an issue of how well it works, as studies have shown it to be highly effective.

The drugs biggest impediment has been, and remains, the simple fact that a number of doctors are still declining to prescribe it.

Some healthcare providers hinge their reluctance on outdated arguments, such as the idea of Paxlovid rebound the chance that people who take the drug have a chance of developing COVID symptoms again, generally about two to eight days after they recover.

As it turns out, anyone who gets COVID-19 has a similar rare chance of rebound.

COVID rebound can occur with or without [Paxlovid] treatment, scientists with the Food and Drug Administration wrote in a study published in December. Viral RNA rebound was not restricted to [Paxlovid] recipients, and rebound rates were generally similar to those in placebo recipients.

When told about one patient who was declined a prescription to Paxlovid because of concern about Paxlovid rebound, UC San Francisco infectious-diseases expert Dr. Peter Chin-Hong groaned.

Oh my God, thats so, like, bogus, Chin-Hong said. Clinicians having this weird idea about rebounds, its just dumb.

Data indicate that most people dont get COVID rebound, Chin-Hong said. And while rebound can occur, the possibility should not dissuade people who might really need it from taking an antiviral.

Even if COVID rebound happens, and symptoms do occur, they tend to be mild and do not require repeating the treatment, according to the California Department of Public Health.

Officials at both the federal and state level have implored healthcare providers to properly prescribe Paxlovid and other antivirals when indicated.

Antivirals are underused, the Centers for Disease Control and Prevention said in a statement Thursday. Dont wait for symptoms to worsen.

In its own advisory, the California Department of Public Health said, Most adults and some children with symptomatic COVID-19 are eligible for treatments ... Providers should have a low threshold for prescribing COVID-19 therapeutics.

Aside from Paxlovid, one alternative oral antiviral treatment is known as molnupiravir. Theres also remdesivir, which is administered intravenously.

The CDC says Paxlovid and remdesivir are the preferred treatments for eligible COVID-19 patients.

Dont delay: Treatment must be started within five to seven days of when you first develop symptoms, the CDC says.

A reference to Paxlovid and other antivirals is even in a musical radio ad from California health authorities that has been broadcast throughout the state: Test it. Treat it. You can beat it, with the ditty later continuing: Medication is key / To slow the virus in your body.

Yet there is wide documentation of the low frequency of prescribing Paxlovid and other antivirals, and that can have significant consequences for higher-risk COVID-19 patients. A report published by the CDC Thursday reviewed 110 COVID-19 patients considered high-risk and found that 80% of them were not offered antiviral treatment.

A big reason given by the patients providers, all of whom were under the Veterans Health Administration, was that their patients COVID symptoms were mild.

But as officials note, thats exactly what antivirals are for.

There is strong scientific evidence that antiviral treatment of persons with mild-to-moderate illness, who are at risk for severe COVID-19, reduces their risk of hospitalization and death, the CDC says.

Risk factors for severe COVID-19 include being age 50 and up; not being current on COVID vaccinations; and a wide array of medical conditions, such as diabetes, asthma, kidney disease, heart disease, having anxiety or depression, and being overweight. Other factors that influence health, such as limited access to healthcare and having a low income, can also heighten someones risk.

Another reason providers may cite to not prescribe COVID antivirals, California officials said, is the chance of serious side effects. But that fear is largely erroneous, as most people have little-to-no side effects, the California Department of Public Health says. Some of the more common side effects after taking Paxlovid are developing a temporary metallic taste in the mouth, which occurs in about 6% of recipients, and diarrhea (3%).

However, some people who do take Paxlovid may need to have other medications adjusted, according to the agency.

The other antiviral pill option, molnupiravir, has very few side effects, but you cannot take it if you are pregnant, the state agency said.

Clinicians may also be reluctant to prescribe Paxlovid for younger adults, not because it causes harm, but because it in some studies doesnt show as much benefit, Chin-Hong said. Younger, healthy people are generally unlikely to die from COVID or become ill enough to require hospitalization even without antiviral treatment.

But some data do suggest that patients who take Paxlovid clear out coronavirus from their bodies faster.

What were finding is that people are turning negative very quickly with Paxlovid, Chin-Hong said.

And one report, published in the journal Emerging Infectious Diseases, suggests widespread use of Paxlovid would not only improve outcomes in treated patients but also ... reduce risks of onward transmission.

So if an initial clinician turns you down for a Paxlovid prescription, and you think you qualify, what other options are there?

One possibility is reaching out to another healthcare provider who might be either more knowledgeable about Paxlovid and other antiviral medications or more open to prescribing them.

Los Angeles County residents can call the countys public health info line, (833) 540-0473, to discuss treatment options with a health provider.

Californians who dont have insurance or have a hard time getting anti-COVID-19 medication can schedule a free telehealth appointment by calling (833) 686-5051 or visiting sesamecare.com/covidca. Medication costs may be subject to a copay, depending on your insurance.

A program funded by the National Institutes of Health, featured at test2treat.org, gives adults who test positive for COVID-19 or flu free access to telehealth care and treatment.

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Paxlovid can still be hard to get. Here's what to know. - Los Angeles Times

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Augusta will host first Camellia Flower Show since COVID-19 - The Augusta Press

The Pacific Martial Arts Conference hosted 10 short classes, which spanned disciplines from around the world.

Nathan Wilk / KLCC

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Oregons martial arts community is still rebuilding after the pandemic. One sign of a comeback: For the first time in four years, students and teachers gathered for a conference last week in Eugene.

The students practiced their spinning back kicks, took turns throwing their partners to the ground, and sparred in slow-motion with wooden sticks.

Some people showed up in uniform white gis and brightly colored belts. Others arrived in T-shirts and sweatpants.

This is the Pacific Martial Arts Conference, or PacMAC. It was held Jan. 20 at Best Martial Arts Institute in Eugene.

Instructors from Oregon and Washington state came together to share about their styles, which ranged from Wing Chun kung fu to medieval Italian knife combat.

Alan Best founded the event in 2007. He said its a chance for martial artists to break out of their insular communities and find common ground.

When we first started the conference, the thing that blew people away was how similar we are, said Best. People think that their art is the only one that does that particular kind of practice. And then they realize, oh, everybody else does this, too.

Best said this months gathering drew over 60 people, despite an ice storm that hit the region just days before. Brazilian jiu-jitsu instructor Padme Grace said she traveled four hours from Long Beach, Washington, to attend.

Im looking for little nuggets of something good that I can pass along to my school, said Grace. So I take a lot of notes.

In bare feet, participants stand ready for one of the days short lessons. Elida Stewart of Eugenes Family Karate Center leads them through a kata, or a choreographed sequence of attacks.

Stewart has had a martial arts studio, or dojo, in Lane County for over three decades. She said at this event, shes found camaraderie and friendship.

Some of these people Ive known for 20-plus years, and I havent seen them since 2020, said Stewart. So to get together is immensely valuable.

Elida Stewart leads her class.

Nathan Wilk / KLCC

The conference comes in the wake of the pandemic, which instructors say left a lasting impact on martial arts, both locally and nationally. Best said many schools couldnt pay their rent during lockdowns and had to shut down permanently.

Thats just so tragic, said Stewart. Will we ever truly recover?

Stewart said dojos arent just for self defense, but building confidence and resistance to adversity.

Kids at some point dont want to listen to their parents, but they never dont want to listen to their sensei, she said. And so its a really awesome way to inspire and help the youth go and have character, and have honor, and integrity, and all the things we want them to have.

Grace said a dojos community is larger than the building itself.

If theres something thats going on in somebodys life, a lot of times theyre divorced or have grandparents that are taking care of their kids, we lend a helping hand wherever we can, said Grace. And we are a village.

For the schools that remain, instructors say theyve seen signs of recovery, and some even report a boom in attendance in recent years.

With this conference, Best said he hopes to encourage the trade of ideas and teaching practices. He said in previous years, instructors have been able to form partnerships and host each other as guest speakers.

Fortunately, martial arts is such a solid part of our American culture now that I think were pretty safe with martial arts being around forever, he said. Its just a matter of how it evolves and honestly, things like PacMAC are right there helping us evolve maybe a little faster than it normally would.

For those curious about Eugenes martial arts community, Stewart said theres no time like the present.

Ive always wanted to do that. I hear that a lot, she said. And I think that the only way you can get it off the bucket list into reality is by taking the first step, which is to show up the first day.

Organizers plan to host the Pacific Martial Arts Conference again later this year.

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Pacific Northwest martial artists reunite in Eugene as impacts of COVID-19 pandemic linger - Oregon Public Broadcasting

Currently, there is a rise in COVID-19 cases nationwide. Therefore, I decided to write an update to my post that I did 1.5 years ago. Let's find out what is new in the COVID-19 and brain research area and how to deal with long-term COVID-19.

Neuroimaging and neuropathological studies results.

There is agreement that COVID-19 is a systemic illness often involving the central nervous system. Reportedly, neurological symptoms affect more than 30 percent of COVID-19 patients. Frequently, there is neurological involvement in all stages of this illness, such as acute, subacute, chronic, and post-acute sequelae.

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Even asymptomatic patients can develop neurological symptoms and conditions such as headaches, myalgia (muscle pain), Guillain-Barre syndrome (in which a person's immune system attacks peripheral nerves), encephalopathy (brain disease or malfunction), and myelopathy (neurologic deficits related to the spinal cord).

The magnetic resonance imaging (MRI) studies of COVID-19 patients revealed a reduction in grey matter thickness mainly in the orbitofrontal cortex (that processes sensory information, decision-making, and emotions.) and the parahippocampal gyrus (that plays an important role in memory encoding and retrieval.) and are associated with the greater reduction in global brain size and greater cognitive decline. The virus can directly infect endothelial cells of the brain (cells that make up the lining of blood vessels), which may promote clot formation and stroke.

In some patients, COVID-19 causes an ischemic stroke (caused by a lack of blood reaching part of the brain). The review of strokes in 455 patients (Finsterer J. et al., 2022) indicates that stroke occurs in all age groups and predominantly in males. The ischemic stroke is multifactorial but usually embolic (a blockade of a blood supply to the part of the brain by a blood clot).

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The researchers also indicated cardiovascular risk factors are frequently present in patients who suffered from a stroke and that COVID-19 stroke may co-occur with the onset of pulmonary symptoms or up to 40 days later. Interestingly, when the virus enters the brain, it mainly affects astrocytes (glial cells in the central nervous system that supply the building blocks of neurotransmitters) and may cause neuronal death or dysfunction (Crunfli, F. et al., 2022). The researchers suggest that these processes could contribute to the structural and functional changes in the brains of COVID-19 patients.

Cognitive and mental health studies results.

Quite a few studies now indicate cognitive changes after COVID-19 illness. The cognitive problems mainly include memory, attention, executive functions, processing speed, and visuospatial deficits, but may also include other brain functions.

The research indicates that cognitive impairments can be seen even in asymptomatic patients. Also, non-hospitalized patients have a significantly higher likelihood of developing mental health problems. These problems may include mood dysregulation, depression, anxiety, insomnia, and even psychosis.

Some research suggests that some patients can develop cognitive and psychotic symptoms even two years after the infection. This, unfortunately, also includes dementia.

About one-third of COVID-19 survivors suffer from a variety of symptoms long after they were first infected. "Brain fog" is the most frequently reported and is one of the symptoms that persist for weeks and months after the disease and significantly affects survivors' everyday functioning.

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Brain fog is not medical terminology. It is rather a description of patient's use for various symptoms they are experiencing. It is mainly described as slow and sluggish thinking and fuzziness, but it can also include searching for words, memory, concentration, problem-solving difficulties, and fatigue. The good news is that usually, there is some improvement in neurological deficits a few months after the infection.

However, there is a correlation between symptom severity and the degree of neurological deficits. An interesting review was published in 2023 (Zhao, S. et al., 2023). The researchers reviewed cognitive changes during the acute and chronic stages of COVID-19.

The results suggest that problems with executive functioning were frequently reported during the acute stage. However, during the chronic phase (three months to two years), mild and moderately infected patients reported attention, executive functioning, and memory deficits. However, the good news is that the recovery can occur within the first year after the infection.

The research indicates that severe COVID-19 is associated with long-term cognitive impairments, but even mild COVID-19 can cause changes in the brain that can lead to cognitive deficits. Therefore, it is best to do whatever it takes not to get sick. However, if you have had COVID-19 and are experiencing some cognitive deficits, it may be beneficial to contact your community neuropsychology office and complete a neuropsychological evaluation.

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This will show if you have some cognitive deficits (memory, attention/concentration, information processing, etc.), how much (mild, moderate, severe impairments), and how to improve or maintain your cognitive functioning.

References

Hingorani, K.S., et al. COVID-19 and the brain. Trends in Cardiovascular Medicine. Volume 32, Issue 6, 2022.

Finsterer, J. et al. Ischemic stroke in 455 COVID-19 patients. Science Direct, Volume 77, January-December 2022

Crunfli, F. et al. Morphological, cellular, and molecular basis of brain infection in COVID-19 patients. PNAS, August 11, 2022 .

Zhao, S. et al. Effects of COVID-19 on cognition and brain health. Trends in Cognitive Sciences. Vol 27, Issue 11, 2023.

Li-Shan Sia, A. et al. Brain fog and COVID-19. The American Journal of the Mdical Sciences. Volume 365, Issue 5, May 2023.

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COVID-19 is a systemic illness that often involves the central nervous system. - Psychology Today

Despite telemedicine visits stabilizing around May 2021 and decreasing after the COVID-19 pandemic peak, mental health video visits continued to increase in the past year, a new study found.1

This [Veteran Affairs] study provides an updated timeline of the fluctuations in use of in-person care and telemedicine since the onset of the COVID-19 pandemic, wrote investigators. A new equilibrium has emerged in which telephone-based care has largely returned to prepandemic levels, whereas video based care accounts for 11% to 12% of outpatient care (2300% increase from a prepandemic level of 0.5%).

During the beginning of the COVID-19 pandemic, many people turned to telemedicine for healthcare visitseven individuals on Medicare.2 Before the pandemic, Medicare beneficiaries could not use the telehealth service unless access to in-person care was limited by location. This changed with the pandemicduring that time, the department of health and human services waived some of its telehealth restrictions for Medicare, making it easier for some individuals to get to their appointments.2

The study, led by Jacqueline M. Ferguson, PhD, from the Center for Innovation to Implementation at Veterans Affairs Palo Alto Health Care System in Menlo Park, California, sought to assess the format trends of clinical outpatient visits between January 1, 2019 and August 2023.1 The team assessed outpatient visits that took place in person, by telephone, and by video before, during, and after the pandemic. Before the pandemic was March 11, 2020, during the pandemic was March 11, 2020May 10, 2023, and after the pandemic was marked by the end of the federal COVID-19 Public Health Emergency declaration on May 11, 2023.

Participants came from the US Department of Veterans Affairs healthcare system, and the investigators identified 277,348,286 clinical outpatient visits through the Veteran Affairs Corporate Data Warehouse. The database contained data from 9 million veterans enrolled in Veteran Affairs services and approximately 5.4 million used Veteran Affairs outpatient healthcare services in 2019.

The data included in the study was comprised of 91% males, 72% White participants, and 65% who lived in urban settings. Health care visits were categorized by care service (primary care, mental health, subspecialty care), and modality (in-person, mental health, and video).

The team observed Veteran Affairs had 1.14 million primary care, subspecialty, or mental health visits every week and 4.9 million visits every month. The number of visits began to decrease at the start of the pandemic and did not stabilize until March 2021.

Notably, this stabilization occurred when vaccines were widely available2 years before the end of the federal COVID-19 Public Health Emergency declaration, wrote investigators.

In-person primary care and mental health services were replaced by telemedicine. As in-person visits decreased, telephone and video-based visits increased, with in-person visits reduced from 81% in February 2020 to 23% in May 2020.

However, telephone and video-based care began decreasing from a peak of 79.6% of care in April 2020 to 36.7% in April 2023. The percentage was mainly due to the decrease in telephone visitsnot video visitsas the number of video visits remained close to the peak at levels 11%13%.

Investigators pointed out people continued to utilize video visits for mental health but use for primary care and specialty visits began to decline as the pandemic neared its end. By August 2023, 34.5% of mental health visits, 3.7% of subspecialty visits, and 3.5% of primary care visits occurred on videoand 20.3%, 34.8%, and 16.7%, respectively, accounted for telephone visits.

The investigators stated 55% of mental health care continues to be provided via telemedicine, which is likely because mental health services have adapted to virtual platforms. Moreover, they pointed out while primary care and subspecialty telemedicine are often limited by the need for in-person evaluation, including physical examinations, 10% of primary and subspecialty care has switched over to telemedicine.

Although these nationwide trends can inform research and policy, they obscure disparities in access to and use of telemedicine that disproportionately affect older adults, individuals in rural regions, and patients from historically marginalized groups, investigators wrote. Future research should consider evaluating quality, safety, and health outcomes of telemedicine in this new equilibrium.

This article originally appeared in HCPLive.

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While Telemedicine Decreased After COVID-19 Peak, Mental Health Video Visits Rose - Drug Topics

Research on COVID-19 misinformation conducted by Dr. Nitin Agarwal, Maulden-Entergy Chair and Distinguished Professor of Information Science and founding director of the Collaboration for Social Media and Online Behavioral Studies (COSMOS) Research Center at the University of Arkansas at Little Rock, has been highlighted as a success story by the U.S. Army Research Office.

The story, Developing Research Infrastructure to Strengthen Socio-Cognitive Security for Combating Misinformation and Deviant Collective Behavior, was featured in the U.S. Army Research Offices year-in-review magazine.

In this research, funded and recognized by the ARO, Dr. Agarwal and his team at the COSMOS research center applied their expertise to study COVID-19related cross-media misinformation campaigns and scams. They put the research in practice by deploying the research-driven solution in collaboration with Arkansas Attorney Generals office to raise awareness and combat misinformation and scams in the state of Arkansas.

This groundbreaking work has garnered recognition from the World Health Organization (WHO), acknowledging its potential as a critical technological innovation in the fight against COVID-19. COSMOS worked closely with the Arkansas Attorney Generals office to understand their need during an intensive barrage of COVID-19related scams and misinformation and delivered effectively for proactive policymaking. Findings from this collaboration were published in the book entitled Coronavirus and Disinformation A Whole of Society Perspective under the auspices of NATO Research and Technology Group (RTG HFM-293).

Our inclusion in the U.S. Army Research Offices year-in-review magazine marks not just a milestone for our team but a beacon of commitment to truth and building community resilience against the tide of misinformation by working closely with our policymakers, Agarwal said. As the digital landscape evolves, so does our dedication to fortifying our socio-cognitive security. The journey continues, and each discovery strengthens the foundation of our collective pursuit.

The funding and recognition of the Army Research Office not only speaks to the importance of such research pursuits, but also to how COSMOS has taken academic theories, such as those on deviant collective behaviors, and applied them with computing algorithms in such a way that government and military officials can now look to such research for practical solutions.

This demonstrates COSMOSs ability to bridge science, society, and policy-making through research, technology, and innovation, Agarwal said. We demonstrated how modern coordinated cognitive threats can be modeled, and its consequences can be mitigated through proactive policy-making supported by research-driven solutions. The recognition puts us at par with research centers and labs at top-tier institutions across the nation working to advance foundational research competencies to enable crucial future Army technologies and capabilities.

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COSMOS' COVID-19 Misinformation Research Featured as Army Success Story - News - UA Little Rock - University of Arkansas at Little Rock

The new Covid-19 sub-variant JN.1 started a new scare among the masses with growing numbers of cases all around the world. The scare was so much that in some parts of the world, people were reminded of old pandemic days.

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The latest figures show that Englands current Covid wave is less likely to get people sick enough to be hospitalised, as compared to previous waves.

It means, people who are infected by Covid-19 recently have less to worry about than at any other time during the pandemic, Paul Hunter at the University of East Anglia in the UK told the NewScientist.

Also Read |Hot springs served as catalyst for life on Earth billions of years ago, study finds

JN.1 subvariant which was first detected in August in Luxembourg was another version of the deadly Omicron variant. It then spread across many European countries quickly and by November it became quite evident by genetic sequencing that JN.1 was rapidly taking over.

These observations led to concerns over JN.1 that it might start a new Covid wave that would be even deadlier than the previous ones. At this time, hospitals in Europe have already been facing a high turnout of patients with seasonal respiratory viruses. This encouraged hospitals in Europe to take preventive measures way before it was established whether JN.1 is as deadly as Omicron.

Also Read |An eruption over 520,000 years ago shows South Aegean Volcanic Arc was far more dangerous

Fortunately, data later suggested that the fear of a bigger wave because of JN.1 variant is not true.

To put things into perspective, the number of Covid-19 infected people in England amid the current wave seems to have peaked in late December, at about 4.5 per cent of the population, as per a large, regular survey by the Office for National Statistics and the UK Health Security Agency. This is similar to that seen during the Covid-19 wave that peaked in December 2022.

Also Read |Many US cities could empty out by 2100, become 'ghost cities' due to population drop: Study

Yet the number of Covid-19 infected people being admitted to hospitals in England seems to have peaked this winter at just over a third of the equivalent figure from 2022. The number was 3300 a year ago in early January, while during the same time in 2024, only 1300 people were hospitalised.

It is unclear whether the fall in severity is because JN.1 is milder than other variants or if people just have more immunity to Covid-19 now due to past infections and vaccinations, I think immunity is probably playing a big role, but it may be that the latest variant is also less virulent, says Hunter.

(With inputs from agencies)

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Data shows the JN.1 variant might be the mildest form of COVID-19 yet - WION