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Tag: coronavirus

  • Study shows long COVID’s hidden effect on women’s sex lives

    Study shows long COVID’s hidden effect on women’s sex lives

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    From work to school to socializing, COVID-19 has impacted just about every part of our lives-;and now Boston University research has shown that also includes what happens in the bedroom. A study of more than 2,000 cisgender women found the coronavirus disease can impair sexual function, with long COVID having an especially detrimental effect.

    If you’re sick with COVID, you’re probably less interested in sex and maybe your body is less prepared to have sex. But what might be surprising to some folks is that long COVID symptoms really may have a physiological and psychological impact on sexual well-being for women.”


    Amelia M. Stanton, BU College of Arts & Sciences assistant professor of psychological and brain sciences

    Although previous research has investigated the effect of the pandemic on peoples’ sex lives-;particularly in men-;Stanton says this is the first study to highlight long COVID’s fallout on sexual health in women. An expert on sexual and mental health, she helped lead the study with researchers from Middlebury College, McLean Hospital, and the University of Vermont. The findings were recently published in the Journal of Sexual Medicine.

    Long COVID and sexual dysfunction

    To figure out COVID’s impact on intimacy, Stanton and her colleagues conducted an online survey. Roughly half of the women taking part had reported never having had COVID, the rest said they’d tested positive. Participants were quizzed using the Female Sexual Function Index (FSFI), an established tool that measures factors like arousal and satisfaction with questions such as, “Over the past 4 weeks, how often did you feel sexual desire?” Only women who’d had sex in the previous month were included in the results.

    Among those who’d had COVID, levels of desire, arousal, lubrication, and satisfaction were all lower than in those who hadn’t; orgasm and pain scores weren’t significantly different between the two groups. But while women in the COVID group were still classed within the index’s functional range, participants with long COVID had “an average FSFI full scale score in the dysfunctional range,” according to the researchers. They found women with long COVID-;a broad condition with cognitive and physical symptoms that linger for weeks, sometimes months, after an initial infection-;had markedly worse arousal, lubrication, orgasm, and pain scores.

    “I hope it’s validating. If women type in ‘sex long COVID,’ something will come up now,” says Stanton, who is also a clinical health psychologist at The Fenway Institute, a Boston clinic focused on the health of sexual and gender minorities. “Sex, sexuality, and sexual function are still relatively taboo subjects. But this offers something patients can bring to their providers and say, ‘This is going on for me,’ and maybe create an open dialogue around sex.”

    In their paper, Stanton and her colleagues say the results suggest “that COVID-19 infection may be associated with impairment of both cognitive and physiological aspects of sexual function.” Just as the body and mind might take some time to get back to firing on all cylinders when it comes to work, study, and exercise, the same may apply to sex. They also speculate that wider societal changes caused by the pandemic may be a factor, with fewer social events and kids hanging around at home more reducing opportunities for shared or solo sexual activities.

    Talking about sex

    While a COVID infection might impact women’s sexual health, previous BU research has found vaccination does not cause infertility, reduce pregnancy chances, or have a significant impact on menstruation.

    “COVID-19 vaccination in either partner is unrelated to fertility among couples trying to conceive through intercourse,” Amelia Wesselink, an SPH research assistant professor of epidemiology, told The Brink in 2022 when discussing her study on vaccines and fertility. That same research did, however, find that men who’d tested positive for COVID within the past 60 days had reduced fertility.

    Stanton is the principal investigator of BU’s Sexual, Reproductive, and Mental Health Disparities Program-;an effort to explore sexual and mental health in minoritized and marginalized populations-;and says possible future routes for the latest project would be to expand the study’s sexual and gender minority diversity, talk to women for their qualitative experiences, and design tools to help providers better support their patients.

    “I’m an interventionist, so I always think about intervention design as a next step,” says Stanton. In other research, she’s working to develop new approaches clinicians can use to talk about sex with their patients, as well as studying how to improve sexual well-being and mental health in low-resource communities.

    “I always encourage providers to initiate conversations about sex,” says Stanton. “If they have someone who’s coming in for long COVID, maybe ask, ‘How are you doing sexually?’ Asking that one question could open the door for people to say, ‘You know, I’ve been ashamed to say that this is going on, and I really need help.’ Any way we can iterate to folks that there is hope and there are strategies-;your symptoms are meaningful and relevant, and they’re important to talk about.”

    Source:

    Journal reference:

    Seehuus, M., et al. (2023). The impact of COVID-19 and long COVID on sexual function in cisgender women. The Journal of Sexual Medicine. doi.org/10.1093/jsxmed/qdad155.

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  • AI’s ability to detect COVID-19 from coughs faces real-world challenges

    AI’s ability to detect COVID-19 from coughs faces real-world challenges

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    A recent Nature Machine Intelligence study investigated the efficacy of audio-based artificial intelligence (AI) classifiers in predicting severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection status. SARS-CoV-2 is the causal organism of the coronavirus disease 2019 (COVID-19) pandemic.

    Study: Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. Image Credit: Aliaksandra Post / ShutterstockStudy: Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. Image Credit: Aliaksandra Post / Shutterstock

    Background

    Since SARS-CoV-2 infection could cause both symptomatic and asymptomatic manifestations, it is important to develop accurate tests to avoid general population quarantine. Previous studies have revealed that AI-based classifiers trained with respiratory audio data could identify SARS-CoV-2 status. 

    Although these studies indicated the effectiveness of AI-based classifiers, many challenges surfaced while applying them in real-world settings. Some factors that withheld AI-based classifier applications were sampling biases, unvalidated data on participants’ COVID-19 status, and delay between infection and audio recording. It is imperative to determine whether the audio biomarkers of COVID-19 are unique to SARS-CoV-2 infection or are inappropriate confounding signals.

    About the Study

    The current study focussed on determining whether audio-based classifiers can be accurately used for COVID-19 screening. A large-scale polymerase chain reaction (PCR) dataset linked to audio-based COVID-19 screening (ABCS) was used. For this study, participants of the Real-time Assessment of Community Transmission (REACT) program and the National Health Service (NHS) Test-and-Trace (T+T) service were invited. All relevant demographic data was extracted from T+T/REACT records.

    Participants were asked to complete survey questions and record four audio clips. For audio recordings, they were asked to read a specific sentence, followed by three successive exhalations, making a “ha” sound. Furthermore, the participants were asked to record forced coughs once and three times in succession. All recordings were documented in .wav format. The quality of the audio recordings was assessed, and 5,157 records were removed for quality-related issues.

    Human figures represent study participants and their corresponding COVID-19 infection status, with the different colours portraying different demographic or symptomatic features. When participants are randomly split into training and test sets, the randomized split models perform well at COVID-19 detection, achieving AUCs in excess of 0.8; however, matched test set performance is seen to drop to estimated AUC between 0.60 and 0.65, with an AUC of 0.5 representing random classification. Inflated classification performance is also seen in engineered out of distribution test sets such as: the designed test set, in which a select set of demographic groups appear solely in the testing set, and the longitudinal test set, in which there is no overlap in the time of submission between train and test instances. The 95% confidence intervals calculated via the normal approximation method are shown, along with the corresponding n numbers of the train and test sets.Human figures represent study participants and their corresponding COVID-19 infection status, with the different colours portraying different demographic or symptomatic features. When participants are randomly split into training and test sets, the randomized split models perform well at COVID-19 detection, achieving AUCs in excess of 0.8; however, matched test set performance is seen to drop to estimated AUC between 0.60 and 0.65, with an AUC of 0.5 representing random classification. Inflated classification performance is also seen in engineered out of distribution test sets such as: the designed test set, in which a select set of demographic groups appear solely in the testing set, and the longitudinal test set, in which there is no overlap in the time of submission between train and test instances. The 95% confidence intervals calculated via the normal approximation method are shown, along with the corresponding n numbers of the train and test sets.

    Study Findings

    In this study, a respiratory acoustic dataset of 67,842 individuals was collected. Among them, 23,514 individuals tested positive for COVID-19. All data were linked with PCR test results. It must be noted that the most significant number of COVID-19-negative participants were recruited from six REACT rounds compared to the T+T channel.

    The dataset considered in this study exhibited promising coverage across England. No significant association between geographical location and COVID-19 status was noted. The highest level of COVID-19 imbalance was found in Cornwall. A previous study indicated recruitment bias in ABCS, particularly linked with age, language, and gender, in both training data and test sets. Despite this bias, the training dataset was balanced in accordance with age and gender across COVID-positive and COVID-negative subgroups. 

    Consistent with previous studies, the unadjusted analysis conducted in this study exhibited that AI classifiers can predict COVID-19 status with high accuracy. However, when measured confounders were matched, a weak performance of AI classifiers in detecting SARS-CoV-2 status was observed.

    Based on the findings, the current study proposed some guidelines to rectify recruitment bias’s effect for future studies. Some of the recommendations are listed below:

    1. Audio samples stored in repositories must include details of the study recruitment criteria. In addition, relevant information about the individuals, including their gender, age, time of COVID-19 test, SARS-CoV-2 symptoms, and locations, must be documented along with the audio recording.
    2. All confounding factors must be identified and matched to help control recruitment bias.
    3. Experimental design must be developed, keeping the possible bias in mind. In most cases, data matching leads to a reduction in sample size. Observational studies recruit participants focusing on the maximized possibility of matching measured confounders.
    4. The predictive values of the classifiers must be compared with standard protocol findings.
    5. AI classifiers’ predictive accuracy must be assessed. However, the predictive accuracy, sensitivity, and specificity vary depending on the targeted population.
    6. The classifiers’ utility must be assessed for each testing outcome.
    7. The replication study must be conducted in randomized cohorts. Furthermore, pilot studies must be conducted in real-world settings based on domain-specific utility.

    Conclusions

    The current study has come with limitations that include the possibility of potential unmeasured confounders across REACT and T+T recruitment channels. For instance, PCR testing for COVID-19 was performed several days after self-screening of symptoms. In contrast, PCR tests in REACT were conducted on a pre-determined date, irrespective of the onset of symptoms. Although the majority of confounders were matched, there is a possibility of the presence of residual predictive variation.

    Despite the limitations, this study highlighted the need to develop accurate machine-learning evaluation procedures to obtain unbiased outputs. Furthermore, it revealed that confounding factors are hard to detect and control across many AI applications.

    Journal reference:

    • Coppock, H. et al. (2024) Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers. Nature Machine Intelligence. 1-14. DOI: 10.1038/s42256-023-00773-8, https://www.nature.com/articles/s42256-023-00773-8

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  • The impact of drinking water quality on mental health and the modifying role of diet

    The impact of drinking water quality on mental health and the modifying role of diet

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    In a recent study published in BMC Medicine, researchers estimate how exposure to various trace elements in drinking water increases the risk of depression and anxiety.

    Study: Association between drinking water quality and mental health and the modifying role of diet: a prospective cohort study. Image Credit: New Africa / Shutterstock.com

    Background

    Mental health disorders, particularly depression and anxiety, remain a leading cause of both disability and premature death throughout the world. Following the coronavirus disease 2019 (COVID-19) pandemic, the prevalence of both anxiety and depression throughout the world rose by about 25%, thus exemplifying the widespread prevalence of these mental diseases.

    Several factors may increase an individual’s risk of depression or anxiety, including genetics, social environment, and physical environment. Within the physical environment, exposure to heavy metals like cadmium has been shown to increase the risk of depression and anxiety, whereas other elements like manganese, copper, and selenium, all of which combat oxidative stress, have the potential to reduce the risk of depression.

    To date, most studies investigating the impact of environmental risk factors on the incidence of depression and anxiety have been cross-sectional. Within China, few longitudinal studies have examined how exposure to metal and nonmetal elements in drinking water may impact the risk of depression and anxiety.

    About the study

    In the present study, researchers identified people diagnosed with depression and anxiety from the Yinzhou district using International Classification of Disease codes F32 and F41, respectively, in data retrieved from the Yinzhou Health Information System (YHIS). Atomic absorption spectrophotometry (AAS) was used to measure manganese, zinc, copper, iron, aluminum, cadmium, selenium, and fluorine levels in tap water samples collected from 37 sites in the Yinzhou district. 

    Water samples were collected four times each year, with at least one sample collected each season. Exposures were assigned to participants based on their residential addresses and the location of tap water collection sites. Daily exposure to all trace elements in drinking water was calculated and adjusted based on the daily drinking water intake of participants, as well as their age and gender.

    A baseline survey was administered to collect data on participants’ frequency of consuming leafy vegetables, meat, fruits, and fish, with their intake of these dietary components categorized as low, moderate, or high. Data on sociodemographic status, lifestyle, and medical history were also acquired.

    Study findings

    The final analysis included 24,285 individuals between 2016 and 2021 without a history of depression or anxiety. From these individuals, 765 and 1,316 depression and anxiety cases, respectively, were reported during a median follow-up period of 4.72 and 4.68 years, respectively.

    Females, as well as those who never smoked or drank, were more likely to have depression, in addition to a greater risk of hypertension, dyslipidemia, cancer, and stroke. Comparatively, females, less educated individuals, older individuals, never drinkers, non-smokers, and those with a lower income were more likely to have anxiety, diabetes, dyslipidemia, cancer, and stroke, in addition to lower levels of seafood and meat consumption.

    Exposure to aluminum in drinking water was more commonly reported in individuals diagnosed with depression, whereas exposure to manganese, iron, and aluminum in drinking water was higher in individuals with anxiety. Individuals with anxiety were also exposed to lower levels of zinc as compared to healthy participants.

    Long-term exposure to zinc, iron, aluminum, selenium, and fluorine did not impact the risk of depression. Likewise, long-term exposure to zinc, copper, aluminum, cadmium, and fluorine did not increase the risk of anxiety.

    Diet did not have a significant effect on the relationship between the risk of depression and manganese, copper, and cadmium exposure in drinking water. However, the risk of anxiety was greater in individuals who consumed less fruits, more seafood, and meat and who were also exposed to manganese and iron in drinking water. Long-term exposure to copper, selenium, and fluorine was also associated with a greater risk of anxiety in individuals who consumed less leafy vegetables and fruits.

    Lower socioeconomic level was associated with increased exposure to heavy metals, particularly copper, in drinking water. Additionally, older, low-income, and less educated individuals who were exposed to cadmium in drinking water were also at a greater risk of depression.

    Higher education levels were more commonly observed in anxious individuals who were exposed to manganese and selenium in drinking water. Comparatively, exposure to iron in drinking water was also more common in older and less educated individuals with anxiety.

    Conclusions

    The present study findings underscore the need to improve the quality of drinking water and adopt healthy dietary habits to reduce the burden of depression and anxiety, as these measures may contribute to the pathophysiology of depression and anxiety. Public health policies should also address the inequitable effect of exposure to various trace elements in drinking water in relation to the increased risk of mental diseases among people in low socioeconomic strata. 

    Journal reference:

    • Zhou, S., Su, M., Shen, P. et al. (2024). Association between drinking water quality and mental health and the modifying role of diet: a prospective cohort study. BMC Medicine 22(53). doi:10.1186/s12916-024-03269-3

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  • Hunters key to early detection of zoonotic diseases, study finds

    Hunters key to early detection of zoonotic diseases, study finds

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    To prevent future health crises, monitoring the emergence of zoonotic diseases in wild meat value chains is essential. In this regard, the role of community hunters is crucial, as they can report early signs of possible disease in game animals.

    Study: An experimental game to assess hunter’s participation in zoonotic diseases surveillance. Image Credit: Virrage Images / Shutterstock.com

    Background

    Since the mid-twentieth century, zoonotic diseases have caused 60% of emerging disease events. More recently, wildlife has been suspected to be the original reservoir of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of the coronavirus disease 2019 (COVID-19) pandemic.

    Wild animal hunting and trade facilitate human-wildlife interactions and spillover events. Community-based surveillance can provide early warning and aid in limiting the spread of a zoonotic disease. However, research has shown that local communities perceive the risk of disease transmission from animals to humans differently.

    About the study

    In a recent study published in BMC Public Health, researchers designed an experimental game (EG) to better understand the response of community hunters when encountered with signs of zoonotic diseases in game animals.

    EGs provide important insights into the decision-making of a group of individuals. These “players” are confronted with hypothetical scenarios and are asked to choose among different options. Observations from EGs are compared to game theoretical predictions, which assume players to be rational utility-maximizers.

    In the forested area of Gabon in central Africa, an EG was developed and tested that mimicked the implementation of a community-based surveillance system. Voluntary reports of hunters were used to monitor zoonotic diseases in wildlife.

    Both subsistence and commercial hunters were included in the EG. The key aim was to identify the characteristics of hunters, surveillance, and epidemiological processes that could influence their probability of participating in wildlife disease surveillance.

    A total of 88 hunters were divided into nine groups, each comprising five to 13 players. Over 21 rounds of the EG were performed, each of which involved a hunting trip simulation where the payers were likely to capture a wild animal with clinical signs of zoonotic disease.

    When signs of the zoonotic disease were visible, the participants were asked to report or sell/consume the animal. Reporting meant lower hunting revenue but also a lower probability of the spread of a zoonotic disease, which could benefit the entire community.

    Key findings

    A false alert, defined as a flagged case not caused by a zoonotic disease, led to reduced case reports in the subsequent round. Concerning hunter characteristics, those who engaged in agricultural activity, in addition to hunting, flagged suspected cases more often than their counterparts. The number of potential case reports rose with each round, thus suggesting a greater inclination to report throughout the game.

    In the game-theoretic model, participation in surveillance was associated with positive externalities. Relevant information benefits the community as a whole; however, it comes at a cost for the reporting player, which could lead to sub-optimal participation in reporting. The game sessions corroborated this theoretical hypothesis.

    The subsequent reduction in reports followed by a false report was due to false reports reducing the anticipated benefit of reporting. Prior research has shown that from a societal point of view, false alerts are acceptable as long their costs do not exceed the benefits of accurate disease detection.

    In the future, community engagement programs should highlight the utility of periodic false alerts. This will help maintain regular surveillance and its proper functioning in the event a zoonotic disease emerges.

    Players engaging in agricultural work were more likely to flag suspected cases of zoonotic disease than their counterparts. For these hunters, agriculture often accounts for a significant portion of household income, thereby reducing their reliance on hunting revenue to support their families. Thus, economic dependence on wild meat likely governs the decision to participate in surveillance systems.

    Conclusions

    The current study highlights the usefulness of EGs in enhancing our understanding of hunters’ willingness to participate in zoonotic disease surveillance. Extending the game to include all potential actors of surveillance along the wild meat value chains could provide helpful information to better manage the risks stemming from zoonotic diseases.

    Journal reference:

    • Pouliquen, A., Mapeyi, G. A. B., Vanthomme, G., et al. (2024) An experimental game to assess hunter’s participation in zoonotic diseases surveillance. BMC Public Health 24(342). doi:10.1186/s12889-024-17696-7

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  • Study reveals high insomnia rates in non-hospitalized COVID-19 survivors

    Study reveals high insomnia rates in non-hospitalized COVID-19 survivors

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    In a recent study published in Frontiers in Public Health, researchers investigated insomnia prevalence and its association with anxiety and depression in the non-hospitalized coronavirus disease 2019 (COVID-19)-recovered community.

    Study: Sleep quality among non-hospitalized COVID-19 survivors: a national cross-sectional study. Image Credit: Stock-Asso/Shutterstock.com
    Study: Sleep quality among non-hospitalized COVID-19 survivors: a national cross-sectional study. Image Credit: Stock-Asso/Shutterstock.com

    Background

    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has afflicted millions globally since late 2019, with most cases resolved by mid-2023. Common symptoms include coughing, weariness, fever, dyspnea, musculoskeletal issues, gastrointestinal complaints, anosmia, dysgeusia, and vertigo. Post-infection and long-term physical and psychological difficulties are serious public health concerns.

    Insomnia is a prevalent complaint, particularly among hospitalized COVID-19 patients. High-risk variables include being female, younger, and more educated, as well as having anxiety, depression, or post-traumatic stress disorder. Poor mental health is associated with insufficient sleep, and chronic disorders such as obstructive sleep apnea (OSA) can affect glycemic control, neurocognitive impairment, and aberrant functional pulmonary alterations.

    About the study

    In the current nationwide cross-sectional study, researchers investigated insomnia prevalence among COVID-19 survivors with no or moderate symptoms who did not require hospitalization throughout the recovery period (six months) and discovered relevant variables.

    Between June and September 2022, the team conducted a web-based survey among 1,056 COVID-19-recovered individuals who recovered within six months of acute SARS-CoV-2 infection and did not need hospitalization. They used the Depression Anxiety and Stress Scale-14 (DASS-14) and the Insomnia Severity Index (ISI). They obtained data on demographics such as age, marital status, sex, educational attainment, occupation, employment status, and comorbidities.

    The team asked the respondents to rate their SARS-CoV-2 infection severity and duration (days from the initial SARS-CoV-2-positive to the initial SARS-CoV-2-negative report). In addition, the respondents compared their sleep quality, sleep initiation, and total sleep duration in the previous two weeks with the time before confirming the SARS-CoV-2 infection.

    The team used multivariate logistic regressions to determine odds ratios (OR) for the relationships between anxiety and depression scores and insomnia levels among the survey respondents. They included adult COVID-19 survivors (who recovered as confirmed using polymerase chain reaction (PCR) within six months and did not require COVID-19-associated hospitalization) in Vietnam’s general population. They excluded individuals diagnosed with insomnia or psychological disorders before the study.

    Results

    The study included 1,056 individuals, with the majority being married (64%), female (69%), and having attended university (69%). After the SARS-CoV-2 infection, almost a third of respondents reported shorter sleep duration, worsened sleep quality, and more difficulties falling asleep, and half of them reported more nocturnal awakenings. Insomnia prevalence was 76%, with 23% of patients reporting severe insomnia.

    Individuals with anxiety (OR, 3.9) or depression (OR, 3.5) had a significantly increased risk of having insomnia. Other characteristics that increased the likelihood of sleeplessness included higher educational attainment and pre-existing medical conditions, but COVID-19 duration and symptoms had no significant relationship.

    Individuals who were divorced or widowed, female, had postgraduate education, were not actively employed, or suffered from chronic medical conditions had higher mean ISI ratings than their peers. Concerning COVID-19, 92% of infected individuals experienced symptoms (mean, 11 weeks). Although these symptomatic individuals showed higher ISI scores (15.2), there was no significant difference compared to individuals without symptoms.

    The mean scores for anxiety and depression were 7.6 and 6.4, respectively, with 439 (42%) and 291 (28%) individuals reporting relevant symptoms, respectively. Individuals with symptoms of anxiety (18.7) and depression (19.1) scored significantly higher on the ISI compared to those without (12.4 and 13.5, respectively). Participants experiencing insomnia scored higher on anxiety (9.2) and depression (7.8) than the overall group mean.

    In univariate analysis, those who were wedded and had a university degree were significantly less likely to experience insomnia than single and formally-educated individuals. Students were significantly more likely to experience insomnia compared to healthcare workers. Individuals with a history of chronic medical conditions were significantly more likely to suffer from insomnia following COVID-19 compared to healthy individuals. After controlling for variables, healthcare professionals had a significantly increased likelihood of insomnia (OR, 1.6) than workers in other professions; however, there were no differences compared to those who did not work or were students.

    Conclusion

    Overall, the study findings highlighted insomnia prevalence among COVID-19 survivors, with more than 75% reporting it. This percentage is much higher than that of the general population (10% to 20%) and hospitalized survivors (12% to 47%). Individuals with chronic medical conditions are more likely to suffer from insomnia, which is underreported. Public health researchers should anticipate a greater frequency of insomnia and sleep disorders in this group, which can last for one-third of healed patients up to one year after infection.

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  • Retrospective study shows decrease in kindergarten readiness

    Retrospective study shows decrease in kindergarten readiness

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    Primary care screening visits for young children serve as useful sources of data for assessing social and developmental markers. It is not clear how these screening data can be used to predict whether children are school ready.

    Study: arly Correlates of School Readiness Before and During the COVID-19 Pandemic Linking Health and School Data. Image Credit: FamVeld/Shutterstock.com
    Study: Early Correlates of School Readiness Before and During the COVID-19 Pandemic Linking Health and School Data. Image Credit: FamVeld/Shutterstock.com

    A new study appeared in JAMA Pediatrics that explored associations between school district early Kindergarten Readiness Assessment (KRA) and electronic health records (EHR) data and linked KRA scores with the changes occurring during the coronavirus disease 2019 (COVID-19) pandemic.

    Background

    Childhood is a watershed period for developing social skills, healthy physical and brain development, and becoming ready for school. Multiple factors may interfere with the acquisition of these skills which are essential in school life, such as social training, emotional regulation, as well as math and literacy skills. These may include socioeconomic and racial characteristics.

    In some regions, up to 4 out of 10 new kindergartners are not ready to enter school. Since there has been no systematic attempt to identify which children are at risk of entering kindergarten without readiness, it is not clear how and which risk factors can be modified to change this situation.

    The COVID-19 pandemic negatively impacted learning in school-age children, but its effect on development in children under five years remains to be described. This motivated the current study that uses KRA scores before and during the pandemic with the EHR data from a cohort of students in a large school district with about 36,000 students.

    The KRA scores are linked to reading proficiency in the third grade and include four skill categories: preliteracy, premath, motor skills, and social-emotional skills.

    What did the study show?

    The study included over 3,000 patients who were screened at primary care level. The mean age was 67 months, with the majority being Black (80%) vs 8% Whites. The passing KRA score was set at 270.

    When correlated with the pandemic dates, the mean KRA scores were significantly lower in 2021, at 260, vs ~263 in 2019 and 2018. About a fifth of students scored above passing levels in 2021, demonstrating school readiness, vs ~30% in 2019 and 32% in 2018.

    About one in four parents said they rarely read to their child, that is, one or less days a week, at least once during the period of the study. About 27% of children were unable to meet ASQ scores at least once, while 12% of the children sometimes experienced food insecurity.

    The risk factors for a low KRA score were one or more failures in the ASQ between 18 and 54 months, being Hispanic, not speaking the language of the healthcare professional during screening visits, being male and being seldom read to, as well as having food insecurity. Only 23% of boys were school-ready vs 32% of girls.

    Having Medicaid insurance, indicative of low socioeconomic status, was associated with school readiness in ~27% of children, vs ~51% if Medicaid was never used.

    Other socioeconomic factors, like housing insecurity, race, depression among the caregivers, and difficulty of any sort in obtaining benefits, did not show an association with the KRA scores. 

    “To interpret our findings using a hypothetical clinical example, starting with the expected score of 270.8 in the adjusted model (equivalent to demonstrating readiness): a boy who is Medicaid insured, who once failed an ASQ, who infrequently reported food insecurity, and was not read to as an infant lost an average of 15 points on the KRA, placing him in bottom category of emerging readiness (score below 257).”

    What are the implications?

    This is among the earliest studies to report that there might have been “a deleterious association of the COVID-19 pandemic with early learning and development.” It is also one of the largest studies to correlate primary care data to outcomes in public schools.

    While other researchers have found conflicting evidence regarding childhood development during the pandemic, multiple factors have been at work, impacting the validity of observed associations. For example, school enrolment was lower during the period. However, the association of lower school readiness with not being read to as an infant has been well documented, as well as with low developmental scores and food insecurity.

    Danger signals picked up in this way could help provide appropriate interventions in early life, whether by speech and language therapy, promoting learning by enrolment in good early childhood education programs, or facilitating library access.

    “These findings suggest substantial untapped potential for primary care pediatrics and school districts to work more closely together given that risks for kindergarten readiness are evident much earlier in primary care.”

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  • Paxlovid enhances treatment options for COVID-19 patients

    Paxlovid enhances treatment options for COVID-19 patients

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    In a recent review published in the Pharmaceutics, a group of authors explored the design, synthesis, and mechanism of action of Paxlovid, a Protease inhibitor (PI) drug combination for treating coronavirus disease 2019 (COVID-19).

    Study: The Design, Synthesis and Mechanism of Action of Paxlovid, a Protease Inhibitor Drug Combination for the Treatment of COVID-19. Image Credit: Tobias Arhelger/Shutterstock.com

    Background 

    The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, significantly challenged global healthcare systems and medical science.

    In response, researchers worldwide developed vaccines with innovative mechanisms and small-molecule antivirals targeting crucial viral proteins.

    Among these, PaxlovidTM, a blend of nirmatrelvir and ritonavir PIs, stands out for its effectiveness in treating COVID-19.

    Nirmatrelvir inhibits SARS-CoV-2’s main protease, vital for viral replication, while ritonavir boosts nirmatrelvir’s effectiveness by inhibiting Cytochrome P450 3A4 (CYP3A4), an enzyme that would otherwise degrade nirmatrelvir quickly.

    Further research is needed to develop alternative main protease (MPro) inhibitors despite the success of the nirmatrelvir-ritonavir combination, ensuring continued effectiveness against COVID-19.

    PIs as antivirals for Hepatitis C virus (HCV) and Human immunodeficiency virus (HIV) 

    PI Drugs for HCV and HIV Infections

    PIs are key in treating HCV and HIV infections. HCV, a small ribonucleic acid (RNA) virus causing hepatic diseases, is targeted by PIs like asunaporevir, telaprevir, and boceprevir, focusing on the nonstructural (NS)3/4A serine protease.

    These inhibitors are peptidomimetics, containing peptide bonds and a ‘warhead’ group that binds covalently but reversibly to the enzyme’s active site.

    HIV PIs target the virus’s aspartic acid protease, which is crucial for viral replication. They are used in antiretroviral therapy, transforming HIV from fatal to chronic.

    Development and mechanism of Nirmatrelvir

    Nirmatrelvir, developed from Pfizer’s earlier SARS-CoV-1 PI .. PF-00835231, faced challenges in oral absorption.

    Modifications like altering the warhead and substituting various molecular components enhanced its binding affinity and antiviral activity, eventually leading to nirmatrelvir with a nitrile warhead, improving solubility and synthesis.

    Despite different warheads, its structural similarity to boceprevir, and its role as a covalent inhibitor of SARS-CoV-2 Mpro makes it significant in COVID-19 treatment.

    Synthesis of nirmatrelvir

    Nirmatrelvir’s synthesis involves coupling the P1 building block and the P2-P3 dipeptide, with the final step being the formation of the nitrile warhead.

    The process starts with protected amino acid derivatives, proceeding through stages like Boc-deprotection, ester cleavage, and dipeptide formation.

    The synthesis yields nirmatrelvir with high efficiency and introduces a new approach involving a Ugi-type three-component reaction for higher diastereoselectivity.

    Synthesis and structure-activity relationship (SAR) study of nirmatrelvir analogs

    Research by Chia and co-workers led to the synthesizing nirmatrelvir analogs with different P1′ moieties, examining the role of the warhead in antiviral activity.

    These studies revealed varying levels of effectiveness in protease inhibition and antiviral activity, with some derivatives showing similar or superior effects to nirmatrelvir. However, challenges in cell penetration and specificity to SARS-CoV-2 limited the broader application of these analogs.

    Novel covalent and non-covalent inhibitors of SARS-CoV-2 Mpro

    Recent developments in SARS-CoV-2 Mpro inhibitors have introduced both peptidomimetic and non-peptidic inhibitors.

    These include warheads, such as epoxide rings and fluoromethyl groups, offering alternative mechanisms of covalent binding to the enzyme.

    Non-covalent inhibitors, like ensitrelvir, show lower reactivity but better selectivity due to their secondary interaction nature. These developments represent crucial steps in diversifying therapeutic options against COVID-19 and its evolving strains.

    Ritonavir as a pharmacokinetic enhancer

    Structure, activity, and interactions of ritonavir

    Originally an HIV protease inhibitor, Ritonavir is known for its efficacy at low doses (~100 mg) in inhibiting the CYP3A4 enzyme, a crucial element in drug metabolism.

    While high doses of Ritonavir are poorly tolerated, its low-dose effectiveness is leveraged in combination therapies with other HIV protease inhibitors, enhancing their half-lives and thus reducing required dosages.

    This unique use of Ritonavir has been explored even in early COVID-19 treatments. However, its use poses risks of significant drug–drug interactions, especially with medications metabolized by CYP3A4, potentially elevating their levels to toxic concentrations.

    Additionally, Ritonavir’s effect on other enzymes and transport proteins is noted, albeit of lesser importance in Paxlovid treatment.

    Synthesis of ritonavir

    developed at Abbott Laboratories, Ritonavir’s synthesis involves complex chemical processes, combining chiral amine and carboxylic acid building blocks.

    The synthesis starts with a cyclocondensation reaction involving thioformamide and ethyl 2-chloroacetate, followed by a series of steps leading to the formation of ritonavir.

    This intricate process involves various intermediate compounds and chemical reactions, including triethylamine and 4-dimethylaminopyridine, highlighting the sophistication required in pharmaceutical synthesis.

    The production of Ritonavir demonstrates the intricate chemical engineering necessary to develop effective pharmaceutical agents.

    Paxlovid—application and activity against mutant variants

    Paxlovid, combining nirmatrelvir and ritonavir, has shown significant efficacy in reducing COVID-19-related hospitalizations and mortality.

    While it has gained emergency use authorization in various regions, its effectiveness against emerging strains and mutant variants is under continuous scrutiny.

    The evolving landscape of SARS-CoV-2 mutations necessitates ongoing monitoring to ensure the sustained efficacy of treatments like Paxlovid.

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  • SARS-CoV-2 fragments found to mimic immune system peptides, fueling inflammation

    SARS-CoV-2 fragments found to mimic immune system peptides, fueling inflammation

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    In a recent study published in the journal Proceedings of the National Academy of Sciences, researchers analyzed the inflammatory capacity of fragmented components of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

    The intensive research during the coronavirus disease 2019 (COVID-19) pandemic has helped understand SARS-CoV-2 infection. Nevertheless, what makes the virus capable of causing a dangerous inflammatory response remains unclear. Research has suggested that amphiphilic, cationic peptides from the innate immune system undergo amyloid-like assembly with anionic nucleic acids and form proinflammatory complexes.

    Study: Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes. Image Credit: NIAIDStudy: Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes. ​​​​​​​Image Credit: NIAID

    The study and findings

    The present study investigated whether fragmented SARS-CoV-2 peptides assemble with anionic double-stranded RNA (dsRNA) into supramolecular complexes. The viral proteome was considered a reservoir of peptide fragments liberating after the proteolytic destruction of virions. The researchers leveraged a support vector machine (SVM) classifier to recognize antimicrobial peptide (AMP)-like sequences (xenoAMPs) in the SARS-CoV-2 proteome.

    Viral protein sequences were scanned via a moving window of 24–34 amino acids to identify potential xenoAMPs and test whether they behave like AMPs if cleaved at different positions. Sequences were selected based on the output provided by the classifier as a sigma (σ) score, wherein a strongly positive score implied the sequence was highly likely to be an AMP.

    Existence of exogenous mimics of pro-inflammatory host antimicrobial peptides (xenoAMPs) in SARS-CoV-2 proteins. (A) SARS-CoV-2 proteins are scanned with a machine-learning AMP classifier. Each queried sequence is given a σ score that measures its AMP-ness. Three representative high-scoring sequences are studied: xenoAMP(ORF1ab), xenoAMP(S), and xenoAMP(M). The grey bars mark the location where the corresponding sequences are selected. (B) SARS-CoV-2 sequences are aligned and compared to their homologs in a common cold human coronavirus HCoV-OC43: Control (ORF1ab), Control(S), and Control(M). Asterisks, colons, and periods indicate positions that have fully conserved residues, those that have strongly similar properties, and those that have weakly similar properties, respectively. Color is assigned to each residue using the ClustalX scheme. (C) σ score heatmaps compare the distribution of high-scoring sequences in three proteins from SARS-CoV-2 and HCoV-OC43. The first amino acid in each sequence is colored according to its average σ score; regions with negative average σ scores (non-AMPs) are colored white. “Hot spot” clusters of high-scoring sequences for SARS-CoV-2 (bright yellow regions bracketed in red boxes) have systematically higher scores and span wider regions of sequence space compared to HCoV-OC43. This trend suggests that hot spots in SARS-CoV-2 can generate higher scoring sequences for a greater diversity of enzymatic cleavage sites than those in HCoV-OC43.

    Existence of exogenous mimics of pro-inflammatory host antimicrobial peptides (xenoAMPs) in SARS-CoV-2 proteins. (A) SARS-CoV-2 proteins are scanned with a machine-learning AMP classifier. Each queried sequence is given a σ score that measures its AMP-ness. Three representative high-scoring sequences are studied: xenoAMP(ORF1ab), xenoAMP(S), and xenoAMP(M). The grey bars mark the location where the corresponding sequences are selected. (B) SARS-CoV-2 sequences are aligned and compared to their homologs in a common cold human coronavirus HCoV-OC43: Control (ORF1ab), Control(S), and Control(M). Asterisks, colons, and periods indicate positions that have fully conserved residues, those that have strongly similar properties, and those that have weakly similar properties, respectively. Color is assigned to each residue using the ClustalX scheme. (C) σ score heatmaps compare the distribution of high-scoring sequences in three proteins from SARS-CoV-2 and HCoV-OC43. The first amino acid in each sequence is colored according to its average σ score; regions with negative average σ scores (non-AMPs) are colored white. “Hot spot” clusters of high-scoring sequences for SARS-CoV-2 (bright yellow regions bracketed in red boxes) have systematically higher scores and span wider regions of sequence space compared to HCoV-OC43. This trend suggests that hot spots in SARS-CoV-2 can generate higher scoring sequences for a greater diversity of enzymatic cleavage sites than those in HCoV-OC43.

    Further, the team selected specific sequences from this population of (high-scoring) sequences with a high cationic charge. Specifically, they focused on prototypical candidates from the membrane (M) protein, spike (S) protein, and open reading frame 1ab (ORF1ab) polyprotein. In silico analyses showed that these xenoAMPs could be generated during proteasomal degradation, with matrix metalloproteinase 9 (MMP9) and neutrophil elastase (NE) capable of generating them.

    Next, the team compared SARS-CoV-2 xenoAMPs with homologous sequences from SARS-CoV-1 and non-pandemic human CoVs. This showed that sequences were partially conserved. A comparison of σ score heat maps of ORF1ab, S, and M proteins between SARS-CoV-2 and HCoV-OC43 revealed that high-scoring sequences were clustered into hotspots, with SARS-CoV-2 hotspots having higher scores and spanning wider regions than those of HCoV-OC43.

    Further, mass spectrometry was performed on tracheal aspirate samples from patients with severe COVID-19. The team detected fragments of host AMP, cathelicidin LL-37, in 20 samples (out of 29). By contrast, 28 samples contained viral peptide fragments, some of which had sufficiently high σ scores to qualify as xenoAMPs.

    The three xenoAMPs, xenoAMP(S), xenoAMP(M), and xenoAMP(ORF1ab), were experimentally observed to chaperone and assemble with dsRNA into complexes similar to LL-37. Polyinosine: polycytidylic acid (Poly(I:C) was used as a synthetic analog to mimic the viral dsRNA generated during replication. The structures of xenoAMPs-poly(I:C) complexes were cognate to host AMPs-dsRNA complexes.

    Next, the team investigated the robustness of these self-assembled proinflammatory complexes under non-optimal conditions. They found that the nanocrystalline structures were preserved when participating xenoAMPs were shortened. Besides, SARS-CoV-2 xenoAMPs were found to co-crystallize with LL-37, suggesting that host AMPs and xenoAMPs could synergistically activate inflammatory responses.

    The immune activation capacity of xenoAMPs from SARS-CoV-2 was compared with that of homolog peptides from HCoV-OC43 using human monocytes. XenoAMP-poly(I:C)-treated monocytes released 1.7-fold more interleukin (IL)-8 than poly(I:C) treated controls. By contrast, complexes formed with homologous peptides from HCoV-OC43 induced much lower IL-8 levels.

    In addition, xenoAMP-poly(I:C) stimulation of primary human dermal microvascular endothelial cells (HDMVECs) triggered robust production of IL-6, which was not observed with complexes formed from HCoV-OC43 peptides. Notably, xenoAMP-poly(I:C)-treated HDMVECs showed significant upregulation of several proinflammatory chemokine and cytokine genes.

    Finally, the researchers measured the immune activation capacity in mice. C57BL/6 mice unexposed to infection were treated with xenoAMP(ORF1ab)-poly(I:C) complexes or poly(I:C)-alone (control). XenoAMP(ORF1ab)-poly(I:C) treatment increased plasma levels of IL-6 and C-X-C motif chemokine ligand 1 (CXCL1) by 1.6 and 2.2 times, respectively, compared to poly(I:C)-alone. Moreover, IL-6 and CXCL1 levels increased 1.2 times in the lung compared to the control treatment.

    Conclusions

    In sum, the study has illustrated an unexpected mechanism of inflammation propagating through uninfected cells in COVID-19, wherein viral fragments mimic AMPs like LL-37. This could be salient to understand why the host immune system in COVID-19 resembles that of individuals with autoimmune conditions like rheumatoid arthritis and lupus.

    The researchers found that host proteases could generate xenoAMPs, suggesting that protease inhibitors suppressing xenoAMP generation could have a clinical impact on viral-induced inflammation. The proteolytic degradation of SARS-CoV-2 could differ across host individuals, possibly explaining the heterogeneity of infection outcomes, e.g., asymptomatic and fatal.

    Journal reference:

    • Zhang Y, Bharathi V, Dokoshi T, et al. Viral afterlife: SARS-CoV-2 as a reservoir of immunomimetic peptides that reassemble into proinflammatory supramolecular complexes. Proc Natl Acad Sci USA, 2024, DOI: 10.1073/pnas.2300644120, https://www.pnas.org/doi/10.1073/pnas.2300644120

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  • Fatigue and cognitive deficits improve over two years

    Fatigue and cognitive deficits improve over two years

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    In a recent study published in the journal EClinicalMedicine, a team of scientists from Germany assessed the long-term trajectories of sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections such as cognitive deficits and fatigue and attempted to identify the risk factors that could predict non-recovery from these sequelae.

    Study: Predictors of non-recovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study. Image Credit: p.ill.i / ShutterstockStudy: Predictors of non-recovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study. Image Credit: p.ill.i / Shutterstock

    Background

    Although worldwide vaccination efforts have successfully limited the transmission and severity of SARS-CoV-2 infections and lowered the morbidity and mortality associated with the coronavirus disease 2019 (COVID-19) pandemic, long coronavirus disease (long-COVID) has emerged as a serious consequential health concern. Over 60 million COVID-19 patients are believed to suffer from long-COVID, with cognitive impairments and fatigue being the most common symptoms.

    Approximately 26% of the long-COVID patients suffer from cognitive deficits, while fatigue impacts 19% of the patients, with both symptoms significantly affecting their overall quality of life and preventing the resumption of everyday activities such as work and exercise.

    Furthermore, while electronic health records of long-COVID patients indicate that cognitive deficits are observed throughout the first two years following a SARS-CoV-2 infection, the longitudinal information on fatigue is sparse. The few existing studies are primarily on older patients with preexisting comorbidities, and the results are conflicting, making it difficult to extrapolate these findings to the general population.

    About the study

    In the present study, the researchers used data from the German National Pandemic Cohort Network to evaluate the trajectories of the two most prevalent long-COVID symptoms — cognitive deficits and fatigue — over a period of 18 months in 3,000 patients. They hypothesized that long-term follow-up would indicate a recovery from both symptoms in most patients.

    The scientists also aimed to identify the risk factors that could indicate non-recovery from cognitive deficits or fatigue following COVID-19, which could be used to predict recovery rates and make informed decisions on treating these conditions. The longitudinal, prospective, multicenter, population-based study included participants above the age of 18 years who tested positive for SARS-CoV-2 through a polymerase chain reaction (PCR) test.

    Baseline assessments were conducted six months after the first SARS-CoV-2 infection, and those with reinfections were excluded from the study. Assessments for follow-up were conducted a minimum of 18 months after the SARS-CoV-2 infection.

    All participants were required to fill out an online questionnaire about fatigue, and those with symptoms that indicated post-COVID syndrome or long-COVID were invited for on-site appointments to undergo cognitive assessments. Matched controls were selected based on the PCR test date, with 30% of the baseline participants and their matched controls being invited for in-person follow-ups.

    The FACIT-Fatigue or Functional Assessment of Chronic Illness Therapy-Fatigue scale, which assesses 13 symptoms related to fatigue on a five-point scale, was used to measure one of the primary measures. Scores below the cut-off indicated recovery from fatigue, while those above the cut-off indicated persistent fatigue. The scores were used to further characterize fatigue severity.

    The Montreal Cognitive Assessment was used to assess cognitive performance, with scores between 0 and 30 indicating severe to no cognitive deficits. Educational levels were considered while assessing these scores to account for learning deficits.

    Results

    The results showed that while cognitive deficits and fatigue were the two most prevalent long-COVID symptoms, these symptoms showed improvements over two years in close to half the patients recovering from post-COVID syndrome. Furthermore, depressive symptoms and headaches were risk factors that predicted non-recovery from fatigue in the long term, while male sex, old age, and school education levels below 12 years were predictors of non-recovery from cognitive deficits.

    Compared to the pre-COVID-19 pandemic levels of fatigue, which were around 9%, clinically relevant fatigue was reported by 21% of the participants, indicating a significant health burden due to fatigue in the post-pandemic period. However, the fatigue scores were seen to improve significantly after the follow-up period of 18 months to two years.

    Psychological distress before the SARS-CoV-2 infection was thought to be linked to the persistence of fatigue since depressive symptoms were found to be one of the significant predictors of non-recovery from fatigue. Depressive symptoms and headaches could potentially be targeted for accurate diagnosis and targeted treatment of fatigue in long-COVID patients.

    Conclusions

    To summarize, the study investigated the long-term trajectories of fatigue and cognitive impairment, the two most prevalent long-COVID symptoms, in a longitudinal cohort of long-COVID patients.

    The findings suggested that while both symptoms showed improvements over a span of two years in approximately 50% of the patients, specific risk factors such as depressive symptoms and headache predicted non-recovery from fatigue in the long term. Old age and male sex were two of the risk factors indicating non-recovery from cognitive deficits in long-COVID patients.

    Journal reference:

    • Hartung, T. J., Bahmer, T., ChaplinskayaSobol, I., Deckert, J., Endres, M., Franzpötter, K., Geritz, J., Haeusler, K. G., Hein, G., Heuschmann, P. U., Hopff, S. M., Horn, A., Keil, T., Krawczak, M., Krist, L., Lieb, W., Maetzler, C., Montellano, F. A., Morbach, C., & Neumann, C. (2024). Predictors of nonrecovery from fatigue and cognitive deficits after COVID-19: a prospective, longitudinal, population-based study. EClinicalMedicine, 69.  DOI: 10.1016/j.eclinm.2024.102456, https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(24)00035-X/fulltext

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  • Congressman off-base in ad claiming Fauci shipped covid to Montana before the pandemic

    Congressman off-base in ad claiming Fauci shipped covid to Montana before the pandemic

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    A fundraising ad for U.S. Rep. Matt Rosendale (R-Mont.) shows a photo of Anthony Fauci, former director of the National Institute of Allergy and Infectious Diseases, behind bars, swarmed by flying bats.

    Rosendale, who is eyeing a challenge to incumbent Sen. Jon Tester, a Democrat, maintains that a Montana biomedical research facility, Rocky Mountain Laboratories in Hamilton, has a dangerous link to the pandemic. This claim is echoed in the ad:

    “It’s been revealed that Fauci brought COVID to the Montana one year before COVID broke out in the U.S!,” it charges in all-caps before asking readers to “Donate today and hold the D.C. bureaucracy accountable!”

    The ad, paid for by Matt Rosendale for Montana, seeks contributions through WinRed, a platform that processes donations for Republican candidates. Rosendale also shared the fundraising pitch on his X account Nov. 1, and it remained live as of early February.

    Rosendale made similar accusations on social media, during a November speech on the U.S. House floor, and through his congressional office. Sometimes his comments, like those on the House floor, are milder, saying the researchers experimented on “a coronavirus” leading up to the pandemic. Other times, as in an interview with One America News Network, he linked the lab’s work to covid-19’s spread.

    In that interview clip, Rosendale recounted pandemic-era shutdowns before saying, “And now we’re finding out that the National Institute of Health, Rocky Mountain Lab, down in Hamilton, Montana, had also played a role in this.”

    Rosendale’s statements echo broader efforts to scrutinize how research into viruses happens in the United States and is part of a continued wave of backlash against scientists who have studied coronaviruses. Rosendale is considering seeking the Republican nomination to challenge Tester, in a toss-up race that could help determine which party controls the Senate in 2025. Political newcomer Tim Sheehy is also seeking the Republican nomination for the Senate.

    Rosendale proposed amendments to a health spending bill that would ban pandemic-related pathogen research funding for Rocky Mountain Laboratories and cut the salary of one of its top researchers, virologist Vincent Munster, to $1. The House has included both amendments in the Health and Human Services budget bill that the Republican majority hopes to pass. A temporary spending bill is funding the health department until March.

    We contacted Rosendale’s congressional office multiple times — with emails, a phone call, and an online request — asking what proof he had to back up his statements that the Montana lab infected bats with covid from China before the outbreak. We got no reply.

    Kathy Donbeck, of the National Institute of Allergy and Infectious Diseases’ Office of Communications and Government Relations, said in an email that the ad’s claims are false. Interviews with virologists and a review of the research paper published shortly before Rosendale’s assertions support that position.

    Where this is coming from

    Rosendale’s statements seem to stem from a Rocky Mountain Laboratories study from 2016 that looked into how a coronavirus, WIV1-CoV, acted in Egyptian fruit bats. The work, published by the journal Viruses in 2018, showed that the specific strain didn’t cause a robust infection in the bats.

    The study did not receive widespread attention at the time. But on Oct. 30, 2023, the study was highlighted by a blog called White Coat Waste Project, which says its mission is to stop taxpayer-funded experiments on animals. Some right-wing media outlets began to connect the Montana lab with the coronavirus that causes covid.

    Rosendale’s office issued an Oct. 31 news release saying the Wuhan Institute of Virology in China “shipped a strain of coronavirus” to the Hamilton lab. “Our government helped create the Wuhan flu, then shut the country down when it escaped from the lab,” Rosendale said.

    It’s a different virus

    Rocky Mountain Laboratories is a federally funded facility as part of NIAID, the nation’s top infectious disease research agency, which Fauci led for nearly 40 years.

    According to the study and Donbeck’s email, the Montana researchers focused on a coronavirus called WIV1-CoV, not the covid-causing SARS-CoV-2. They’re different viruses.

    “The genetics of the viruses are very different, and their behavior biologically is very different,” said Troy Sutton, a virologist with Pennsylvania State University who has studied the evolution of pandemic influenza viruses.

    In a review of media reports on the Montana study, Health Feedback, a network of scientists that fact-checks health and medical media coverage, showed the virus’s lineage indicated that WIV1 “is not a direct ancestor or even a close relative of SARS-CoV-2.”

    Additionally, the description of the coronavirus strain as being “shipped” suggests that it physically traveled across the world. That’s not what happened.

    The Wuhan Institute of Virology provided the WIV1 virus’s sequence that allowed researchers to make a lab-grown copy. A separate study, published in 2013 by the journal Nature, outlines the origins of the lab-created virus.

    According to the study’s methodology, the researchers used a clone of WIV1. An NIAID statement to Lee Enterprises, a media company, said the virus “was generated using common laboratory techniques, based on genetic information that was publicly shared by Chinese scientists.”

    Stanley Perlman, a University of Iowa professor who studies coronaviruses and serves on the federal advisory committee that reviews vaccines, said Rosendale’s claim is off-base.

    He said Rosendale’s focus on where the lab got its materials is irrelevant and serves “only to make people wary and scared.”

    Rosendale’s efforts to prohibit particular research at Rocky Mountain Laboratories appear ill-informed, too. Rosendale targeted banning gain-of-function research, which involves altering a pathogen to study its spread. In her email, NIAID’s Donbeck said the Rocky Mountain Laboratories study didn’t involve gain-of-function research.

    This type of research has long been controversial, and people who study viruses have said the definition of “gain of function” is problematic and insufficient to show when research, or even work to create vaccines, could cross into that type of research.

    But both Sutton and Perlman said that, any way you look at it, the Rocky Mountain Laboratories study published in 2018 didn’t change the virus. It put a virus in bats and showed it didn’t grow.

    And it had no effect on the covid outbreak a year later, first detected in Washington state.

    Our ruling

    Rosendale’s ad said, “It’s been revealed that Fauci brought COVID to the Montana one year before COVID broke out in the U.S!” The campaign ad and Rosendale’s similar statements refer to research at the Rocky Mountain Laboratories involving WIV1, a coronavirus that researchers say is not even distantly close in genetic structure to SARS-CoV-2, the virus that caused covid-19.

    Rosendale’s claim is wrong about when the scientists began their work, what they were studying, and where they got the materials. The researchers began their work in 2016 and, although they were studying a coronavirus, it wasn’t the virus that causes covid. The Montana scientists used a lab-grown clone of WIV1 for their research. The first laboratory-confirmed case of covid was not detected in the U.S. until Jan. 20, 2020. Rosendale’s ad is inaccurate and ridiculous. We rate it Pants on Fire!

    Sources:

    Viruses, “SARS-Like Coronavirus WIV1-CoV Does Not Replicate in Egyptian Fruit Bats (Rousettus aegyptiacus),” Dec. 19, 2018

    White Coat Waste Project, “Horror Show: Shady Zoo Sent Bats to NIH to Be Infected With a Wuhan Lab Coronavirus,” Oct. 30, 2023

    MattForMontana X post, Nov. 1, 2023

    Campaign ad, accessed Dec. 14, 2023

    Rep. Matt Rosendale, House floor speech, Nov. 14, 2023

    One America News Network, interview, accessed Dec. 14, 2023

    Rosendale congressional office, “Rep. Rosendale Reacts to Reports That Wuhan Lab Shipped Coronavirus to Fauci-Run Lab in Hamilton Prior to Pandemic,” Oct. 31, 2023

    National Institute of Allergy and Infectious Diseases, “History of Rocky Mountain Labs (RML),” accessed Dec. 14, 2023

    Email exchange with NIAID, beginning Dec. 14, 2023

    Statement from NIAID provided to Lee Enterprises, accessed Jan. 2, 2024

    Nature, “Isolation and Characterization of a Bat SARS-Like Coronavirus That Uses the ACE2 Receptor,” Oct. 30, 2013

    Ravalli Republic, “Rosendale Moves to Strip Rocky Mountain Lab Research Funding,” Nov. 17, 2023

    Interview, Troy Sutton, assistant professor of veterinary and biomedical sciences at Pennsylvania State University, Jan. 5, 2024

    Interview, Stanley Perlman, professor of microbiology and immunology and professor of pediatrics at the University of Iowa, Jan. 13, 2024

    FDA, “Roster of the Vaccines and Related Biological Products Advisory Committee,” accessed Jan. 16, 2024

    Health Feedback, “2018 Coronavirus Research in NIAID Montana Lab Is Unrelated to the COVID-19 Pandemic, Contrary to Claim by Fox News’s Jesse Watters,” last accessed Jan. 17, 2024

    Email exchange with OpenSecrets, an independent research group tracking money in politics, beginning Jan. 30, 2024

    CDC Museum COVID-19 Timeline, accessed Feb. 2, 2024




    Kaiser Health NewsThis article was reprinted from khn.org, a national newsroom that produces in-depth journalism about health issues and is one of the core operating programs at KFF – the independent source for health policy research, polling, and journalism.



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