An interactive web-based dashboard to track COVID-19 in real time

In vivo delivery of engineered synthetic DNA-encoded SARS-CoV-2 monoclonal antibodies for pre-exposure prophylaxis in non-human primates

COVID-19 remains a major public health concern. Monoclonal antibodies have received emergency use authorization (EUA) for pre-exposure prophylaxis against COVID-19 among high-risk groups for treatment of mild to moderate COVID-19. In addition to recombinant biologics, engineered synthetic DNA-encoded antibodies (DMAb) are an important strategy for direct in vivo delivery of protective mAb. A DMAb cocktail was synthetically engineered to encode the immunoglobulin heavy and light chains of two different two different Fc-engineered anti-SARS-CoV-2 antibodies. The DMAbs were designed to enhance in vivo expression and delivered intramuscularly to cynomolgus and rhesus macaques with a modified in vivo delivery regimen. Serum levels were detected in macaques, along with specific binding to SARS-CoV-2 spike receptor binding domain protein and neutralization of multiple SARS-CoV-2 variants of concern in pseudovirus and authentic live virus assays. Prophylactic administration was protective in rhesus macaques against signs of SARS-CoV-2 (USA-WA1/2020) associated disease in the lungs. Overall, the data support further study of DNA-encoded antibodies as an additional delivery mode for prevention of COVID-19 severe disease. These data have implications for human translation of gene-encoded mAbs for emerging infectious diseases and low dose mAb delivery against COVID-19.

The comparison of decay rates of infectious SARS-CoV-2 and viral RNA in environmental waters and wastewater

Understanding the decay characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater and ambient waters is important for multiple applications including assessment of risk of exposure associated with handling wastewater samples, public health risk associated with recreation in wastewater polluted ambient waters and better understanding and interpretation of wastewater-based epidemiology (WBE) results. We evaluated the decay rates of infectious SARS-CoV-2 and viral RNA in wastewater and ambient waters under temperature regimes representative of seasonal fluctuations. Infectious virus was seeded in autoclaved primary wastewater effluent, final dechlorinated wastewater effluent, lake water, and marine water at a final concentration of 6.26 ± 0.07 log10 plaque forming units per milliliter. Each suspension was incubated at either 4°, 25°, and 37 °C. Samples were initially collected on an hourly basis, then approximately every other day for 15 days. All samples were analyzed for infectious virus via a plaque assay using the Vero E6 cell line, and viral gene copy levels were quantified with the US CDC's N1 and N2 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays. The infectious virus decayed significantly faster (p ≤ 0.0214) compared to viral RNA, which persisted for the duration of the study irrespective of the incubation conditions. The initial loss (within 15 min of seeding) as well as decay of infectious SARS-CoV-2 was significantly faster (p ≤ 0.0387) in primary treated wastewater compared to other water types, but viral RNA did not degrade appreciably in this matrix until day 15. Overall, temperature was the most important driver of decay, and after 24 h, no infectious SARS-CoV-2 was detected at 37 °C in any water type. Moreover, the CDC N2 gene assay target decayed significantly (p ≤ 0.0174) faster at elevated temperatures compared to CDC N1, which has important implications for RT-qPCR assay selection for WBE approach.

Notch4 regulatory T cells and SARS-CoV-2 viremia shape COVID19 survival outcome

BACKGROUND

Immune dysregulation and SARS-CoV-2 plasma viremia have been implicated in fatal COVID-19 disease. However, how these two factors interact to shape disease outcomes is unclear.

METHODS

We carried out viral and immunological phenotyping on a prospective cohort of 280 patients with COVID-19 presenting to acute care hospitals in Boston, Massachusetts and Genoa, Italy between June 1, 2020 and February 8, 2022. Disease severity, mortality, plasma viremia, and immune dysregulation were assessed. A mouse model of lethal H1N1 influenza infection was used to analyze the therapeutic potential of Notch4 and pyroptosis inhibition in disease outcome.

RESULTS

Stratifying patients based on %Notch4+ Treg cells and/or the presence of plasma viremia identified four subgroups with different clinical trajectories and immune phenotypes. Patients with both high %Notch4+ Treg cells and viremia suffered the most disease severity and 90-day mortality compared to the other groups even after adjusting for baseline comorbidities. Increased Notch4 and plasma viremia impacted different arms of the immune response in SARS-CoV-2 infection. Increased Notch4 was associated with decreased Treg cell amphiregulin expression and suppressive function whereas plasma viremia was associated with increased monocyte cell pyroptosis. Combinatorial therapies using Notch4 blockade and pyroptosis inhibition induced stepwise protection against mortality in a mouse model of lethal H1N1 influenza infection.

CONCLUSIONS

The clinical trajectory and survival outcome in hospitalized patients with COVID-19 is predicated on two cardinal factors in disease pathogenesis: viremia and Notch4+ Treg cells. Intervention strategies aimed at resetting the immune dysregulation in COVID-19 by antagonizing Notch4 and pyroptosis may be effective in severe cases of viral lung infection.

SARS-CoV-2 replication and drug discovery

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has killed millions of people and continues to wreak havoc across the globe. This sudden and deadly pandemic emphasizes the necessity for anti-viral drug development that can be rapidly administered to reduce morbidity, mortality, and virus propagation. Thus, lacking efficient anti-COVID-19 treatment, and especially given the lengthy drug development process as well as the critical death tool that has been associated with SARS-CoV-2 since its outbreak, drug repurposing (or repositioning) constitutes so far, the ideal and ready-to-go best approach in mitigating viral spread, containing the infection, and reducing the COVID-19-associated death rate. Indeed, based on the molecular similarity approach of SARS-CoV-2 with previous coronaviruses (CoVs), repurposed drugs have been reported to hamper SARS-CoV-2 replication. Therefore, understanding the inhibition mechanisms of viral replication by repurposed anti-viral drugs and chemicals known to block CoV and SARS-CoV-2 multiplication is crucial, and it opens the way for particular treatment options and COVID-19 therapeutics. In this review, we highlighted molecular basics underlying drug-repurposing strategies against SARS-CoV-2. Notably, we discussed inhibition mechanisms of viral replication, involving and including inhibition of SARS-CoV-2 proteases (3C-like protease, 3CLpro or Papain-like protease, PLpro) by protease inhibitors such as Carmofur, Ebselen, and GRL017, polymerases (RNA-dependent RNA-polymerase, RdRp) by drugs like Suramin, Remdesivir, or Favipiravir, and proteins/peptides inhibiting virus-cell fusion and host cell replication pathways, such as Disulfiram, GC376, and Molnupiravir. When applicable, comparisons with SARS-CoV inhibitors approved for clinical use were made to provide further insights to understand molecular basics in inhibiting SARS-CoV-2 replication and draw conclusions for future drug discovery research.

Cost-effectiveness analysis of COVID-19 booster doses and oral antivirals: Case studies in the Indo-Pacific

BACKGROUND

Decision-makers in middle-income countries need evidence on the cost-effectiveness of COVID-19 booster doses and oral antivirals to appropriately prioritise these healthcare interventions.

METHODS

We used a dynamic transmission model to assess the cost-effectiveness of COVID-19 booster doses and oral antivirals in Fiji, Indonesia, Papua New Guinea, and Timor-Leste. We conducted cost-effectiveness analysis from both healthcare and societal perspectives using data collated from publicly available sources. We developed an interactive R Shiny which allows the user to vary key model assumptions, such as the choice of discounting rate, and view how these assumptions affect model results.

FINDINGS

Booster doses were cost saving and therefore cost-effective in all four middle-income settings from both healthcare and societal perspectives using 3% discounting. Providing oral antivirals was cost-effective from a healthcare perspective if procured at a low generic price (US$25) or middle-income reference price (US$250); however, their cost-effectiveness was strongly influenced by rates of wastage or misuse, and the ongoing costs of care for patients hospitalised with COVID-19. The cost or wastage of rapid antigen tests did not appear strongly influential over the cost-effectiveness of oral antivirals in any of the four study settings.

CONCLUSIONS

Our results support that COVID-19 booster programs are cost-effective in middle-income settings. Oral antivirals demonstrate the potential to be cost-effective if procured at or below a middle-income reference price of US$250 per schedule. Further research should quantify the rates of wastage or misuse of oral COVID-19 antivirals in middle-income settings.

Measurement of economic resilience under the COVID-19 based on nighttime light remote sensing: Case of Hubei province

This study investigates the economic resilience of cities in Hubei province during the COVID-19 pandemic, utilizing highway nighttime lights as a proxy indicator. By February 2020, the pandemic had caused a significant economic contraction in Hubei. However, by May 2021, a strong recovery was observed, with most cities experiencing growth rates of over 20%. Despite initially similar responses to the crisis, this study reveals significant heterogeneity in economic resilience across the examined cities in Hubei. The spatial distribution follows a core-periphery structure, with Wuhan exhibiting greater resistance to economic shocks compared to peripheral cities. Furthermore, the response capacity within the Wuhan urban agglomeration area exhibits regional variations. In summary, lockdown policies had spatially varied impacts on economic resilience across Hubei's cities. These results offer valuable insights into regional economic resilience and contribute to the formulation of strategies aimed at effectively addressing future unforeseen events.

Shared fate was associated with sustained cooperation during the COVID-19 pandemic

Did the COVID-19 pandemic bring people together or push them apart? While infectious diseases tend to push people apart, crises can also bring people together through positive interdependence. We studied this question by asking an international sample (N = 1,006) about their inclinations to cooperate, perceptions of interdependence (i.e., shared fate), and perceived risk as well as local prevalence of COVID-19 infection across 14 time points from March to August, 2020. While perceived interdependence with others tended to increase during this time period, inclinations to cooperate decreased over time. At the within-person level, higher local prevalence of COVID-19 attenuated increases in perceived interdependence with others, and was associated with lower inclinations to cooperate. At the between-person level, people with high perceived interdependence with others reported more stable, or increasing, inclinations to cooperate over time than people with low perceived interdependence. Establishing a high sense of perceived interdependence with others may thus allow people to maintain cooperation during crises, even in the face of challenging circumstances such as those posed by a highly transmissible virus.

Assessment of the Effective Sensitivity of SARS-CoV-2 Sample Pooling Based on a Large-Scale Screening Experience: Retrospective Analysis

BACKGROUND

The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2.

OBJECTIVE

This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections.

METHODS

Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period.

RESULTS

Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling's impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP - log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and -0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 - FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices.

CONCLUSIONS

Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR.

Changes in physical activity among Canadian adults more than 6 months into the COVID-19 pandemic: a secondary analysis of the INTERACT cohort study

OBJECTIVE

To estimate the effect of (a) the COVID-19 pandemic and (b) COVID-19 restriction stringency on daily minutes of device-measured moderate-to-vigorous physical activity (MVPA).

DESIGN

Physical activity data were collected from the INTerventions, Equity, Research and Action in Cities Team (INTERACT) cohorts in Montreal, Saskatoon and Vancouver before (May 2018 to February 2019, 'phase 1') and during the pandemic (October 2020 to February 2021, 'phase 2'). We estimated the effect of the two exposures by comparing daily MVPA measured (a) before vs during the pandemic (phase 1 vs phase 2) and (b) at different levels of COVID-19 restriction stringency during phase 2. Separate mixed effects negative binomial regression models were used to estimate the association between each exposure and daily MVPA, with and without controlling for confounders. Analyses were conducted on person-days with at least 600 min of wear time. Effect modification by gender, age, income, employment status, education, children in the home and city was assessed via stratification.

SETTING

Montreal (Quebec), Saskatoon (Saskatchewan) and Vancouver (British Columbia), Canada.

MAIN OUTCOME MEASURE

Daily minutes of MVPA, as measured using SenseDoc, a research-grade accelerometer device.

RESULTS

Daily minutes of MVPA were 21% lower in phase 2 (October 2020 to February 2021) compared with phase 1 (May 2018 to February 2019), controlling for gender, age, employment status, household income, education, city, weather and wear time (rate ratio=0.79, 95% CI 0.69, 0.92). This did not appear to be driven by changes in the sample or timing of data collection between phases. The results suggested effect modification by employment, household income and education. Restriction stringency was not associated with daily MVPA between October 2020 and February 2021 (adjusted rate ratio=0.99, 95% CI 0.96, 1.03).

CONCLUSIONS

Between October 2020 and February 2021, daily minutes of MVPA were significantly lower than 2 years prior, but were not associated with daily COVID-19 restriction stringency.

Managing vector-borne diseases in a geoAI-enabled society. Malaria as an example

More than 17 % of all infectious diseases are caused by vector-borne diseases resulting in more than 1 billion cases and over 1 million deaths each year. Of these malaria continues to be a global burden in over eighty countries. As societies become more digitalised, the availability of geospatially enabled health and disease information will become more abundant. With this, the ability to assess health and disease risks in real-time will become a reality. The purpose of this study was to examine how geographic information, geospatial technologies and spatial data science are being used to reduce the burden of vector-borne diseases such as malaria and explore the opportunities that lie ahead with GeoAI and other geospatial technology advancements. Malaria is a dynamic and complex system and as such a range of data and approaches are needed to tackle different parts of the malaria cycle at different local and global scales. Geospatial technologies provide an integrated framework vital for monitoring, analysing and managing vector-borne diseases. GeoAI and technological advancements are useful for enhancing real-time assessments, accelerating the decision making process and spatial targeting of interventions. Training is needed to enhance the use of geospatial information for the management of vector-borne diseases.

Palmitoylation of SARS-CoV-2 Envelope protein is central to virus particle formation

The Envelope (E) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an integral structural protein in the virus particles. However, its role in the assembly of virions and the underlying molecular mechanisms are yet to be elucidated, including whether the function of E protein is regulated by post-translational modifications. In the present study, we report that SARS-CoV-2 E protein is palmitoylated at C40, C43, and C44 by palmitoyltransferases zDHHC3, 6, 12, 15, and 20. Mutating these three cysteines to serines (C40/43/44S) reduced the stability of E protein, decreased the interaction of E with structural proteins Spike, Membrane, and Nucleocapsid, and thereby inhibited the production of virus-like particles (VLPs) and VLP-mediated luciferase transcriptional delivery. Specifically, the C40/43/44S mutation of E protein reduced the density of VLPs. Collectively, these results demonstrate that palmitoylation of E protein is vital for its function in the assembly of SARS-CoV-2 particles.IMPORTANCEIn this study, we systematically examined the biochemistry of palmitoylation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) E protein and demonstrated that palmitoylation of SARS-CoV-2 E protein is required for virus-like particle (VLP) production and maintaining normal particle density. These results suggest that palmitoylated E protein is central for proper morphogenesis of SARS-CoV-2 VLPs in densities required for viral infectivity. This study presents a significant advancement in the understanding of how palmitoylation of viral proteins is vital for assembling SARS-CoV-2 particles and supports that palmitoyl acyltransferases can be potential therapeutic targets for the development of SARS-CoV-2 inhibitors.

Assessing marginal effects of non-pharmaceutical interventions on the transmission of SARS-CoV-2 across Africa: a hybrid modeling study

Since 2019, a new strain of coronavirus has challenged global health systems. Due its fragile healthcare systems, Africa was predicted to be the most affected continent. However, past experiences of African countries with epidemics and other factors, including actions taken by governments, have contributed to reducing the spread of SARS-CoV-2. This study aims to assess the marginal impact of non-pharmaceutical interventions in fifteen African countries during the pre-vaccination period. To describe the transmission dynamics and control of SARS-CoV-2 spread, an extended time-dependent SEIR model was used. The transmission rate of each infectious stage was obtained using a logistic model with NPI intensity as a covariate. The results revealed that the effects of NPIs varied between countries. Overall, restrictive measures related to assembly had, in most countries, the largest reducing effects on the pre-symptomatic and mild transmission, while the transmission by severe individuals is influenced by privacy measures (more than $10\%$). Countries should develop efficient alternatives to assembly restrictions to preserve the economic sector. This involves e.g. training in digital tools and strengthening digital infrastructures.

Effectiveness of non-pharmaceutical interventions for COVID-19 in USA

Worldwide, governments imposed non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to contain the pandemic more effectively. We examined the effectiveness of individual NPIs in the United States during the first wave of the pandemic. Three types of analyses were performed. First, a prototypical Bayesian hierarchical model was employed to gauge the effectiveness of five NPIs and they are gathering restriction, restaurant capacity restriction, business closure, school closure, and stay-at-home order in the 42 states with over 100 deaths by the end of the wave. Second, we examined the effectiveness of the face mask mandate, the sixth and most controversial NPI by counterfactual modeling, which is a variant of the prototypical Bayesian hierarchical model allowing us to answer the question of what if the state had imposed the mandate or not. The third analysis used an advanced Bayesian hierarchical model to evaluate the effectiveness of all six NPIs in all 50 states and the District of Columbia, and thereby provide a full-scale estimation of the effectiveness of NPIs and the relative effectiveness of each NPI in the entire United States. Our results have enhanced the collective knowledge on the general effectiveness of NPIs in arresting the spread of COVID-19.

Impact of a University-Led COVID-19 Case Investigation and Contact Tracing Program

CONTEXT

The coronavirus disease 2019 (COVID-19) pandemic devastated societies and economies worldwide. Given the major disruptions to higher education, reflection on university responses to the COVID-19 pandemic may provide insights for future outbreaks.

OBJECTIVE

Here, we describe the epidemiology of COVID-19 on the Emory University campus during the 2020-2021 academic year and provide an evaluation of the performance of a university-led program with the purpose of describing the effectiveness of efforts to augment the public health authority's case investigation and contact tracing efforts during a public health emergency.

DESIGN

Evaluation of a case investigation and contact tracing program regarding operations, timeliness, and performance.

MAIN OUTCOME MEASURES

We analyzed quality metrics to determine the proportion of cases and contacts interviewed and the time to completion of each step from case diagnosis to testing of contacts.

RESULTS

During the 2020-2021 academic year, 1267 COVID-19 cases among Emory students, faculty, and staff were confirmed by polymerase chain reaction, with 1132 reported close contacts. Among cases, the median test turnaround time was 1 day (interquartile range: 1, 2). Among both cases and close contacts, 98% were successfully interviewed. The team called a majority of cases on the same day as their test result was reported to the program (87%; n = 1052). Almost all (98%; n = 1247) cases completed isolation or were advised to isolate during the review period. Close to half (46%; n = 513) of contacts interviewed began quarantine before their interview. Among close contacts interviewed, 13% (n = 145) subsequently converted to an index case.

CONCLUSIONS

The impact and performance of Emory's program may provide useful and actionable data for future university-led infectious disease outbreak response programs. The program structure, performance metrics, and information collected via interviews provide practical implications and an organized structure to guide other programs during future outbreaks.

Association of depressive symptoms with incidence and mortality rates of COVID-19 over 2 years among healthcare workers in 20 countries: multi-country serial cross-sectional study

BACKGROUND

Long-term deterioration in the mental health of healthcare workers (HCWs) has been reported during and after the COVID-19 pandemic. Determining the impact of COVID-19 incidence and mortality rates on the mental health of HCWs is essential to prepare for potential new pandemics. This study aimed to investigate the association of COVID-19 incidence and mortality rates with depressive symptoms over 2 years among HCWs in 20 countries during and after the COVID-19 pandemic.

METHODS

This was a multi-country serial cross-sectional study using data from the first and second survey waves of the COVID-19 HEalth caRe wOrkErS (HEROES) global study. The HEROES study prospectively collected data from HCWs at various health facilities. The target population included HCWs with both clinical and non-clinical roles. In most countries, healthcare centers were recruited based on convenience sampling. As an independent variable, daily COVID-19 incidence and mortality rates were calculated using confirmed cases and deaths reported by Johns Hopkins University. These rates represent the average for the 7 days preceding the participants' response date. The primary outcome was depressive symptoms, assessed by the Patient Health Questionnaire-9. A multilevel linear mixed model (LMM) was conducted to investigate the association of depressive symptoms with the average incidence and mortality rates.

RESULTS

A total of 32,223 responses from the participants who responded to all measures used in this study on either the first or second survey, and on both the first and second surveys in 20 countries were included in the analysis. The mean age was 40.1 (SD = 11.1), and 23,619 responses (73.3%) were from females. The 9323 responses (28.9%) were nurses and 9119 (28.3%) were physicians. LMM showed that the incidence rate was significantly and positively associated with depressive symptoms (coefficient = 0.008, standard error 0.003, p = 0.003). The mortality rate was significantly and positively associated with depressive symptoms (coefficient = 0.049, se = 0.020, p = 0.017).

CONCLUSIONS

This is the first study to show an association between COVID-19 incidence and mortality rates with depressive symptoms among HCWs during the first 2 years of the outbreak in multiple countries. This study's findings indicate that additional mental health support for HCWs was needed when the COVID-19 incidence and mortality rates increase during and after the early phase of the pandemic, and these findings may apply to future pandemics.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT04352634.

The Collaborative Outcome Study on Health and Functioning during Infection Times (COH-FIT): Results from Cyprus

Many studies have shown that COVID-19 caused many problems in mental health. This paper presents the results of the Cyprus sample, part of the global initiative named "The Collaborative Outcomes Study on Health and Functioning during Infection Times" (COH-FIT). Methods: The study took place from April 2019 to January 2022, using the Greek version of the online standard COH-FIT questionnaire on 917 Cypriot adults. Weighted t-tests were applied to test the differences between pre-pandemic and intra-pandemic scores using the anesrake package. Results: Participant responses indicated a significant negative impact of the pandemic on measures of mental health (-7.55; 95% CI: -9.01 to -6.07), with worsening in the scores for anxiety (12.05; 95% CI: 9.33 to 14.77), well-being (-11.06; 95% CI: -12.69 to -9.45) and depression (4.60; 95% CI: 2.06 to 7.14). Similar negative effects were observed for feelings of anger (12.92; 95% CI: 10.54 to 15.29), helplessness (9.66; 95% CI: 7.25 to 12.07), fear (22.25; 95% CI: 19.25 to 25.26), and loneliness (12.52; 95% CI: 9.94 to15.11). Increased use of social media (0.89; 95% CI: 0.71 to 1.09), internet (0.86; 95% CI: 0.67 to 1.04), and substance consumption (0.06; 95% CI: 0.00 to 0.11) were reported, along with a significant decrease in physical health (-3.45; 95% CI: -4.59 to -2.32), self-care (-7.10; 95% CI: -9.00 to -5.20), and social function (-11.27; 95% CI: -13.19 to -9.35), including support (-0.72; 95% CI: -1.09 to -0.34) and family function (-7.97; 95% CI: -9.90 to -6.05). Conclusions: The COVID-19 pandemic significantly affected the daily life and emotional well-being of Cypriots. Identifying factors that influence vulnerability and resilience is essential to prioritize mental health support and address the long-term effects of the pandemic.

Mendelian randomization provides causal association between COVID-19 and thyroid cancer: insights from a multi-cancer analysis

Since the onset of the COVID-19 pandemic, the SARS-CoV-2 virus has caused over 600 million confirmed infections and more than 6.8 million deaths worldwide, with ongoing implications for human health. COVID-19 has been extensively documented to have extrapulmonary manifestations due to the widespread expression of necessary ACE2 receptors in the human body. Nevertheless, the association between COVID-19 and cancer risk remains inadequately explored. This study employs Mendelian randomization (MR) methods to examine the causal relationship between genetic variations associated with COVID-19 and the risk of developing cancer. The findings indicate that COVID-19 has negligible impact on most cancer risks. Interestingly, a higher COVID-19 impact is associated with a decreased risk of thyroid cancer. In summary, our findings demonstrate a genetic correlation between COVID-19 and thyroid cancer, contributing to our understanding of the interplay between COVID-19 and cancer risk.

Evolving patterns of COVID-19 mortality in US counties: A longitudinal study of healthcare, socioeconomic, and vaccination associations

The COVID-19 pandemic emphasized the need for pandemic preparedness strategies to mitigate its impacts, particularly in the United States, which experienced multiple waves with varying policies, population response, and vaccination effects. This study explores the relationships between county-level factors and COVID-19 mortality outcomes in the U.S. from 2020 to 2023, focusing on disparities in healthcare access, vaccination coverage, and socioeconomic characteristics. We conduct multi-variable rolling regression analyses to reveal associations between various factors and COVID-19 mortality outcomes, defined as Case Fatality Rate (CFR) and Overall Mortality to Hospitalization Rate (OMHR), at the U.S. county level. Each analysis examines the association between mortality outcomes and one of the three hierarchical levels of the Social Vulnerability Index (SVI), along with other factors such as access to hospital beds, vaccination coverage, and demographic characteristics. Our results reveal persistent and dynamic correlations between various factors and COVID-19 mortality measures. Access to hospital beds and higher vaccination coverage showed persistent protective effects, while higher Social Vulnerability Index was associated with worse outcomes persistently. Socioeconomic status and vulnerable household characteristics within the SVI consistently associated with elevated mortality. Poverty, lower education, unemployment, housing cost burden, single-parent households, and disability population showed significant associations with Case Fatality Rates during different stages of the pandemic. Vulnerable age groups demonstrated varying associations with mortality measures, with worse outcomes predominantly during the Original strain. Rural-Urban Continuum Code exhibited predominantly positive associations with CFR and OMHR, while it starts with a positive OMHR association during the Original strain. This study reveals longitudinal persistent and dynamic factors associated with two mortality rate measures throughout the pandemic, disproportionately affecting marginalized communities. The findings emphasize the urgency of implementing targeted policies and interventions to address disparities in the fight against future pandemics and the pursuit of improved public health outcomes.

Deep learning infused SIRVD model for COVID-19 prediction: XGBoost-SIRVD-LSTM approach

The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19's future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group's membership over time. To increase the SIRVD model's accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R 2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

The impact of COVID-19 restrictions on HIV prevention and treatment services for key populations in South Africa: an interrupted time series analysis

BACKGROUND

Key populations (KP), including men who have sex with men (MSM), female sex workers (FSW), and transgender women (TGW), experience a disproportionate burden of HIV, even in generalized epidemics like South Africa. Given this disproportionate burden and unique barriers to accessing health services, sustained provision of care is particularly relevant. It is unclear how the COVID-19 pandemic and its associated restrictions may have impacted this delivery. In this study, we aimed to describe patterns of engagement in HIV prevention and treatment services among KP in South Africa and assess the impact of different COVID-19 restriction levels on service delivery.

METHODS

We leveraged programmatic data collected by the US President's Emergency Plan for AIDS Relief (PEPFAR)-supported KP partners in South Africa. We divided data into three discrete time periods based on national COVID-19 restriction periods: (i) Pre-restriction period, (ii) High-level restriction period, and (iii) After-high level restriction period. Primary outcomes included monthly total HIV tests, new HIV cases identified, new initiations of pre-exposure prophylaxis (PrEP), and new enrollments in antiretroviral therapy (ART). We conducted interrupted time series segmented regression analyses to estimate the impact of COVID-19 restrictions on HIV prevention and treatment service utilization.

RESULTS

Between January 2018 and June 2022, there were a total of 231,086 HIV tests, 27,051 HIV positive cases, 27,656 pre-exposure prophylaxis (PrEP) initiations, and 15,949 antiretroviral therapy initiations among MSM, FSW and TGW in PEPFAR-supported KP programs in South Africa. We recorded 90,457 total HIV tests during the 'pre-restriction' period, with 13,593 confirmed new HIV diagnoses; 26,134 total HIV tests with 2,771 new diagnoses during the 'high-level restriction' period; and 114,495 HIV tests with 10,687 new diagnoses during the after high-level restriction period. Our Poisson regression model estimates indicate an immediate and significant decrease in service engagement at the onset of COVID-19 restrictions, including declines in HIV testing, treatment, and PrEP use, which persisted. As programs adjusted to the new restrictions, there was a gradual rebound in service engagement, particularly among MSM and FSW. Towards the end of the high-level restriction period, with some aspects of daily life returning to normal but others still restricted, there was more variability. Some indicators continued to improve, while others stagnated or decreased.

CONCLUSION

Service provision rebounded from the initial shock created by pandemic-related restrictions, and HIV services were largely maintained for KP in South Africa. These results suggest that HIV service delivery among programs designed for KP was able to be flexible and resilient to the evolving restrictions. The results of this study can inform plans for future pandemics and large-scale disruptions to the delivery of HIV services.

Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 and venous thromboembolism

BACKGROUND

A growing body of research has shown that patients with coronavirus disease 2019 (COVID-19) have significantly higher rates of venous thromboembolism (VTE) than healthy. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication.

METHODS

The gene expression profiles of COVID-19 and VTE were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for COVID-19 and VTE, functional annotation, a protein-protein interactions (PPI) network, module construction, and hub gene identification were performed. Finally, we constructed a transcription factor (TF)-gene regulatory network and a TF-miRNA regulatory network for hub genes.

RESULTS

A total of 42 common DEGs were selected for subsequent analyses. Functional analyses showed that biological function and signaling pathways collectively participated in the development and progression of VTE and COVID-19. Finally, 8 significant hub genes were identified using the cytoHubba plugin, including RSL24D1, RPS17, RPS27, HINT1, COX7C, RPL35, RPL34, and NDUFA4, which had preferable values as diagnostic markers for COVID-19 and VTE.

CONCLUSIONS

Our study revealed the common pathogenesis of COVID-19 and VTE. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.

Targeting COVID-19 and varicocele by blocking inflammasome: Ligand-based virtual screening

COVID-19 is a new generation of outbreaks that invade not only local emerging region, continental but also the whole globe. Varicocele on the other hand, is a testicular vascular disease that underlies 40 % of male infertility cases. Fortunately, the two diseases can be blocked through targeting one common target, NLRP3 inflammasome. Upon searching for similar drugs that gained FDA-approval in ChEMBL library along with examining their potential blockade of the receptor through docking using CB-DOCK-2, three potential approved drugs can be repurposed, ChEMBL 4297185, ChEMBL 1201749, ChEMBL 1200545 which had binding energy of -9.8 and -9.7 kcal/mol (stronger than the reference inhibitor, -9.3 kcal/mol). Also, ADME profile of the top 3 drugs showed better attributes. Also, the simulated proteins exhibited stable pattern with strong free binding energies. Among the potential inhibitor drugs ChEMBL 4297185 was found to remain inside the binding site of the protein during the 200 ns simulation time. Hence, it is anticipated to have the highest binding and thus inhibition potential against the protein. The suggested drugs, especially ChEMBL 4297185, are potentially repurposable toward treating COVID-19 and varicocele which deserve further experimental validation.

Depression during the COVID pandemic in La Manouba Governorate, Tunisia: A community survey

BACKGROUND

It is difficult to quantify the impact of the COVID-19 pandemic on mental health as many community surveys have limited quality, rely on screening tests to measure mental health conditions and distress, and often use convenience samples. Moreover, robust surveys come mainly from high-income countries.

AIM

To measure the prevalence of depressive disorders with onset during the pandemic in a community of a Lower-Middle-income country (LMIC)-Tunisia.

METHODS

Clinical semi-structured face-to-face interviews were carried out during the pandemic (September-December 2021) by medical doctors among a representative sample of the general population in the governorate of La Manouba, Tunisia. Psychiatric diagnoses were established according to DSM-IV.

RESULTS

The prevalence of Major Depressive Disorder (MDD) started or recurred after the pandemic was 5.66%. The factors associated with MDD were loss of job and considerably diminished income due to the pandemic (OR = 2.1, 95% CI [1.5, 2.9]) and the perception of having the family's financial situation below the Tunisian average (OR = 2.3, 95% CI [1.7, 3.2]). Female sex, marital status as separated/divorced, and having a COVID-19 infection were associated with MDD only in the overall sample and urban areas, but not in rural areas. Age and having loved ones who passed away due to COVID-19 were not associated with MDD.

CONCLUSION

In Tunisia, the pandemic seems to have increased the risk of depression in people experiencing a precarious financial situation, also due to the pre-existing economic crisis. Specific local level factors, such as not establishing a rigid lockdown for an extended period, may have protected young people and allowed for better mourning in families suffering the loss of a loved one.

Mathematical analysis of simple behavioral epidemic models

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible-Exposed-Infectious-Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model's ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.

Far from the threatening crowd: Generalisation of conditioned threat expectancy and fear in COVID-19 lockdown

Fear and anxiety are rarely confined to specific stimuli or situations. In fear generalisation, there is a spread of fear responses elicited by physically dissimilar generalisation stimuli (GS) along a continuum between danger and safety. The current study investigated fear generalisation with a novel online task using COVID-19-relevant stimuli (i.e., busy or quiet shopping street/mall scenes) during pandemic lockdown restrictions in the United Kingdom. Participants (N = 50) first completed clinically relevant trait measures before commencing a habituation phase, where two conditioned stimuli (CSs; i.e., a busy or quiet high street/mall scene) were presented. Participants then underwent fear conditioning where one conditioned stimulus (CS+) was followed by an aversive unconditioned stimulus (US; a loud female scream accompanied by a facial photograph of a female displaying a fearful emotion) and another (CS-) was not. In a test phase, six generalisation stimuli were presented where the US was withheld, and participants provided threat expectancy and fear ratings for all stimuli. Following successful conditioning, fear generalization was observed for both threat expectancy and fear ratings. Trait worry partially predicted generalised threat expectancy and COVID-19 fear strongly predicted generalised fear. In conclusion, a generalisation gradient was evident using an online remote generalisation task with images of busy/quiet streets during the pandemic. Worry and fear of COVID-19 predicted fear generalisation.

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions that facilitate more informed decision-making. There are three major types of models: mechanistic models including the SEIR-type paradigm, alternative data-driven (DD) approaches, and hybrid models that combine mechanistic models with DD approaches. In this paper, we summarize our work in the COVID-19 Scenario Modeling Hub (SMH) for more than 12 rounds since early 2021 for informed decision support. We emphasize the importance of deep learning techniques for epidemic modeling via a flexible DD framework that substantially complements the mechanistic paradigm to evaluate various future epidemic scenarios. We start with a traditional curve-fitting approach to model cumulative COVID-19 based on the underlying SEIR-type mechanisms. Hospitalizations and deaths are modeled as binomial processes of cases and hospitalization, respectively. We further formulate two types of deep learning models based on multivariate long short term memory (LSTM) to address the challenges of more traditional DD models. The first LSTM is structurally similar to the curve fitting approach and assumes that hospitalizations and deaths are binomial processes of cases. Instead of using a predefined exponential curve, LSTM relies on the underlying data to identify the most appropriate functions, and is capable of capturing both long-term and short-term epidemic behaviors. We then relax the assumption of dependent inputs among cases, hospitalizations, and death. Another type of LSTM that handles all input time series as parallel signals, the independent multivariate LSTM, is developed. Independent multivariate LSTM can incorporate a wide range of data sources beyond traditional case-based epidemiological surveillance. The DD framework unleashes its potential in big data era with previously neglected heterogeneous surveillance data sources, such as syndromic, environment, genomic, serologic, infoveillance, and mobility data. DD approaches, especially LSTM, complement and integrate with the mechanistic modeling paradigm, provide a feasible alternative approach to model today's complex socio-epidemiological systems, and further leverage our ability to explore different scenarios for more informed decision-making during health emergencies.

A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations

Comparing COVID-19 response strategies across nations is a key step in preparing for future pandemics. Conventional comparisons, which rank individual non-pharmaceutical intervention (NPI) effects, are limited by: (i) a focus on epidemiological outcomes; (ii) NPIs typically being applied as packages of interventions; and (iii) different political, economic and social conditions among nations. Here, we develop a coupled epidemiological-behavioural-macroeconomic model that can transfer NPI effects from a reference nation to a focal nation. This approach quantifies epidemiological, behavioural and economic outcomes while accounting for both packaged NPIs and differing conditions among nations. As a first proof of concept, we take Germany as our focal nation during Spring 2020, and New Zealand and Switzerland as reference nations with contrasting NPI strategies. Our results suggest that, while New Zealand's more aggressive strategy would have yielded modest epidemiological gains in Germany, it would have resulted in substantially higher economic costs while dramatically reducing social contacts. In contrast, Switzerland's more lenient strategy would have prolonged the first wave in Germany, but would also have increased relative costs. More generally, these findings indicate that our approach can provide novel, multifaceted insights on the efficacy of pandemic response strategies, and therefore merits further exploration and development.

Public health use and lessons learned from a statewide SARS-CoV-2 wastewater monitoring program (MiNET)

The global SARS-CoV-2 monitoring effort has been extensive, resulting in many states and countries establishing wastewater-based epidemiology programs to address the spread of the virus during the pandemic. Challenges for programs include concurrently optimizing methods, training new laboratories, and implementing successful surveillance programs that can rapidly translate results for public health, and policy making. Surveillance in Michigan early in the pandemic in 2020 highlights the importance of quality-controlled data and explores correlations with wastewater and clinical case data aggregated at the state level. The lessons learned and potential measures to improve public utilization of results are discussed. The Michigan Network for Environmental Health and Technology (MiNET) established a network of laboratories that partnered with local health departments, universities, wastewater treatment plants (WWTPs) and other stakeholders to monitor SARS-CoV-2 in wastewater at 214 sites in Michigan. MiNET consisted of nineteen laboratories, twenty-nine local health departments, 6 Native American tribes, and 60 WWTPs monitoring sites representing 45 % of Michigan's population from April 6 and December 29, 2020. Three result datasets were created based on quality control criteria. Wastewater results that met all quality assurance criteria (Dataset Mp) produced strongest correlations with reported clinical cases at 16 days lag (rho = 0.866, p < 0.05). The project demonstrated the ability to successfully track SARS-CoV-2 on a large, state-wide scale, particularly data that met the outlined quality criteria and provided an early warning of increasing COVID-19 cases. MiNET is currently poised to leverage its competency to complement public health surveillance networks through environmental monitoring for new and emerging pathogens of concern and provides a valuable resource to state and federal agencies to support future responses.

The limits of nudging: Results of a randomized trial of text messages to promote home-based caregiving and reduce perceptions of stigma for COVID-19 patients in Kenyan informal settlements

During the early stages of the global COVID-19 pandemic, governments searched for effective means to rapidly disseminate information about how to prevent the disease and care for sick household members. In June 2020, the government of Kenya considered sending text messages, a behavioral nudging approach, to inform and persuade the public to practice home-based care for those who were infected. We conducted a randomized evaluation of simple informational messages compared to messages targeting personal and social benefits for those receiving the messages. We hypothesized that those that received messages tailored around social or personal benefit would be more likely to undertake the promoted behaviors of isolating if infected with COVID-19 and intending to care for an infected family member. While fear and perceptions of stigma were widespread, more than two-thirds of respondents in the control condition expressed an intention to care for an infected family member at home. Despite greater recall of the personal benefit message, which used reciprocity as its key behavioral lever, intentions to provide care at home and perceptions of stigma did not differ across study groups. Rather, capabilities such as wealth and having sufficient room at home were the key determinants. While text messages as behavioral nudges may be useful for some behaviors, policymakers should consider a broader range of tools for behaviors that are influenced by people's capabilities, since even low-cost interventions may crowd out the time and energy needed for other responses during an emergency.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

Characterization of the SARS-CoV-2 Genome 3'-Untranslated Region Interactions with Host MicroRNAs

The 2019 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has marked the spread of a novel human coronavirus. SARS-CoV-2 has exhibited increased disease severity and immune evasion across its variants, and the molecular mechanisms behind these phenomena remain largely unknown. Conserved elements of the viral genome, such as secondary structures within the 3'-untranslated region (UTR), could prove crucial in furthering our understanding of the host-virus interface. Analysis of the SARS-CoV-2 viral genome 3'-UTR revealed the potential for host microRNA (miR) binding sites, allowing for sequence-specific interactions. In this study, we demonstrate that the SARS-CoV-2 genome 3'-UTR binds the host cellular miRs miR-34a-5p, miR-34b-5p, and miR-760-3p in vitro. Native gel electrophoresis and steady-state fluorescence spectroscopy were utilized to biophysically characterize the binding of these miRs to their predicted sites within the SARS-CoV-2 genome 3'-UTR. Additionally, we investigated 2'-fluoro-d-arabinonucleic acid (FANA) analogs as competitive binding inhibitors for these interactions. These miRs modulate the translation of granulin (GRN), interleukin-6 (IL-6), and the IL-6 receptor (IL-6R), all of which are key modulators and activators of JAK/STAT3 signaling and are implicated in regulation of the immune response. Thus, we propose that hijacking of these miRs by SARS-CoV-2 could identify a mechanism of host immune modulation by the virus. The mechanisms detailed in this study have the potential to drive the development of antiviral treatments for SARS-CoV-2, through direct targeting of the virus-host interface.

Assessment of myocardial injury by SPECT myocardial perfusion imaging in patients with COVID-19 infection in a single center after lifting the restrictions in China

PURPOSE

To assess myocardial injury using rest single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) in patients with COVID-19 and to evaluate whether myocardial injury detected by rest MPI predict the prognosis of symptoms after 6 months follow-up.

METHODS

Patients suspected of myocarditis between December 2022 and March 2023, after the lifting of COVID-19 pandemic restrictions, and between December 2018 and March 2019, prior to the pandemic, were referred to our study. All patients underwent rest MPI. One hundred and sixty four patients with COVID-19 infection after the lifting of pandemic restrictions and 101 patients before the pandemic were included as the study and control groups, respectively. One hundred and fifty three patients of the study group and 83 of the control group presented symptoms when they initially visit to our department. Compare the parameters of myocardial injury detected by rest SPECT MPI between the two groups and then investigate the association between myocardial injury and symptom prognosis in symptomatic patients of both groups.

RESULTS

Total perfusion defect (TPD) (4.2% ± 3.3% vs. 2.3% ± 2.2%, P < 0.001), summed rest score (SRS) (5.3 ± 5.4 vs. 2.7 ± 2.0, P < 0.001), the proportion of patients with TPD > 4% (43.3% vs. 17.8%, P < 0.001), TPD > 10% (6.71% vs 0, P < 0.001), SRS > 4 (40.2% vs 15.8%, P < 0.001), SRS > 10 (12.8% vs 0, P < 0.001), the number of abnormal perfusion segments (3.9 ± 3.1 vs. 2.4 ± 1.7, P < 0.001) were all significantly higher in the study group. All the parameters of rest MPI were not associated with the prognosis of symptoms in symptomatic patients of both groups after 6 months follow-up.

CONCLUSION

Myocardial injury in COVID-19 patients could be assessed by rest SPECT MPI. The COVID-19 patients could exhibited a higher frequency and greater severity of myocardial injury than uninfected control patients. Myocardial injury assessed by rest MPI did not predict for the prognosis of symptoms in symptomatic patients of both COVID-19 patients and uninfected patients.

Sensitivity-improved SERS detection of SARS-CoV-2 spike protein by Au NPs/COFs integrated with catalytic-hairpin-assembly amplification technology

BACKGROUND

The COVID-19 pandemic, caused by the novel coronavirus, has had a profound impact on global health and economies worldwide. This unprecedented crisis has affected individuals, communities, and nations in diverse manners. Developing simple and accurate diagnostic methods is an imperative task for frequent testing to mitigate the spread of the virus. Among these methods, SARS-CoV-2 antigen tests in clinical specimens have emerged as a promising diagnostic method for COVID-19 due to their sensitive and accurate detection of spike (S) protein, which plays a crucial role in viral infection initiation.

RESULTS

In this work, a dual-signal amplification surface enhanced Raman scattering (SERS)-based S protein biosensor was constructed based on Au NPs/COFs and enzyme-free catalytic hairpin assembly (CHA) amplification method. The approach relies on a released free DNA sequence (T), which is generated from the competition reaction between Aptamer/T and Aptamer/S protein, to trigger a CHA reaction. Due to the high binding affinity and selectivity between the S protein and its aptamer, CHA process was triggered with the maximum SERS tags (H2-conjugated Au@4-mercaptobenzonitrile@Ag) anchored onto Au NPs/COFs substrate surface. This SERS platform could detect the S protein at concentrations with high sensitivity (limit of detection = 3.0 × 10-16 g/mL), wide detection range (1 × 10-16 to 1 × 10-11 g/mL), acceptable reproducibility (relative standard deviation = 7.01 %) and excellent specificity. The biosensor was also employed to detect S protein in artificial human salivas.

SIGNIFICANCE

Thus, this study not only developed a novel Au NPs/COFs substrate exhibiting strong SERS enhancement ability and high reproducibility, but also proposed a promising dual-signal amplification SERS-based diagnostic method for COVID-19, holding immense potential for the detection of a wide range of antigens and infectious diseases in future applications.

The Mediating Role of Human Mobility in Temporal-Lagged Relationships Between Risk Perception and COVID-19 Dynamics in Taiwan: Statistical Modeling for Comparing the Pre-Omicron and Omicron Eras

Background

The COVID-19 pandemic has profoundly impacted all aspects of human life for over 3 years. Understanding the evolution of public risk perception during these periods is crucial. Few studies explore the mechanisms for reducing disease transmission due to risk perception. Thus, we hypothesize that changes in human mobility play a mediating role between risk perception and the progression of the pandemic.

Objective

The study aims to explore how various forms of human mobility, including essential, nonessential, and job-related behaviors, mediate the temporal relationships between risk perception and pandemic dynamics.

Methods

We used distributed-lag linear structural equation models to compare the mediating impact of human mobility across different virus variant periods. These models examined the temporal dynamics and time-lagged effects among risk perception, changes in mobility, and virus transmission in Taiwan, focusing on two distinct periods: (1) April-August 2021 (pre-Omicron era) and (2) February-September 2022 (Omicron era).

Results

In the pre-Omicron era, our findings showed that an increase in public risk perception correlated with significant reductions in COVID-19 cases across various types of mobility within specific time frames. Specifically, we observed a decrease of 5.59 (95% CI -4.35 to -6.83) COVID-19 cases per million individuals after 7 weeks in nonessential mobility, while essential mobility demonstrated a reduction of 10.73 (95% CI -9.6030 to -11.8615) cases after 8 weeks. Additionally, job-related mobility resulted in a decrease of 3.96 (95% CI -3.5039 to -4.4254) cases after 11 weeks. However, during the Omicron era, these effects notably diminished. A reduction of 0.85 (95% CI -1.0046 to -0.6953) cases through nonessential mobility after 10 weeks and a decrease of 0.69 (95% CI -0.7827 to -0.6054) cases through essential mobility after 12 weeks were observed.

Conclusions

This study confirms that changes in mobility serve as a mediating factor between heightened risk perception and pandemic mitigation in both pre-Omicron and Omicron periods. This suggests that elevating risk perception is notably effective in impeding virus progression, especially when vaccines are unavailable or their coverage remains limited. Our findings provide significant value for health authorities in devising policies to address the global threats posed by emerging infectious diseases.

Thromboembolic events in people with cancer during the COVID-19 pandemic: case-control study

OBJECTIVE

to analyze the association between coronavirus disease infection and thromboembolic events in people with cancer in the first year of the pandemic.

METHOD

case-control study carried out by collecting medical records. The selected cases were adults with cancer, diagnosed with a thromboembolic event, treated in the selected service units during the first year of the pandemic. The control group included adults with cancer without a diagnosis of a thromboembolic event. Pearson's chi-square test was applied to verify the association between risk factors and the outcome and logistic regression techniques were applied to identify the odds ratio for the occurrence of a thromboembolic event.

RESULTS

there were 388 cases and 440 control cases included in the study (ratio 1/1). Females predominated, who were white, with mean age of 58.2 (±14.8) years. Antineoplastic chemotherapy was the most used treatment and coronavirus disease was identified in 11.59% of participants. In the case group, deep vein thrombosis was more prevalent.

CONCLUSION

the study confirmed the hypothesis that coronavirus disease infection did not increase the chance of thromboembolic events in people with cancer. For the population studied, the factors that were associated with these events were those related to cancer and its treatment.

HIGHLIGHTS

(1) Deep vein thrombosis was what prevailed in the studied population. (2) Chemotherapy increased the chance of thromboembolic events by 65%. (3) Thromboembolic events showed a significant association with a higher death rate as the outcome. (4) COVID-19 did not increase the risk of thromboembolic events in people with cancer.

Factors associated with oropharyngeal dysphagia in individuals with cardiovascular disease and COVID-19

PURPOSE

Oropharyngeal dysphagia (OD) is one of the possible outcomes in patients hospitalized with COVID-19 and also in the population hospitalized for the treatment of cardiovascular disease. Thus, knowing the predictive risk factors for OD may help with referral and early intervention. This study aimed to verify the association of different factors with OD in hospitalized individuals with cardiovascular disease and COVID-19.

METHODS

Cross-sectional clinical study approved by the Research Ethics Committee (4,521,771). Clinical evaluation of swallowing was carried out in 72 adult patients with cardiovascular disease and COVID-19 hospitalized from April to September 2020. Individuals under 18 years of age and without previous cardiovascular disease were excluded. The presence of general clinical and/or neurological complications, pronation, stay in the intensive care unit (ICU), orotracheal intubation (OTI), tracheostomy tube, oxygen support and age were considered as predictive risk factors for oropharyngeal dysphagia. Fisher's exact test, Mann Whitney test and logistic regression model were used for analysis.

RESULTS

General clinical complications (p=0.001), pronation (p=0.003), ICU stay (p=0.043), in addition to the need for oxygen supplementation (p=0.023) and age (p= 0 .037) were statistically significant factors associated. The pronation (0.013) and age (0.038) were independently associated with dysphagia. OTI (p=0.208), tracheostomy (p=0.707) and the presence of previous cerebrovascular accidents (p=0.493) were not statistically significant.

CONCLUSION

In this study, age and prone position were factors independently associated with oropharyngeal dysphagia, complications such as the need for oxygen supplementation, in addition to the need for ICU admission, were also associated factors in the population.

The impact of official recommendations during the COVID-19 pandemic on the clinical activity and business turnover of manual therapists in Sweden-The CAMP cohort study

BACKGROUND

This study examined manual therapy business owners' perception of official recommendations during the COVID-19 pandemic and the impact on their clinics' economic performance, including clinic activity hours and business turnover.

MATERIALS AND METHODS

In a longitudinal study design, data were collected in November 2021 (baseline), and after three months, six months, and 12 months. Participants were manual therapists who were business owners. A growth curve model was used to analyze differences in clinical activity trajectories. Multinomial logistic regression analysis was used to assess the relationship between perceived disruptions in business and turnover. Qualitative text analysis was used to examine participants' responses to open-ended questions concerning economic measures taken to sustain their business during the pandemic.

RESULTS

This study of 443 manual therapy business owners found that clinics were initially active with minimal variation, but activity changed following COVID-19 recommendations. Business owners perceived that the disruptions had no significant impact on turnover during the initial stages of the official recommendations. Economic support and the previous decrease in turnover increased the likelihood of experiencing a decreased turnover at 12 months. Business owners implemented cost-cutting measures and diversified income sources to navigate COVID-19 challenges and sustain their businesses.

CONCLUSION

The official recommendations in Sweden had an impact on manual therapists' businesses as the COVID-19 pandemic lingered. Some business owners were concerned at the early stages about lower turnover but showed financial resilience by cutting costs and finding new revenue sources to overcome COVID-19 challenges.

Booster Vaccination with BNT162b2 Improves Cellular and Humoral Immune Response in the Pediatric Population Immunized with CoronaVac

The SARS-CoV-2 Omicron variant and its sublineages continue to cause COVID-19-associated pediatric hospitalizations, severe disease, and death globally. BNT162b2 and CoronaVac are the main vaccines used in Chile. Much less is known about the Wuhan-Hu-1 strain-based vaccines in the pediatric population compared to adults. Given the worldwide need for booster vaccinations to stimulate the immune response against new Omicron variants of SARS-CoV-2, we characterized the humoral and cellular immune response against Omicron variant BA.1 in a pediatric cohort aged 10 to 16 years who received heterologous vaccination based on two doses of CoronaVac, two doses of CoronaVac (2x) plus one booster dose of BNT162b2 [CoronaVac(2x) + BNT162b2 (1x)], two doses of CoronaVac plus two booster doses of BNT162b2 [CoronaVac(2x) + BNT162b2 (2x)], and three doses of BNT162b2. We observed that the [CoronaVac(2x) + BNT162b2 (2x)] vaccination showed higher anti-S1 and neutralizing antibody titers and CD4 and CD8 T cell immunity specific to the Omicron variant compared to immunization with two doses of CoronaVac alone. Furthermore, from all groups tested, immunity against Omicron was highest in individuals who received three doses of BNT162b2. We conclude that booster vaccination with BNT162b2, compared to two doses of CoronaVac alone, induces a greater protective immunity.

A parameter free relative density based biclustering method for identifying non-linear feature relations

The existing biclustering algorithms often depend on assumptions like monotonicity or linearity of feature relations for finding biclusters. Though a few algorithms overcome this problem using density-based methods, they tend to miss out many biclusters because they use global criteria for identifying dense regions. The proposed method, PF-RelDenBi, uses local variations in marginal and joint densities for each pair of features to find the subset of observations, forming the basis of the relation between them. It then finds the set of features connected by a common set of observations using a non-linear feature relation index, resulting in a bicluster. This approach allows us to find biclusters based on feature relations, even if the relations are non-linear or non-monotonous. Additionally, the proposed method does not require the user to provide any parameters, allowing its application to datasets from different domains. To study the behaviour of PF-RelDenBi on datasets with different properties, experiments were carried out on sixteen simulated datasets and the performance has been compared with eleven state-of-the-art algorithms. The proposed method is seen to produce better results for most of the simulated datasets. Experiments were conducted with five benchmark datasets and biclusters were detected using PF-RelDenBi. For the first two datasets, the detected biclusters were used to generate additional features that improved classification performance. For the other three datasets, the performance of PF-RelDenBi was compared with the eleven state-of-the-art methods in terms of accuracy, NMI and ARI. The proposed method is seen to detect biclusters with greater accuracy. The proposed technique has also been applied to the COVID-19 dataset to identify some demographic features that are likely to affect the spread of COVID-19.

Torsional Twist of the SARS-CoV and SARS-CoV-2 SUD-N and SUD-M domains

Coronavirus non-structural protein 3 (nsp3) forms hexameric crowns of pores in the double membrane vacuole that houses the replication-transcription complex. Nsp3 in SARS-like viruses has three unique domains absent in other coronavirus nsp3 proteins. Two of these, SUD-N (Macrodomain 2) and SUD-M (Macrodomain 3), form two lobes connected by a peptide linker and an interdomain disulfide bridge. We resolve the first complete x-ray structure of SARS-CoV SUD-N/M as well as a mutant variant of SARS-CoV-2 SUD-N/M modified to restore cysteines for interdomain disulfide bond naturally lost by evolution. Comparative analysis of all structures revealed SUD-N and SUD-M are not rigidly associated, but rather, have significant rotational flexibility. Phylogenetic analysis supports that the disulfide bond cysteines are also absent in pangolin-SARS and closely related viruses, consistent with pangolins being the presumed intermediate host in the emergence of SARS-CoV-2. The absence of these cysteines does not impact viral replication or protein translation.

The global incidence and trends of three common flavivirus infections (Dengue, yellow fever, and Zika) from 2011 to 2021

Background

Flavivirus pose a continued threat to global health, yet their worldwide burden and trends remain poorly quantified. We aimed to evaluate the global, regional, and national incidence of three common flavivirus infections (Dengue, yellow fever, and Zika) from 2011 to 2021.

Methods

Data on the number and rate of incidence for the three common flavivirus infection in 204 countries and territories were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. The estimated annual percent change (EAPC) was calculated to quantify the temporal trend during 2011-2016, 2016-2019, and 2019-2021, respectively.

Results

In 2021, an estimated 59,220,428 individuals were infected globally, comprising 58,964,185 cases of dengue, 86,509 cases of yellow fever, and 169,734 cases of Zika virus infection. The age-standardized incidence rate (ASIR) of the three common flavivirus infections increased by an annual average of 5.08% (95% CI 4.12 to 6.05) globally from 2011 to 2016, whereas decreased by an annual average of -8.37% (95% CI -12.46 to -4.08) per year between 2016 to 2019. The ASIR remained stable during 2019-2021, with an average change of 0.69% (95% CI -0.96 to 2.37) per year globally for the three common flavivirus infections. Regionally, the burden of the three common flavivirus infections was primarily concentrated in those regions with middle income, such as South Asia, Southeast Asia, and Tropical Latin America. Additionally, at the country level, there was an inverted "U" relationship between the SDI level and the ASI. Notably, an increase in the average age of infected cases has been observed worldwide, particularly in higher-income regions.

Conclusion

Flavivirus infections are an expanding public health concern worldwide, with considerable regional and demographic variation in the incidence. Policymakers and healthcare providers must stay vigilant regarding the impact of COVID-19 and other environmental factors on the risk of flavivirus infection and be prepared for potential future outbreaks.

A modeling framework for the analysis of the SARS-CoV2 transmission dynamics

Despite the progress in medical data collection the actual burden of SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under-reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths. We adopt this framework and assume that the dynamics generating the total number of infections can be described by a continuous time transmission model expressed through a system of nonlinear ordinary differential equations where the transmission rate is modeled as a diffusion process allowing to reveal both the effect of control strategies and the changes in individuals behavior. We develop this flexible Bayesian tool in Stan and study 3 pairs of European countries, estimating the time-varying reproduction number (R t $$ {R}_t $$ ) as well as the true cumulative number of infected individuals. As we estimate the true number of infections we offer a more accurate estimate ofR t $$ {R}_t $$ . We also provide an estimate of the daily reporting ratio and discuss the effects of changes in mobility and testing on the inferred quantities.

Impact of Gamma COVID-19 variant on the prognosis of hospitalized pregnant and postpartum women with cardiovascular disease

OBJECTIVE

The aim of the study was to assess the impact of the Gamma coronavirus disease 2019 (COVID-19) variant on pregnant and postpartum women with Cardiovascular Disease (CVD).

METHODS

The Influenza Epidemiological Surveillance System database (SIVEP-Gripe), a compulsory notification system for cases of Severe Acute Respiratory Syndrome (SARS), was investigated for notified cases of pregnant and postpartum women with reported CVD and SARS due to COVID-19 between February 16, 2020 and May 1, 2021 (when vaccination began), was investigated. In this retrospective cohort, two groups were formed based on symptom onset date, according to the predominance of the variants: original (group 2020) and Gamma (group 2021). Cases with missing information on the presence or absence of CVD were excluded. The comparative analysis was controlled for confounding variables.

RESULTS

Among 703 COVID-19 cases notified with CVD (406 patients in 2020 and 297 patients in 2021), compared to 2020, cases in 2021 had more respiratory symptoms (90.6 % vs. 80.1 %, p < 0.001), greater ventilatory support need (75.3 % vs. 53.9 %, p < 0.001), more ICU admission (46.6 % vs. 34.3 %, p = 0.002), longer duration (20.59 ± 14.47 vs. 16.52 ± 12.98 days, p < 0.001), higher mortality (25.6 % vs. 15.5 %, p < 0.001), with more than two-times mortality likelihood in the third trimester (adjusted OR = 2.41, 95 % CI 1.50-3.88, p < 0.001) or puerperium periods (adjusted_OR = 2.15, 95 % CI 1.34-3.44, p = 0.001).

CONCLUSIONS

In Brazil, pregnant and postpartum women with CVDs in the Gamma variant phase have higher morbidity and mortality than those affected by the original variant of Coronavirus-19.

Exploiting Hydrophobic Amino Acid Scanning to Develop Cyclic Peptide Inhibitors of the SARS-CoV-2 Main Protease with Antiviral Activity

The development of novel antivirals is crucial not only for managing current COVID-19 infections but for addressing potential future zoonotic outbreaks. SARS-CoV-2 main protease (Mpro) is vital for viral replication and viability and therefore serves as an attractive target for antiviral intervention. Herein, we report the optimization of a cyclic peptide inhibitor that emerged from an mRNA display selection against the SARS-CoV-2 Mpro to enhance its cell permeability and in vitro antiviral activity. By identifying mutation-tolerant amino acid residues within the peptide sequence, we describe the development of a second-generation Mpro inhibitor bearing five cyclohexylalanine residues. This cyclic peptide analogue exhibited significantly improved cell permeability and antiviral activity compared to the parent peptide. This approach highlights the importance of optimizing cyclic peptide hits for activity against intracellular targets such as the SARS-CoV-2 Mpro.

Ultrafast and highly sensitive detection of SARS-CoV-2 spike protein by field-effect transistor graphene-based biosensors

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), etiological agent for the coronavirus disease 2019 (COVID-19), has resulted in over 775 million global infections. Early diagnosis remains pivotal for effective epidemiological surveillance despite the availability of vaccines. Antigen-based assays are advantageous for early COVID-19 detection due to their simplicity, cost-effectiveness, and suitability for point-of-care testing (PoCT). This study introduces a graphene field-effect transistor-based biosensor designed for high sensitivity and rapid response to the SARS-CoV-2 spike protein. By functionalizing graphene with monoclonal antibodies and applying short-duration gate voltage pulses, we achieve selective detection of the viral spike protein in human serum within 100 µs and at concentrations as low as 1 fg ml-1, equivalent to 8 antigen molecules perµl of blood. Furthermore, the biosensor estimates spike protein concentrations in serum from COVID-19 patients. Our platform demonstrates potential for next-generation PoCT antigen assays, promising fast and sensitive diagnostics for COVID-19 and other infectious diseases.

More severe comorbidities, advanced age, and incomplete vaccination increase the risk of COVID-19 mortality

Numerous studies have stated that comorbidities are risk factors for coronavirus disease 2019 (COVID-19) mortality, but few have considered the severity or stage of these comorbidities. The aim of this study was to determine the association between the severity of comorbidity, age, and number of COVID-19 vaccinations with COVID-19 mortality. This case-control study was conducted from July 2021 until December 2022 at the Dr. Soetomo General Academic Hospital, Surabaya, Indonesia. The patients were divided into non-survived patients (case group) and survived patients (control group). The inclusion criteria for cases were adult patients hospitalized with confirmed COVID-19, based on reverse transcriptase-polymerase chain reaction (RT-PCR) testing of nasopharyngeal swabs. Using total sampling, 1,046 confirmed COVID-19 patients, which consisted of 450 (43%) non-survived patients and 596 (57%) survived patients, were included. The most common comorbidity was diabetes mellitus (DM) (82.7%), chronic kidney disease (CKD) (43%), hypertension (25.7%), and obesity (23.6%). Our multivariate analysis indicated that older age (aOR: 1.03; 95%CI: 1.02-1.04, p<0.001), male sex (aOR: 1.29; 95%CI: 1.11- 2.00, p=0.007), severe COVID-19 at first admission (aOR: 3.13; 95%CI: 2.08-4.73, p<0.001), having pneumonia (aOR: 1.99; 95%CI: 1.21-3.33, p=0.005), poorly controlled DM with HbA1c≥9% (aOR: 2.90; 95%CI: 1.72-4.89, p<0.001), severe obesity with body mass index (BMI)≥30 (OR: 2.90; 95%CI: 1.72-4.89, p<0.001), hypertension stage 2 (aOR: 1.99; 95%CI: 1.12-3.53, p=0.019) or stage 3 (aOR: 6.59; 95%CI: 2.39-18.17, p<0.001), CKD stage 3 (aOR: 2.50; 95%CI: 1.36-4.59, p=0.003), stage 4 (aOR: 5.47; 95%CI: 2.18-13.69, p<0.001) or stage 5 (aOR: 1.71; 95%CI: 1.04-2.81, p=0.036), and having chronic lung disease (aOR: 3.08; 95%CI: 1.22-7.77, p=0.017) significantly increased the risk of COVID-19 mortality. In contrast, COVID-19 vaccination reduced the risk of COVID-19-associated death. This study highlights that more severe comorbidities, advanced age, and incomplete vaccination were associated with COVID-19 mortality.

Enhanced Treatment in Severe-Critical COVID-19 With Tocilizumab, Remdesivir, Dexamethasone: A Jordanian Cohort Study

BACKGROUND

Several medications have been proposed to manage COVID-19, with controversial data regarding their clinical benefits. We aimed to investigate the clinical efficacy of using remdesivir (RDV) with and without tocilizumab (TCZ) and standard therapy in treating severe COVID-19.

METHODS

This retrospective cohort study was conducted in a Jordanian tertiary hospital (September 26th, 2020 - August 28th, 2021) and included adult COVID-19 patients requiring oxygen support. Patients were categorized into three groups based on treatment: TCZ+RDV and standard therapy; RDV and standard therapy; and standard therapy alone, which included dexamethasone, vitamins, anticoagulants, and ceftriaxone.

RESULTS

Of 1,556 screened, 1,244 patients (mean age 62.33, 60.8% men) were included. Distribution was 106 in TCZ+RDV, 520 in RDV, and 618 in standard therapy. No significant differences were observed in age, gender, or BMI. Mortality was lowest in TCZ+RDV (32.1%), followed by RDV (40.6%) and standard therapy (47.1%) (p=0.005). Among ICU patients, TCZ+RDV showed significantly lower mortality (51.1%) compared to RDV (75%) and standard therapy (85.8%) (p<0.001). The ICU stays and invasive mandatory ventilation (IMV) durations were significantly shorter with TCZ+RDV (4.30 and 2.69 days, respectively) compared to RDV (7.61 and 4.52 days) and standard therapy (7.98 and 5.32 days) (p<0.001 for ICU stays, p=0.025 for IMV durations).

CONCLUSIONS

Combining TCZ, RDV, and dexamethasone shows promise in reducing mortality and ICU/IMV duration for severe COVID-19.

Healthcare system resilience and adaptability to pandemic disruptions in the United States

Understanding healthcare system resilience has become paramount, particularly in the wake of the COVID-19 pandemic, which imposed unprecedented burdens on healthcare services and severely impacted public health. Resilience is defined as the system's ability to absorb, recover from and adapt to disruptions; however, despite extensive studies on this subject, we still lack empirical evidence and mathematical tools to quantify its adaptability (the ability of the system to adjust to and learn from disruptions). By analyzing millions of patients' electronic medical records across US states, we find that the COVID-19 pandemic caused two successive waves of disruptions within the healthcare systems, enabling natural experiment analysis of the adaptive capacity of each system to adapt to past disruptions. We generalized the quantification framework and found that the US healthcare systems exhibit substantial adaptability (ρ = 0.58) but only a moderate level of resilience (r = 0.70). When considering system responses across racial groups, Black and Hispanic groups were more severely impacted by pandemic disruptions than white and Asian groups. Physician abundance was the key characteristic for determining healthcare system resilience. Our results offer vital guidance in designing resilient and sustainable healthcare systems to prepare for future waves of disruptions akin to COVID-19 pandemics.

Protein S-Nitrosylation: A Chemical Modification with Ubiquitous Biological Activities

Nitric oxide (NO) induces protein posttranslational modification (PTM), known as S-nitrosylation, which has started to gain attention as a critical regulator of thousands of substrate proteins. However, our understanding of the biological consequences of this emerging PTM is incomplete because of the limited number of identified S-nitrosylated proteins (S-NO proteins). Recent advances in detection methods have effectively contributed to broadening the spectrum of discovered S-NO proteins. This article briefly reviews the progress in S-NO protein detection methods and discusses how these methods are involved in characterizing the biological consequences of this PTM. Additionally, we provide insight into S-NO protein-related diseases, focusing on the role of these proteins in mitigating the severity of infectious diseases.

A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration

PURPOSE

We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability.

METHODS

Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score.

RESULTS

Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort.

CONCLUSION

The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.