Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis.

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers.

Background: Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods: This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results: Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI, .69-.80); days 1 and 2, 0.84 (95% CI, .73-.94); days 3 and 4, 0.94 (95% CI, .88-1.0); and days 5 and 6, 0.96 (95% CI, .90-1.0). Conclusions: This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care.

Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements.

Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.

Investigating the etiologies of non-malarial febrile illness in Senegal using metagenomic sequencing.

The worldwide decline in malaria incidence is revealing the extensive burden of non-malarial febrile illness (NMFI), which remains poorly understood and difficult to diagnose. To characterize NMFI in Senegal, we collected venous blood and clinical metadata in a cross-sectional study of febrile patients and healthy controls in a low malaria burden area. Using 16S and untargeted sequencing, we detected viral, bacterial, or eukaryotic pathogens in 23% (38/163) of NMFI cases. Bacteria were the most common, with relapsing fever Borrelia and spotted fever Rickettsia found in 15.5% and 3.8% of cases, respectively. Four viral pathogens were found in a total of 7 febrile cases (3.5%). Sequencing also detected undiagnosed Plasmodium, including one putative P. ovale infection. We developed a logistic regression model that can distinguish Borrelia from NMFIs with similar presentation based on symptoms and vital signs (F1 score: 0.823). These results highlight the challenge and importance of improved diagnostics, especially for Borrelia, to support diagnosis and surveillance.

Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study.

Background: Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods: The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results: The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions: The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.