Effect of COVID-19 Curriculum Changes on Medical Student Exam Performance: A Case Series.

BACKGROUND: The COVID-19 pandemic caused medical schools to convert to an online format, necessitating a swift change in medical education delivery. New teaching methods were adapted, with some schools having greater success than others. Kirk Kerkorian School of Medicine (KSOM) employed a small-group interactive learning style that consists of eight or fewer medical students and one faculty mentor engaging in group problem-based learning (PBL) twice weekly. This style had clear signs of struggle with a significant decrease in exam performance. Rocky Vista University College of Osteopathic Medicine (RVUCOM) employed a large-group didactic lecture style that consisted of one faculty mentor lecturing hundreds of medical students in a pre-recorded setting five times weekly. This style had greater success with its curriculum adaptation leading to minimal effect on their exam performance. This study aims to investigate whether the type of medical school curriculum (small-group interactive vs. large-group didactic) impacts student exam performance during online learning transitions forced by the COVID-19 pandemic. METHODOLOGY: KSOM and RVUCOM students were grouped into above-expectations and below-expectations categories based on each institution's standardized exam performance metrics. Independently sampled t-tests were performed to compare groups. KSOM was classified as a small-group interactive curriculum through its heavy reliance on student-led PBL, whereas RVUCOM was classified as a large-group didactic curriculum through its extensive proctor-led slideshow lectures. RESULTS: KSOM's transition to online PBL resulted in fewer students scoring above the national average on the National Board of Medical Examiners (NBME) exams compared to previous cohorts (55% vs. 77%, respectively; N = 47 and 78; P < 0.01). RVUCOM's transition to online large-group lectures yielded no significant differences between students who performed above expectations and students who performed below expectations between their cohorts (63% vs. 65%, respectively; N = 305 and 300; P > 0.05). CONCLUSIONS: KSOM's COVID-19 cohort performed significantly worse than RVUCOM's COVID-19 cohort during their medical school organ-system exams. We believe that the small-group learning at KSOM is less resilient for online curricula compared to the large-group didactics seen at RVUCOM. Understanding which didactic methods can transition to online learning more effectively than others is vital in guiding effective curriculum adjustments as online delivery becomes more prominent.

Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: A systematic review and meta-analysis.

BACKGROUND: The objective of this study is to examine the application of AI algorithms in detecting OPMD and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes. MATERIALS AND METHODS: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools. RESULTS: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95%CI= [39.484, 118.623], I2 = 86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95%CI= [0.866,0.925]; I2 = 81%), specificity of 89.2% (95%CI= [0.851,0.922], I2 = 79%), and a high negative predictive value of 89.5% (95%CI= [0.851; 0.927], I2 = 96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95%CI [0.873,0.929], I2=4%), 87.0% (95%CI [0.813,0.912], I2=49%) and 90.1% (95%CI [0.860,0.931], I2=57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy. CONCLUSIONS: AI-based detection using clinical photography shows a high diagnostic odds ratio and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.

Prediction of high-risk emergency department revisits from a machine-learning algorithm: a proof-of-concept study.

BACKGROUND: High-risk emergency department (ED) revisit is considered an important quality indicator that may reflect an increase in complications and medical burden. However, because of its multidimensional and highly complex nature, this factor has not been comprehensively investigated. This study aimed to predict high-risk ED revisit with a machine-learning (ML) approach. METHODS: This 3-year retrospective cohort study assessed adult patients between January 2019 and December 2021 from National Taiwan University Hospital Hsin-Chu Branch with high-risk ED revisit, defined as hospital or intensive care unit admission after ED return within 72 hours. A total of 150 features were preliminarily screened, and 79 were used in the prediction model. Deep learning, random forest, extreme gradient boosting (XGBoost) and stacked ensemble algorithm were used. The stacked ensemble model combined multiple ML models and performed model stacking as a meta-level algorithm. Confusion matrix, accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUROC) were used to evaluate performance. RESULTS: Analysis was performed for 6282 eligible adult patients: 5025 (80.0%) in the training set and 1257 (20.0%) in the testing set. High-risk ED revisit occurred for 971 (19.3%) of training set patients vs 252 (20.1%) in the testing set. Leading predictors of high-risk ED revisit were age, systolic blood pressure and heart rate. The stacked ensemble model showed more favourable prediction performance (AUROC 0.82) than the other models: deep learning (0.69), random forest (0.78) and XGBoost (0.79). Also, the stacked ensemble model achieved favourable accuracy and specificity. CONCLUSION: The stacked ensemble algorithm exhibited better prediction performance in which the predictions were generated from different ML algorithms to optimally maximise the final set of results. Patients with older age and abnormal systolic blood pressure and heart rate at the index ED visit were vulnerable to high-risk ED revisit. Further studies should be conducted to externally validate the model.

Transdiaphragmatic intercostal herniation in the setting of trauma.

Transdiaphragmatic intercostal herniation is a rare injury that can be associated with blunt trauma. Since its first documentation within the literature in 1946, there have been less than 50 cases reported. We present a case involving a 56-year old female who presented to our Trauma Center with transdiaphragmatic intercostal herniation caused by blunt trauma from a high-velocity T-bone vehicular collision. Upon presentation, she exhibited bilateral breath sounds; however, with labored breathing, chest pain, and hypoxia. The initial chest radiograph interpretation indicated the presence of "left lower lobe infiltrates", and subsequent computed tomography imaging identified "a small lateral hernia along the left mid abdomen". After initial resuscitation, her condition deteriorated, exhibiting respiratory distress and becoming increasingly hypercarbic, requiring intubation. Review of the imaging showed disruption of the left hemidiaphragm with intrathoracic herniation of colon and stomach through the thoracic wall between the ninth and tenth ribs. Consequently, a thoracotomy was performed in the operating room, revealing a large defect between the two ribs with disruption of the intercostal muscles and inferior displacement of rib space. Lung and omentum had herniated through the disrupted rib space and the diaphragmatic rupture was attenuated anteriorly, measuring 11x6cm. After reduction of the herniated organs, a biologic porcine mesh was placed and an intermediate complex closure of the thoracic wall hernia was performed. The patient was later extubated, recovered from her injuries with no complications and was discharged. With the low incidence of transdiaphragmatic intercostal herniation, there is no standardized surgical management. Recent literature suggests that these injuries should be managed with mesh, rather than sutures only, due to high rates of recurrence. Furthermore, diaphragmatic injuries may suffer a delay in diagnosis. Therefore, a high index of suspicion should be maintained in patients with respiratory distress following a blunt trauma, with close review of computed tomography.

MetaQuad: shared informative variants discovery in metagenomic samples.

Motivation: Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge. Results: MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments. Availability and implementation: MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad.

Reported outcome measures in necrotising soft tissue infections: a systematic review.

Introduction: There are inconsistencies in outcome reporting for patients with necrotising soft tissue infections (NSTI). The aim of this study was to evaluate reported outcome measures in NSTI literature that could inform a core outcome set (COS) such as could be used in a study of hyperbaric oxygen in this indication. Methods: A systematic review of all NSTI literature identified from Cochrane, Ovid MEDLINE and Scopus databases as well as grey literature sources OpenGrey and the New York Academy of Medicine databases which met inclusion criteria and were published between 2010 and 2020 was performed. Studies were included if they reported on > 5 cases and presented clinical endpoints, patient related outcomes, or resource utilisation in NSTI patients. Studies did not have to include intervention. Two independent researchers then extracted reported outcome measures. Similar outcomes were grouped and classified into domains to produce a structured inventory. An attempt was made to identify trends in outcome measures over time and by study design. Results: Three hundred and seventy-five studies were identified and included a total of 311 outcome measures. Forty eight percent (150/311) of outcome measures were reported by two or more studies. The four most frequently reported outcome measures were mortality without time specified, length of hospital stay, amputation performed, and number of debridements, reported in 298 (79.5%), 260 (69.3%), 156 (41.6%) and 151 (40.3%) studies respectively. Mortality outcomes were reported in 23 different ways. Randomised controlled trials (RCTs) were more likely to report 28-day mortality or 90-day mortality. The second most frequent amputation related outcome was level of amputation, reported in 7.5% (28/375) of studies. The most commonly reported patient-centred outcome was the SF-36 which was reported in 1.6% (6/375) of all studies and in 2/10 RCTs. Conclusions: There was wide variance in outcome measures in NSTI studies, further highlighting the need for a COS.

The Effect of the COVID-19 Pandemic on Non-COVID-19 Deaths: Population-Wide Retrospective Cohort Study.

BACKGROUND: Health care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths. OBJECTIVE: This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong. METHODS: This was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits. RESULTS: Compared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths. CONCLUSIONS: We demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences.

In vitro and in vivo models define a molecular signature reference for human embryonic notochordal cells.

Understanding the emergence of human notochordal cells (NC) is essential for the development of regenerative approaches. We present a comprehensive investigation into the specification and generation of bona fide NC using a straightforward pluripotent stem cell (PSC)-based system benchmarked with human fetal notochord. By integrating in vitro and in vivo transcriptomic data at single-cell resolution, we establish an extended molecular signature and overcome the limitations associated with studying human notochordal lineage at early developmental stages. We show that TGF-ß inhibition enhances the yield and homogeneity of notochordal lineage commitment in vitro. Furthermore, this study characterizes regulators of cell-fate decision and matrisome enriched in the notochordal niche. Importantly, we identify specific cell-surface markers opening avenues for differentiation refinement, NC purification, and functional studies. Altogether, this study provides a human notochord transcriptomic reference that will serve as a resource for notochord identification in human systems, diseased-tissues modeling, and facilitating future biomedical research.

scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

A single-cell atlas of conventional central chondrosarcoma reveals the role of endoplasmic reticulum stress in malignant transformation.

The transformation of benign lesions to malignant tumours is a crucial aspect of understanding chondrosarcomas, which are malignant cartilage tumours that could develop from benign chondroid lesions. However, the process of malignant transformation for chondroid lesions remains poorly understood, and no reliable markers are available to aid clinical decision-making. To address this issue, we conducted a study analysing 11 primary cartilage tumours and controls using single-cell RNA sequencing. By creating a single-cell atlas, we were able to identify the role of endoplasmic reticulum (ER) stress in the malignant transformation of conventional central chondrosarcomas (CCCS). Our research revealed that lower levels of ER stress promote chondrosarcoma growth in a patient-derived xenograft mouse model, while intensive ER stress reduces primary chondrosarcoma cell viability. Furthermore, we discovered that the NF-κB pathway alleviates ER stress-induced apoptosis during chondrosarcoma progression. Our single-cell signatures and large public data support the use of key ER stress regulators, such as DNA Damage Inducible Transcript 3 (DDIT3; also known as CHOP), as malignant markers for overall patient survival. Ultimately, our study highlights the significant role that ER stress plays in the malignant transformation of cartilaginous tumours and provides a valuable resource for future diagnostic markers and therapeutic strategies.

Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data.

The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12 vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.

PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells.

Programmed death ligand 1 (PD-L1) serves as a pivotal immune checkpoint in both the innate and adaptive immune systems. PD-L1 is expressed in macrophages in response to IFNγ. We examined whether PD-L1 might regulate macrophage development. We established PD-L1 KO (CD274 -/- ) human pluripotent stem cells and differentiated them into macrophages and observed a 60% reduction in CD11B+CD45+ macrophages in CD274 -/- ; this was orthogonally verified, with the PD-L1 inhibitor BMS-1166 reducing macrophages to the same fold. Single-cell RNA sequencing further confirmed the down-regulation of the macrophage-defining transcription factors SPI1 and MAFB Furthermore, CD274 -/- macrophages reduced the level of inflammatory signals such as NF-κB and TNF, and chemokine secretion of the CXCL and CCL families. Anti-inflammatory TGF-ß was up-regulated. Finally, we identified that CD274 -/- macrophages significantly down-regulated interferon-stimulated genes despite the presence of IFNγ in the differentiation media. These data suggest that PD-L1 regulates inflammatory programs of macrophages from human pluripotent stem cells.

Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models.

The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.

Multi-Level Biomarkers for Early Diagnosis of Ischaemic Stroke: A Systematic Review and Meta-Analysis.

Blood biomarkers hold potential for the early diagnosis of ischaemic stroke (IS). We aimed to evaluate the current weight of evidence and identify potential biomarkers and biological pathways for further investigation. We searched PubMed, EMBASE, the Cochrane Library and Web of Science, used R package meta4diag for diagnostic meta-analysis and applied Gene Ontology (GO) analysis to identify vital biological processes (BPs). Among 8544 studies, we included 182 articles with a total of 30,446 participants: 15675 IS, 2317 haemorrhagic stroke (HS), 1798 stroke mimics, 846 transient ischaemic attack and 9810 control subjects. There were 518 pooled biomarkers including 203 proteins, 114 genes, 108 metabolites and 88 transcripts. Our study generated two shortlists of biomarkers for future research: one with optimal diagnostic performance and another with low selection bias. Glial fibrillary acidic protein was eligible for diagnostic meta-analysis, with summary sensitivities and specificities for differentiating HS from IS between 3 h and 24 h after stroke onset ranging from 73% to 80% and 77% to 97%, respectively. GO analysis revealed the top five BPs associated with IS. This study provides a holistic view of early diagnostic biomarkers in IS. Two shortlists of biomarkers and five BPs warrant future investigation.

MOF-mediated acetylation of SIRT6 disrupts SIRT6-FOXA2 interaction and represses SIRT6 tumor-suppressive function by upregulating ZEB2 in NSCLC.

Mammalian sirtuin 6 (SIRT6) regulates a spectrum of vital biological processes and has long been implicated in the progression of cancer. However, the mechanisms underlying the regulation of SIRT6 in tumorigenesis remain elusive. Here, we report that the tumor-suppressive function of SIRT6 in non-small cell lung cancer (NSCLC) is regulated by acetylation. Specifically, males absent on the first (MOF) acetylates SIRT6 at K128, K160, and K267, resulting in a decreased deacetylase activity of SIRT6 and attenuated SIRT6 tumor-suppressive function in NSCLC. Mechanistically, MOF-mediated SIRT6 acetylation hinders the interaction between SIRT6 and transcriptional factor FOXA2, which in turn leads to the transcriptional activation of ZEB2, thus promoting NSCLC progression. Collectively, these data indicate an acetylation-dependent mechanism that modulates SIRT6 tumor-suppressive function in NSCLC. Our findings suggest that the MOF-SIRT6-ZEB2 axis may represent a promising therapeutic target for the management of NSCLC.

Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER.

Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.

DCATS: differential composition analysis for flexible single-cell experimental designs.

Differential composition analysis - the identification of cell types that have statistically significant changes in abundance between multiple experimental conditions - is one of the most common tasks in single cell omic data analysis. However, it remains challenging to perform differential composition analysis in the presence of flexible experimental designs and uncertainty in cell type assignment. Here, we introduce a statistical model and an open source R package, DCATS, for differential composition analysis based on a beta-binomial regression framework that addresses these challenges. Our empirical evaluation shows that DCATS consistently maintains high sensitivity and specificity compared to state-of-the-art methods.

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

Association of Molnupiravir and Nirmatrelvir-Ritonavir with reduced mortality and sepsis in hospitalized omicron patients: a territory-wide study.

This study evaluates the association between antivirals (Molnupiravir and Nirmatrelvir-Ritonavir) and all-cause and respiratory mortality and organ dysfunction among high-risk COVID-19 patients during an Omicron outbreak. Two cohorts, Nirmatrelvir-Ritonavir versus control and Molnupiravir versus control, were constructed with inverse probability treatment weighting to balance baseline characteristics. Cox proportional hazards models evaluated the association of their use with all-cause mortality, respiratory mortality, and all-cause sepsis (a composite of circulatory shock, respiratory failure, acute liver injury, coagulopathy, and acute liver impairment). Patients recruited were hospitalized and diagnosed with the COVID-19 Omicron variant between February 22, 2022 and April 15, 2022, and followed up until May 15, 2022. The study included 17,704 patients. There were 4.67 and 22.7 total mortalities per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio, - 18.1 [95% CI - 23.0 to - 13.2]; hazard ratio, 0.18 [95% CI, 0.11-0.29]). There were 6.64 and 25.9 total mortalities per 1000 person-days in the Molnupiravir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 19.3 [95% CI - 22.6 to - 15.9]; hazard ratio, 0.23 [95% CI 0.18-0.30]). In all-cause sepsis, there were 13.7 and 35.4 organ dysfunction events per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 21.7 [95% CI - 26.3 to - 17.1]; hazard ratio, 0.44 [95% CI 0.38-0.52]). There were 23.7 and 40.8 organ dysfunction events in the Molnupiravir and control groups respectively before adjustment (weighted incidence ratio per 1000 person-days, - 17.1 [95% CI, - 20.6 to - 13.6]; hazard ratio, 0.63 [95% CI 0.58-0.69]). Among COVID-19 hospitalized patients, use of either Nirmatrelvir-Ritonavir or Molnupiravir compared with no antiviral use was associated with a significantly lower incidence of 28-days all-cause and respiratory mortality and sepsis.