Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases (preprint)

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. We introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This innovative tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI adeptly learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI’s cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI’s predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes (preprint)

Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developed DENetwork. This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model, DENetwork enhances the capabilities of traditional differential gene analysis tools, such as DESeq2. Our application of DENetwork to an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms. DENetwork offers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.

RAMEN identifies effective indicators for severe COVID and Long COVID patients (preprint)

The outbreak of the COVID-19 pandemic caused catastrophic socioeconomic consequences and fundamentally reshaped the lives of billions across the globe. Our current understanding of the relationships between clinical variables (demographics, symptoms, follow-up symptoms, comorbidities, treatments, lab results, complications, and other clinical measurements) and COVID-19 outcomes remains obscure. Various computational approaches have been employed to elucidate the relationships between different COVID-19 clinical variables and their contributions to the disease outcomes. However, it is often challenging to capture the indirect relationships, as well as the direction of those relationships, with the conventional pairwise correlation methods. Graphical models (e.g., Bayesian networks) can address these limitations but are computationally expensive, which substantially limits their applications in reconstructing relationship networks ofumpteen clinical variables. In this study, we have developed a method named RAMEN, which employs Genetic Algorithm and random walks to infer the Bayesian relationship network between clinical variables. We applied RAMEN to a comprehensive COVID-19 dataset, Biobanque Quebecoise de la COVID-19 (BQC19). Most of the clinical variables in our reconstructed Bayesian network associated with COVID-19 severity, or long COVID, are supported by existing literature. We further computationally verified the effectiveness of the RAMEN method with statistical examinations of the multi-omics measurements (Clinical variables, RNA-seq, and Somascan) of the BQC19 data and simulations. The accurate inference of the relationships between clinical variables and disease outcomes powered by RAMEN will significantly advance the development of effective and early diagnostics of severe COVID-19 and long COVID, which can help save millions of lives.

Novel mRNA vaccines encoding Monkeypox virus M1R and A35R protect mice from a lethal virus challenge (preprint)

The outbreak of Monkeypox virus infection urgently need effective vaccines. However, the vaccines so far approved are all based on whole-virus, which raises safety concerns. MRNA vaccines has demonstrated its high efficacy and safety against SARS-Cov-2 infection. Here, we developed three mRNA vaccines encoding Monkeypox proteins M1R and A35R, including A35R-M1R fusions (VGPox1 and VGPox 2) and a combination of encapsulated full-length mRNAs for A35R and M1R (VGPox 3). All three vaccines induced anti-A35R total IgGs as early as day 7 following a single vaccination. However, only VGPox 1 and 2 produced anti-M1R total IgGs at early dates following vaccination while VGPox 3 did not show significant anti-M1R antibody till day 35. Similar results were also found in neutralizing antibodies and T cell immune response. However, all mRNA vaccine groups completely protected mice from a lethal dose virus challenge and effectively cleared virus in lungs. Collectively, our results indicate that the novel mRNA vaccines coding for a fusion protein of A35R and M1R had a better anti-virus immunity than co-expression of the two individual proteins. The mRNA vaccines are highly effective and can be an alternative to the current whole-virus vaccines to defend Monkeypox virus infection.

Heterologous boost with mRNA vaccines against SARS-CoV-2 Delta/Omicron variants following an inactivated whole-virus vaccine (preprint)

The coronavirus SARS-CoV-2 has mutated quickly and caused significant global damage. This study characterizes two mRNA vaccines ZSVG-02 (Delta) and ZSVG-02-O (Omicron BA.1), and associating heterologous prime-boost strategy following the prime of a most widely administrated inactivated whole-virus vaccine (BBIBP-CorV). The ZSVG-02-O induces neutralizing antibodies that effectively cross-react with Omicron subvariants following an order of BA.1>BA.2>BA.4/5. In native animals, ZSVG-02 or ZSVG-02-O induce humoral responses skewed to the vaccine's targeting strains, but cellular immune responses cross-react to all variants of concern (VOCs) tested. Following heterologous prime-boost regimes, animals present comparable neutralizing antibody levels and superior protection across all VOCs. Single-boost only generated ancestral and omicron dual-responsive antibodies, probably by "recall" and "reshape" the prime immunity. New Omicron-specific antibody populations, however, appeared only following the second boost with ZSVG-02-O. Overall, our results support a heterologous boost with ZSVG-02-O, providing the best protection against current VOCs in inactivated virus vaccine-primed populations.

Electrochemical Oxygen Generator With 99.9% Oxygen Purity and High Energy Efficiency

Under the growing crisis of the coronavirus disease 2019 pandemic, the global medical system is facing the predicament of an acute shortage of medical‐grade oxygen (O2, ≥ 99.5% purity). Herein, an oxygen generation device is manufactured that relies on electrochemical technology. The performance of the electrochemical oxygen generator (EOG) is remarkably improved to a practically applicable level, achieving long‐term (>200 h), stable, and quick production (>1.5 L min−1) of high purity O2 (99.9%) at high energy efficiency (496 L kW−1 h−1), via simultaneous optimization for intrinsic electrochemical reaction mechanisms, electrocatalysts, and external cell structure. The EOG also presents powerful competitiveness in user experience, which finds expression in high portability (4.7 kg), nearly instant O2 production (<1 s), and a quiet working condition (<39 dB). The EOG shows great potential to substitute commercial pressure swing adsorption O2 generation devices, which may significantly impact the traditional oxygen production industry. [ FROM AUTHOR] Copyright of Advanced Energy Materials is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia in Young People: Preliminary Findings from Systematic Review and Meta-Analysis.

Various forms of cognitive behavioral therapy for insomnia (CBT-i) have been developed to improve its scalability and accessibility for insomnia management in young people, but the efficacy of digitally-delivered cognitive behavioral therapy for insomnia (dCBT-i) remains uncertain. This study systematically reviewed and evaluated the effectiveness of dCBT-i among young individuals with insomnia. We conducted comprehensive searches using four electronic databases (PubMed, Cochrane Library, PsycINFO, and Embase; until October 2021) and examined eligible records. The search strategy comprised the following three main concepts: (1) participants were adolescents or active college students; (2) dCBT-I was employed; (3) standardized tools were used for outcome measurement. Four randomized controlled trials qualified for meta-analysis. A significant improvement in self-reported sleep quality with a medium-to-large effect size after treatment (Hedges's g = -0.58~-0.80) was noted. However, a limited effect was detected regarding objective sleep quality improvement (total sleep time and sleep efficiency measured using actigraphy). These preliminary findings from the meta-analysis suggest that dCBT-i is a moderately effective treatment in managing insomnia in younger age groups, and CBT-i delivered through the web or a mobile application is an acceptable approach for promoting sleep health in young people.

Novel Coronavirus Pandemic Impacts Children and Adolescents' Psychological Well-Being in Heavily Hit Chinese Provinces (preprint)

AbstractsIn light of the novel coronaviruss (COVID-19s) threat to public health worldwide, we sought to elucidate COVID-19s impacts on the mental health of children and adolescents in China. Through online self-report questionnaires, we aimed to discover the psychological effects of the pandemic and its associated risk factors for developing mental health symptoms in young people. We disseminated a mental health survey through online social media, WeChat, and QQ in the five Chinese provinces with the most confirmed cases of COVID-19 during the late stage of the country-wide lockdown. We used a self-made questionnaire that queried children and adolescents aged 6 to 18 on demographic information, psychological status, and other lifestyle and COVID-related variables. A total of 17,740 children and adolescents with valid survey data participated in the study. 10,022 (56.5%), 11,611 (65.5%), 10,697 (60.3%), 6,868 (38.7%), and 6,225 (35.1%) participants presented, respectively, more depressive, anxious, compulsive, inattentive, and sleep-related problems compared to before the outbreak of COVID-19. High school students reported a greater change in depression and anxiety than did middle school and primary school students. Despite the fact that very few children (0.1%) or their family members (0.1%) contracted the virus in this study, the psychological impact of the pandemic was clearly profound. Fathers anxiety appeared to have the strongest influence on a childrens psychological symptoms, explaining about 33% of variation in the childs overall symptoms. Other factors only explained less than 2% of the variance in symptoms once parents anxiety was accounted for. The spread of COVID-19 significantly influenced the psychological state of children and adolescents. It is clear that children and adolescents, particularly older adolescents, need mental health support during the pandemic. The risk factors we uncovered suggest that reducing fathers anxiety is particularly critical to addressing young peoples mental health disorders in this time.

A network-informed analysis of SARS-CoV-2 and hemophagocytic lymphohistiocytosis genes' interactions points to Neutrophil Extracellular Traps as mediators of thrombosis in COVID-19 (preprint)

Abnormal coagulation and an increased risk of thrombosis are features of severe COVID-19, with parallels proposed with hemophagocytic lymphohistiocytosis (HLH), a life-threating condition associated with hyperinflammation. The presence of HLH was described in severely ill patients during the H1N1 influenza epidemic, presenting with pulmonary vascular thrombosis. We tested the hypothesis that genes causing primary HLH regulate pathways linking pulmonary thromboembolism to the presence of SARS-CoV-2 using novel network-informed computational algorithms. This approach led to the identification of Neutrophils Extracellular Traps (NETs) as plausible mediators of vascular thrombosis in severe COVID-19 in children and adults. Taken together, the network-informed analysis led us to propose the following model: the release of NETs in response to inflammatory signals acting in concert with SARS-CoV-2 damage the endothelium and direct platelet-activation promoting abnormal coagulation leading to serious complications of COVID-19. The underlying hypothesis is that genetic and/or environmental conditions that favor the release of NETs may predispose individuals to thrombotic complications of COVID-19 due to an increase risk of abnormal coagulation. This would be a common pathogenic mechanism in conditions including autoimmune/infectious diseases, hematologic and metabolic disorders.

Reconstructing SARS-CoV-2 response signaling and regulatory networks (preprint)

Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify the key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins though some were shown to impact other coronaviruses highlighting the importance of large scale data integration for understanding virus and host activity.