Tlda: A Transfer Learning Based Dual-Augmentation Strategy for Traditional Chinese Medicine Rare Disease Syndrome Differentiation (preprint)

The Traditional Chinese Medicine (TCM) has demonstrated its significant medical value over the decades, particularly during the COVID-19 pandemic. TCM-AI interdisciplinary models have been proposed to model TCM knowledge, diagnosis, and treatment experiments in clinical practice. Among them, numerous models have been developed to simulate the syndrome differentiation process of human TCM doctors for automatic syndrome diagnosis. However, these models are designed for normal scenarios and trained using a supervised learning paradigm which needs tens of thousands of training samples. They fail to effectively differentiate syndromes in rare disease scenarios where the available TCM electronic medical records (EMRs) are very limited for each unique syndrome. To address the challenge of rare diseases, this study proposes a simple yet effective method called Transfer Learning based Dual-Augmentation (TLDA). TLDA aims to augment the limited EMRs at both the sample-level and feature-level, enriching the pathological and medical information during training. Extended experiments involving 11 comparison models, including the state-of-the-art model, demonstrate the effectiveness of TLDA. TLDA outperforms all comparison models by a significant margin. Furthermore, TLDA can also be extended to other medical tasks when the EMRs for diagnosis are limited in samples.

Antiviral drug design based on structural insights into the N-terminal domain and C-terminal domain of the SARS-CoV-2 nucleocapsid protein.

Nucleocapsid (N) protein plays crucial roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including the formation of ribonucleoprotein (RNP) complex with the viral RNA. Here we reported the crystal structures of the N-terminal domain (NTD) and C-terminal domain (CTD) of the N protein and an NTD-RNA complex. Our structures reveal a unique tetramer organization of NTD and identify a distinct RNA binding mode in the NTD-RNA complex, which could contribute to the formation of the RNP complex. We also screened small molecule inhibitors of N-NTD and N-CTD and discovered that ceftriaxone sodium, an antibiotic, can block the binding of RNA to NTD and inhibit the formation of the RNP complex. These results together could facilitate the further research of antiviral drug design targeting N protein.

Estimates of Power Shortages and Affected Populations during the Initial Period of the Ukrainian-Russian Conflict

Since the outbreak of the Ukrainian-Russian conflict on 24 February 2022, Ukraine’s economy, society, and cities have been devastated and struck on multiple fronts, with large numbers of refugees fleeing to neighboring countries. The lighting systems in Ukrainian cities have been severely restricted due to Russian missile bombing and curfew policies. The power shortages adversely affected the livelihoods of the Ukrainian residents dramatically. For a timely assessment of the power shortages’ extent and the affected population in Ukraine, this study tracked the dynamics of nighttime light emissions in Ukraine based on the newly developed daily Black Marble product (VNP46A2) from NASA. The results show that the average light radiance in Ukrainian urban areas has decreased by about 37% since the eruption of the war, with Kiev city being the most dramatic region, having a post-conflict decrease of about 51%. In addition, by introducing near-real-time population data, we have implemented a survey of the affected population in Ukraine suffering from war-induced power shortages. Estimates show that about 17.3 million Ukrainian residents were affected by power shortages. In more detail, the number of children under 10 years old was about 2.35 million (about 5.24% of the total population), while the number of elderly people over 60 years old was about 3.53 million (about 7.86% of the total population). Generally, the results of this study could contribute positively to the timely assessment of the impact of the conflict and the implementation of humanitarian relief.

Recent Trends in AI-Based Intelligent Sensing

In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field.

Thinking like a structural biologist: A pocket-based 3D molecule generative model fueled by electron density (preprint)

We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets including kinase (HPK1), protease (Covid19-3CL), and nuclear receptor (VDR), and evaluated with a reference dataset composed of over 8,000 compounds that have their activities reported in the literature. The evaluation examined the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can reproduce classical active compounds and can also generate novel molecules with similar binding modes as active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create .

Clinical Characteristics and Risk Factors for Acute Kidney Injury in COVID-19

Objective: The objective of the study is to describe the clinical characteristics, risk factors, and prognosis for acute kidney injury (AKI) among patients with coronavirus disease (COVID-19). Methods: Retrospective study of 456 consecutive patients with confirmed COVID-19 infection at the whole hospital from January 1 to March 1, 2020 was enrolled. Demographic, clinical characteristics, the risk factors, and prognosis were collected and analyzed. Results: Of 456 patients with COVID-19, 38 patients developed AKI. Patients with AKI were older and predominantly male sex and were more likely to have comorbidities such as hypertension, cardiovascular, and cerebrovascular diseases. Among patients with AKI, the white blood cell count, neutrophil count, neutrophil-to-lymphocyte ratio, alanine aminotransferase, and C-reaction protein were increased, and lymphocyte and platelet count were decreased. Multivariate analysis showed that age, hypertension, and lymphocyte count were independent risk factors for AKI. The overall mortality rate of 456 patients was 9.9%, and the mortality rate of patients with AKI was 23.7%. In particular, increasing AKI severity was associated with increased risk. Conclusions: The risk of AKI was high in patients with COVID-19. Older age, hypertension, and lower lymphocyte count were independent risk factors for AKI. COVID-19-associated AKI was associated with higher risk of death in patients with COVID-19.

Estimation of secondary PM2.5 in China and the United States using a multi-tracer approach

PM2.5, generated via both direct emission and secondary formation, can have varying environmental impacts due to different physical and chemical properties of its components. However, traditional methods to quantify different PM2.5 components are often based on online or offline observations and numerical models, which are generally high economic cost- or labor-intensive. In this study, we develop a new method, named Multi-Tracer Estimation Algorithm (MTEA), to identify the primary and secondary components from routine observation of PM2.5. By comparing with long-term and short-term measurements of aerosol chemical components in China and the United States, it is proven that MTEA can successfully capture the magnitude and variation of the primary PM2.5 (PPM) and secondary PM2.5 (SPM). Applying MTEA to the China National Air Quality Network, we find that (1) SPM accounted for 63.5 % of the PM2.5 in cities in southern China on average during 2014–2018, while the proportion dropped to 57.1 % in the north of China, and at the same time the secondary proportion in regional background regions was ∼ 19 % higher than that in populous regions;(2) the summertime secondary PM2.5 proportion presented a slight but consistent increasing trend (from 58.5 % to 59.2 %) in most populous cities, mainly because of the recent increase in O3 pollution in China;(3) the secondary PM2.5 proportion in Beijing significantly increased by 34 % during the COVID-19 lockdown, which might be the main reason for the observed unexpected PM pollution in this special period;and finally, (4) SPM and O3 showed similar positive correlations in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions, but the correlations between total PM2.5 and O3 in these two regions, as determined from PPM levels, were quite different. In general, MTEA is a promising tool for efficiently estimating PPM and SPM, and has huge potential for future PM mitigation.

Evaluation of multiplex combined real-time PCR detection kit for nucleic acid detection of 22 respiratory pathogens

Objective: To evaluate the detection consistency and power of a multiplex combined real-time PCR detection kits, and provide reference for the prevention and control of influenza plus SARS-CoV-2 infection.

A mathematical model reveals the influence of NPIs and vaccination on SARS-CoV-2 Omicron Variant (preprint)

A SVEIR SARS-CoV-2 Omicron variant model is proposed to provide some insight to coordinate non-pharmaceutical interventions(NPIs) and vaccination. Mathematically, we define the basic reproduction number R0 and the effective reproduction number Re to measure the infection potential of Omicron variant and formulate a optimal disease control strategy. Our inversion results imply that the sick period of Omicron variant in the United States is longer than that of Delta variant in Indian; The decreasing of the infectious period of the infection with infectiousness implies that the risk of hospitalization is reduced; but the increasing period of the infection with non-infectiousness signifies that Omicron variant lengthens the period of nucleic acid test being negative; Optimistically, Omicron's death rate is only a quarter of Delta's. Moreover, we forecast that the cumulative cases will exceed 100 million in the United States on 28 February, 2022 and the daily confirmed cases will reach a peak on 2 February, 2022. The results of parameters sensitivity analysis imply that NPIs is helpful to reduce the number of confirmed cases. Especially, NPIs are indispensable even if all the people were vaccinated when the efficiency of vaccine is relatively low. By simulating the relation ships of the effective reproduction number Re , the vaccination rate and the efficacy of vaccine, we find that it is impossible to achieve the herd immunity without NPIs while the efficiency of vaccine is lower than 88.7%. Therefore, the herd immunity area is defined by the evolution of relationships between the vaccination rate and the efficacy of vaccine. Finally, we present that the disease-induced mortality rate demonstrates the periodic oscillation and an almost periodic function is deduced to match the curve. A discussion completes the paper.AMS Subject Classification (2020): 34A34; 34D20; 92D30

SARS-CoV-2 Viral Genes Compromise Survival and Functions of Human Pluripotent Stem Cell-derived Cardiomyocytes via Reducing Cellular ATP Level (preprint)

Cardiac manifestations are commonly observed in COVID-19 patients and prominently contributed to overall mortality. Human myocardium could be infected by SARS-CoV-2, and human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are susceptible to SARS-CoV-2 infection. However, molecular mechanisms of SARS-CoV-2 gene-induced injury and dysfunction of human CMs remain elusive. Here, we find overexpression of three SARS-CoV-2 coding genes, Nsp6, Nsp8 and M, could globally compromise transcriptome of hPSC-CMs. Integrated transcriptomic analyses of hPSC-CMs infected by SARS-CoV-2 with hPSC-CMs of Nsp6, Nsp8 or M overexpression identified concordantly activated genes enriched into apoptosis and immune/inflammation responses, whereas reduced genes related to heart contraction and functions. Further, Nsp6, Nsp8 or M overexpression induce prominent apoptosis and electrical dysfunctions of hPSC-CMs. Global interactome analysis find Nsp6, Nsp8 and M all interact with ATPase subunits, leading to significantly reduced cellular ATP level of hPSC-CMs. Finally, we find two FDA-approved drugs, ivermectin and meclizine, could enhance the ATP level, and ameliorate cell death and dysfunctions of hPSC-CMs overexpressing Nsp6, Nsp8 or M. Overall, we uncover the global detrimental impacts of SARS-CoV-2 genes Nsp6, Nsp8 and M on the whole transcriptome and interactome of hPSC-CMs, define the crucial role of ATP level reduced by SARS-CoV-2 genes in CM death and functional abnormalities, and explore the potentially pharmaceutical approaches to ameliorate SARS-CoV-2 genes-induced CM injury and abnormalities.

Common mtDNA variations at C5178a and A249d/T6392C/G10310A decrease the risk of severe COVID-19 in a Han Chinese population from Central China.

BACKGROUND: Mitochondria have been shown to play vital roles during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) development. Currently, it is unclear whether mitochondrial DNA (mtDNA) variants, which define mtDNA haplogroups and determine oxidative phosphorylation performance and reactive oxygen species production, are associated with COVID-19 risk. METHODS: A population-based case-control study was conducted to compare the distribution of mtDNA variations defining mtDNA haplogroups between healthy controls (n = 615) and COVID-19 patients (n = 536). COVID-19 patients were diagnosed based on molecular diagnostics of the viral genome by qPCR and chest X-ray or computed tomography scanning. The exclusion criteria for the healthy controls were any history of disease in the month preceding the study assessment. MtDNA variants defining mtDNA haplogroups were identified by PCR-RFLPs and HVS-I sequencing and determined based on mtDNA phylogenetic analysis using Mitomap Phylogeny. Student's t-test was used for continuous variables, and Pearson's chi-squared test or Fisher's exact test was used for categorical variables. To assess the independent effect of each mtDNA variant defining mtDNA haplogroups, multivariate logistic regression analyses were performed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) with adjustments for possible confounding factors of age, sex, smoking and diseases (including cardiopulmonary diseases, diabetes, obesity and hypertension) as determined through clinical and radiographic examinations. RESULTS: Multivariate logistic regression analyses revealed that the most common investigated mtDNA variations (> 10% in the control population) at C5178a (in NADH dehydrogenase subunit 2 gene, ND2) and A249d (in the displacement loop region, D-loop)/T6392C (in cytochrome c oxidase I gene, CO1)/G10310A (in ND3) were associated with a reduced risk of severe COVID-19 (OR = 0.590, 95% CI 0.428-0.814, P = 0.001; and OR = 0.654, 95% CI 0.457-0.936, P = 0.020, respectively), while A4833G (ND2), A4715G (ND2), T3394C (ND1) and G5417A (ND2)/C16257a (D-loop)/C16261T (D-loop) were related to an increased risk of severe COVID-19 (OR = 2.336, 95% CI 1.179-4.608, P = 0.015; OR = 2.033, 95% CI 1.242-3.322, P = 0.005; OR = 3.040, 95% CI 1.522-6.061, P = 0.002; and OR = 2.890, 95% CI 1.199-6.993, P = 0.018, respectively). CONCLUSIONS: This is the first study to explore the association of mtDNA variants with individual's risk of developing severe COVID-19. Based on the case-control study, we concluded that the common mtDNA variants at C5178a and A249d/T6392C/G10310A might contribute to an individual's resistance to developing severe COVID-19, whereas A4833G, A4715G, T3394C and G5417A/C16257a/C16261T might increase an individual's risk of developing severe COVID-19.

First-principles DFT investigations of the vibrational spectra of chloro-quine and hydroxychloroquine

Since the COVID-19 pandemic began, two drugs, chloroquine (CQ) and hydroxychloroquine (HCQ), have received renewed attention. Using the density functional theory method in the CASTEP and DMol3 packages, we calculated both molecules’ infrared spectra and the partial phonon density of states of the hydroxyl group to identify the origin of the differences between the two spectra. Some characteristic vibrational modes of the hydroxyl group in HCQ were analysed individually. We also compared their Fukui functions and found that the oxygen atom in HCQ possesses electrophilic properties. This finding may be related to the large difference in toxicity between these two drugs. The method herein presents a new pathway to investigate organic molecules from the view of physics.

The Impact of Physical Education Classes on Health and Quality of Life during the COVID-19

The purpose of this study was to examine the effects of the COVID-19 pandemic on the well-being and the quality of life of college students participating in physical education classes. We consider this study to be relevant because during the COVID-19 pandemic we tested whether the boost in effective activity among physical education class participants affected well-being and quality of life. A sectional questionnaire survey was conducted across Taiwan in 2019–2020. Data were collected in two stages within 6 months from a sample of 1011 university students in Taiwan (328 male, 683 female). A series of one-way ANOVAs was adopted to examine each outcome across groups and time phases. The results provide support for a positive relationship between well-being (β = 0.25, p <0.001), and quality of life (β = 0.92, p <0.001), supporting our expectations. Findings from our study suggest that physical activity was positively associated with mental health;and participation in physical activity had an effect on the well-being and quality of life in college students.

Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity (preprint)

The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.

Delayed rise of oral fluid antibodies, elevated BMI, and absence of early fever correlate with longer time to SARS-CoV-2 RNA clearance in an longitudinally sampled cohort of COVID-19 outpatients (preprint)

BackgroundSustained molecular detection of SARS-CoV-2 RNA in the upper respiratory tract (URT) in mild to moderate COVID-19 is common. We sought to identify host and immune determinants of prolonged SARS-CoV-2 RNA detection. MethodsNinety-five outpatients self-collected mid-turbinate nasal, oropharyngeal (OP), and gingival crevicular fluid (oral fluid) samples at home and in a research clinic a median of 6 times over 1-3 months. Samples were tested for viral RNA, virus culture, and SARS-CoV-2 and other human coronavirus antibodies, and associations were estimated using Cox proportional hazards models. ResultsViral RNA clearance, as measured by SARS-CoV-2 RT-PCR, in 507 URT samples occurred a median (IQR) 33.5 (17-63.5) days post-symptom onset. Sixteen nasal-OP samples collected 2-11 days post-symptom onset were virus culture positive out of 183 RT-PCR positive samples tested. All participants but one with positive virus culture were negative for concomitant oral fluid anti-SARS-CoV-2 antibodies. The mean time to first antibody detection in oral fluid was 8-13 days post-symptom onset. A longer time to first detection of oral fluid anti-SARS-CoV-2 S antibodies (aHR 0.96, 95% CI 0.92-0.99, p=0.020) and BMI [≥] 25kg/m2 (aHR 0.37, 95% CI 0.18-0.78, p=0.009) were independently associated with a longer time to SARS-CoV-2 viral RNA clearance. Fever as one of first three COVID-19 symptoms correlated with shorter time to viral RNA clearance (aHR 2.06, 95% CI 1.02-4.18, p=0.044). ConclusionsWe demonstrate that delayed rise of oral fluid SARS-CoV-2-specific antibodies, elevated BMI, and absence of early fever are independently associated with delayed URT viral RNA clearance.

Impact of Pre-Event Testing and Quarantine On Reducing The Risk of COVID-19 Epidemic Rebound: A Modelling Study (preprint)

BACKGROUND With the evolving growth of the COVID-19 epidemic, travel restriction policies would need to be adjusted accordingly. Prohibition of mass event may be relaxed for social and economic benefits when virus transmission stops but could bear the risk of epidemic rebound. Against the background of the varied SARS-CoV-2 prevalence internationally, we modelled the potential impacts of pre-event interventions on epidemic risk of holding a mass event when COVID-19 is under control. METHODS We developed a mathematical model of SARS-CoV-2 transmission in Guangdong Province, China, where local virus transmission ceased to occur. A large-scale international trade fair was assumed to be held, with influx of people from overseas and rest of China over a short period of time, who participated for 2-week. Scenarios of pre-event intervention (none, quarantine arrangement and polymerase chain reaction (PCR) testing for participants) were compared. The influence of contact pattern, SARS-CoV-2 prevalence outside the province and China, and testing coverage were examined in sensitivity analyses. RESULTS In basecase scenario (no event), the epidemic has been under control since March 2020. The event would lead to the detection of 1% more confirmed cases by 31 July when community contact rate increases to pre-epidemic level. In event scenario without additional interventions, there would be 599 (93%) more new infections comparing with basecase scenario. To avert new infections, quarantining all participants before the event would be the most effective strategy, followed by quarantining all overseas participants and testing all other participants, and testing all participants before the event and on day 7. However, testing strategy is likely to be affected by the SARS-CoV-2 prevalence outside the event province.  CONCLUSIONS Pre-event interventions are effective for reducing the risk of epidemic rebound caused by an international large-scale event. Universal testing for participants is likely to be an effective and feasible intervention. 

Impact Of Pre-event Testing And Quarantine On Reducing The Risk Of COVID-19 Epidemic Rebound: A Modelling Study (preprint)

BACKGROUND With the evolving growth of the COVID-19 epidemic, travel restriction policies would need to be adjusted accordingly. Prohibition of mass event may be relaxed for social and economic benefits when virus transmission stops but could bear the risk of epidemic rebound. Against the background of the varied SARS-CoV-2 prevalence internationally, we modelled the potential impacts of pre-event interventions on epidemic risk of holding a mass event when COVID-19 is under control. METHODS We developed a mathematical model of SARS-CoV-2 transmission in Guangdong Province, China, where local virus transmission ceased to occur. A large-scale international trade fair was assumed to be held, with influx of people from overseas and rest of China over a short period of time, who participated for 2-week. Scenarios of pre-event intervention (none, quarantine arrangement and polymerase chain reaction (PCR) testing for participants) were compared. The influence of contact pattern, SARS-CoV-2 prevalence outside the province and China, and testing coverage were examined in sensitivity analyses. RESULTS In basecase scenario (no event), the epidemic has been under control since March 2020. The event would lead to the detection of 1% more confirmed cases by 31 July when community contact rate increased to pre-epidemic level. In event scenario without additional interventions, there would be 599 (93%) more new infections comparing with basecase scenario. To avert new infections, quarantining all participants before the event would be the most effective strategy, followed by quarantining all overseas participants and testing all other participants, and testing all participants before the event and on day 7. However, testing strategy is likely to be affected by the SARS-CoV-2 prevalence outside the event province.  CONCLUSIONS Pre-event interventions are effective for reducing the risk of epidemic rebound caused by an international large-scale event. Universal testing for participants is likely to be an effective and feasible intervention.