Thrombotic microangiopathy with kidney involvement in a patient with coronavirus disease 2019: A case report and literature review (preprint)

Background and aim: Millions of people worldwide have suffered from coronavirus disease 2019 (COVID-19). COVID-19 can lead to coagulopathy and thrombosis, presenting as pulmonary artery thromboembolism, deep vein thrombosis, and thrombotic microangiopathy (TMA), the latter being a rare finding in affected patients’ kidneys. Prior reports have rarely addressed the pathophysiology, clinical presentations, and therapeutic options in patients with COVID-19-associated TMA. Case presentation: We herein described a case of renal biopsy-proven TMA after COVID-19 in a 36-year-old woman. Initial examination revealed inflammation, acute kidney injury (AKI), anemia, and thrombocytopenia. She was diagnosed with hemolytic uremic syndrome, pulmonary infection, and COVID-19. After treatment, her condition stabilized but remained hemodialysis-dependent after discharge. One week later, she was re-hospitalized, and physical examination showed anemia and bilateral lower extremity edema. Abdominal ultrasound showed increased bilateral kidney echogenicity. Whole-exome sequencing detected an unknown variant of the C3 gene associated with hemolytic uremic syndrome susceptibility type 5/complement C3 deficiency. Kidney biopsy showed renal artery lesions, including small arteriole endothelial swelling, intimal thickening, mucinous degeneration, luminal occlusion, and small arterial wall necrosis. She received plasma exchange and steroids with significant renal function recovery. Conclusion: TMA likely contributed to AKI after COVID-19,thus supporting the notion that TMA plays an important role in the pathogenesis of COVID-19-related kidney injury. When diagnosing and treating COVID-19 patients with abnormal renal function, clinicians should incorporate kidney biopsy and genetic testing for the complement system, identify renal-limited and systemic TMA, and treat accordingly, which can improve patient outcomes.

Predictors of Alcohol Use Among Latinx Men in South Florida: Machismo as a Correlate of Alcohol Use Frequency and Quantity.

Previous studies have found Latinx cultural values to be positively associated with healthy behaviors. This study aims to examine socioeconomic and cultural correlates of alcohol use among Latinx adult men living in Miami-Dade County, Florida. The study sample included 122 Latinx adult men (mean age = 44, SD = 10), predominantly of South and Central American origin. Data was collected using REDCap. Interviews included the Timeline Follow-Back scale for alcohol use. Results indicate that Caribbean participants were significantly less likely to report drinking in the past 90 days (aOR = 0.08, p = 0.042) compared to their Venezuelan counterparts. Higher machismo scores were associated with low drinking frequency (aRR = 0.67, p = 0.043), while no significant associations were found between machismo and other drinking outcomes. Drinking quantity and frequency are significantly associated with higher income and authorized immigration status in the US among Latinx men in South Florida. Higher machismo scores were associated with low drinking frequency.

HIV Care Access During the COVID-19 Pandemic as Perceived by Racial/Ethnic Minority Groups Served by the Ryan White Program, Miami-Dade County, Florida.

The Ryan White Program (RWP) in Miami-Dade County, Florida made several modifications to keep HIV care accessible during the COVID-19 Pandemic, including expanding telehealth services, increasing access to HIV medications, and waiving required lab tests for service recertification. We assessed ease of access to medical providers, medical case managers, and antiretroviral medications during the COVID-19 Pandemic among 298 Non-Hispanic Black, Hispanic, and Haitian people with HIV (PWH) served by the RWP Part A, Miami-Dade County, Florida using a telephone-administered survey between October 2020 and January 2021. Overall, most clients reported similar or better access compared to before the Pandemic. Use of videocalls to communicate with HIV medical providers varied by race/ethnicity: Hispanics (49.6%), Non-Hispanic Blacks (37.7%), and Haitian clients (16.0%). Results suggest the modifications helped maintain access to care during an unprecedented health crisis. Permanently adopting many of these modifications should be considered to continue to facilitate access to care.

Total Infectomes Characterization of Respiratory Infections in pre-COVID-19 Wuhan, China (preprint)

At the end of 2019 Wuhan witnessed an outbreak of “atypical pneumonia” that later developed into a global pandemic. Metagenomic sequencing rapidly revealed the causative agent of this outbreak to be a novel coronavirus - SARS-CoV-2. Herein, to provide a snapshot of the pathogens in pneumonia-associated respiratory samples from Wuhan prior to the emergence of SARS-CoV-2, we collected bronchoalveolar lavage fluid samples from 408 patients presenting with pneumonia and acute respiratory infections at the Central Hospital of Wuhan between 2016 and 2017. Unbiased total RNA sequencing was performed to reveal their “total infectome”, including viruses, bacteria and fungi. Consequently, we identified 37 pathogen species, comprising 15 RNA viruses, 3 DNA viruses, 16 bacteria and 3 fungi, often at high abundance and including multiple co-infections (12.8%). However, SARS-CoV-2 was not present. These data depict a stable core infectome comprising common respiratory pathogens such as rhinoviruses and influenza viruses, an atypical respiratory virus (EV-D68), and a single case of a sporadic zoonotic pathogen – Chlamydia psittaci . Samples from patients experiencing respiratory disease on average had higher pathogen abundance than healthy controls. Phylogenetic analyses of individual pathogens revealed multiple origins and global transmission histories, highlighting the connectedness of the Wuhan population. This study provides a comprehensive overview of the pathogens associated with acute respiratory infections and pneumonia, which were more diverse and complex than obtained using targeted PCR or qPCR approaches. These data also suggest that SARS-CoV-2 or closely related viruses were absent from Wuhan in 2016-2017.

High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor (preprint)

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 uM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers (preprint)

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

Scalable HPC and AI Infrastructure for COVID-19 Therapeutics (preprint)

COVID-19 has claimed more 1 million lives and resulted in over 40 million infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. In response, the DOE recently established the Medical Therapeutics project as part of the National Virtual Biotechnology Laboratory, and tasked it with creating the computational infrastructure and methods necessary to advance therapeutics development. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation and characterize their performance, and highlight science advances that these capabilities have enabled.

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads (preprint)

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.

Prevalence and impact of burnout, secondary traumatic stress and compassion satisfaction on hand hygiene of healthcare workers in medical aid team during COVID-19 pandemic (preprint)

Background Healthcare workers suffered mental burden, especially in the period of COVID-19. Professional quality of life quality is suitable to measure how healthcare workers feel in medical aid team. Current evidence of impact of professional quality of life on hand hygiene behavior or even IPC measures was limited, especially in emerging infectious disease period. This study aimed to assess the prevalence of burnout, secondary traumatic stress and compassion satisfaction and explore their impact on self-reported hand hygiene behavior among medical aid team in Wuhan, China, where strict management was conducted to prevent COVID-19 spread and guarantee healthcare workers’ health. Results A cross-sectional study was conducted using online questionnaire covering professional quality of life and self-reported hand hygiene behavior based on COVID-19 guideline. A total of 1,734 healthcare workers were surveyed. The prevalence of burnout, secondary trauma and compassion satisfaction were low and average levels (69.61 and 30.39%), low and average levels (33.33 and 66.21%), average and high levels (49.65 and 49.71%), respectively. Burnout was negatively associated with overall hand hygiene (Coef. =-0.088, p<0.001), low hand hygiene (Coef. =-0.109, p<0.001), medium hand hygiene (Coef. =-0.088, p<0.001) and high hand hygiene (Coef. =-0.065, p<0.001). Conclusions Healthcare workers with higher compassion satisfaction reported higher hand hygiene compared to the lower. Healthcare workers in medical aid team experience lower level burnout, and higher level of compassion satisfaction during COVID-19 pandemic compared to the general period. The lower burnout and higher compassion satisfaction are associated with higher self-reported hand hygiene behavior. Burnout and compassion satisfaction in healthcare workers should be emphasize and need interventions targeting. The management of healthcare workers in Wuhan, China may be constructive for the future medical aid team.

Validation of reported risk factors for disease classification and prognosis in COVID-19: a descriptive and retrospective study (preprint)

Risk indicators viral load (ORF1ab Ct), lymphocyte percentage (LYM%), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and lactic acid (LA) in COVID-19 patients have been proposed in recent studies. However, the predictive effects of those indicators on disease classification and prognosis remains largely unknown. We dynamically measured those reported indicators in 132 cases of COVID-19 patients including the moderate-cured (moderated and cured), severe-cured (severe and cured) and critically ill (died). Our data showed that CRP, PCT, IL-6, LYM%, lactic acid and viral load could predict prognosis and guide classification of COVID-19 patients in different degrees. CRP, IL-6 and LYM% were more effective than other three factors in predicting prognosis. For disease classification, CRP and LYM% were sensitive in identifying the types between critically ill and severe (or moderate). Notably, among the investigated factors, LYM% was the only one that could distinguish between the severe and moderate types. Collectively, we concluded that LYM% was the most sensitive and reliable predictor for disease typing and prognosis. During the COVID-19 pandemic, the precise classification and prognosis prediction are critical for saving the insufficient medical resources, stratified treatment and improving the survival rate of critically ill patients. We recommend that LYM% be used independently or in combination with other indicators in the management of COVID-19.

A special case of COVID-19 with long duration of viral shedding for 49 days (preprint)

Prolonged viral shedding is associated with severe status and poor prognosis of COVID-19 patients. Unexpectedly, here we report a non-severe patient with the longest duration of viral shedding. According to the investigation on the clinical and epidemiological information of this case, we concluded that this type of virus might have a low toxicity and transmissibility, but have a prolonged infective ability and was hardly to be eliminated in the body with regular therapy. However, infusion of plasma from recovered patients showed high efficiency in elimination of this virus. Our findings might shed light on the management of COVID-19.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

Evaluation of Nucleocapsid and Spike Protein-based ELISAs for detecting antibodies against SARS-CoV-2 (preprint)

Background: At present, PCR-based nucleic acid detection cannot meet the demands for coronavirus infectious disease (COVID-19) diagnosis. Methods: 214 confirmed COVID-19 patients who were hospitalized in the General Hospital of Central Theater Command of the People's Liberation Army between January 18 and February 26, 2020, were recruited. Two Enzyme-Linked Immunosorbent Assay (ELISA) kits based on recombinant SARS-CoV-2 nucleocapsid protein (rN) and spike protein (rS) were used for detecting IgM and IgG antibodies, and their diagnostic feasibility was evaluated. Results: Among the 214 patients, 146 (68.2%) and 150 (70.1%) were successfully diagnosed with the rN-based IgM and IgG ELISAs, respectively; 165 (77.1%) and 159 (74.3%) were successfully diagnosed with the rS-based IgM and IgG ELISAs, respectively. The positive rates of the rN-based and rS-based ELISAs for antibody (IgM and/or IgG) detection were 80.4% and 82.2%, respectively. The sensitivity of the rS-based ELISA for IgM detection was significantly higher than that of the rN-based ELISA. We observed an increase in the positive rate for IgM and IgG with an increasing number of days post-disease onset (d.p.o.), but the positive rate of IgM dropped after 35 d.p.o. The positive rate of rN-based and rS-based IgM and IgG ELISAs was less than 60% during the early stage of the illness 0-10 d.p.o., and that of IgM and IgG was obviously increased after 10 d.p.o. Conclusions: ELISA has a high sensitivity, especially for the detection of serum samples from patients after 10 d.p.o, it can be an important supplementary method for COVID-19 diagnosis.

Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study (preprint)

BackgroundCoronavirus disease-2019 (COVID-19) is a rapidly escalating epidemic caused by SARS-CoV-2. Identification of a simple and effective indicator to assess disease severity and prognosis is urgently needed. MethodsDynamic changes of blood lymphocyte percentage (LYM%) in 15 death cases, 15 severe cases as well as 40 moderate cases of COVID-19 patients were retrospectively analyzed. A Time-LYM% model (TLM) was established according to the descriptive studies and was validated in 92 hospitalized cases. ResultsResults from death and severe cases showed that LYM% in blood tests were inversely associated with the severity and prognosis of COVID-19. LYM% in moderate type of patients with COVID-19 remained higher than 20% 10-12 days after symptom onset. In contrast, LYM% was lower than 20% in severe cases. However, LYM% in severe cases was higher than 5% 17-19 days after the onset of the disease, while it fell below 5% in death cases. Accordingly, we established a Time-LYM% model (TLM), which was validated as an independent criterion of disease classification in another 92 hospitalized patients with COVID-19. ConclusionLymphopenia can be used as an indicator of disease severity and prognosis of COVID-19 patients. TLM is worth of application in the clinical practice.