Value of urinary al - microglobulin and N -acetyl - D - glucosaminidase/urinary creatinine in monitoring early renal injury in patients with chronic hepatitis B virus - related liver diseases. (Theme Issue: Diagnosis and treatment of liver failure in children.) [Chinese]

Objective: To investigate the value of urinary al - microglobulin (al - MC) and N - acetyl - - D -glucosaminidase/urinary creatinine (NAG/UCr) in monitoring renal injury in patients with chronic hepatitis B virus (HBV) - related liver diseases.

Establishment of a Rapid Detection System for ISG20-Dependent SARS-CoV-2 Subreplicon RNA Degradation Induced by Interferon-α.

Inhaled nebulized interferon (IFN)-α and IFN-ß have been shown to be effective in the management of coronavirus disease 2019 (COVID-19). We aimed to construct a virus-free rapid detection system for high-throughput screening of IFN-like compounds that induce viral RNA degradation and suppress the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We prepared a SARS-CoV-2 subreplicon RNA expression vector which contained the SARS-CoV-2 5'-UTR, the partial sequence of ORF1a, luciferase, nucleocapsid, ORF10, and 3'-UTR under the control of the cytomegalovirus promoter. The expression vector was transfected into Calu-3 cells and treated with IFN-α and the IFNAR2 agonist CDM-3008 (RO8191) for 3 days. SARS-CoV-2 subreplicon RNA degradation was subsequently evaluated based on luciferase levels. IFN-α and CDM-3008 suppressed SARS-CoV-2 subreplicon RNA in a dose-dependent manner, with IC50 values of 193 IU/mL and 2.54 µM, respectively. HeLa cells stably expressing SARS-CoV-2 subreplicon RNA were prepared and treated with the IFN-α and pan-JAK inhibitor Pyridone 6 or siRNA-targeting ISG20. IFN-α activity was canceled with Pyridone 6. The knockdown of ISG20 partially canceled IFN-α activity. Collectively, we constructed a virus-free rapid detection system to measure SARS-CoV-2 RNA suppression. Our data suggest that the SARS-CoV-2 subreplicon RNA was degraded by IFN-α-induced ISG20 exonuclease activity.

Dynamic Blood Tests Reveal Predicted Week of Death And Tricky Week For Surveillance In Severely Ill COVID-19 Patients (preprint)

Background: Blood laboratory tests are the most reliable methods for the diagnosis and assessment of vital organs’ functions and the body’s response to infection. Herein, we compared the results of dynamic blood tests between the survivor and non-survivor group of patients with coronavirus disease 2019 (COVID-19) and aimed to determine the predicted and tricky week for death and surveillance.Methods: The survivor and non-survivor groups were compared using biochemical blood tests, routine blood tests, and coagulation blood tests over four weeks of investigation.Results: Blood urea nitrogen, creatinine, high-sensitivity C-reactive protein, total bile acid, neutrophil count, white blood cell count, D-dimer, fibrin and fibrinogen degradation product, and prothrombin time showed significantly higher levels in the non-survivor group than the survivor group. Only pre-albumin, eosinophil count, lymphocyte count, red blood cell count, platelet count, hemoglobin, and prothrombin activity tests were significantly higher in the survivor group than the non-survivor group. Generally, the third week of the non-survivor’s group could be regarded as the predicted week for death based on all tests except for creatinine, pre-albumin, total bile acid, monocyte count, white blood cell count, and prothrombin activity. The tricky week in the non-survivor group was the second week in all tests except for pre-albumin, basophil count, eosinophil count, lymphocyte count, platelet count, D-dimer, and fibrin and fibrinogen degradation product.Conclusions: Based on our study, specific attention should be given to some weeks with respect to their related tests as predicted or tricky for death or surveillance, respectively.

Semen quality and windows of susceptibility: A case study during COVID-19 outbreak in China.

BACKGROUND: To evaluate the impact of air pollution exposure on semen quality parameters during COVID-19 outbreak in China, and to identify potential windows of susceptibility for semen quality. METHODS: A retrospective observational study was carried out on 1991 semen samples collected between November 23, 2019 and July 23, 2020 (a period covering COVID-19 lock-down in China) from 781 sperm donor candidates at University-affiliated Sichuan Provincial Human Sperm Bank. Multivariate mixed-effects regression models were constructed to investigate the relationship between pollution exposure, windows of susceptibility, and semen quality, while controlling for biographic and meteorologic confounders. RESULT(S): The results indicated multiple windows of susceptibility for semen quality, especially sperm motility, due to ambient pollution exposure. Exposure to particulate matters (PM2.5 and PM10), O3 and NO2 during late stages of spermatogenesis appeared to have weak but positive association with semen quality. Exposure to CO late in sperm development appeared to have inverse relationship with sperm movement parameters. Exposure to SO2 appeared to influence semen quality throughout spermatogenesis. CONCLUSION(S): Potential windows of susceptibility for semen quality varied depending on air pollutants. Sperm motility was sensitive to pollution exposure. Findings from current study further elucidate the importance of sensitive periods during spermatogenesis and provide new evidence for the determinants of male fertility.

Revealing the Transmission Dynamics of COVID-19: A Bayesian Framework for Rt Estimation (preprint)

In epidemiological modelling, the instantaneous reproduction number, Rt, is important to understand the transmission dynamics of infectious diseases. Current Rt estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of Rt. To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for Rt estimation, resulting in the state-of-the-art ‘DARt’ system for Rt estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.

Bayesian data assimilation for estimating epidemic evolution: a COVID-19 study (preprint)

The evolution of epidemiological parameters, such as instantaneous reproduction number Rt, is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to Rt estimation, resulting in the state-of-the-art DARt system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and Rt; the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in revealing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for accurate and timely estimating transmission dynamics from reported data.

Utility of Screening Chest Radiographs in Patients with Asymptomatic or Minimally Symptomatic COVID-19 in Singapore.

Background Singapore saw an escalation of coronavirus disease 2019 (COVID-19) cases from fewer than 4000 in April 2020 to more than 40 000 in June 2020, with most of these cases attributed to spread within shared facilities housing foreign workers. Appropriate triage and escalation of clinical care are crucial for this patient group managed in community care facilities (CCFs). Purpose To evaluate the imaging guideline recommendations for COVID-19 from the Fleischner Society and to analyze the clinical utility of screening chest radiography for asymptomatic or minimally symptomatic patients with COVID-19. Materials and Methods In this retrospective study, patients with reverse-transcription polymerase chain reaction-confirmed COVID-19 who were admitted to a designated CCF for continuation of their treatment during May 3-31, 2020, were identified. Upon admission, patients aged 36 years and older without any baseline chest images underwent chest radiography. All chest radiographs and clinical outcomes of patients, including those who were subsequently transferred to acute hospitals for escalation of care, were reviewed. Key proportions of patients with findings of pulmonary infection and those requiring further inpatient treatment were calculated, and 95% binomial proportion CIs were obtained using the Clopper-Pearson method. Results The study included 5621 patients. All patients were men (100%; 5621 of 5621), and the mean patient age was 37 years ± 8 (range, 17-60 years). A total of 1964 chest radiographs were obtained, of which normal images accounted for 98.0% (1925 of 1964 radiographs) and findings of pulmonary infection represented 2.0% (39 of 1964 radiographs). Only 0.2% of patients (four of 1964) with findings of pulmonary infection at chest radiography (all of whom were symptomatic) required supplemental oxygenation and inpatient treatment. None of the asymptomatic patients with findings of pulmonary infection required supplemental oxygenation, and they received only symptomatic treatment. Conclusion In accordance with Fleischner Society recommendations, screening chest radiography is not indicated in patients with coronavirus disease 2019 who are aged 17-60 years with mild or no symptoms unless there is risk of clinical deterioration. © RSNA, 2021 See also the editorial by Schaefer-Prokop and Prokop in this issue.

A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19 (preprint)

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA.

A collaborative online AI engine for CT-based COVID-19 diagnosis (preprint)

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.