Discriminant Ability of In-Hospital Mortality Based on Prognostic Scores of Elderly Patients with COVID-19: A Taiwan Medical Center Study

Introduction: The coronavirus disease 2019 (COVID-19) has brought excessive patients in emergency departments. Several COVID-19 prediction scores have been developed to aid in the patient disposition of emergency physicians. This study aimed to validate different COVID-19 prediction scores. Method: ???DynaMed??? was used to retrieve high-quality COVID-19 prediction scores for the evaluation of in-hospital mortality rate. SEIMC score, 4C-Mortality score, SOARS score, and Veterans Health Administration COVID-19 (VACO) Index were selected. A retrospective, single-center study was done on elderly patients hospitalized for COVID-19 from May 2021 to July 2021 in MacKay Memorial Hospital. Patients who were (I) negative for COVID-19 examination, (II) aged 65 years old, (III) previously infected with COVID-19 and de-isolated (IV) hospital-acquired COVID-19 infection, (V) not admitted for hospitalization, and (VI) with missing of demographic characteristics were excluded. The area under the receiver operating characteristic curves (AUC) was computed to predict the in-hospital mortality rate. Result: Of 66,090 patients who underwent COVID-19 examination in MacKay Memorial Hospital, 133 patients were included in this study, with 26 deceased patients (19.5%). Among included patients, the median age was 74.38 years and 53% patients were male. Of the selected COVID-19 prediction scores, 4C-Mortality Score (AUC = 0.8), SEIMC score (AUC = 0.75), and SOARS score (AUC = 0.72) contained a good prognostic value, with an AUC 0.70. VACO index demonstrated less predictive value (AUC = 0.61). Conclusion: COVID-19 prediction scores were validated, and it was found that 4C-Mortality Score, SEIMC score, and SOARS score performed well in predicting the in-hospital mortality rate of elderly patients with COVID-19, and 4C-Mortality score is best appreciated.

Development of Ultrapure and Potent Tannic Acids as a Pan-coronal Antiviral Therapeutic

The rampageous transmission of SARS-CoV-2 has been devastatingly impacting human life and public health since late 2019. The waves of pandemic events caused by distinct coronaviruses at present and over the past decades have prompted the need to develop broad-spectrum antiviral drugs against them. In this study, our Pentarlandir ultrapure and potent tannic acids (UPPTA) showed activities against two coronaviral strains, SARSCoV-2 and HCoV-OC43, the earliest-known coronaviruses. The mode of inhibition of Pentarlandir UPPTA is likely to act on 3-chymotrypsin-like protease (3CLpro) to prevent viral replication, as supported by results of biochemical analysis, a 3CLpro assay, and a "gain-of-function" 3CLpro overexpressed cell-based method. Even in the 3CLpro overexpressed environment, Pentarlandir UPPTA remained its antiviral characteristic. Utilizing cell-based virucidal and cytotoxicity assays, the 50% effective concentrations (EC50) and 50% cytotoxicity concentration (CC50) of Pentarlandir UPPTA were determined to be similar to 0.5 and 52.5 mu M against SARS-CoV-2, while they were 1.3 and 205.9 mu M against HCoV-OC43, respectively. In the pharmacokinetic studies, Pentarlandir UPPTA was distributable at a high level to the lung tissue with no accumulation in the body, although the distribution was affected by the food effect. With further investigation in toxicology, Pentarlandir UPPTA demonstrated an overall safe toxicology profile. Taking these findings together, Pentarlandir UPPTA is considered to be a safe and efficacious pancoronal antiviral drug candidate that has been advanced to clinical development.

CRYO-RALIB - A MODULAR LIBRARY FOR ACCELERATING ALIGNMENT IN CRYO-EM

Thanks to GPU-accelerated processing, cryo-EM has become a rapid structure determination method that permits capture of dynamical structures of molecules in solution, which has been recently demonstrated by the determination of COVID-19 spike protein in March, shortly after its breakout in late January 2020. This rapidity is critical for vaccine development in response to the emerging pandemic. Compared to the Bayesian-based 2D classification widely used in the workflow, the multi-reference alignment (MRA) is less popular. It is time-consuming despite its superior in differentiating structural variations. Interestingly, the Bayesian approach has higher complexity than MRA. We thereby reason that the popularity of Bayesian is gained through GPU acceleration, where a modular acceleration library for MRA is lacking. Here, we introduce a library called Cryo-RALib that expands the functionality of CUDA library used by GPU ISAC. It contains a GPU-accelerated MRA routine for accelerating MRA-based classification algorithms. In addition, we connect the cryo-EM image analysis with the python data science stack to make it easier for users to perform data analysis and visualization. Benchmarking on the TaiWan Computing Cloud (TWCC) shows that our implementation can accelerate the computation by one order of magnitude. The library is available at https://github.com/phonchi/Cryo-RAlib. © 2021 IEEE

Big Data Analysis for Effects of the COVID-19 Outbreak on Ambient PM2.5 in Areas that Were Not Locked Down

COVID-19, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first broke out at the end of 2019. Despite rapidly spreading around the world during the first half of 2020, it remained well controlled in Taiwan without the implementation of a nationwide lockdown. This study aimed to evaluate the PM2.5 concentrations in this country during the 2020 COVID-19 pandemic and compare them with those during the corresponding period from 2019. We obtained measurements (taken every minute or every 3 minutes) from approximately 1,500 PM2.5 sensors deployed in industrial areas of northern and southern Taiwan for the first quarters (January-March) of both years. Our big data analysis revealed that the median hourly PM2.5 levels decreased by 3.70% (from 16.3 to 15.7 mu g m(-3)) and 10.6% (from 32.4 to 29.3 mu g m(-3)) in the north and south, respectively, between these periods owing to lower domestic emissions of PM2.5 precursors (viz., nitrogen dioxide and sulfur dioxide) and, to a lesser degree, smaller transported emissions of PM2.5, e.g., from China. Additionally, the spatial patterns of the PM2.5 in both northern and southern Taiwan during 2020 resembled those from the previous year. Finally, controlling local PM2.5 emission sources critically contributes to reducing the number of COVID-19 cases.