Optimized synthesis of anti-COVID-19 drugs aided by retrosynthesis software

Considering the millions of COVID-19 patients worldwide, a global critical challenge of low-cost and efficient anti-COVID-19 drug production has emerged. Favipiravir is one of the potential anti-COVID-19 drugs, but its original synthetic route with 7 harsh steps gives a low product yield (0.8%) and has a high cost ($68 per g). Herein, we demonstrated a low-cost and efficient synthesis route for favipiravir designed using improved retrosynthesis software, which involves only 3 steps under safe and near-ambient air conditions. A yield of 32% and cost of $1.54 per g were achieved by this synthetic route. We also used the same strategy to optimize the synthesis of sabizabulin. We anticipate that these synthetic routes will contribute to the prevention and treatment of COVID-19.

Perinatal Care during the COVID-19 Pandemic

Background: Following the pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), and considering its capacity for rapid mutation, there have been many studies and articles on this novel coronavirus over the past three years. Therefore, providing knowledge and directions for management of SARS-CoV-2, for hospital staff is crucial. Hence, we collected the research information from different perspectives and summarized the guidelines for perinatal care on the topic of SARS-CoV-2, and for possible future viral pandemics. Method(s): A systematic review aimed at assessing the publications written in English and Chinese, offering different perspectives on the topic of perinatal care concerning SARS-CoV-2, was conducted using PubMed and Google Scholar from 2020 to 2022. In addition, we summarized the guidelines from the Taiwan Association of Obstetrics and Gynecology, American College of Obstetricians and Gynecologists, Society for Maternal-Fetal Medicine, Maternal Immunization Task Force and Partners, and Academy of Breastfeeding Medicine. Result(s): Due to physiological changes, pregnant patients may be prone to have complications, especially pre-eclampsia, affecting morbidity and mortality. Most neonates of coronavirus disease (COVID-19) infected mothers did not show any clinical abnormalities due to the infection. However, compared to the general population, infected neonates needed more invasive ventilation care, while the proportion of asymptomatic neonates was less than that in the general population. Further, long term complications are still under investigation. Evidence of vertical transmission via the placenta and umbilical cord is rare but not absent. Paxlovid (nirmatrelvir/ritonavir) can be administered to patients with comorbidities, and indications for cesarean delivery does not include COVID-19 infection. Vaccination against COVID-19 should not be delayed during pregnancy and lactation. Conclusion(s): Obstetricians and gynecologists should pay more attention to pregnant women with SARS-CoV-2 because of the physiological changes and higher risks of complications, morbidity, and mortality. Early prevention with vaccination in pregnant women is the key to controlling the COVID-19 pandemic, from which we can learn how to manage the next pandemic.Copyright © 2023 The Author(s).

Using the big data analysis and basic information from lecture Halls to predict air change rate

School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate. © 2023 Elsevier Ltd

Defying the Odds: A National Perspective of Bariatric and Metabolic Surgery Amidst the Covid19 Pandemic

Background: Elective Bariatric and Metabolic Surgeries (BMS) were stopped all over the world during this COVID-19 pandemic to ensure the availability of hospital resources to combat the pandemic and also to protect its first responders and other care givers. Only emergency and urgent cases were permitted. However, the actions taken early in the pandemic by the government of Taiwan and our center's collective efforts allowed us to be the only center in the world to safely perform elective BMS unhampered. Method(s): A retrospective review and analysis of the trends, complications and safety for all elective BMS from January to April 2020 was done. We reviewed the preparations, healthcare policies, and protocols created by the government of Taiwan against COVID-19 and our center's robust algorithm for patient and healthcare workers (HCW) surveillance and safety. Results and Discussion: A total of 99 patients underwent elective BMS from January to April 2020. The breakdown was 59 females and 40 males with an average body mass index (BMI) of 35.20 kg/m2 and 40.68 kg/m2 respectively. Compared to the previous year when a total 117 patients had surgery, a decline of 18 elective operations (-15.38%) was noted. There were no reported cases of a patient developing postoperative COVID-19 or a HCW. Conclusion(s): Elective operations may not need to be postponed if you have already in place early mitigation measures to prevent a pandemic spread, including but not limited to a prompt implementation of protocols and strict adherence to these measures. (Figure Presented).

A Novel Facial Mask Detection Using Fast-YOLO Algorithm

The extremely high transmission rate of the COVID-19 has made the supply of medical resources in countries around the world in short supply. The implementation of quarantine in order to avoid group infections has a serious impact on the economy, transportation, education and other aspects. Epidemic prevention will be a routine task that needs to be carried out for a long time and cannot be neglected. In view of the fact that wearing masks is currently an effective method of epidemic prevention, and the current face detection models are not effective for masked faces, and pedestrians who have not worn masks in the correct way. It may spread the epidemic. This research will establish a face data set with three kinds of annotations, and combine a variety of deep learning convolutional neural network architectures and methods to design a face detection model that can quickly train and detect wearing a mask, not wearing a mask, and wearing a mask incorrectly faces. In the hope of contributing to the epidemic prevention, we use an adaptive algorithm to adjust the image size to reduce unnecessary operations, and modify the CIOU_LOSS error function to speed up the operation. Experiments have confirmed that our algorithm saves 70% of the time compared to YOLO v5m with the same accuracy. © 2022 IEEE.

The interactive computer simulation and learning activity for facilitating students' conceptual understanding on the buoyant force through the CoSci learning platform

Learning science, especially in the physics field, there are many varieties of invisible and phenomena that are hard and difficult for students to observe and learn. One of the tools that can help students to understand those phenomena in a better way is computer simulations. The computer simulations are usually used in both on-site classroom and on-line learning platforms. Learning in the COVID-19 pandemic era at present, the computer simulations are very important for helping students to understand the physics concept. Interactive computer simulation can be considered as one of the effective methods of facilitating inquiry learning in science, as it allows students to experience the scientific inquiry process and facilitates students to understand an conception and to understand the relationship between variables of invisible phenomena more clearly in reasonable ways. This study aimed to develop the interactive computer simulation and learning activity for enhancing students' conceptual understanding of the buoyant force on the CoSci learning platform. Totally eighteen participants were studied in the twelfth grade in science classrooms of a university-affiliated school project (SCiUS), Khon Kaen University, Thailand, in 2019. The learning activity was developed based on students' alternative concepts and used to facilitate students' conceptual understanding of the buoyant force. There were six basic concepts related to the buoyant force constructed based on the predict-observe-explain strategy (POE) with the interactive computer simulation (i.e., the CoSci learning platform) in the learning activity. The learning activity on the CoSci learning platform consisted of eight pie charts such as 1) main question pie chart, 2) density pie chart, 3) water level pie chart, 4) volume pie chart, 5) mass pie chart, 6) weight pie chart, 7) submerged depth pie chart, and 8) answer pie chart. There were six interactive computer simulations used in this research including 1) density simulation, 2) water level simulation, 3) volume simulation, 4) mass simulation, 5) submerged depth simulation, and 6) weight simulation. All of these simulations were developed on the CoSci learning platform (https://cosci.tw/). The findings showed that 72% of students performed better in the post-test scores than in the pre-test score in all six basic concepts related to the buoyant force after learning buoyant force on the CoSci platform. Furthermore, the most difficulty in changing misconception in learning of the buoyant force was the concept related to the mass of the object. © 2022 Institute of Physics Publishing. All rights reserved.

Inferring the Trend of COVID-19 Epidemic with Close Contacts Counting

Close contacts with high-risk exposure to COVID-19 cases are more robust in statistics for inferring future development of COVID-19 epidemic. In Beijing, the proportion of close contact cases in newly confirmed cases had increased from about 50% at the end of January to nearly 100% in mid-February, indicating that contact tracing and quarantine measures are effective non-pharmaceutical interventions for containing the epidemic. In addition, we show at the national level that the cumulative number of close contacts was stabilized at about eight times as much as infected individuals, and the growth rate of daily close contacts was consistent with that of daily confirmed cases 5~6 days later. Consequently, tracking the daily change of close contacts is beneficial to predict the trend of the epidemic, based on which advanced medical supplies scheduling and effective epidemic prevention can be achieved. © 2020, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

Analysis of clinical features and imaging signs of COVID-19 with the assistance of artificial intelligence.

OBJECTIVE: To explore the CT imaging features/signs of patients with different clinical types of Coronavirus Disease 2019 (COVID-19) via the application of artificial intelligence (AI), thus improving the understanding of COVID-19. PANTIENTS AND METHODS: Clinical data and chest CT imaging features of 58 patients confirmed with COVID-19 in the Fifth Medical Center of PLA General Hospital were retrospectively analyzed. According to the Guidelines on Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment (Provisional 6th Edition), COVID-19 patients were divided into mild type (7), common type (34), severe type (7) and critical type (10 patients). The CT imaging features of the patients with different clinical types of COVID-19 types were analyzed, and the volume percentage of pneumonia lesions with respect to the lung lobes (where the lesion was located) and to the whole lung was calculated with the use of AI software. SPSS 21.0 software was used for statistical analysis. RESULTS: Common clinical manifestations of COVID-19 patients: fever was found in 47 patients (81.0%), cough in 31 (53.4%) and weakness in 10 (17.2%). Laboratory examinations: normal or decreased white blood cell (WBC) counts were observed in 52 patients (89.7%), decreased lymphocyte counts (LCs) in 14 (24.1%) and increased C-reactive protein (CRP) levels in 18 (31.0%). CT imaging features: there were 48 patients (94.1%) with lesions distributed in both lungs and 46 patients (90.2%) had lesions most visible in the lower lungs; the primary manifestations in patients with common type COVID-19 were ground-glass opacities (GGOs) (23/34, 67.6%) or mixed type (17/34, 50.0%), with lesions mainly distributed in the periphery of the lungs (28/34, 82.4%); the primary manifestations of patients with severe/critical type COVID-19 were consolidations (13/17, 76.5%) or mixed type (14/17, 82.4%), with lesions distributed in both the peripheral and central areas of lungs (14/17,82.4%); other common signs, including pleural parallel signs, halo signs, vascular thickening signs, crazy-paving signs and air bronchogram signs, were visible in patients with different clinical types, and pleural effusion was found in 5 patients with severe/critical COVID-19. AI software was used to calculate the volume percentages of pneumonia lesions with respect to the lung lobes (where the lesion was located) and to the whole lung. There were significant differences in the volume percentages of pneumonia lesions for the superior lobe of the left lung, the inferior lobe of the left lung, the superior lobe of the right lung, the inferior lobe of the right lung and the whole lung among patients with different clinical types (p<0.05). The area under the ROC curve (AUC) of the volume percentage of pneumonia lesions for the whole lung for the diagnosis of severe/critical type COVID-19 was 0.740, with sensitivity and specificity of 91.2% and 58.8%, respectively. CONCLUSIONS: The clinical and CT imaging features of COVID-19 patients were characteristic to a certain degree; thus, the clinical course and severity of COVID-19 could be evaluated with a combination of an analysis of clinical features and CT imaging features and assistant diagnosis by AI software.