Depression Severity Among Patients with Psychiatric Disorders during the COVID-19 Pandemic

Objectives: The impact of the COVID-19 pandemic on mental health is not yet well-studied. This study's objective is to describe demographic characteristics of the population diagnosed with depression or anxiety, and to compare PHQ9 scores before and after the pandemic. Method(s): A retrospective cohort study was performed using Komodo Health's healthcare claims and EMR data, which included Patient Health Questionnaire-9 (PHQ9) survey responses. The study's baseline and follow-up periods were set as one year before and after 03/01/2020. Patients selected were >=18 years of age, had a MDD, GAD, or other psychiatric diagnosis in both periods, and had taken at least one PHQ9 survey in both periods, resulting in 10,433 patients. Demographic characteristics were described across age, gender, and race/ethnicity, and a subgroup analysis was performed on PHQ9 scores and depression categories using averages (mean, SD) and odds ratios. Result(s): Demographic analysis showed depression severity correlated with patients who were younger, female, and Black or Hispanic. Younger patients (<30) were more likely than older (>=30) to be in the moderately severe category or worse (PHQ9 score >=15) in both time periods (ORs 1.72 and 1.62, p<0.001). This was also true for female as compared to male (ORs 1.45 and 1.49, p<0.001), and Black or Hispanic as compared to White (ORs 1.87 and 1.47, p<0.001). However, mean PHQ9 scores tended to decrease in the follow-up period. The overall mean decreased slightly from 6.28 (SD 6.05) to 5.68 (SD 5.82), which was consistent in nearly all age, gender, and race/ethnicity subcategories. Conclusion(s): While the improvements in average PHQ9 scores were counterintuitive, given the harmful impacts of the pandemic, existing correlations between demographics and depression severity remained. One possible explanation is that this cohort definition selected for patients who received more consistent mental healthcare. Further study will investigate this and other possible factors.Copyright © 2023

Dynamic Safety Assessment and Enhancement of Port Operational Infrastructure Systems during the COVID-19 Era

Seaports function as lifeline systems in maritime transportation, facilitating critical processes like shipping, distribution, and allied cargo handling. These diverse subsystems constitute the Port Infrastructure System (PIS) and have intricate functional interdependencies. The PIS is vulnerable to several external disruptions, and the impact of COVID-19 is severe and unprecedented in this domain. Therefore, this study proposes a novel general port safety framework to cope with recurring hazards and crisis events like COVID-19 and to augment PIS safety through a multi-state failure system. The PIS is divided into three critical subsystems: shipping, terminal, and distribution infrastructure, thereby capturing its functional interdependency and intricacy. A dynamic input-output model is employed, incorporating the spatial variability and average delay of the disruption, to determine the PIS resilience capacity under the stated disruptions. This study simulates three disruption scenarios and determines the functional failure capacity of the system by generating a functional change curve in Simulink. This study offers viable solutions to port managers, terminal operators, and concerned authorities in the efficient running of intricate interdependent processes and in devising efficient risk control measures to enhance overall PIS resilience and reliability. As part of future studies, given the difficulty in obtaining relevant data and the relatively limited validation of the current model, we aim to improve the accuracy and reliability of our model and enhance its practical applicability to real-world situations with data collected from a real-world case study of a PIS system.

Analysis of case-based malaria surveillance and response during the period of COVID-19 outbreak in China

Objective To assess the case-based malaria surveillance and response during the period of COVID-19 outbreak in China, in order to provide reference for malaria elimination under the COVID-19 pandemic. Methods Information of malaria cases reported during the four months pre - and post-COVID -19 outbreak (December 1, 2019-March 31, 2020) and in the same time period of past two years in China (excluding Hong Kong, Macau and Taiwan regions) was obtained from the Parasitic Disease Control Information Management System. Cross-sectional survey and comparison were conducted for malaria surveillance and response data in 3 four-month time periods (December 1, 2019 to January 22, 2020;January 23 to March 17, 2020;and March 18-31, 2020). The number of malaria cases including deaths, the median and average time interval from disease onset to the first visit, the median and average of time interval from the first visit to the confirmed diagnosis, the completion status of the #1-3-7$ task and the source of infections in each period were analyzed and compared to the same times in the past two years. Results From December 1, 2019 to March 31, 2020, a total of 750 malaria cases, which were all imported cases, were reported in China, decreased by 9.2% from that reported during December 2018 and March 2019 (826 cases) and by 13.1% from that reported during December 2017 to March 2018 (863 cases). The decrease mainly occurred in February and March in 2020;there were no statistical differences in the time interval from onset to first visit (median 1 day, mean 2.0 days), time interval from first visit to confirmed diagnosis (median 1 day, mean 1.8 days), case reporting rate within 1 day (100%), case epidemiological investigation rate within 3 days (98.4%), epidemic site disposal rate within 7 days (100%) between the time period of COVID-19 outbreak and the same time in the past year (December 2018 to March 2019). In addition, no statistical difference (! > 0.05) was found in the time intervals from onset to first visit among the first period [median 1 d, average (1.9 +/- 0.2) d], the second period [median 1 d, average (2.1 +/- 0.3) d] and the third period [median 1 d, mean (1.5 +/- 0.3) d], while the time interval from the first visit to the confirmed diagnosis was statistically different (! X 0.05) among the first period [median 0 d, average (1.5 +/- 0.2) d], the second period [median 1 d, mean (2.3 +/- 0.3) d] and the third period [median 0.5 d, average (1.5 +/- 0.4) d], where the time interval in the second period was longer than that in the first period (! X 0.01). Conclusion China' s core measures to eliminate malaria have been carried out as planned, although the timely malaria diagnosis was slightly affected in the second time period (January 23 to March 17, 2020).Copyright © 2021, National Institute of Parasitic Diseases. All rights reserved.

Design and Construction of the New Web Learning System of Tsinghua University

Online learning platforms play an important role in supporting colleges and universities in integrating online and offline learning. The project team designed and developed the Web Learning System of Tsinghua University, which has become a basic supporting platform for teachers and students to carry out teaching and learning activities. The Web Learning System possesses functionalities such as course announcement management, courseware management, assignment management, discussion, Q & A, and online timetables, providing supporting services for course teaching, teacher-student interaction, and especially online and offline integrated learning during the COVID-19 pandemic. © 2022 IEEE.

Integrated surveillance and early warning system for emerging infectious diseases based on One Health concept: Structures and innovations.

One Health is an upgrade and optimization of health concepts, which recognizes the integrated health of the human-animal-environment. It emphasizes the use of interdisciplinary collaboration, multi-sectoral coordination, and multi-organizational One Health approaches to solve scientific questions. The surveillance and early warning system is the basis of public health emergency prevention and control. The COVID-19 pandemic and the emerging infectious disease (EID) have put great challenges on the existing surveillance and early warning systems worldwide. Guided by the concept of One Health, we attempt to build a new pattern of integrated surveillance and early warning system for EID. We will detail the system including the One Health-based organizational structure, zoonotic and environmental science surveillance network, EID reporting process, and support and guarantee from education and policy. The integrated surveillance and early warning system for EID constructed here has practical and application prospects, and will provide guidance for the prevention and control of COVID-19 and the possible EID in the future.Copyright © 2022, National Institute of Parasitic Diseases. All rights reserved.

Bond Portfolio Optimization with Long-Range Dependent Credits

Consider the optimal allocation between money market account and corporate bond fund. While the money market account is free of credit risk, corporate bonds are defaultable and exhibit long-range dependence (LRD) in credit risk. We propose a Volterra default intensity model to capture the LRD in credit risk. Using utility maximization, we derive the novel optimal investment strategy for a corporate bond fund. As empirical study shows that the COVID-19 pandemic has lowered the level of LRD in credit risk, we conduct sensitivity analysis and empirically investigate the changes in demand for corporate bonds before and during the pandemic period.

Forecast of China's Economic Growth Rate in 2023 and Policy Suggestions

China's economic growth slowed down in 2022 due to the COVID-19 pandemic and the corresponding measures. There are great uncertainties in China's economic development in 2023. It is expected that China's medium and long-term economic growth rate will show a wavy downward trend. Based on input-output technology, econometrics, prosperity analysis, expert analysis, and scenario analysis, this study proposes a systematic integrated factor prediction approach on annual GDP growth. Through analysis of China's economic growth in 2022 and the current situation worldwide, China's economic growth rate is predicted to be about 6.0% in 2023, reverting to the normal level. The policy recommendations are further put forward based on the analysis, including strengthening the adjustment of macro-policy, implementing proactive fiscal policy and prudent monetary policy, boosting domestic consumption, increasing employment and promoting investment, striving to stabilize the macro-economic market, preventing and defusing risks in major fields, and leveraging China's advantages in the global industrial chain. © 2023, Science Press. All rights reserved.

Case report of congenital disorder of glycosylation type Ia combined with dilated cardiomyopathy.

Clinical data and follow-up of a case of congenital disorder of glycosylation type Ia (CDG-Ia) combined with dilated cardiomyopathy admitted to the Department of Cardiology, Children's Hospital of Nanjing Medical University were analyzed retrospectively.The 5-year-old female patient was admitted in December 2016 due to recu-rrent shortness of breath for 2 months.Clinical symptoms and signs included repeated attacks of shortness of breath, physical retardation, malnutrition, binocular esotropia, multiple episodes of hypoglycemia, hepatosplenomegaly, hypotonia and other multi-system damages.Cardiac echocardiography suggested the feature of dilated cardiomyopathy, including the significant enlargement of the left ventricle, and decreased systolic function.Genetic testing revealed a compound heterozygous mutation in the PMM2 gene, and as a result, the patient was diagnosed as CDG-Ia.The patient's condition improved after symptomatic treatments such as Cedilanid, Dopamine, Dobutamine, Furosemide, as well as support treatments like myocardium nutrition, blood sugar maintenance, liver protection, etc.After discharge, the patient was given oral Digoxin, Betaloc, Captopril and diuretics, and hypoglycemia-controlling agents.The patient was followed up every 3-6 months.After more than 2 years of follow-up, the heart function and heart enlargement gradually returned to normal.During the Corona Virus Disease 2019 outbreak, self-withdrawal continued for 2 months.Re-examinations showed decreased cardiac function and enlarged left ventricle again.Medications were resumed again, and the patient was followed up closely.This case report suggested that CDG-Ia may be associated with dilated cardiomyopathy, and the cardiac phenotype may be improved by symptomatic supportive treatment.Copyright © 2021 by the Chinese Medical Association.

A Face Mask Detection Algorithm Based on YOLOv5

As a new machine learning method, deep learning has been widely used in computer vision. YOLOv5, a target detection algorithm based on deep learning, has a good detection effect. In the case of COVID-19, masks should be worn correctly in public places. Therefore, it is urgent to design an accurate and effective face mask detection algorithm. To solve the problem of mask-wearing detection, a face mask detection algorithm based on YOLOv5 is proposed. The main research contents include training of the YOLOv5 model, verification of face mask detection function, and analysis and comparison of detection effects of three different sizes of detection models: YOLOv5s, YOLOv5m and YOLOv5l. The proposed model realizes the mask detection function and obtains the advantages and disadvantages of different scale models through performance evaluation. The maximum mAP of the model reached 88.1%, with good detection accuracy. © 2022 IEEE.

Delivery Strategy Under Epidemic Based on Neural Network and ALNS

Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.

An investigation into the aging of disposable face masks in landfill leachate

Due to the excessive use of disposable face masks during the COVID-19 pandemic, their accumulation has posed a great threat to the environment. In this study, we explored the fate of masks after being disposed in landfill. We simulated the possible process that masks would experience, including the exposure to sunlight before being covered and the contact with landfill leachate. After exposure to UV radiation, all three mask layers exhibited abrasions and fractures on the surface and became unstable with the increased UV radiation duration showed aging process. The alterations in chemical groups of masks as well as the lower mechanical strength of masks after UV weathering were detected to prove the happened aging process. Then it was found that the aging of masks in landfill leachate was further accelerated compared to these processes occurring in deionized water. Furthermore, the carbonyl index and isotacticity of the mask samples after aging for 30 days in leachate were higher than those of pristine materials, especially for those endured longer UV radiation. Similarly, the weight and tensile strength of the aged masks were also found lower than the original samples. Masks were likely to release more microparticles and high concentration of metal elements into leachate than deionized water after UV radiation and aging. After being exposed to UV radiation for 48 h, the concentration of released particles in leachate was 39.45 muL/L after 1 day and then grew to 309.45 muL/L after 30 days of aging. Seven elements (Al, Cr, Cu, Zn, Cd, Sb and Pb) were detected in leachate and the concentration of this metal elements increased with the longer aging time. The findings of this study can advance our understanding of the fate of disposable masks in the landfill and develop the strategy to address this challenge in waste management. Copyright © 2023 Elsevier B.V.

Delivery Strategy Under Epidemic Based on Neural Network and ALNS

Under the background of the continuous spread of covid-19, fresh food delivery platforms need to make decisions on how to incorporate epidemic factors into their delivery strategies. In this paper, considering the factors of large activity range, long path, low efficiency and high risk of delivery staff in reservation-type fresh food delivery, combined with the perspective of delivery platform, a path planning model is constructed. we apply the ALNS algorithm to the proposed model and compares it with other classical heuristic algorithms. The results show that our proposed model can effectively reduce risks and improve delivery efficiency. © 2022 SPIE.

Integrated surveillance and early warning system for emerging infectious diseases based on One Health concept: Structures and innovations

One Health is an upgrade and optimization of health concepts, which recognizes the integrated health of the human-animal-environment. It emphasizes the use of interdisciplinary collaboration, multi-sectoral coordination, and multi-organizational One Health approaches to solve scientific questions. The surveillance and early warning system is the basis of public health emergency prevention and control. The COVID-19 pandemic and the emerging infectious disease (EID) have put great challenges on the existing surveillance and early warning systems worldwide. Guided by the concept of One Health, we attempt to build a new pattern of integrated surveillance and early warning system for EID. We will detail the system including the One Health-based organizational structure, zoonotic and environmental science surveillance network, EID reporting process, and support and guarantee from education and policy. The integrated surveillance and early warning system for EID constructed here has practical and application prospects, and will provide guidance for the prevention and control of COVID-19 and the possible EID in the future. © 2022, National Institute of Parasitic Diseases. All rights reserved.

Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis.

OBJECTIVES: The COVID-19 pandemic has significantly affected healthcare systems and daily well-being. However, the reports of the indirect impacts of the pandemic on preterm birth remain conflicting. We performed a meta-analysis to examine whether the pandemic altered the risk of preterm birth. STUDY DESIGN: This was a systematic review and meta-analysis of the previous literature. METHODS: We searched MEDLINE and Embase databases until March 2022 using appropriate keywords and extracted 63 eligible studies that compared preterm between the COVID-19 pandemic period and the prepandemic period. A random effects model was used to obtain the pooled odds of each outcome. The study protocol was registered with PROSPERO (No. CRD42022326717). RESULTS: The search identified 3827 studies, of which 63 reports were included. A total of 3,220,370 pregnancies during the COVID-19 pandemic period and 6,122,615 pregnancies during the prepandemic period were studied. Compared with the prepandemic period, we identified a significant decreased odds of preterm birth (PTB; <37 weeks' gestation; pooled odds ratio [OR; 95% confidence interval (CI)] = 0.96 [0.94, 0.98]; I2 = 78.7%; 62 studies) and extremely PTB (<28 weeks' gestation; pooled OR [95% CI] = 0.92 [0.87, 0.97]; I2 = 26.4%; 25 studies) during the pandemic, whereas there was only a borderline significant reduction in the odds of very PTB (<32 weeks' gestation; pooled OR [95% CI] = 0.93 [0.86, 1.01]; I2 = 90.1%; 33 studies) between the two periods. There was significant publication bias for PTB. CONCLUSION: Pooled results suggested the COVID-19 pandemic was associated with preterm birth, although there was only a borderline significant reduction for very PTB during the pandemic compared with the prepandemic period. Large studies showed conflicting results, and further research on whether the change is related to pandemic mitigation measures was warranted.

Study on effective components of Ganoderma applanation and its mechanism of action in preventing COVID-19 based on UHPLC-Q- Exactive-Orbitrap-MS integrated network pharmacology

Aim To elucidate the effective components of Ganoderma applanatum and its mechanism of preventing the coronavirus disease 2019(COVID-19).Methods To begin with, UHPLC-Q-Exactive-Orbitrap-MS was established to identify the main chemical constituents of G.applanatum.Then, the predicted targets of G.applanatum were selected by Swiss Target Prediction.GO analysis and KEGG analysis of core target genes were performed using the DAVID database.Finally, to explore the potential mechanism of G.applanatum against COVID-19, core functional components-core target-metabolism path network diagram was constructed using Cytoscape 3.8.0, and molecular docking was used to analyze the binding force of the core effective compounds with angiotensin-converting enzyme II(ACE2)and three SARS CoV-2 proteins, nonstructural protein-15 Endoribonuclease(NSP15), the receptor-binding domain of spike protein(RBD of S protein), and main protease(Mpro/3CLpro).Results Sixty-two components were identified from G.applanatum by UHPLC-Q-Exactive-Orbitrap-MS study;30 active components were closely associated with 32 core targets including IL6, PTGS2, and MAPK1;KEGG analysis showed that it might treat COVID-19 through signaling pathways, such as PI3K-Akt signaling pathway, TNF signaling pathway, tuberculosis, and so on;molecular docking analysis showed that 1,4-Dihydroxy-2-naphthoic acid, parthenolide, 7,8-Dihydroxycoumarin, and other vital compounds had a certain degree of affinity with ACE2 and three SARS CoV-2 proteins.Conclusion This study clarifies the chemical composition and the potential mechanism of G.applanatum, providing a scientific basis for screening the effective ingredients of G.applanatum. Copyright © 2022 Publication Centre of Anhui Medical University. All rights reserved.

Revisiting Key Issues in Applying the Communicative Approach in Korea: Follow up after 26 Years of Implementation

While CLT has continued to be one of the most widely applied approach in teaching English as a foreign language (TEFL) worldwide, there has been numerous studies that have pointed out various obstacles in applying CLT in the East Asian context. Jeon (1997) identified key issues in implementing CLT in the Korean context and followed up on the same issues in Jeon (2009) to examine whether there had been any changes after 12 years of implementation. In particular, three research questions were considered: 1) What are the key issues in applying the communicative approach in Korea? 2) Is there an order of priority in the importance of these issues? and 3) Are there any changes in the importance of these issues after 12 years of implementation? The results showed that while some new issues had come up, the top key issues had remained the same. This was a surprising finding since there had not been any major changes in the top key issues 12 years after the first study and pointed out the persistent need to seek out obstacles over the years. As there had been a global upheaval in the educational context due to COVID-19, major changes in English education was expected. Accordingly, this study is another follow up study that focused on revisiting key issues regarding the implementation of CLT in the Korean EFL context. In order to identify the key issues, a three-round Delphi technique was used. A total of 36 teachers participated in identifying the key issues, ranking the issues and revisiting the ranked issues to see if there needs to be any adjustments. The results showed that, after 26 years of implementation, some of the key issues had been modified and have either increased or decreased their importance. While there were some issues that have newly emerged, the issues that had stayed in the top ranking have remained the same. This calls for an urgent need to address such issues as the curriculum continues to stress the importance of CLT in Korea. Without ameliorating the hindering factors, proper implementation of CLT will be challenging for English teachers in Korea. © 2022 AsiaTEFL All rights reserved.

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

COVID-19 positive cases prediction based on LSTM algorithm and its variants

In this paper, deep learning methods are applied to predict positive cases reported in Wuhan and four states in USA. Recurrent neural network based on long-short term memory (LSTM) and its variants including bidirectional LSTM, stacked LSTM and traditional SEIR model are applied on Wuhan dataset to compare and select the best model in task of predicting positive cases. The results reveal that our models based on LSTM significantly perform better than traditional SEIR model. Besides, since bidirectional LSTM can learn information from history and future, it achieves the highest prediction accuracy. Then we use bidirectional LSTM to make prediction on another USA dataset, which contains more recent data. The bidirectional LSTM shows its power and accuracy on this data, which demonstrates its effectiveness on predicting COVID-19 positive cases once again. The model we proposed alos provide some insight into the research of epidemics and the understanding of the spread of the COVID-19. © 2022 IEEE.