Delivery and neonatal outcomes of pregnant women during the Shanghai lockdown: A retrospective analysis.

Objectives: Shanghai witnessed an unprecedented outbreak of COVID-19 and experienced a strict lockdown from March 28, 2022 to May 31, 2022. Most studies to date are on the first lockdown after the outbreak in December 2019. This study aimed to examine the impact of lockdown on delivery and neonatal outcomes among uninfected pregnant women in the new phase of the COVID-19 outbreak. Methods: A retrospective analysis was conducted in the Obstetrics and Gynecology Hospital of Fudan University. Pregnant women without COVID-19 who delivered from March 28, 2022 to May 31, 2022 (lockdown group) and the same period in 2021 (non-lockdown group) were recruited for this study. Logistic regression models and 1 : 1 propensity score matching (PSM) were used to assess the effect of lockdown on delivery outcomes. Results: A total of 2,962 patients were included in this study, 1,339 of whom were from the lockdown group. Compared with the non-lockdown group, pregnant women giving birth during lockdown had an increased risk of term prelabor rupture of membranes (TPROM) (aOR = 1.253, 95% CI: 1.026-1.530), and decreased risks of postpartum hemorrhage (PPH) (aOR = 0.362, 95% CI: 0.216-0.606) and fetal malformation (aOR = 0.309, 95% CI: 0.164-0.582). The risk of large for gestational age (LGA) (aOR = 0.802, 95% CI: 0.648-0.992) and rate of admission to the neonatal intensive care unit (NICU) (aOR = 0.722, 95% CI: 0.589-0.885) also significantly declined. After 1 : 1 PSM, the impact of lockdown on the risk of TPROM (aOR = 1.501, 95% CI: 1.083-2.080), PPH (aOR = 0.371, 95% CI: 0.211-0.654), fetal malformation (aOR = 0.332, 95% CI: 0.161-0.684), LGA (aOR = 0.749, 95% CI: 0.594-0.945) and rate of admission to the NICU (aOR = 0.700, 95% CI: 0.564-0.869) all remained. There were no other delivery or neonatal outcomes affected by the lockdown after the COVID-19 outbreak. Conclusion: This study indicated a significant increase in the risk of term PROM, significant decreases in the risk of PPH, fetal malformation and LGA, and a marked decline in the rate of admission to the NICU during Shanghai Lockdown.

Willingness to practice medicine and related influential factors among Medical Undergraduates during COVID-19: a cross-sectional study (preprint)

Background Medical undergraduates are the future workforce, and their job preferences can directly impact the quality of healthcare services in China, especially during the coronavirus (COVID-19) pandemic. We aim to understand the status of the willingness to practice medicine among medical undergraduates and to analyze the related influential factors.Methods During the COVID-19 epidemic, we conducted a cross-sectional survey from 15 February 2022 to 31 May 2022 through an online platform to collect information about characteristics, psychology, and motivations for the career choice. The general self-efficacy scale (GSES) was used to assess medical students’ self-efficacy perceptions. Multivariate logistic regression was used to analyse influencing factors of willingness to practice medicine.Results A total of 2348 valid questionnaires were included, and 1573 (66.99%) were willing to practice medicine for medical undergraduates after graduation. The mean GESE scores in the willingness group (2.87 ± 0.54) were significantly higher than those in the unwillingness group (2.73 ± 0.49). The multiple logistic regression showed that several factors were positively associated with willingness to practice medicine as a career, including students’ GSES score (OR = 1.72), personal ideals (OR = 2.04), family support (OR = 1.48), high income (OR = 1.81), and social respect (OR = 2.14). Compared with those very afraid of COVID-19, students who were not afraid at all had a higher preference for choosing the medical profession as a career. In addition, students thinking of high tension in the doctor-patient relationship, heavy workload, and long training were less likely to choose medical work after graduation.Conclusions There was a relatively high level of willingness to practice medicine among medical undergraduates after graduation. Psychological factors, personal preferences, career needs or preferences, and the impact of the COVID-19 pandemic are associated with this willingness.

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks (preprint)

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.

Proteomics Investigation of Diverse Serological Patterns in COVID-19 (preprint)

Serum antibodies IgM and IgG are elevated during COVID-19 to defend against viral attack. Atypical results such as negative and abnormally high antibody expression were frequently observed whereas the underlying molecular mechanisms are elusive. In our cohort of 144 COVID-19 patients, 3.5% were both IgM and IgG negative whereas 29.2% remained only IgM negative. The remaining patients exhibited positive IgM and IgG expression, with 9.3% of them exhibiting over 20-fold higher titers of IgM than the others at their plateau. IgG titers in all of them were significantly boosted after vaccination in the second year. To investigate the underlying molecular mechanisms, we classed the patients into four groups with diverse serological patterns and analyzed their two-year clinical indicators. Additionally, we collected 111 serum samples for TMTpro-based longitudinal proteomic profiling and characterized 1494 proteins in total. We found that the continuously negative IgM and IgG expression during COVID-19 were associated with mild inflammatory reactions and high T cell responses. Low levels of serum IgD, inferior complement 1 activation of complement cascades, and insufficient cellular immune responses might collectively lead to compensatory serological responses, causing overexpression of IgM. Serum CD163 was positively correlated with antibody titers during seroconversion. This study suggests that patients with negative serology still developed cellular immunity for viral defense, and that high titers of IgM might not be favorable to COVID-19 recovery.

Challenges and Innovation in Community-Based Organization HIV/AIDS Services During COVID-19 in China (preprint)

Introduction People living with HIV relied on community-based organizations (CBOs) in accessing HIV care and support during the COVID-19 pandemic in China. However, little is known on the impact of, and challenges faced by Chinese CBOs supporting PLHIV during lockdowns. Methods A mixed methods study was conducted among 29 CBOs serving PLHIV in China between November 10 and November 23, 2020. Participants were asked to complete a 20-minute online survey on their routine operations, organizational capacity building, service provided, and challenges during the pandemic. A focus group interview was conducted with CBOs after the survey to gain further insights on data interpretations. Quantitative data analysis was conducted using STATA 17.0 while qualitative data was examined using thematic analysis. Results HIV-focused CBOs in China serve diverse clients including PLHIV, high-risk groups, and the general public. The scope of services provided is broad, ranging from HIV testing to peer support. All CBOs maintained their services during the pandemic, many by switching to online or hybrid mode. Many CBOs reported adding new clients and services, such as mailing medications. Top challenges faced by CBOs included service reduction due to staff shortage, lack of PPE for staff, and lack of funding during COVID-19 lockdowns. In addition to the staff and funding needs, the ability to better network with other CBOs and other sectors (e.g., clinics, governments), a standard emergency response guideline, and ready strategies to help PLHIV build resilience are critical for future emergency preparation. Conclusions Chinese CBOs serving vulnerable populations affected by HIV/AIDS are instrumental in building resilience in their communities during the COVID-19 pandemic, and they can play significant roles in providing uninterrupted services during emergencies by mobilizing resources, creating new services and operation methods, and utilizing existing networks. Chinese CBOs’ experiences, challenges, and their policy recommendations can inform policy makers on how to support future CBO capacity building to bridge service gaps during crises and reduce health inequalities in China and globally.

Pulmonary and Renal Long COVID at Two-year Revisit (preprint)

More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.

Feasibility to Reduce Swab RT-PCR Tests by Serum Proteomics for COVID-19 Disease Course Monitoring (preprint)

The diagnosis and disease course monitoring of COVID-19 are mainly based on RT-PCR analysis of RNAs extracted from pharyngeal or nasopharyngeal swabs with potential live virus, posing a high risk to medical practitioners. Here, we investigated the feasibility of applying serum proteomics to classify COVID-19 patients in the nucleic acid positive (NCP) and negative (NCN) stages. We analyzed the proteome of 320 inactivated serum samples from 144 COVID-19 patients, and 45 controls and shortlisted 42 regulated proteins in the severe group and 12 regulated proteins in the non-severe group. Together with several key clinical indexes including days after symptom onset, platelet counts and magnesium, we developed machine learning models to classify NCP and NCN with an AUC of 0.94 for the severe cases and 0.89 for the non-severe cases. This study suggests the feasibility of utilizing quantitative serum proteomics for NCP-NCN classification.Funding: This work was supported by grants from the National Key R&D Program of China(No. 2020YFE0202200), National Natural Science Foundation of China (81672086), Zhejiang Province Analysis Test Project (2018C37032), the National Natural Science Foundation of China (81972492, 21904107), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Zhejiang Medical and Health Science and Technology Plan (2021KY394), Hangzhou Agriculture andSociety Advancement Program (20190101A04), and Westlake Education Foundation, Tencent Foundation.Conflict of Interest: Tiannan Guo is shareholder of Westlake Omics Inc. W.G. and N.X. are employees of Westlake Omics Inc. The remaining authors declare no competing interests.Ethical Approval: This study has been approved by both the Ethical/Institutional Review Boards of Taizhou Hospital and Westlake University. Informed contents from patients were waived by the boards.

Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk (preprint)

As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we develop a framework with machine learning assisted to extract epidemic dynamics from the infection data, in which contains a county-level spatiotemporal epidemiological model that combines a spatial Cellular Automaton (CA) with a temporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared with the existing time risk prediction models, the proposed CA-SUIR model shows the multi-level risk of the county to the government and coronavirus transmission patterns under different policies. This new toolbox is first utilized to the projection of the multi-level COVID-19 prevalence over 412 Landkreis (counties) in Germany, including t-day-ahead risk forecast and the risk assessment to the travel restriction policy. As a practical illustration, we predict the situation at Christmas where the worst fatalities are 34.5 thousand, effective policies could contain it to below 21 thousand. Such intervenable evaluation system could help decide on economic restarting and public health policies making in pandemic.

Eleven Routine Clinical Features Predict COVID-19 Severity Uncovered by Machine Learning of Longitudinal Measurements (preprint)

Background: Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. In this study, we aim to establish a model for COVID-19 severity prediction and depict dynamic changes of key clinical features over 7 weeks.Methods: In our retrospective study, a total of 841 patients have been screened with the SARS-CoV-2 nucleic acid test, of which 144 patients were virus RNA (COVID-19) positive, resulting in a data matrix containing of 3,065 readings for 124 types of measurements from 17 categories. We built a support vector machine model assisted with genetic algorithm for feature selection based on the longitudinal measurement. 25 patients as a test cohort were included from an independent hospital.Findings: A panel of 11 routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving an accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved an accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. This study presents a practical model for timely severity prediction for COVID-19, which is freely available at a webserver https://guomics.shinyapps.io/covidAI/.Interpretation: The model provided a classifier composed of 11 routine clinical features which are widely available during COVID-19 management which could predict the severity and may guide the medical care of COVID-19 patients.Funding: This work is supported by grants from Tencent Foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).Declaration of Interests: NAEthics Approval Statement: This study was approved by the Medical Ethics Committee of Taizhou Hospital, Shaoxing People’s Hospital and Westlake University, Zhejiang province of China, and informed consent was obtained from each enrolled subject.

Eleven Routine Clinical Features Predict COVID-19 Severity (preprint)

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

Detection of SARS-CoV-2 in Exhaled Breath from COVID-19 Patients Ready for Hospital Discharge (preprint)

The COVID-19 pandemic has brought an unprecedented crisis to the global health sector1. When recovering COVID-19 patients are discharged in accordance with throat or nasal swab protocols using reverse transcription polymerase chain reaction (RT-PCR), the potential risk of re-introducing the infection source to humans and the environment must be resolved 2,3,4. Here we show that 20% of COVID-19 patients, who were ready for a hospital discharge based on current guidelines, had SARS-CoV-2 in their exhaled breath (~105 RNA copies/m3). They were estimated to emit about 1400 RNA copies into the air per minute. Although fewer surface swabs (1.3%, N=318) tested positive, medical equipment frequently contacted by healthcare workers and the work shift floor were contaminated by SARS-CoV-2 in four hospitals in Wuhan. All air samples (N=44) appeared negative likely due to the dilution or inactivation through natural ventilation (1.6-3.3 m/s) and applied disinfection. Despite the low risk of cross environmental contamination in the studied hospitals, there is a critical need for strengthening the hospital discharge standards in preventing re-emergence of COVID-19 spread.