Impact of COVID-19 Pandemic on Hospital Admissions of Acute Coronary Syndrome: A Beijing Inpatient Database Study.

BACKGROUND: Consequences of reduced acute coronary syndrome (ACS) admissions during COVID-19 pandemic periods were reported by different countries. However, admissions, treatments, and prognosis of ACS during and after COVID-19 pandemic in Beijing, China was unknown. METHODS: Information on ACS admissions and heart failure (HF) admission were identified from database of Beijing Municipal Health Commission Information Center. Study period was defined as December 1, 2019 to June 30, 2020, and control period was defined as December 1, 2018 to June 30, 2019. Numbers of admission for HF during the control period, the study period, and seven months after study period were compared to evaluate the consequence of changed ACS care during the COVID-19 pandemic. FINDINGS: Admissions for ST-elevation myocardial infarction (STEMI), Non-ST-elevation myocardial infarction (Non-STEMI), and unstable angina (UAP) reduced by 38·0%, 41·0%, and 63·3% (N = 1953, 1991, 7664 between January 24, 2020 to June 30, 2020 vs. N = 3150, 3373, and 20,868 between January 24, 2019 to June 30, 2019) in study period. Percutaneous coronary intervention performed within 24 h were significantly more frequent during study period in patients with STEMI (37·9% vs. 31·7%, P<0·0001), but significantly less frequent in patients with Non-STEMI (7·9% vs. 9·5%, P = 0·049), and in patients with UAP (1·7% vs. 3·5%, P<0·0001). In-hospital mortality rates in patients with ACS were similar during the study period and the control period (3·1% vs 2·5%, P = 0·174 for STEMI; 2·7% vs 2·3%, P = 0·429 for Non-STEMI; 0·2% vs 0·1%, P = 0·222 for UAP). A fall by 23.9% for HF admissions was also observed during the seven months following the study period than equivalent period in 2019. INTERPRETATION: During COVID-19 pandemic, ACS admissions reduced significantly in Beijing; however, increase of HF admissions was not observed within seven months post-pandemic period, implying the pandemic didn't deteriorate the short-term prognosis for ACS. FUNDING: the National Natural Science Foundation of China (82,103,904), the National Key Research and Development Program of China (Grant number: 2020YFC2004803).

Depression and anxiety of medical students at Kunming Medical University during COVID-19: A cross-sectional survey.

An isolation strategy was used to control the transmission and rapid spread of COVID-19 in Yunnan. As a result, students were supposed to stay at home and disrupted their outside activities. It led to a detrimental influence on students' mental health. The purpose of this study was to investigate the prevalence and risk factors of depression and anxiety among medical students and to provide ideas for the prevention of depression and anxiety in medical students. A cross-sectional survey was conducted among 2,116 medical students at Kunming Medical University from July 8 to July 16, 2020. Participants' demographic and living conditions were collected. Depression and anxiety were measured using the Patient Health Questionnaire 9 and General Anxiety Disorder-7, respectively. Uni- and multivariate logistic regression analyses were performed to detect risk factors associated with depression and anxiety. The prevalence rates of depression and anxiety among medical students were 52.5 and 29.6%, respectively. Depression was more likely to be caused by low grades, lack of physical exercise, drug use, irregular diet, extensive screen time on mobile phones, being greatly affected by the COVID-19 pandemic, and inadaptability to offline courses. Anxiety was more likely to be caused by lack of physical exercise, drug use, irregular diet, and inadaptability to offline courses. Depression and anxiety are highly comorbid. Our study showed predictive factors for depression and anxiety and identified a major mental health burden on medical students during the COVID-19 outbreak. More targeted measures should be taken to improve the mental state of students to reduce the incidence of depression and anxiety.

Geriatric risk and protective factors for serious COVID-19 outcomes among older adults in Shanghai Omicron wave.

Shanghai has been experiencing the Omicron wave since March 2022. Though several studies have evaluated the risk factors of severe infections, the analyses of BA.2 infection risk and protective factors among geriatric people were much limited. This multicentre cohort study described clinical characteristics, and assessed risk and protective factors for geriatric Omicron severe infections. A total of 1377 patients older than 60 were enrolled, with 75.96% having comorbidities. The median viral shedding time and hospitalization time were nine and eight days, respectively. Severe and critical were associated with longer virus clearance time (aOR [95%CI]:0.706 (0.533-0.935), P = .015), while fully vaccinated/booster and paxlovid use shortened viral shedding time (1.229 [1.076-1.402], P = .002; 1.140 [0.019-1.274], P = .022, respectively). Older age (>80), cerebrovascular disease, and chronic kidney disease were risk factors of severe/critical. Fully vaccination was a significant protective factor against severe infections (0.237 [0.071-0.793], P = .019). We found patients with more than two comorbidities were more likely to get serious outcomes. These findings demonstrated that in the elderly older than 60 years old, older age (aged over 80), cerebrovascular disease, and chronic kidney disease were risk factors for severe infection. Patients with more than two comorbidities were more likely to get serious outcomes. Fully vaccinated/booster patients were less likely to be severe and vaccinations could shorten viral shedding time. The limitation of lacking an overall spectrum of COVID-19 infections among elders could be compensated in other larger-scale studies in the future.

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning (preprint)

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such a machine learning approach for analyzing Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (for example, amide, amino acid, carboxylic acid), we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Pedestrian epidemic coupling modeling and simulation based on social force model

In order to meet the global common needs of COVID-19 epidemic containment, it is particularly important to establish a pedestrian walking model considering the interaction between pedestrians and epidemic situation, aiming at the problem of massive epidemic spread caused by pedestrian aggregation in public places. In this paper, based on the social force model, the authors established the pedestrian movement model under the epidemic background firstly. Then, according to the characteristics of COVID-19 epidemic, an infection model is established to simulate the single person's dynamic behavior of infection, thus, the pedestrian walking model with population epidemic coupling is proposed. Finally, the spread law of the epidemic situation was simulated by using computer simulation technology. The results showed that pedestrian flow rate, coverage rate of pedestrian protection measures, pedestrian spacing and pedestrian walking speed were the key factors affecting the spread of the epidemic. When the pedestrian flow rate was 1700 person / h, the number of infected persons within 500s had reached 27. Therefore, the control of pedestrian flow rate can be the most important direction of epidemic containment.

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning (preprint)

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum. In this paper, we present a machine learning analysis on Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (e.g. amide, amino acid, carboxylic acid) we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. The accurate and interpretable machine learning model developed for Raman virus identification presents promising potential in a real-time virus detection system. Significance Statement A portable micro-fluidic platform for virus capture promises rapid enrichment and label-free optical identification of viruses by Raman spectroscopy. A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with the portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses.

Depressive Symptoms, Sleep Quality and Diet During the 2019 Novel Coronavirus Epidemic in China: A Survey of Medical Students.

The psychological condition of medical students may be influenced by the 2019 novel coronavirus (COVID-19) outbreak. This study investigated the prevalence and influencing factors of depressive symptoms, poor sleep quality and poor diet in students at Kunming Medical University during the early part of the COVID-19 outbreak. A cross-sectional study was used from a questionnaire survey in February 2020. Of a total of 1,026 study participants, the prevalence of depressive symptoms, poor sleep quality, and poor diet was, respectively, 22.4, 33.2, and 17.4%. Male students and students with a low degree of focus on COVID-19 had a high risk of depressive symptoms. A high percentage of females and students in the fifth grade, as well as students with high levels of concern about the negative impact of COVID-19 on their education or employment, comprised those with poor sleep quality. Students in the fifth grade and students with high levels of concern about the negative impact of COVID-19 on their education or employment were more likely to report poor diet. This study suggests the importance of monitoring medical students' depressive state during the COVID-19 outbreak, and universities are encouraged to institute policies and programs to provide educational counseling and psychological support to help students to cope with these problems.

Single-Cell Analysis Reveals Macrophage-Driven T Cell Dysfunction in Severe COVID-19 Patients (preprint)

The vastly spreading COVID-19 pneumonia is caused by SARS-CoV-2. Lymphopenia and cytokine levels are tightly associated with disease severity. However, virus-induced immune dysregulation at cellular and molecular levels remains largely undefined. Here, the leukocytes in the pleural effusion, sputum, and peripheral blood biopsies from severe and mild patients were analyzed at single-cell resolution. Drastic T cell hyperactivation accompanying elevated T cell exhaustion was observed, predominantly in pleural effusion. The mechanistic investigation identified a group of CD14+ monocytes and macrophages highly expressing CD163 and MRC1 in the biopsies from severe patients, suggesting M2 macrophage polarization. These M2-like cells exhibited up-regulated IL10, CCL18, APOE, CSF1 (M-CSF), and CCL2 signaling pathways. Further, SARS-CoV-2-specific T cells were observed in pleural effusion earlier than in peripheral blood. Together, our results suggest that severe SARS-CoV-2 infection causes immune dysregulation by inducing M2 polarization and subsequent T cell exhaustion. This study improves our understanding of COVID-19 pathogenesis.