LS-MolGen: Ligand-and-Structure Dual-driven Deep Reinforcement Learning for Target-specific Molecular Generation Improves Binding Affinity and Novelty (preprint)

Molecule generative models based on deep learning have attracted significant attention in de novo drug design. However, most current generative approaches are either only ligand-based or only structure-based, which do not leverage the complementary knowledge from ligands and the structure of binding target. In this work, we proposed a new ligand and structure combined molecular generative model, LS-MolGen, that integrates representation learning, transfer learning, and reinforcement learning. Focus knowledge from transfer learning and special explore strategy in reinforcement learning enables LS-MolGen to generate novel and active molecules efficiently. The results of evaluation using EGFR and case study of inhibitor design for SARS-CoV-2 Mpro showed that LS-MolGen outperformed other state-of-the-art ligand-based or structure-based generative models and was capable of de novo designing promising compounds with novel scaffold and high binding affinity. Thus, we recommend that this proof-of-concept ligand-and-structure-based generative model will provide a promising new tool for target-specific molecular generation and drug design.

Neuropilin-1-Mediated SARS-CoV-2 Infection in Bone Marrow-Derived Macrophages Inhibits Osteoclast Differentiation.

In humans, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause medical complications across various tissues and organs. Despite the advances to understanding the pathogenesis of SARS-CoV-2, its tissue tropism and interactions with host cells have not been fully understood. Existing clinical data have revealed disordered calcium and phosphorus metabolism in Coronavirus Disease 2019 (COVID-19) patients, suggesting possible infection or damage in the human skeleton system by SARS-CoV-2. Herein, SARS-CoV-2 infection in mouse models with wild-type and beta strain (B.1.351) viruses is investigated, and it is found that bone marrow-derived macrophages (BMMs) can be efficiently infected in vivo. Single-cell RNA sequencing (scRNA-Seq) analyses of infected BMMs identify distinct clusters of susceptible macrophages, including those related to osteoblast differentiation. Interestingly, SARS-CoV-2 entry on BMMs is dependent on the expression of neuropilin-1 (NRP1) rather than the widely recognized receptor angiotensin-converting enzyme 2 (ACE2). The loss of NRP1 expression during BMM-to-osteoclast differentiation or NRP1 neutralization and knockdown can significantly inhibit SARS-CoV-2 infection in BMMs. Importantly, it is found that authentic SARS-CoV-2 infection impedes BMM-to-osteoclast differentiation. Collectively, this study provides evidence for NRP1-mediated SARS-CoV-2 infection in BMMs and establishes a potential link between disturbed osteoclast differentiation and disordered skeleton metabolism in COVID-19 patients.

Efficiency Analysis of New Rural Cooperative Medical System in China: Implications for the COVID-19 Era.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has caused a huge impact on the Chinese residents' health and economic level. In the pandemic background, the country and its institutions have introduced pandemic-related insurance to stabilize the national situation. At this stage, insurance has played an increasingly important role in social life. With the popularization of insurance, the idea of buying insurance to avoid risk has gradually become popular among people. Among them, the New Rural Cooperative Medical System (NRCMS) has been farmers' common choice. The NRCMS, a mutual aid system created by farmers spontaneously in the country, plays a great role in guaranteeing farmers access to basic health services, alleviating poverty caused by disease and returning to poverty due to disease, and promoting poverty alleviation and rural revitalization. Given this backdrop, we study the efficiency of the NRCMS that can effectively promote poverty alleviation and rural revitalization and ensure the people's happy life. Implementing the Data Envelopment Analysis (DEA), we find that technological progress is one of the main factors influencing the efficiency of the NRCMS. Therefore, it is important to improve the technology for providing the efficiency of the NRCMS and promoting the happiness of the society.

Neuropilin-1 Mediates SARS-CoV-2 Infection in Bone Marrow-derived Macrophages (preprint)

SARS-CoV-2 infection in human can cause medical complications across various tissues and organs. Despite of the advances to understanding the pathogenesis of SARS-CoV-2, its tissue tropism and interactions with host cells have not been fully understood. Existing clinical data have suggested possible SARS-CoV-2 infection in human skeleton system. In the present study, we found that authentic SARS-CoV-2 could efficiently infect human and mouse bone marrow-derived macrophages (BMMs) and alter the expression of macrophage chemotaxis and osteoclast-related genes. Importantly, in a mouse SARS-CoV-2 infection model that was enabled by the intranasal adenoviral (AdV) delivery of human angiotensin converting enzyme 2 (hACE2), SARS-CoV-2 was found to be present in femoral BMMs as determined by in situ immunofluorescence analysis. Using single-cell RNA sequencing (scRNA-Seq), we characterized SARS-CoV-2 infection in BMMs. Importantly, SARS-CoV-2 entry on BMMs appeared to be dependent on the expression of neuropilin-1 (NRP1) rather than the widely recognized receptor ACE2. It was also noted that unlike brain macrophages which displayed aging-dependent NRP1 expression, BMMs from neonatal and aged mice had constant NRP1 expression, making BMMs constantly vulnerable target cells for SARS-CoV-2. Furthermore, it was found that the abolished SARS-CoV-2 entry in BMM-derived osteoclasts was associated with the loss of NRP1 expression during BMM-to-osteoclast differentiation. Collectively, our study has suggested that NRP1 can mediate SARS-CoV-2 infection in BMMs, which precautions the potential impact of SARS-CoV-2 infection on human skeleton system.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study.

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.

Impact of the 2019 Novel Coronavirus Disease Pandemic on the Performance of a Cardiovascular Department in a Non-epidemic Center in Beijing, China.

Background: Knowledge of the impact of the 2019 novel coronavirus disease (COVID-19) pandemic on the performance of a cardiovascular department in a medical referral hub center from a non-epidemic area of China is limited. Method: The data on the total number of non-emergency medical cares (including the number of out-patient clinic attendances, the number of patients who were hospitalized in non-intensive care wards, and patients who underwent elective cardiac intervention procedures) and emergency medical cares [including the number of emergency department (ED attendances) and chest pain center (CPC attendances), as well as the number of patients who were hospitalized in coronary care unit (CCU) and the number of patients who underwent emergency cardiac intervention procedures] before and during the pandemic (time before the pandemic: 20th January 2019 to 31st March 2019 and time during the pandemic: 20th January 2020 to 31st March 2020) in the Department of Cardiology and Macrovascular Disease, Beijing Tiantan Hospital, Capital Medical University were collected and compared. Results: Both the non-emergency medical and emergency medical cares were affected by the pandemic. The total number of out-patient clinic attendance decreased by 44.8% and the total number of patients who were hospitalized in non-intensive care wards decreased by 56.4%. Pearson correlation analysis showed that the number of out-patient clinic attendance per day was not associated with the number of new confirmed COVID-19 cases and the cumulative number of confirmed COVID-19 patients in Beijing (r = -0.080, p = 0.506 and r = -0.071, p = 0.552, respectively). The total number of patients who underwent non-emergency cardiac intervention procedures decreased during the pandemic, although there were no statistically significant differences except for patent foramen ovale (PFO) occlusion (1.7 ± 2.9 vs. 8.3 ± 2.3, p = 0.035). As for the emergency medical cares, the ED attendances decreased by 22.4%, the total number of CPC attendances increased by 10.3%, and the number of patients who were hospitalized in CCU increased by 8.9%: these differences were not statistically significant. During the pandemic, the proportion of hospitalized patients with ST segment elevation myocardial infarction (STEMI) and non-ST segment elevation myocardial infarction (NSTEMI) significantly increased (19.0 vs. 8.7%, p < 0.001; 28.8 vs. 18.0%, p < 0.001, respectively); also, the number of primary percutaneous coronary intervention (PCI) increased by 10.3%. There was no significant difference between patients before and during the pandemic regarding the age, gender, baseline and discharge medication therapy, as well as length of stay and in-hospital mortality. Conclusions: Our preliminary results demonstrate that both the non-emergency and emergency medical cares were affected by the COVID-19 pandemic even in a referral medical center with low cross-infection risk. The number of the out-patient clinic attendances not associated with the number of confirmed COVID-19 cases could be due to different factors, such as the local government contamination measures. The proportion of hospitalized patients with acute myocardial infarction increased in our center during the pandemic since other hospitals stopped performing primary angioplasty. A hub-and-spoke model could be effective in limiting the collateral damage for patients affected by cardiovascular diseases when the medical system is stressed by disasters, such as COVID-19 pandemic.

Comparison of Clinical Outcomes in Patients with ST Elevation Myocardial Infarction with Percutaneous Coronary Intervention and the Use of a Telemedicine App Before and After the COVID-19 Pandemic at a Center in Beijing, China, from August 2019 to March 2020.

BACKGROUND The efficacy of telemedicine in reducing delay times and short-term adverse clinical outcomes in patients with ST segment elevation myocardial infarction (STEMI) during the coronavirus disease 2019 (COVID-19) pandemic is unclear. This study compared outcomes in patients with STEMI who had percutaneous coronary intervention (PCI) and the use of a telemedicine app from August 2019 to March 2020 at a single center in Beijing, China. MATERIAL AND METHODS A total of 243 patients with STEMI who underwent PCI were consecutively enrolled and divided into 2 groups according to the date, before or after the pandemic. The 2 groups were further divided into patients who used the app for consulting and those who did not. RESULTS The time from symptom onset to calling an ambulance (SCT), door to balloon time (DTB), and total ischemia time (TIT) were significantly prolonged in patients after the pandemic. Patients who used the app had shorter SCT, DTB, and TIT before and after the pandemic compared to those who did not. Adverse clinical outcomes were significantly higher after compared with before the pandemic, despite the incidence rate of stroke, any revascularization, and stent thrombosis. However, there was no significant difference in short-term adverse clinical outcomes between patients who used the app and those who did not before and after the pandemic. CONCLUSIONS Telemedicine reduced the delay time of STEMI patients during the COVID-19 pandemic. The difference in short-term adverse clinical outcomes was not statistically significant between patients who used the app and those who did not.