Identification and characterization of 7-azaindole derivatives as inhibitors of the SARS-CoV-2 spike-hACE2 protein interaction.

The COVID-19 pandemic, caused by SARS-CoV-2, has become a global public health crisis. The entry of SARS-CoV-2 into host cells is facilitated by the binding of its spike protein (S1-RBD) to the host receptor hACE2. Small molecule compounds targeting S1-RBD-hACE2 interaction could provide an alternative therapeutic strategy sensitive to viral mutations. In this study, we identified G7a as a hit compound that targets the S1-RBD-hACE2 interaction, using high-throughput screening in the SARS2-S pseudovirus model. To enhance the antiviral activity of G7a, we designed and synthesized a series of novel 7-azaindole derivatives that bind to the S1-RBD-hACE2 interface. Surprisingly, ASM-7 showed excellent antiviral activity and low cytotoxicity, as confirmed by pseudovirus and native virus assays. Molecular docking and molecular dynamics simulations revealed that ASM-7 could stably bind to the binding interface of S1-RBD-hACE2, forming strong non-covalent interactions with key residues. Furthermore, the binding of ASM-7 caused alterations in the structural dynamics of both S1-RBD and hACE2, resulting in a decrease in their binding affinity and ultimately impeding the viral invasion of host cells. Our findings demonstrate that ASM-7 is a promising lead compound for developing novel therapeutics against SARS-CoV-2.

Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.

PURPOSE: Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation. METHODS: We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre-SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri-COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models. RESULTS: Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri-COVID-19 sample. The algorithm performance was stable across both pre- and peri-COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm. CONCLUSION: Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high-ACU risk patients and enable preventive interventions to avoid ACU.

Impact of Contextual Factors on the Attendance and Role in the Evidence-Based Chronic Disease Prevention Programs Among Primary Care Practitioners in Shanghai, China.

BACKGROUND: The implementation of evidence-based approaches by general practitioners (GPs) is new in the primary care setting, and few quantitative studies have evaluated the impact of contextual factors on the attendance of these approaches. METHODS: In total, 892 GPs from 75 community healthcare centers (CHCs) in Shanghai completed our survey. We used logistic regression to analyze factors affecting the number of evidence-based chronic disease programs attended by GPs and whether they had held the lead position in such a program. RESULTS: A total of 346 (38.8%) of the practitioners had never participated in any evidence-based chronic disease prevention (EBCDP) program. The EBCDP interventions in which the GPs had participated were predominantly related to hypertension, diabetes, and cardiovascular disease. However, the proportion of GPs in the lead role was relatively low, between 0.8% (programs involving prevention and control of asthma) and 5.0% (diabetes). Organizational factors and areas were significantly associated with evidence-based practices (EBPs) of the GP, while monthly income and department were the most significantly related to GPs who have the lead role in a program. The results indicated that GPs who had taken the lead position had higher scores for policy and economic impeding factors. GPs who were men, had a higher income, and worked in prevention and healthcare departments and urban areas were more likely to take the lead position. CONCLUSION: Evidence-based programs for chronic diseases should be extended to different types of diseases. Personal, organizational, political, and economic factors and the factors of female sex, lower income, department type, and suburban area environment should be considered to facilitate the translation of evidence to practice.

[Variation Characteristics and Source Analysis of Carbonaceous Aerosols in Winter in Jiashan].

Organic carbon (OC), elemental carbon (EC), and PM2.5 concentration data obtained from Shanxi Super Station in Jiashan County of Jiaxing City, in the winter of 2018 and 2019, were analyzed to determine the variation and potential source areas of carbonaceous aerosols. The results show that OC concentrations in the winter of 2018 and 2019 were 6.90 µg·m-3 and 5.63 µg·m-3, respectively, while EC concentrations were 2.47 µg·m-3 and 1.57 µg·m-3, respectively. The concentrations of OC and EC in the winter of 2019 were lower than those in the winter of 2018, by approximately 18.4% and 36.4%, respectively. In 2018 and 2019, the concentrations of secondary organic carbon (SOC), calculated using the minimum R-squared (MRS) method, were 1.49 µg·m-3 and 1.97 µg·m-3, respectively, and the concentrations of primary organic carbon (POC) were 5.41 µg·m-3 and 3.66 µg·m-3, respectively. The proportion of POC in OC showed a downward trend, from 96.0% in December 2018 to 64.9% in February 2020, indicating a decrease of 31.1 percentage points. SOC showed an upward trend, increasing by 31.1 percentage points from 4.0% in December 2018 to 35.1% in February 2020. It is worth noting that with the increase in PM2.5 concentration, the concentration of OC and EC increased by 474.7% and 408.2%, respectively, although the proportion of OC in PM2.5 decreased from 18.8% to 12.3%. and the percentage of OC decreased from 5.8% to 3.3%. The contribution of POC to PM2.5 did not fluctuate, and only decreased significantly above 150 µg·m-3, while the contribution of SOC to PM2.5 first decreased and then increased. In Jiaxing, the potential sources of OC and EC were mainly southern Jiangsu, southeastern Anhui, local Jiaxing, and northern Zhejiang. In the winter of the contribution concentrations of OC and EC in the main potential source regions were approximately 2 µg·m-3 and 6 µg·m-3 lower, respectively, than in winter 2018. The range of high values in the potential source regions also decreased in 2019. Before the COVID-19 epidemic, it was affected by both motor vehicle exhaust emissions and coal burning. During the Spring Festival and home isolation, due to traffic control and other reasons, motor vehicle emissions were reduced, which leaving coal burning as the main contributor.

Oral etoposide combined with oral arsenic plus retinoic acid for two cases with newly diagnosed high-risk acute promyelocytic leukemia during COVID19 pandemic.

Acute promyelocytic leukemia (APL) is a highly curable hematology malignancy. The major factor influence prognosis of APL is early deaths (ED) during the course of induction therapy, especially in high-risk APL. Therefore, effective reduction of white blood cells and correction of coagulation abnormalities are the key points of treatment for high-risk APL. Due to COVID19 pandemic in China since Jan 2020, some patients with hematologic malignancies suspected of COVID-19 infection had been isolated and traditional intravenous chemotherapy drugs is not available in isolated wards. We had explored a regimen of an oral etoposide to reduce the tumor burden for high-risk APL and dual induction with retinoic acid (ATRA) and oral arsenic realgar-Indigo nautralis formula (RIF), and finally two cases of high-risk APL patients received complete remission in one month. It is indicated that pure oral induction regimen: oral etoposide, ATRA and RIF provides a novel therapy in outpatient clinics.

The experience of treating patients with pneumonia secondary to the coronavirus disease 2019 using the Guizhi method of traditional Chinese medicine

This paper is a discussion of Professor Tang Nong's approach to the diagnosis and treatment of the coronavirus disease 2019 (COVID-19) while providing a case report at the end. Professor Tang Nong considered that the main etiologies of the disease are 'cold, wet, and poisonous.' He suggested resolving the body's dampness by balancing internal organ functions, detoxifying the lungs, and providing heat. However, the treatment of cold with herbs and cleansing heat must not be performed too early to prevent the spread of the disease. Using principles from the basic theory of Fuyang Pai from traditional Chinese medicine (TCM), this project used the Huashi Qingfei immune formula (modified Guizhi Erchen decoction), which has been shown to be effective, to treat patients diagnosed with COVID-19. At present, the participation of TCM in our hospital is over 96% with a cure rate of approximately 90%.