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The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current traditional village surveys usually require a large number of experts and scholars to conduct field research, which is an expensive and time-consuming method, especially for large-scale tasks. Therefore, this study proposes an automatic classification method based on deep learning (DL) for the identification of traditional village heritage value elements (TVHVE). The study evaluates four selected convolutional neural network (CNN) frames using traditional villages in Hubei Province as a sample dataset. The results show that Residual Network152 (ResNet152) is the most suitable CNN frame for identifying TVHVE in Hubei. The stability and consistency of various TVHVE present in the ResNet152 model were evaluated using Area Under Curve (AUC) and Precision Recall Curve (PRC), which indicated satisfactory prediction performance for most elements, except for specific elements such as tombstones and stone carvings, which showed lower accuracy. In addition, the study sheds light on the areas of concern of the model with respect to different TVHVE images and elucidates the reasons behind the confusion between elements through semantic clustering based on image classification and interpretability analysis using the Gradient-Weighted Class Activation Mapping (Grad-CAM) heat map. By using an automated classification method based on DL, this study significantly reduces the cost and effort associated with traditional surveys. At the same time, insight into areas of concern and confusion in the model improves guidance for conservation efforts and provides valuable references for subsequent research. [ABSTRACT FROM AUTHOR]

Objectives The objectives of present study were to analyze the association of the streptococcal infection with childhood Henoch–Schönlein purpura (HSP) in China. Methods: We performed a case-control study over a period of five years to evaluate the epidemiology and clinical characteristics of group A β-hemolytic streptococcal (GABHS) triggered HSP. Results: 1. The frequency of GABHS-triggered HSP was 15.1%, while that of GABHS infection developing HSP in children was 4.7%. 2.The epidemiological characteristics of HSP with streptococcal infection were similar to those of HSP alone. 3. The GABHS-triggered HSP cases had a significantly higher frequency of renal involvement than the noninfectious group. 4. IgA and IgG were significantly increased in the streptococcal infection group than in the noninfectious group, while the levels of C3 and C4 decreased significantly. Conclusions: GABHS infection is the most frequent agent in HSP children, and may aggravate the immune dysfunction and prolong the course of HSP. [ABSTRACT FROM AUTHOR]

Background: It would be rational to describe the pattern of the clinical characteristics of the survivors and the nonsurvivors during the critical intensive-infection era of coronavirus disease 2019 (COVID-19). The explicit objective of the current scoping review was to delineate the predictive risk factors associated with case fatality rate (CFR). Methods: Six retrospective studies of subjects infected with COVID-19 published between December 1, 2020, and March 30, 2020, describing nonsurvivors in Wuhan/Hubei, China, were identified. Results: There were 1769 subjects with a mean age of 52 years, and 65.9% were male. The highest comorbidity reported was cardiovascular diseases at 22.2% (393/1769). The overall number of cases admitted to the intensive care unit was 228 (12.9%). The reported overall CFR was 7.7% (136/1769), with the highest at 28.2% (54/191), and the lowest at 1.4% (15/1099). The mean duration of onset until death for nonsurvivors was 15.3 days. Conclusion: We have found that older age, male gender, the longer duration from onset till death (days), development of acute respiratory distress syndrome/shock, preexisting diabetes, and preexisting cardiovascular diseases were the major risk factors associated with high CFR. [ABSTRACT FROM AUTHOR]

At the beginning of 2020, the COVID-19 pandemic was able to spread quickly in Wuhan and in the province of Hubei due to a lack of experience with this novel virus. Additionally, authories had no proven experience with applying insufficient medical, communication and crisis management tools. For a considerable period of time, the actual number of people infected was unknown. There were great uncertainties regarding the dynamics and spread of the Covid-19 virus infection. In this paper, we develop a system dynamics model for the three connected regions (Wuhan, Hubei excl. Wuhan, China excl. Hubei) to understand the infection and spread dynamics of the virus and provide a more accurate estimate of the number of infected people in Wuhan and discuss the necessity and effectivity of protective measures against this epidemic, such as the quarantines imposed throughout China. We use the statistics of confirmed cases of China excl. Hubei. Also the daily data on travel activity within China was utilized, in order to determine the actual numerical development of the infected people in Wuhan City and Hubei Province. We used a multivariate Monte Carlo optimization to parameterize the model to match the official statistics. In particular, we used the model to calculate the infections, which had already broken out, but were not diagnosed for various reasons. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19), caused by a novel corinavirus (later named SARS-CoV-2 virus), was fistly reported in Wuhan, Hubei Province, China towards the end of 2019. Large-scale spread within China and internationally led the World Health Organization to declare a Public Health Emergency of International Concern on 30th January 2020. The clinical manifestations of COVID-19 virus infection include asymptomatic infection, mild upper respiratory symptoms, severe viral pneumonia with respiratory failure, and even death. There are no antivirals of proven clinical efficacy in coronavirus infections. Remdesivir (GS-5734), a nucleoside analogue, has inhibitory effects on animal and human highly pathogenic coronaviruses, including MERS-CoV and SARS-CoV, in in vitro and in vivo experiments. It is also inhibitory against the COVID-19 virus in vitro. The aim of this study is to assess the efficacy and safety of remdesivir in adult patients with severe COVID-19.Methods: The protocol is prepared in accordance with the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidelines. This is a phase 3, randomized, double-blind, placebo-controlled, multicentre trial. Adults (≥ 18 years) with laboratory-confirmed COVID-19 virus infection, severe pneumonia signs or symptoms, and radiologically confirmed severe pneumonia are randomly assigned in a 2:1 ratio to intravenously administered remdesivir or placebo for 10 days. The primary endpoint is time to clinical improvement (censored at day 28), defined as the time (in days) from randomization of study treatment (remdesivir or placebo) until a decline of two categories on a six-category ordinal scale of clinical status (1 = discharged; 6 = death) or live discharge from hospital. One interim analysis for efficacy and futility will be conducted once half of the total number of events required has been observed.Discussion: This is the first randomized, placebo-controlled trial in COVID-19. Enrolment began in sites in Wuhan, Hubei Province, China on 6th February 2020.Trial Registration: ClinicalTrials.gov: NCT04257656. Registered on 6 February 2020. [ABSTRACT FROM AUTHOR]

Background: Rates of thrombolysis in most countries are well below best practice benchmarks. We aimed to investigate thrombolysis utilization and its associated factors in acute ischemic stroke (AIS) patients in Hubei province, China, to assess neurologists' experiences of the treatment, and to identify barriers against the treatment from perspective of AIS patients and neurologists.Methods: Survey of 2096 AIS patients and 709 neurologists from 66 hospitals was conducted in Hubei province between 2014 and 2015. A multivariable logistic regression model was utilized to identify the factors associated with thrombolysis utilization and neurologists' experiences with thrombolysis.Results: Of the 2096 AIS patients, only 3.8% received thrombolysis. Of the 709 neurologists, 66.0% reported using thrombolysis for AIS patients. The main reasons for not using thrombolysis were late arrival of patients, fear of the risk of complications of thrombolysis, and light or quickly recovered stroke symptoms. The behavior and clinical characteristics of patients, including early admission to hospital (odds ratio [OR] = 5.81, 95% confidence intervals [CI] 3.31-10.20), using emergency medical services to be hospitalized (OR = 3.36, 95% CI 2.00-5.62), stroke history (OR = 0.53, 95% CI 0.28-0.99), and National Institute of Health Stroke Scale score < 4 (OR = 0.46, 95% CI 0.27-0.77) were shown to significantly affect the thrombolysis utilization in the multivariate model. In addition, hospital grade (OR = 2.84, 95% CI 1.84-4.37), education level (OR = 2.49, 95% CI 1.09-5.73), and working years (OR = 1.88, 95% CI 1.18-3.00) were strongly associated with neurologists' experiences of thrombolysis.Conclusions: A very low proportion of AIS patients received thrombolysis in Hubei province, China. Considerable education programs and interventions were required regarding knowledge of stroke treatment for clinicians and proper behavior after stroke for AIS patients and their families. [ABSTRACT FROM AUTHOR]