ObjectiveTo construct a nomogram prediction model for non-valvular persistent atrial fibrillation （PeAF） patients with left ventricular hypertrophy （LVH） ， followed by prognostic analysis through follow-up. MethodsThis study retrospectively enrolled 949 patients with newly diagnosed and hospitalized non-valvular PeAF. Among them， 403 patients presented with LVH. The cohort was randomly stratified into a training set （n=665） and a validation set （n=284）. Univariate and multivariate Logistic regression analyses were employed to identify independent risk factors for PeAF complicated by LVH. A nomogram prediction model was subsequently constructed and evaluated for discriminative ability， calibration， and clinical utility using receiver operating characteristic （ROC） curve analysis， calibration plots， and decision curve analysis （DCA）. ResultsSeven independent risk factors were ultimately identified and included in the prediction model： female sex， hypertension， diabetes， red blood cell distribution width-SD （RDW-SD）， body mass index （BMI）， left atrial diameter （LAD）， and left ventricular ejection fraction （LVEF）. The area under the ROC curve （AUC） in the training set was 0.862 （95% CI： 0.834-0.890）， and in the validation set， it was 0.870 （95% CI： 0.829-0.911）， demonstrating excellent predictive performance. ConclusionIndependent risk factors for LVH in PeAF patients include female， hypertension， diabetes， RDW-SD， BMI， LAD， and LVEF. The prediction model built based on this can help early identification of PeAF patients with high risk of LVH. At the same time， the incidence of major adverse cardiovascular events （MACE） is higher in PeAF patients with LVH. Patients with atrial fibrillation combined with LVH may benefit from catheter ablation.

Objective:This study aims to investigate the relationship between plague epidemics and environmental factors in Lincang City.Methods:Epidemiological survey data and environmental factors (including altitude, temperature, rainfall, and season) from plague occurrence sites in Lincang City from 1990 to 2020 were collected from Lincang Center for Disease Control and Prevention and the data sharing platform of the data sharing service network of the China Meteorological Administration, and analyzed using multiple correspondence analysis (MCA).Results:From 1990 to 2005, a total of 38 outbreaks of plague among rodents had been reported, 17 of which involved human beings. Since 2006, the plague had entered a dormant period, with no further epidemics reported up to 2020. The plague epidemic in Lincang City exhibited a clear seasonal variation, with the primary epidemic season occurred from autumn to early winter, peaking in September and October. Furthermore, the plague epidemic in Lincang City exhibited a pronounced spatial aggregation, with the primary affected regions including Linxiang County, Gengma Dai and Va Autonomous County, Zhenkang County, and Yun County. MCA showed that the Cronbach' α coefficients of all variables in the first and second dimensions were 0.87 and 0.82, respectively, and the characteristic roots were 3.27 and 2.91, respectively. The cumulative contribution rate of the two dimensions was 84.60%. Plague outbreaks were more likely in winter in areas at altitudes of > 1 400 - 1 650 m with 1 000 - 1 200 mm average annual rainfall and 16.0 - 17.9 ℃ average annual temperature, with increased severity. Regions at altitudes of > 500 - 700 m and > 900 - 1 150 m, with average annual rainfall of < 1 000, 1 201 - 1 400 and > 1 600 mm and average annual temperature of 18.0 - 19.9 ℃, showed a higher susceptibility to plague outbreaks, with notably pronounced incidences.Conclusions:The prevalence of plague epidemics is closely related to the local climatic conditions of natural foci in Lincang City. Therefore, it is imperative to enhance the monitoring of these climatic conditions, particularly meteorological data, to facilitate more effective prevention and control of plague outbreaks.

Objective:This study aims to investigate the relationship between plague epidemics and environmental factors in Lincang City.Methods:Epidemiological survey data and environmental factors (including altitude, temperature, rainfall, and season) from plague occurrence sites in Lincang City from 1990 to 2020 were collected from Lincang Center for Disease Control and Prevention and the data sharing platform of the data sharing service network of the China Meteorological Administration, and analyzed using multiple correspondence analysis (MCA).Results:From 1990 to 2005, a total of 38 outbreaks of plague among rodents had been reported, 17 of which involved human beings. Since 2006, the plague had entered a dormant period, with no further epidemics reported up to 2020. The plague epidemic in Lincang City exhibited a clear seasonal variation, with the primary epidemic season occurred from autumn to early winter, peaking in September and October. Furthermore, the plague epidemic in Lincang City exhibited a pronounced spatial aggregation, with the primary affected regions including Linxiang County, Gengma Dai and Va Autonomous County, Zhenkang County, and Yun County. MCA showed that the Cronbach' α coefficients of all variables in the first and second dimensions were 0.87 and 0.82, respectively, and the characteristic roots were 3.27 and 2.91, respectively. The cumulative contribution rate of the two dimensions was 84.60%. Plague outbreaks were more likely in winter in areas at altitudes of > 1 400 - 1 650 m with 1 000 - 1 200 mm average annual rainfall and 16.0 - 17.9 ℃ average annual temperature, with increased severity. Regions at altitudes of > 500 - 700 m and > 900 - 1 150 m, with average annual rainfall of < 1 000, 1 201 - 1 400 and > 1 600 mm and average annual temperature of 18.0 - 19.9 ℃, showed a higher susceptibility to plague outbreaks, with notably pronounced incidences.Conclusions:The prevalence of plague epidemics is closely related to the local climatic conditions of natural foci in Lincang City. Therefore, it is imperative to enhance the monitoring of these climatic conditions, particularly meteorological data, to facilitate more effective prevention and control of plague outbreaks.

Objective To evaluate the consistency of DNA coding region single nucleotide polymorphism(cSNP)genotyping at the DNA and RNA levels in common body fluid samples based on the next-generation sequencing platform.Methods After extensive literature retrieval,25 cSNP loci of 8 human tissue-specific mRNAs in peripheral blood,semen and vaginal secretion were selected.Two cSNP multiplex genotyping panels based on DNA and RNA,respectively,were developed for use on the MiSeq FGx sequencing platform.45 body fluid samples(including 14 peripheral blood samples,15 semen samples and 16 vaginal secretion samples)were sequenced and analyzed.The inconsistent typing results of DNA and RNA were rechecked by Sanger sequencing.Results The results of cSNP genotyping at the DNA and RNA levels in peripheral blood were completely consistent.Among the 15 semen samples,the genotypes of rs1995640 and rs 1995641 on the TGM4 gene were inconsistent in 3 cases.Among the 16 vaginal secretion samples,there were 2 cases,1 case and 2 case with inconsistent results of rs3869098,rs10947121 and rs12110470 in MUC22 gene,respectively.Conclusion In this study,MiSeq FGx sequencing and Sanger sequencing were used to test 25 cSNP loci with body fluid tissue specificity.The same typing results at the DNA and RNA levels were observed at 20 cSNPs.Inconsistent genotypes at the DNA and RNA levels were observed at 5 cSNPs on the TGM4 and MUC22 genes.This study provides experimental methods and data for forensic cSNP studies.

Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

ObjectiveTo develop a scientifically rigorous and contextually appropriate instrument for evaluating the health service experience of pulmonary tuberculosis patients in China, to enable systematic assessment of core medical care dimensions, and to provide quantitative evidence for service improvement. MethodsGrounded in the theoretical framework of healthcare accessibility and the clinical care pathway for tuberculosis patients, the tool was developed through a systematic literature review and the Delphi expert consultation method. A multi-stage cluster sampling strategy was employed to survey pulmonary tuberculosis patients who had been receiving treatment for more than two months, aimed to explore the scale’s applicability in real-world settings. Reliability was assessed using Cronbach’s α and split-half reliability coefficients. Validity was evaluated through content validity, structural validity, convergent validity, and discriminant validity. ResultsThe tool was composed of 21 items across four dimensions: awareness, accessibility, affordability, and acceptability of tuberculosis medical care. It demonstrated a Cronbach’s α coefficient of 0.838 and a split-half reliability coefficient of 0.859. Exploratory factor analyses extracted six factors: satisfaction with healthcare services, supportive role of nurses, affordability of treatment costs, doctor-patient communication, waiting time for medical appointments, and transportation cost. The goodness-of-fit index (GFI) and other indices met the recommended standards, with the loading matrix indicating robust structural validity of the tool. The constructed factor model exhibited satisfactory content validity and discriminant validity. ConclusionThe scale for assessing patients’ experiences with tuberculosis-related medical care developed in this study demonstrates good reliability and validity and serves as a practical tool for evaluating patient experiences of tuberculosis medical care in China.

The variants of pathogens causing major infectious diseases have a significant impact on the prevention, diagnosis, and treatment of these diseases. Rapid identification and efficient treatment rely on the detection and identification technologies for these variants. Molecular diagnostic techniques have greatly improved the sensitivity, specificity, and speed of detecting variants of major infectious diseases. With the rapid advancement of sequencing technologies, both second-generation and third-generation sequencing have shown substantial potentials in identifying variants of major infectious diseases. This article reviews the research and application status of molecular diagnostic techniques for variants of severe acute respiratory coronavirus-2, mycobacterium tuberculosis, and human immunodeficiency virus, and prospects development directions in this field, aiming to provide references for the detection and identification of major infectious diseases variants.

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.