ObjectiveTo analyze the epidemiological characteristics of latent tuberculosis infection (LTBI) among newly detained populations in eastern China, to identify high-risk groups, and to provide a scientific basis for formulating tuberculosis prevention and control strategies in the prison system. MethodsA cross-sectional study was conducted among the newly admitted detainees in two prisons in eastern China in 2022. Data on demographic characteristics, behavioral risk factors and previous disease history of the research subjects were collected through a structured questionnaire survey. The LTBI status of the detainees was determined using the QuantiFERON-TB Gold In-Tube (QFT-GIT) method. Lasso regression was used to screen variables, followed by multivariate logistic regression analysis to investigate the influencing factors of LTBI. ResultsA total of 305 detainees were included in the study. The median age of detainees was 35 (31, 43) years. The study population was predominantly male (67.21%), of Han ethnicity (95.41%), had a junior or senior high school education (59.34%), and was unemployed (31.80%). A history of smoking was reported by 52.79% of participants, while 57.70% reported no alcohol consumption. The majority had no history of hypertension (85.90%), diabetes mellitus (93.77%), human immunodeficiency virus (HIV) infection (97.38%), familial genetic diseases (95.08%), surgery or trauma (73.77%), drug use (92.79%), or hepatitis (93.77%). The LTBI rate was 14.75%. After comparing the demographic characteristics of LTBI and non-infected groups, it was found that smoking history (χ²=7.40, P=0.025), drug use history (χ²=5.49, P=0.019), and HIV infection (χ²=8.12, P=0.004) were statistically correlated with LTBI infection. The results of multivariate logistic regression analyses showed that smoking [adjusted odds ratio (aOR)=4.08, 95%CI: 1.60‒10.42, P=0.003], HIV infection (aOR=11.57, 95%CI: 2.50‒53.51, P=0.002) and drug use (aOR=3.04, 95%CI: 1.02‒9.09, P=0.046) were risk factors for LTBI. ConclusionThe LTBI rate among newly detainees in two prisons in eastern China is slightly lower than that among long-term detainees. Early screening and intervention should be implemented for newly detainees, with particular attention focused on high-risk groups such as those with a history of smoking, HIV infection, or drug use.

BACKGROUND:Liver organoids are of great significance to elucidate the exact pathological mechanism of liver diseases and the treatment of liver diseases. OBJECTIVE:To summarize the basic research in this field at home and abroad,review the important research progress in the construction of liver organoids,disease modeling and transplantation therapy,and discuss the application prospect of combined tissue engineering technology of liver organoids. METHODS:The relevant articles included in PubMed and CNKI databases were searched.The English and Chinese search terms were"liver,organoids,liver diseases."The main search time was from April 2018 to April 2024.Duplicate literature was excluded by manual reading.Finally,94 articles were included for review and analysis. RESULTS AND CONCLUSION:The seed cells constructed by liver organoids are mainly concentrated in adult cells and pluripotent stem cells,which promote the generation of organoids by assisting various cytokines to participate in signal guidance and providing 3D microenvironment by extracellular matrix.However,the overall maturity is not high,which is expected to improve this problem by combining tissue engineering technology.In vitro disease modeling is mainly studied in the field of simple diseases and single-gene genetic diseases.Organoids highly retain patient genetic characteristics,and it is expected to simulate more complex liver diseases and clarify deeper pathological mechanisms by combining CRISPR-Cas9 gene correction and other emerging technologies.In vivo transplantation treatment,liver organoids can be safely and effectively implanted,showing amazing liver function replacement potential,tissue regeneration ability,and may also be combined with other tissue engineering materials to achieve therapeutic purposes.

Objective To analyze effects of caragliflozin and empagliflozin on inflammatory markers,glucose and lipid metabolism and miR-144 expression in obese patients with type 2 diabetes mellitus(T2DM).Methods A total of 148 obese T2DM patients admitted to our hospital from June 2021 to May 2024 were selected and divided into the caragliflozin group and the empagliflozin group by random number table method.The two groups were treated with canagliflozin and empagliflozin on the basis of conventional treatment for 6 months.The inflammatory indicators,glucose metabolism indicators,lipid metabolism indicators,microRNA-144(miR-144)expression,body mass index(BMI),clinical efficacy and incidence of adverse reactions were compared between the two groups.Results After a total of 7 cases were excluded during the treatment period,there were 71 cases in the caragliflozin group and 70 cases in the empagliflozin group.After treatment,the levels of tumor necrosis factor-α,interleukin-6,C-reactive protein,fasting blood glucose(FBG),2 h postprandial blood glucose(2 h-PPG),glycosylated hemoglobin(HbA1c),triglyceride(TG),total cholesterol,low density lipoprotein cholesterol,BMI and miR-144 expression were lower than those before treatment in two groups of patients(P＜0.05),and the levels of FBG,2 h-PPG,HbA1c,TG and miR-144 expression were lower in the caragliflozin group than those of the empagliflozin group(P＜0.05).After treatment,high density lipoprotein cholesterol was higher than that before treatment in the two groups(P＜0.05),and that in the canagliflozin group was higher than the empagliflozin group(P＜0.05).There were no significant differences in the clinical efficacy and incidence of adverse reactions between the two groups after treatment(P＞0.05).Conclusion Both caragliflozin and empagliflozin have certain therapeutic efficacy and good safety for obese T2DM patients,and caragliflozin is more effective in improving glucose and lipid metabolism.

Objective:To explore the distribution of Traditional Chinese Medicine constitution among patients with impaired awareness of hypoglycemia (IAH) and identify risk factors for IAH in patients with diabetes mellitus, to develop a risk prediction model. The aim is to validate the models′ predictive accuracy to facilitate early prevention and treatment of IAH.Methods:A case control study employing convenience sampling model was conducted on 1351 hospitalized patients with diabetes mellitus in the endocrinology departments of Changshu Hospital Affiliated to Nanjing University of Chinese Medicine and Affiliated Hospital of Jiangsu University, between August 2021 and December 2023. Traditional Chinese medicine constitution types were determined using the Traditional Chinese Medicine Constitution Classification and Judgment (ZYYXH/T157-2009). Data were divided into training and test sets at a ratio of 7∶3. Two prediction models were developed: Model 1, a conventional IAH prediction model for patients with diabetes mellitus, and Model 2, an IAH prediction model for patients with diabetes mellitus incorporating traditional Chinese medicine constitution. Nomograms were drawn for both models. The Hosmer-Lemeshow goodness-of-fit test, calibration curve, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated to evaluate the effectiveness of models 1 and 2. The improvement in prediction performance between Models 1 and 2 was assessed using Delong test, AUC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).Results:The study included 1 283 patients with diabetes mellitus, including 578 males and 705 females, aged (59.61 ± 14.09) years. The incidence of IAH among patients with diabetes mellitus was 20.50% (263/1283), with yang deficiency constitution being the most prevalent traditional Chinese medicine constitution type, at 47.53% (125/263). Multivariate analysis revealed that age, body mass index, course of diabetes, neurological hypoglycemia symptoms, hypoglycemia symptoms and severe hypoglycemia history were the influencing factors of Model 1 (all P<0.05); age, body mass index, neurological hypoglycemic symptoms, hypoglycemic symptoms, history of severe hypoglycemia, and traditional Chinese medicine constitution were the influencing factors of Model 2 (all P<0.05). The Hosmer-Lemeshow goodness-of-fit test showed a good fit of Model 2 [training set ( χ2=8.48, P>0.05), test set ( χ2=3.92, P>0.05)]. The Delong test results showed that the AUC for Model 2 was 0.96 for both the training and test sets, significantly higher than the AUCs of the 0.90 and 0.91 for Model 1 ( Z=-7.27, -3.70, both P<0.01). Furthermore, NRI was 0.66 ( 95%CI 0.53-0.79, P<0.01) and IDI was 0.02 (95% CI 0.01-0.03, P<0.05) for Model 2. Comparative analysis of clinical utility demonstrated that the net benefit of Model 2 for predicting IAH in patients with diabetes mellitus surpassed that of Model 1 across threshold probabilities ranging from 5% to 100%. Conclusions:The study constructed a nomogram prediction model included traditional Chinese medicine constitution with good predictive performance for IAH in patients with diabetes mellitus, and is of significant clinical value for identifying high-risk IAH populations.IAH patients mainly have a biased constitution, indicating that medical staff can reduce the incidence of IAH by improving the patients′ constitution.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Rheumatoid arthritis(RA)is a common autoimmune disease with synovitis as core pathological manifestation,with strong injury and high disability rate,which can not be cured completely at present.Recent studies have shown that tumor necro-sis factor receptor-related factor 6(TRAF6),as a ubiquitin ligase E3,plays a key role in autoimmune diseases and inflammatory diseases through a variety of pathways.This article reviews structure,function,ubiquitin regulation of TRAF6 and research progress of pathogenesis of RA.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective:To explore the expression and mechanism of lncRNA MALAT1 in negative sputum for tuberculous bac-terium.Methods:Expression of lncRNA MALAT1 in peripheral blood mononuclear cells(PBMC)of patients with positive sputum bacteria(Positive group)and negative sputum bacteria(Negative group)and healthy volunteers(HC group)was detected by RT-PCR.ELISA was used to detect expression levels of TNF-α,IL-1β and IL-6 in plasma.Expression of lncRNA MALAT1 in mice macro-phages RAW264.7 was silenced by siRNA interference,and RAW264.7 cells were infected with mycobacterium tuberculosis(MTB)H37Rv.Cells were divided into four groups:Control group,Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group.Proliferation activity of RAW264.7 cells in each group was detected by CCK-8 method.The number of MTB in each group was detected by CFU.Expressions of TNF-α,IL-1β and IL-6 in supernatant of RAW264.7 cells were detected by ELISA.Results:Compared with HC group,expressions of lncRNA MALAT1 in PBMC,and TNF-α,IL-1β and IL-6 in plasma were significantly increased in Positive group and Negative group(P<0.01).Compared with Control group,expression level of lncRNA MALAT1,proliferation activity,CFU value,and concentrations of TNF-α,IL-1β and IL-6 in supernatant of Control+MTB group,MTB+si-NC group and MTB+si-MALAT1 group were significantly increased(P<0.05).Compared with MTB+si-NC group,the above detection indexes in MTB+si-MALAT1 group were significantly decreased(P<0.05),while there was no significant difference in Control+MTB group(P>0.05).Conclusion:The significantly increased expression of MALAT1 in patients with negative sputum for tuberculous bacterium is positively correlated with expression of plasma inflammatory factors,while the silence of MALAT1 expression can reduce MTB induced inflammatory response by inhibiting the proliferation and phagocytosis of MTB infected macrophages.

Objective To analyze effects of caragliflozin and empagliflozin on inflammatory markers,glucose and lipid metabolism and miR-144 expression in obese patients with type 2 diabetes mellitus(T2DM).Methods A total of 148 obese T2DM patients admitted to our hospital from June 2021 to May 2024 were selected and divided into the caragliflozin group and the empagliflozin group by random number table method.The two groups were treated with canagliflozin and empagliflozin on the basis of conventional treatment for 6 months.The inflammatory indicators,glucose metabolism indicators,lipid metabolism indicators,microRNA-144(miR-144)expression,body mass index(BMI),clinical efficacy and incidence of adverse reactions were compared between the two groups.Results After a total of 7 cases were excluded during the treatment period,there were 71 cases in the caragliflozin group and 70 cases in the empagliflozin group.After treatment,the levels of tumor necrosis factor-α,interleukin-6,C-reactive protein,fasting blood glucose(FBG),2 h postprandial blood glucose(2 h-PPG),glycosylated hemoglobin(HbA1c),triglyceride(TG),total cholesterol,low density lipoprotein cholesterol,BMI and miR-144 expression were lower than those before treatment in two groups of patients(P＜0.05),and the levels of FBG,2 h-PPG,HbA1c,TG and miR-144 expression were lower in the caragliflozin group than those of the empagliflozin group(P＜0.05).After treatment,high density lipoprotein cholesterol was higher than that before treatment in the two groups(P＜0.05),and that in the canagliflozin group was higher than the empagliflozin group(P＜0.05).There were no significant differences in the clinical efficacy and incidence of adverse reactions between the two groups after treatment(P＞0.05).Conclusion Both caragliflozin and empagliflozin have certain therapeutic efficacy and good safety for obese T2DM patients,and caragliflozin is more effective in improving glucose and lipid metabolism.

miR-101-3p is an evolutionarily conserved non-coding RNA that has shown aberrant expression and regulatory functios in various cardiovascular diseases(CVDs)in recent years.It participates in key pathological processes including post-infarction myocardial remodeling,myocardial fibrosis in heart failure and the development of atherosclerosis by modulating pathways such as apoptosis,autophagy and metabolism.In addition,it exhibits immunoregulatory potential in myocardium injury related to systemic diseases.This review summarizes the recent advances in understanding the role of miR-101-3p in CVDs,focusing on its expression dynamics and regulatory mechanisms in different disease models.It further elaborates on the biological effects of miR-101-3p in cardiovascular pathology and explores its potential as a diagnostic biomarker and therapeutic target.miR-101-3p is expected to provide novel insights for early detection and targeted intervention of cardiovascular diseases.