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.

Chronic obstructive pulmonary disease (COPD) currently lacks effective treatments to halt disease progression, making the search for preventive and therapeutic drugs a pressing issue. Natural products, with their accessibility, affordability, and low toxicity, offer promising avenues. Investigating the pharmacological effects and related signaling mechanisms of active components from natural products on COPD animal models induced by various triggers has become an important focus. In animal models induced by cigarette smoke, cigarette smoke combined with lipopolysaccharide (LPS), air pollution, elastase, bacterial or viral infections, the active compounds of natural products, such as flavonoids, terpenoids, and phenolics, can exert anti-inflammatory, antioxidant, mucus-regulating, and airway remodeling-inhibiting effects through key signaling pathways including nuclear factor-erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and mitogen-activated protein kinase (MAPK). These findings not only provide a theoretical basis for the clinical diagnosis and treatment of COPD but also point to new directions for future scientific research.
Pulmonary Disease, Chronic Obstructive/etiology* ; Animals ; Disease Models, Animal ; Biological Products/pharmacology* ; Humans ; NF-kappa B/metabolism* ; Flavonoids/pharmacology* ; Signal Transduction/drug effects* ; Anti-Inflammatory Agents/pharmacology* ; Heme Oxygenase-1/metabolism* ; Terpenes/pharmacology* ; Antioxidants/pharmacology* ; NF-E2-Related Factor 2/metabolism* ; Smoke/adverse effects* ; Phenols/therapeutic use*

Abstract: Objective To evaluate the performance of QuantiFERON-TB Gold Plus (QFT-Plus) in screening the prison population for latent tuberculosis infection (LTBI), and analyze the related influencing factors of latent infection, so as to provide the basis for screening the prison population for latent tuberculosis infection. Methods The basic characteristics and screening results of the newly admitted prison population were collected, using QuantiFERON-TB Gold (QFT-GIT) assay as the gold standard. The kappa value and the area under the ROC curve were used to evaluate the detection performance of QFT-Plus. At the same time, Kruskal-Wallis test was used to compare the difference of IFN-γ production between tubes. A logistic regression model was established to analyze the influencing factors of latent tuberculosis infection in the prison population. Results A total of 100 valid subjects were included in this study, and 24 LTBI patients were screened. The median age of the participants was 38 years, with a median LTBI age of 44 years and a median healthy age of 37 years. In addition, the education level was mainly concentrated in college and above. Consistency checks of various screening methods revealed that the highest coincidence rate of QFT-Plus and QFT-GIT detection results was 0.917, with the largest area under the curve at 0.958. The K-W analysis showed that the amount of IFN-γ in QFT-Plus TB tube was significantly higher than that in QFT-GIT TB tube, and the variation range of IFN-γ in QFT-Plus tube was greater. In addition, multivariate analysis showed that increasing age (aOR: 1.046, 95%CI: 1.004-1.089) was a risk factor for LTBI in the prison population, while having an education level of college or above (aOR: 0.263, 95%CI: 0.071-0.972) was a protective factor. Conclusions QFT-Plus, as a newly developed method for detecting LTBI, showed basic consistency with QFT-GIT, and the consistency was higher than that of other skin test methods. However, due to the large variation of IFN-γ production in QFT-Plus, it is still necessary to further explore the diagnostic threshold. In the screening of LTBI in prisons, special attention should be given to the elderly population and the population with low education levels.

Objective:To investigate the status of anticipatory grief among the main caregivers of maintenance hemodialysis patients, and to analyze its influencing factors.Methods:From April 2021 to July 2021, the main caregivers of 180 patients undergoing maintenance hemodialysis in Xiangdong Hospital Affiliated to Hunan Normal University and Liling Traditional Chinese Medicine Hospital in Hunan Province, were selected by convenience sampling method for the research object. The survey was carried out using the General Information Questionnaire, the Anticipatory Grief Scale and the Zarit Caregiver Burden Interview, multiple linear regression was used to analyze the influencing factors of anticipatory grief in the main caregivers of maintenance hemodialysis patients.Results:The total score of Anticipatory Grief Scale in the main caregivers of maintenance hemodialysis patients was 84.43±12.02, and the total score of Zarit Caregiver Burden Interview was 24.92 ± 7.98, which were positively correlated ( r = 0.557, P<0.01).In the multiple linear regression analysis, the caregiver ′s education level, age, gender, care burden and per capita monthly income and the patient ′s age were the influencing factors of anticipatory grief for the main caregivers of maintenance hemodialysis patients ( t values were -5.54-8.75, all P<0.05), which could explain 54.1% of the total variance. Conclusions:The anticipatory grief of the main caregivers of maintenance hemodialysis patients is at a relatively serious level. Medical care should pay more attention to the caregivers and their sadness in their communication, so as to detect problems in time, so as to take targeted measures to the current situation, which is effective to improve their level of grief and improve the quality of care.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.

Objective To prepare a human meningococcal reference serum and standardize IgG concentrations to capsular polysaccharides and in vitro bactericidal activities of the reference serum against serogroup A, C, Y and W135 strains.Methods Twenty healthy adults were recruited and given one dose of immunization with tetravalent (serogroups A, C, Y and W135) meningococcal polysaccharide vaccine . Plasma samples were collected and gone through a series of process treatments including defibrination , filtra-tion, and lyophilization to prepare the meningococcal reference serum Men 10.The IgG concentrations of Men10 to capsular polysaccharides of serogroups A , C, Y and W135 were calibrated by using an internation-al reference serum CDC1992 as the standard in enzyme-linked immunosorbent assay (ELISA).Provisional IgG concentrations of Men10 were intensively validated by testing a panel of 12 calibration serum samples from Centers for Disease Control and Prevention , USA ( US CDC) and a panel of 56 serum samples immu-nized with A, C, Y and W135 meningococcal polysaccharide vaccine from Lanzhou Institute of Biological Products Co., Ltd.(LIBP) with the assays using Men10 and CDC1992 as the standard and/or test sam-ples, respectively.The bactericidal titers against serogroup A , C, Y and W135 strains were measured by se-rum bactericidal assay (SBA).Results Four thousand vials (0.5 ml/vial)of lyophilized human meningo-coccal reference serum Men10 were successfully prepared with 2.5%of residual moisture .Reference serum Men10 was sterile and free from contamination by hepatitis B virus , hepatitis C virus , human immunodefi-ciency virus and syphilis .Provisional IgG concentration of Men 10 to capsular polysaccharide of serogroups A, C, Y and W135 was calibrated by using CDC1992 as the standard.Furthermore, IgG concentrations of both panels of 12 CDC calibration serum samples and 56 LIBP serum samples calibrated by using Men 10 as the standard correlated well with those by using CDC1992 as the standard (r=0.99,P<0.05).The IgG concentrations of CDC1992 as calibrated by using Men10 as the standard showed significant correlation with its previously determined values with variation <10%.SBA titers for serotype A , C, Y and W135 strains were established as well .Conclusion A panel of new human meningococcal reference serum Men 10 with accurately calibrated IgG concentration against capsular polysaccharide of serogroups A , C, Y and W135 as well as SBA titers was successfully established .

Objective To develop a new continent cutaneous diversion using a tapered ileum wrapped by rectus abdominis muscular flap. Methods Ten dogs underwent a procedure in which two 6 cm ileal segments were tapered and connected to the U-shaped reservoir which was detubularized by a 30-40 cm ileal segment with end-to-side anastomosis. A continent tube was created by a 6 cm length and 3 cm width rectus abdominis muscular flap with its blood supplied which was penetrated be-tween the mesenteric vessels of one tapered ileum (study tube) and wrapped around its periphery. The other tapered ileum (control tube) was brought out into the abdominal skin directly without any extra-mural support. Urodynamic studies were carried out at 1, 3 and 6 months postoperatively. The data of maximum inner pressure (MIP) and functional pressure length (FPL) of every study and control tubes at every study phase were recorded and statistics analysis were taken. Retrograde radiograms of the ef-ferent tubes were performed prior to sacrifice and histological examinations of study group were carried out after sacrificed. Results The average MIP in experimental group was significantly higher than that in control group at 1, 3 and 6 months post-operatively (P<0.01). However, no significant differences in MIP or FPL were found in study group between empty and full reservoir. In the control group, MIP was found rising significantly (P<0.001) and FPL (P<0.05) decreased sharply com-pared with empty and full reservoir. Retrograde radiograms confirmed that all efferent tubes were pa-tent and positioned straightly beneath the abdominal wall. Histological examination of study group at 6 months postoperatively demonstrated a layer of striated muscle around the serosa of the ileum. Conclusions The continent mechanism of tapered ileum could be enhanced by extramural support from wrapped rectus abdominis muscular flap. This technique could setup a reliable, easy catheterization continent cutaneous efferent tube.