OBJECTIVE: The relationship between benign prostatic hyperplasia (BPH) and the oxidative balance score (OBS) will be discussed in this study. METHODS: The clinical data on 16 dimensions of diet and 4 dimensions of lifestyle from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2008 were used to calculate OBS. We considered BPH as the outcome and investigated the linear and nonlinear relationships between the two. Additionally, subgroup analyses and interaction tests were conducted as well. Furthermore, the methods of machine learning including XGBoost, support vector machine (SVM) and naive Bayes (NB) were used to establish a predictive model for BPH. RESULTS: Higher OBS was consistently associated with an increased prevalence of BPH, with Restricted Cubic Splines highlighting a significant positive nonlinear association (P=0.015). Subgroup analyses revealed differences and interactive relationships based on alcohol consumption. Among the seven machine learning models that we included the OBS score in, the XGBoost model emerged as the best, with an AUC value of 0.769. CONCLUSION There is a significant association between OBS and the prevalence of BPH in the American population, which provides a valuable insight for further diagnosis and research of the disease.
Humans ; Male ; Prostatic Hyperplasia/epidemiology* ; Machine Learning ; Bayes Theorem ; Nutrition Surveys ; Support Vector Machine ; Life Style ; Oxidative Stress ; Aged ; Diet ; Prevalence

Objective： To investigate the relationship between the lipid metabolism-related gene sterol carrier protein 2(SCP2) and prostate cancer (PCa) from a multi-omics perspective using single-cell transcriptomes combined with Mendelian randomization. Methods： Single-cell transcriptome data of benign and malignant prostate tissues were obtained from GSE120716, GSE157703 and GSE141445 datasets, respectively. Integration, quality control and annotation were performed on the data to categorize the epithelial cells into high and low SCP2 expression groups, followed by further differential and trajectory analyses. Single nucleotide polymorphism (SNP) data for SCP2 expression quantitative trait loci (eQTL) were subsequently downloaded from Genotype-Tissue Expression (GTEx) and investigated from the PCa Society Cancer-Related Genomic Alteration Panel for the Investigation of Cancer-Related Alterations (PRACTICAL) to obtain PCa outcome data for Mendelian randomization analysis to validate the causal relationship between SCP2 and PCa. Results： High SCP2-expressing epithelial cells had higher energy metabolism and proliferation capacity with low immunotherapy response and metastatic tendency. Trajectory analysis showed that epithelial cells with high SCP2 expression may have a higher degree of malignancy, and SCP2 may be a key marker gene for differentiation of malignant epithelial cells in the prostate. Further Mendelian randomization results showed a significant causal relationship between SCP2 and PCa development (OR=1.045, 95% CI: 1.010 －1.083, P=0.011). Conclusion： By combining single-cell transcriptome and Mendelian randomization, the role of the lipid metabolism-related gene SCP2 in PCa development has been confirmed, and new targets and therapeutic directions for PCa treatment have been provided.
Humans ; Prostatic Neoplasms/genetics* ; Male ; Mendelian Randomization Analysis ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Single-Cell Analysis ; Sequence Analysis, RNA ; Carrier Proteins/genetics* ; Transcriptome ; Lipid Metabolism

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Objective:To investigate the therapeutic efficacy of autologous hematopoietic stem cell transplantation (auto-HSCT) for treatment of diffuse large B-cell lymphoma (DLBCL) and the factors affecting the prognosis.Methods:A retrospective case series study was conducted. The clinical data of 51 patients with DLBCL who underwent auto-HSCT in the First Affiliated Hospital of Xinjiang Medical University from March 2019 to January 2024 were retrospectively analyzed. Patients were divided into high-risk group (19 cases) and non-high-risk group (low-risk, low-moderate-risk and moderate-high-risk groups, 32 cases) based on different risk stratifications; patients were divided into the germinal center B-cell (GCB) group (29 cases) and non-GCB group (22 cases) based on different cellular origins; patients were divided into BEAM group (39 cases) and BeEAM group (12 cases) based on different conditioning regimens before auto-HSCT; patients were divided into auto-HSCT consolidation therapy group (41 cases) and auto-HSCT after relapsed/refractory group (10 cases) based on different transplantation timings. The Kaplan-Meier method was used for survival analysis and log-rank was used for subgroup comparison.Results:All 51 patients achieved the hematopoietic reconstitution with no transplantation-related death within 100 d. Before auto-HSCT, 39 cases achieved complete remission and 12 cases (23.5%) achieved partial remission. After auto-HSCT, all cases achieved complete remission. Follow-up was until May 31, 2024, and the median follow-up time [ M ( Q1, Q3)] of 51 DLBCL patients was 33 (8, 43) months. After 51 DLBCL patients receiving auto-HSCT, 7 patients relapsed and 6 cases died including 3 cases with relapse-related death and 3 cases with non relapse-related death. The 3-year progression-free survival (PFS) and overall survival (OS) rates were 78.5% (95% CI: 64.4%-92.6%) and 85.5% (95% CI: 73.2%-97.8%), respectively. The 3-year PFS rate was 94.7% (95% CI: 84.7%-104.7%) in the high-risk group, 82.2% (95% CI: 67.9%-96.5%) in the non-high-risk group, and the difference in the PFS was not statistically significant between the high-risk group and the non-high-risk group ( P = 0.158). The 3-year PFS rate was 80.1% (95% CI: 64.4%-95.8%) in the GCB group, 88.1% (95% CI: 72.3%-104.2%) in the non-GCB group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.803). The 3-year PFS rate was 84.9% (95% CI: 72.6%-97.2%) in BEAM group, 61.1% (95% CI: 25.0%-97.2%) in the BeEAM group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.106). The 3-year PFS rate was 85.4% (95% CI: 73.4%-97.4%) in the auto-HSCT consolidation therapy group, 64.3% (95% CI: 31.4%-96.4%) in the auto-HSCT after relapsed/refractory group, and the difference in PFS was not statistically significant between the 2 groups ( P = 0.171). Conclusions:auto-HSCT is an effective therapy method for DLBCL.

Objective: To systematically evaluate the influencing factors for medication compliance in patients with comorbidities of chronic diseases, so as to provide the evidence for improving medication compliance. Methods: Literature on influencing factors for medication compliance in patients with comorbidities of chronic diseases were retrived from CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, Cochrane Library and Embase from inception to January 20, 2024. After independent literature screening, data extraction, and quality assessment by two researchers, a meta-analysis was performed using RevMan 5.4 and Stata 16.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was assessed using Egger's test. Results: Initially, 7 365 relevant articles were retrieved, and 35 of them were finally included, with a total sample size of about 150 000 individuals. There were 30 cross-sectional studies and 5 cohort studies; and 11 high-quality studies and 24 medium-quality studies. The meta-analysis showed that the demographic factors of lower level of education (OR=2.148, 95%CI: 1.711-2.696), lower economic income (OR=1.897, 95%CI: 1.589-2.264), male (OR=0.877, 95%CI: 0.782-0.985), living alone (OR=2.833, 95%CI: 1.756-4.569) and unmarried (OR=2.784, 95%CI: 1.251-6.196); the medication treatment factors of polypharmacy (OR=1.794, 95%CI: 1.190-2.706), potentially inappropriate medication (OR=2.988, 95%CI: 1.527-5.847), low frequency of daily medication (OR=0.533, 95%CI: 0.376-0.754) and adverse drug reactions (OR=3.319, 95%CI: 1.967-5.602); the disease factors of long course of disease (OR=2.118, 95%CI: 1.643-2.730), more comorbidities (OR=1.667, 95%CI: 1.143-2.431) and cognitive impairment (OR=2.007, 95%CI: 1.401-2.874); and the psychosocial factors of poor belief in taking medication (OR=1.251, 95%CI: 1.011-1.547), poor self-rated health (OR=1.990, 95%CI: 1.571-2.522) and being guided by healthcare professionals (OR=0.151, 95%CI: 0.062-0.368) were the influencing factors for medication compliance in patients with chronic comorbidities. Conclusion The medication compliance in patients with comorbidities of chronic diseases is associated with demographic factors, pharmacological factors, disease factors and psychosocial factors, mainly including living alone, adverse drug reactions, course of disease, number of comorbidities and medication beliefs.

Objective: To conduct a scoping review on prognostic prediction models for patients with comorbidity of chronic diseases, and understand modeling methods, predictive factors and predictive effect of the models, so as to provide the reference for prognostic evaluation on patients with comorbidity of chronic diseases. Methods: Literature on prognostic prediction models for patients with comorbidity of chronic diseases was collected through SinoMed, CNKI, Wanfang Data, VIP, PubMed, Embase, Cochrane Library and Web of Science published from the time of their establishment to November 1, 2023. The quality of literature was assessed using prediction model risk of bias assessment tool (PROBAST), then modeling methods, predictive factors and predictive effects were reviewed. Results: Totally 2 130 publications were retrieved, and nine publications were finally enrolled, with an overall high risk of bias. Thirteen models were involved, with three established using machine learning methods and ten established using logistic regression. The prediction results of four models were death, with main predictive factors being age, gender, body mass index (BMI), Barthel index and pressure ulcers; the prediction results of nine models were rehospitalization, with main predictive factors being age, BMI, hospitalization frequency, duration of hospital stay and hospitalization costs. Eleven models reported the area under the receiver operating characteristic curve (AUC), ranging from 0.663 to 0.991 6; two models reported the C-index, ranging from 0.64 to 0.70. Eight models performed internal validation, one model performed external validation, and four models did not reported verification methods. Conclusions The prognostic prediction models for patients with comorbidity of chronic diseases are established by logistic regression and machine learning methods with common nursing evaluation indicators, and perform well. Laboratory indicators should be considered to add in the models to further improve the predictive effects.

ObjectiveThis study aimed to observe the impact of sinomenine hydrochloride on the proliferation of fibroblasts and the mRNA expression of related genes in knee joint adhesion and contracture in rabbits. Additionally, we sought to explore its potential mechanisms in combating knee joint adhesion and contracture. MethodsFibroblasts were cultured in vitro, and experimental groups with varying concentrations of sinomenine hydrochloride were established alongside a control group. Cell proliferation was assessed using the CCK-8 assay. Changes in the mRNA expression of fibroblast-related genes following sinomenine hydrochloride treatment were evaluated using RT-qPCR. The impact of the drug on serum levels of inflammatory cytokines was determined using the ELISA method, and the expression of related proteins was assessed using Western blot. ResultsSinomenine hydrochloride was found to inhibit fibroblast viability, with viability decreasing as the concentration of sinomenine hydrochloride increased. The effects of sinomenine hydrochloride in all experimental groups were highly significant (P<0.05). At the mRNA expression level, compared to the control group, sinomenine hydrochloride led to a significant downregulation of inflammatory cytokines in all groups (P<0.05). Additionally, the expression levels of apoptosis-related proteins significantly increased, while Bcl-2 mRNA expression decreased (P<0.05). The mRNA expression levels of the PI3K/mTOR/AKT3 signaling pathway also decreased (P<0.05). At the protein expression level, in comparison to the control group, the levels of inflammatory cytokines IL-6, IL-8, IL-1β, and TGF-β were significantly downregulated in the middle and high-dose sinomenine hydrochloride groups (P<0.05). The expression levels of cleaved-PARP, cleaved caspase-3/7, and Bax increased and were positively correlated with the dose, while the expression levels of the anti-apoptotic protein Bcl-2 and the PI3K/AKT3/mTOR signaling pathway were negatively correlated with the dose. Sinomenine hydrochloride exhibited a significant inhibitory effect on the viability of rabbit knee joint fibroblasts, which may be associated with the downregulation of inflammatory cytokines IL-6, IL-8, and IL-1β, promotion of apoptosis-related proteins cleaved-PARP, cleaved caspase-3/7, and Bax, suppression of Bcl-2 expression, and inhibition of gene expression in the downstream PI3K/AKT3/mTOR signaling pathway. ConclusionSinomenine hydrochloride can inhibit the inflammatory response of fibroblasts in adhesive knee joints and accelerate fibroblast apoptosis. This mechanism may offer a novel approach to improving and treating knee joint adhesion.

Objective To construct a risk prediction model of pulmonary involvement based on chest CT and clinical feature in patients with primary Sjogren's syndrome(pSS),and to explore the risk prediction value of the model.Methods A total of 360 pSS patients who had been treated at Handan Hospital of Traditional Chinese Medicine from October 2020 to August 2023 were retrospectively selected as study objects,and were then divided into a modeling group(252 patients)and a verification group(108 patients)according to a ratio of 7∶3.The patients in the modeling group were divided into a control group(201 patients)and an involvement group(51 patients)based on presence or absence of lung involvement.The data on clinical characteristics and features of chest high-resolution CT(HRCT)in the modeling group was collected.Univariate analysis was performed among the groups to determine the relevant factors affecting lung involvement in pSS patients.Binary logistic regression analysis was performed on related factors to screen independent risk factors.A prediction model was established based on the independent risk factors.A verification and value analysis of the column-line prediction model were completed through data collection of the verification group.Results Age,disease course,cough,Raynaud's phenomenon,C-reactive protein(CRP),anti-SSA antibody,and HRCT were the relevant factors affecting lung involvement in pSS patients(all P＜0.05).Further binary logistic regression analysis showed that old age,prolonged disease course,cough and abnormal HRCT imaging were independent risk factors for lung involvement in SS patients(all P＜0.05).A nomogram risk prediction model was constructed based on independent factors.The model verification results indicated that the calibration chart showed better performance in the prediction model.The AUC of the area under the receiver operating characteristic(ROC)curve was 0.993 the modeling group and 0.995 in the validation group.Conclusions The clinical characteristics and the results of chest CT are closely related with lung involvement in patients with pSS.Old age,prolonged disease course,cough,and abnormal HRCT imaging are independent risk factors affecting lung involvement in patients with pSS.The prediction model established on this basis has a higher predictive value for the occurrence of lung involvement in patients receiving after-loading radiotherapy.

Objective:To study the diagnostic value of artificial intelligence B-ultrasound image computer-aided diagnosis system (hereinafter referred to as intelligent ultrasound system) in adult goiter.Methods:In June 2022 and March 2023, two phases of thyroid disease survey were carried out in 4 cities in Anhui Province. One village was selected in each city, and 250 adults were selected as survey subjects in each village. Adult bilateral thyroid area was scanned by both intelligent ultrasound system and conventional ultrasound scanning equipment, and the effectiveness of intelligent ultrasound system in the diagnosis of goiter was analyzed based on the results of conventional ultrasound examination. Receiver operating characteristic (ROC) curve was drawn, and Kappa test was used to analyze the consistency between intelligent ultrasound system and conventional ultrasound examination in the diagnosis of goiter. At the same time, Spearman correlation analysis and Bland-Altman method were used to evaluate the consistency of the two methods in measuring thyroid volume.Results:After screening and removing outliers and missing values, a total of 910 adults were included, including 253 males (27.80%) and 657 females (72.20%). The age was (45.92 ± 10.20) years old, ranging from 18 to 60 years old. The sensitivity, specificity, and accuracy of the intelligent ultrasound system for diagnosing adult goiter were 80.00%, 99.67%, and 99.56%, respectively. The area under the ROC curve (AUC) was 0.996, which was consistent with the results of conventional ultrasound examination for diagnosing goiter ( κ = 0.67, P < 0.001). After controlling for variables such as gender, thyroid function, and thyroid nodules, the intelligent ultrasound system showed good consistency with conventional ultrasound examination in the diagnosis of goiter in females, adults with thyroid dysfunction, and adults without thyroid nodules ( κ = 0.66, 0.80, 0.80, P < 0.001). The consistency in the diagnosis of goiter in adults with thyroid nodules was moderate ( κ = 0.56, P < 0.001). Spearman correlation analysis showed a highly positive correlation between the measurement results of adult thyroid volume by intelligent ultrasound system and conventional ultrasound examination ( r = 0.88, P < 0.001). The Bland-Altman method results showed that only 4.62% (42/910) of points in adults were outside the 95% consistency limit, indicating good consistency between intelligent ultrasound system and conventional ultrasound examination in measuring thyroid volume (< 5%). The proportion of points outside the 95% consistency limit in males, adults with thyroid dysfunction, and adults with thyroid nodules was 6.72% (17/253), 5.83% (12/206), and 6.45% (12/186), respectively. Conclusions:The intelligent ultrasound system has certain diagnostic value for adult goiter and has good consistency with conventional ultrasound examination for thyroid volume measurement. However, the accuracy of diagnosis for males and adults with thyroid nodules still needs to be improved.