Background As one of the most severe occupational diseases in China, pneumoconiosis is significantly burdened by its complications, which adversely affects patients' quality of life. Objective To identify the influencing factors of complications in pneumoconiosis and to construct an early prediction model for pneumoconiosis complications, providing theoretical guidance for clinical diagnosis, treatment, and rehabilitation. Methods A case-control study was conducted using data from the Chongqing 5G Pneumoconiosis Rehabilitation Management Information Platform. A total of 1872 pneumoconiosis patients with complications, who received rehabilitation at various rehabilitation stations in Chongqing from January 2021 to December 2024, were enrolled as the case group. Concurrently, 2348 patients without complications from the same platform and period were included as the control group. Univariate analysis, LASSO regression, and logistic regression were employed to identify influencing factors for complications. An extreme gradient boosting (XGBoost) model was trained using ten-fold cross-validation to predict pneumoconiosis complications. The SHapley Additive exPlanations (SHAP) method was applied for model visualization. Results The analysis of influencing factors revealed that increased age, silicosis type, advanced stage of pneumoconiosis, dependence in activities of daily living, abnormal muscle strength, increased dyspnea index, presence of cough, viscous sputum, impaired cardiac function, severe abnormality in the 6-minute walk test (6MWT), and increased Borg score were risk factors for complications (P<0.05). The XGBoost model demonstrated an area under the curve (AUC) of receiver operating characteristic of 0.718, and a Brier score of 0.211 for the calibration curve. Decision curve analysis demonstrated a superior net benefit within the threshold probability range of 0.2 to 1.0 compared to "all-patient intervention" and "no-patient intervention" strategies, indicating good predictive performance and significant clinical utility. SHAP visualization identified the top six important features as Borg score, age, 6MWT, muscle strength, pneumoconiosis stage, and cough symptoms. Conclusion Multiple factors influence the occurrence of complications in pneumoconiosis patients. Clinical attention should be focused on elderly patients with decreased cardiopulmonary reserve, pronounced respiratory symptoms, and diminished muscle strength. The XGBoost model demonstrates satisfactory discrimination, calibration, and clinical applicability in predicting pneumoconiosis complications and may serve as a useful reference for clinical decision-making.

Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

Objective To investigate the epidemiological characteristics and changing trend of typhus in Shaanxi Province from 2005 to 2023 to provide a scientific basis for its prevention and control. Methods Excel 2007, SPSS 25.0, Joinpoint 4.9.1.0, and Geoda 1.6 were used for data collection and statistical analysis. Super Map was used for data visualization to describe the changing characteristics of the disease. Results A total of 394 typhus cases were reported in Shaanxi Province from 2005 to 2023. The average annual incidence of typhus was 0.054/100 000, showing a dynamic fluctuation trend(AAPC=-3.3, t=-0.3, P>0.05). The cases were mainly concentrated in Baoji, Hanzhong and Xi′an, accounting for 78.68%. The incidence peak was from May to October, accounting for 64.21% of annual incidence. The epidemic season was from May to October and December. The incidence of the disease was concentrated in the 40-69 age group, accounting for 58.88%, and the sex was 1.07:1. The main occupation was farmers, accounting for 72.08%. The median time from onset to diagnosis was 7 days. Global spatial autocorrelation analysis showed that there were significant spatial autocorrelations in 11 years from 2005 to 2023(P<0.05). Local spatial autocorrelation analysis detected a total of 43“high-high”clustering areas, mainly concentrated in Baoji City. Conclusions The overall incidence of typhus in Shaanxi Province showed a dynamic fluctuation trend, with notable seasonal and regional aggregation. The incidence of typhus was higher in middle-aged and elderly people in rural areas. Surveillance should be strengthened in typhus endemic areas in summer and autumn, and health education should be conducted for key population to form good health habits and reduce the incidence of typhus.

Objective To construct a dynamic prediction model for JC polyomavirus (JCV) reactivation after kidney transplantation. Methods A retrospective analysis was conducted on the clinical data of 128 recipients who met the inclusion criteria and received kidney transplantation in Nanfang Hospital, Southern Medical University, from June 2021 to December 2024. Dynamic Cox regression and dynamic restricted mean survival time (RMST) regression based on the landmark method were adopted to establish the dynamic prediction models, and Monte Carlo cross-validation was used to evaluate model performance. Results The dynamic models could predict the incidence and onset time of JCV reactivation within the subsequent 3 months according to covariates at each landmark time point from the 1st to the 6th month after transplantation. Remuzzi score, body mass index and warm ischemia time were independent risk factors for JCV reactivation, while a history of urinary BK polyomavirus reactivation served as a protective factor. The median values of the area under the curve, C-index and Brier score of the dynamic Cox model were 0.873, 0.851 and 0.065 respectively, and the C-index of the dynamic RMST model was 0.829, all of which were superior to those of the static model. Conclusions The landmark-based dynamic models exhibit excellent predictive performance, which may integrate baseline data and post-transplant follow-up information to realize dynamic assessment of the risk and time window of JCV reactivation, thereby providing evidence for optimizing post-operative monitoring and individualized intervention strategies.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

Mitochondria, functioning not only as the central hub of cellular energy metabolism but also as semi-autonomous organelles, orchestrate cellular fate decisions through their endogenous mitochondrial DNA (mtDNA), which encodes core components of the electron transport chain. Emerging research has identified microRNAs localized within mitochondria, termed mitochondria-located microRNAs (mitomiRs). Recent studies have revealed that mitomiRs are transcribed from nuclear DNA (nDNA), processed and matured in the cytoplasm, and subsequently transported into mitochondria. mitomiRs regulate mtDNA through diverse mechanisms, including modulation of mtDNA expression at the translational level and direct binding to mtDNA to influence transcription. Aberrant expression of mitomiRs leads to mitochondrial dysfunction and contributes to the pathogenesis of metabolic diseases. Restoring mitomiR expression to physiological levels using mitomiRs mimics or inhibitors has been shown to improve mitochondrial function and alleviate related diseases. Consequently, the regulatory mechanisms of mitomiRs have become a major focus in mitochondrial research. Given that mitomiRs are located in mitochondria, targeted delivery strategies designed for mtDNA can be adapted for the delivery of mitomiRs mimics or inhibitors. However, numerous intracellular and extracellular barriers remain, highlighting the need for more precise and efficient delivery systems in the future. The regulation of mtDNA expression mediated by mitomiRs not only expands our understanding of miRNA functions in post-transcriptional gene regulation but also provides promising molecular targets for the treatment of mitochondrial-related diseases. This review systematically summarizes recent research progress on mitomiRs in regulating mtDNA expression and discusses the underlying mechanisms of mitomiRs-mtDNA interactions. Additionally, it provides new perspectives on precision therapeutic strategies, with a particular emphasis on mitomiRs-based regulation of mitochondrial function in mitochondrial-related diseases.

Objective To explore the correlation between the rs7296288 single nucleotide polymorphism(SNP)of the DHH gene and the onset of depression.Methods From March 2018 to December 2019,480 patients with depression in Sichuan Provincial People's Hospital,Jining Mental Hospital,Yunnan Mental Health Center were collected as the case group,and 329 patients without depression were collected as the control group.Peripheral blood samples were collected and DNA was extracted.After multiple amplification and high-throughput sequencing,statistical software was used to analyze the relevant data.Results The rs7296288 polymorphism of the DHH gene,the AA genotype was 32.9％in the control group and 36.7％in the case group,with no statistical difference;the AC genotype was 52.8％in the control group and 47.7％in the case group,with no statistical difference;the CC genotype was 14.3％in the control group and 15.6％in the case group,with no statistical difference.The co-dominant,dominant,and recessive statistical models were not statistically significant.Subgroup analysis of the degree of depressive episodes,suicide attempts,and first-episode patients did not find an association between the rs7296288 SNP locus of the DHH gene and the clinical features of depression,that is,the SNP rs7296288 of the DHH gene had no significant correlation with depression.Conclusion The rs7296288 polymorphism of DHH gene is not correlated with the pathogenesis of depression.

BACKGROUND: China is seeing a growing demand for rehabilitation treatments for post-stroke upper limb spastic paresis (PSSP-UL). Although acupuncture is known to be effective for PSSP-UL, there is room to enhance its efficacy. OBJECTIVE: This study explored a semi-personalized acupuncture approach for PSSP-UL that used three-dimensional kinematic analysis (3DKA) results to select additional acupoints, and investigated the feasibility, efficacy and safety of this approach. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: This single-blind, single-center, randomized, controlled trial involved 74 participants who experienced a first-ever ischemic or hemorrhagic stroke with spastic upper limb paresis. The participants were then randomly assigned to the intervention group or the control group in a 1:1 ratio. Both groups received conventional treatments and acupuncture treatment 5 days a week for 4 weeks. The main acupoints in both groups were the same, while participants in the intervention group received additional acupoints selected on the basis of 3DKA results. Follow-up assessments were conducted for 8 weeks after the treatment. MAIN OUTCOME MEASURES: The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) response rate (≥ 6-point change) at week 4. Secondary outcomes included changes in motor function (FMA-UE), Brunnstrom recovery stage (BRS), manual muscle test (MMT), spasticity (Modified Ashworth Scale, MAS), and activities of daily life (Modified Barthel Index, MBI) at week 4 and week 12. RESULTS: Sixty-four participants completed the trial and underwent analyses. Compared with control group, the intervention group exhibited a significantly higher FMA-UE response rate at week 4 (χ² = 5.479, P = 0.019) and greater improvements in FMA-UE at both week 4 and week 12 (both P < 0.001). The intervention group also showed bigger improvements from baseline in the MMT grades for shoulder adduction and elbow flexion at weeks 4 and 12 as well as thumb adduction at week 4 (P = 0.007, P = 0.049, P = 0.019, P = 0.008, P = 0.029, respectively). The intervention group showed a better change in the MBI at both week 4 and week 12 (P = 0.004 and P = 0.010, respectively). Although the intervention group had a higher BRS for the hand at week 12 (P = 0.041), no intergroup differences were observed at week 4 (all P > 0.05). The two groups showed no differences in MAS grades as well as in BRS for the arm at weeks 4 and 12 (all P > 0.05). CONCLUSION: Semi-personalized acupuncture prescription based on 3DKA results significantly improved motor function, muscle strength, and activities of daily living in patients with PSSP-UL. TRIAL REGISTRATION Chinese Clinical Trial Registry ChiCTR2200056216. Please cite this article as: Huang XY, Liao OP, Jiang SY, Tao JM, Li Y, Lu XY, Li YY, Wang C, Li J, Ma XP. Three-dimensional kinematic analysis can improve the efficacy of acupoint selection for post-stroke patients with upper limb spastic paresis: A randomized controlled trial. J Integr Med. 2025; 23(1): 15-24.
Humans ; Male ; Female ; Middle Aged ; Acupuncture Points ; Upper Extremity/physiopathology* ; Biomechanical Phenomena ; Single-Blind Method ; Aged ; Stroke/therapy* ; Acupuncture Therapy/methods* ; Stroke Rehabilitation/methods* ; Adult ; Muscle Spasticity/therapy* ; Paresis/physiopathology* ; Treatment Outcome

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis