Objective:To explore the genetic and clinical characteristics of Guizhou patients with enlarged vestibular aqueduct（EVA） syndrome through combined SLC26A4 variant analysis and clinical phenotype analysis. Methods:Seventy-two EVA patients underwent comprehensive genetic testing using a multiplex PCR-based deafness gene panel and next-generation sequencing（NGS）. The audiological and temporal bone imaging characteristics were compared across mutation subtypes. Results:A total of 27 pathogenic loci of SLC26A4 were detected in 72 patients, including c.919-2A>G in 79.2%（57/72）. A novel deletion（c.1703_1707+6del） was discovered. Among 65 cases, truncated mutations were 89.2%（58/65）, 52.3%（34/65）, 28（43.1%） and 7（10.8%）. No significant differences were observed in the midpoint diameter of the vestibular aqueduct and the incidence of incomplete partitioning typeⅡ（IP-Ⅱ） of the cochlea among the three groups of patients. Moreover, there was no difference in the midpoint diameter of different vestibular pipes or the combination with IP-Ⅱ. Conclusion:The most common mutation site of SLC26A4 in EVA patients in Guizhou is c.919-2A>G, though genotype-phenotype correlations remain elusive. The detection of 27 mutation sites and the discovery of new mutation sites suggested the precise diagnostic significance of NGS technology in EVA patients in Guizhou.
Humans ; Sulfate Transporters ; Vestibular Aqueduct/abnormalities* ; Mutation ; Membrane Transport Proteins/genetics* ; Hearing Loss, Sensorineural/genetics* ; Male ; Female ; Child ; Adolescent ; Child, Preschool ; Adult ; Young Adult ; Phenotype ; High-Throughput Nucleotide Sequencing

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

OBJECTIVE: This study examines the sequential mediating roles of body pain and self-reported health in the association between sleep duration and self-reported life satisfaction among elderly Chinese adults. METHODS: Data from the fifth wave of the China Health and Retirement Longitudinal Survey (CHARLS) were used to analyse the relationships between sleep duration and body pain, self-reported health, and life satisfaction through logistic regression and Restricted Cubic Spline (RCS) analyses. The sequential mediation effects of body pain and self-reported health status were examined via chain mediation analysis. RESULTS: Logistic regression analysis showed that sleeping fewer than 6 hours or 6-7 hours was linked to higher risks of body pain, poor health, and dissatisfaction with life compared to sleeping 7-8 hours (all P < 0.05). Additionally, those sleeping more than 9 hours also had increased risks of poor health and dissatisfaction with life compared to those sleeping 7-8 hours (all P < 0.05). Chain mediation analysis showed that body pain and self-reported health status sequentially mediated 46.15% of the association between sleep duration and life satisfaction. CONCLUSION Body pain and self-reported health may shape the relationship between sleep duration and life satisfaction in elderly Chinese adults.
Humans ; Male ; Female ; Aged ; Personal Satisfaction ; Sleep ; Health Status ; Self Report ; China ; Middle Aged ; Longitudinal Studies ; Pain/psychology* ; Sleep Duration

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.

Clinical prediction models are statistical tools that incorporate multiple variables to predict the likelihood of specific outcomes, by which the accuracy and efficiency of medical decision-making can be facilitated and patient health outcomes can be improved. However, many current studies face problems, such as model construction and reporting irregularities, as well as questionable reliability, which limit their clinical application of clinical prediction model. Therefore, this study systematically reviews relevant literatures, including publications from journals like BMJ, and outline the steps involved in constructing clinical prediction models based on practical research experience. It also provides an in-depth comparison of commonly used methods during the construction process and proposes a comprehensive guiding framework to help researchers in the field to better understand and master the core concepts and practical skills of clinical prediction models for the purpose of improving their professional capabilities in the development, validation, and application of clinical prediction models.

Objective:To systematically evaluate the application of artificial intelligence (AI) in cervical cytopathology diagnosis.Methods:A systematic search was conducted using the keywords ′′cervical cancer′′ ′′cytology′′ ′′artificial intelligence′′ ′′sensitivity′′ and ′′specificity′′ (in both English and Chinese) across databases including PubMed, Web of Science, Embase, Cochrane Library, IEEE Xplore, CNKI, Wanfang, VIP Chinese Science and Technology Journals, and SinoMed. The search covered literature from inception until January 1, 2024, on the application of AI in cervical cytopathological diagnosis. Data were extracted using a predefined data extraction form to compile the contingency table data, from which sensitivity, specificity and area under the curve (AUC) were calculated.Results:A total of 1 616 articles were initially retrieved, and 27 articles were finally included in this study according to the inclusion and exclusion criteria. Five researches were conducted on the diagnosis of cytopathology slides, with pooled AUC, sensitivity and specificity of 0.92 (95% CI: 0.89-0.94), 0.91 (95% CI: 0.77-0.97) and 0.84 (95% CI: 0.77-0.90), respectively. About 22 researches were conducted on the diagnosis of cytopathology images (individual cells or cell clusters), with pooled AUC, sensitivity and specificity of 1.00 (95% CI: 0.99-1.00), 0.98 (95% CI: 0.97-0.99) and 0.98 (95% CI: 0.97-0.99), respectively. Conclusion:The application of AI in the field of cervical cytopathology shows certain diagnostic performance and potential clinical application value.

Objective:Exploring the association rules of factors influencing high hospitalization costs for cancer patients, providing references for hospitals to optimize medical cost management measures.Methods:In the inpatient case information system of a tertiary general hospital, the medical record homepages of inpatients in the DRG groups of the oncology department in 2022 were obtained. The upper four scores of hospitalization costs was used as the threshold for patient grouping. Patients with hospitalization costs≥this threshold were the high-cost group, while other patients were control group; 12 factors, including age, gender, and admission condition, etc, were considered as potential influencing factors of high hospitalization costs. FP-Growth and Apriori algorithms were used to excavate the potential association rules between the influencing factors of high hospitalization costs. Logistic regression was used to analyze the independent influencing factors of high hospitalization costs.Results:A total of 5 512 hospitalized patients were included, including 1 378 patients in the high-cost group. Thirteen validated strong association rules for factors influencing high hospitalization costs were obtained, of which the rule antecedents included age (≥70 years), number of days in hospital (≥7 days), other diagnoses (≥5), surgery, planned readmission, use of antibiotics, admission (general/critical), living admission score (61~99), level of care (level 1/level 2), non-day ward, criticality during hospitalisation. Logistic regression results showed that all nine influencing factors except gender, use of antibiotics, and readmission plans were independent influences on high hospitalization costs ( P<0.05). Conclusions:The joint application of FP-Growth and Apriori algorithm could effectively explore the association rules of high hospitalization costs for oncology patients. The early warning information mainly included the number of hospitalization days, the number of other diagnoses, surgeries, and so on. It was suggested that medical institutions can reasonably control the high hospitalization costs through clinical pathway management, diagnosis and treatment process reengineering, admission risk assessment, and multidisciplinary collaborative diagnosis and treatment strategies.

Objective:To analyze the influencing factors of China′s DRG payment system reform(DRG reform) and its hierarchical relationship, for references for the in-depth promotion of China′s medical insurance payment reform.Methods:Relevant literature on DRG reform in China from databases such as CNKI, Wanfang Database, Pubmed, etc, were obtained. Content analysis method was used to extract the influencing factors of DRG reform. The correlation between each influencing factor was determined through expert discussion. An interpretive structural model(ISM) was constructed to analyze the hierarchical relationship of factors influencing DRG reform.Results:After analysis, the influencing factors(12) of DRG reform in China were included such as medical level, hospital management, and medical staff′s cognition and behavior. Among them, the local situation was the deep-level factor affecting DRG reform, 9 factors such as data quality assurance and policy design/implementation were the middle-level factors, and patients′ interests/needs and disease grouping were the surface-level factors.Conclusions:There were many influencing factors on the reform of China′s DRG payment system. It was suggested that relevant management departments in various regions should focus on the actual situation of the locality, take data quality and policy design and implementation as the key points of reform, formulate a scientific and reasonable DRG grouping scheme, safeguard the interests of patients, so as to promote the deepening of DRG reform.