Digital intelligence technology is a comprehensive technical system with data as the key element and artificial intelligence as the core driving force. It provides innovative methodological support for addressing core bottlenecks in rare disease etiology research, including sample scarcity, high-dimensional datacomplexity, intricate pathogenic mechanisms, and challenges in causal validation. This paper systematically reviews the application pathways and current implementation of digital intelligence across four major stages of rare disease research, including etiological clue mining, causal inference, mechanism analysis, and clinical translation. Furthermore, it summarizes the technical strengths, existing limitations, and developmental challenges, aiming to inform optimization of the research paradigm and promote the sustainable advancement of rare disease research.

Objective:To investigate the occurrence of work-related musculoskeletal disorders (WMSDs) among underground coal mine workers, identify the risk factors for WMSDs, and provide a scientific evidence for the prevention and treatment of WMSDs.Methods:In March 2024, through cluster sampling, the on-the-job workers who underwent questionnaire surveys and health examinations at a certain coal mine from July to August 2018 were selected as the research subjects. Basic information of employees, ergonomics-related characteristics, and the occurrence status of WMSDs in each part were collected, and multivariate logistic regression was used for analysis.Results:The incidence rate of WMSDs in at least one site among underground coal mine workers within the past year was 62.22% (219/352). The top three sites in sequence were the lower back (44.32%, 156/352), neck (26.14%, 92/352), and knee (26.14%, 92/352). Multivariate logistic regression analysis showed that frequently exerting great force with arms or hands during work ( OR=2.223, 95% CI: 1.022-4.836), prolonged static forward bending ( OR=1.544, 95% CI: 1.305-1.972), and frequently exerting great effort to operate tools or machines ( OR=2.206, 95% CI: 1.011-4.813), absence of external support systems ( OR=1.589, 95% CI: 1.349-1.996), and repetitive full-body twisting ( OR=1.523, 95% CI: 1.298-1.916) were all risk factors for the occurrence of WMSDs in the lower back ( P<0.05). Both night shift work ( OR=1.564, 95% CI: 1.339-1.939) and frequent forward neck flexion ( OR=1.532, 95% CI: 1.312-1.907) were all risk factors for the occurrence of WMSDs in the neck ( P<0.05). Lifting heavy objects above the shoulder ( OR=1.333, 95% CI: 1.142-1.782), uncomfortable posture and inability to exert force ( OR=1.873, 95% CI: 1.104-2.712), the use of vibration tools ( OR=2.958, 95% CI: 1.255-6.972), and length of service >10 years ( OR=1.525, 95% CI: 1.105-1.967) were all risk factors for the occurrence of WMSDs in the knee ( P<0.05) . Conclusion:The incidence of WMSDs among underground coal miners is relatively high, mainly concentrated in the lower back, neck and knee, and is related to factors such as poor working postures, and work organization. Coal mining enterprises should strengthen work organization, provide appropriate working equipment, and ensure reasonable distribution of workloads.

Objective:To identify risk factors influencing the incidence of hypertension among steelworkers (Homo sapiens) and establish an effective and easily implementable hypertension prediction model.Methods:In September 2023, 2214 steelworkers (Homo sapiens) were selected as study subjects. Basic demographic information, lifestyle, and occupational exposure data were collected, along with physiological measurements such as height, weight, and blood pressure. Multivariate unconditional logistic regression analysis was employed based on relevant literature to determine influencing factors for hypertension among steelworkers (Homo sapiens). Python 3.9 software was used to construct and compare logistic regression, support vector machine (SVM), random forest, extreme gradient boosting tree (XGBoost), and LGBM models. Model performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, calibration curves, and F1 scores. The Shapley Additive Explanations (SHAP) model was introduced for feature importance analysis to enhance the interpretability of the prediction model.Results:A total of 432 cases of hypertension were detected among 2214 study subjects, with a detection rate of 19.51%. Age, smoking status, salt intake, use of cooling equipment, carbon monoxide exposure, family history of hypertension, fasting blood glucose, triglycerides, and hemoglobin were identified as independent risk factors for hypertension ( P<0.05). A comparison of the five models revealed the following performance metrics: logistic regression achieved an accuracy of 0.853, F1 score of 0.680, Brier score of 0.108, and AUC of 0.907; SVM demonstrated an accuracy of 0.863, F1 score of 0.687, Brier score of 0.081, and AUC of 0.910; random forest showed an accuracy of 0.857, F1 score of 0.603, Brier score of 0.105, and AUC of 0.861; XGBoost yielded an accuracy of 0.850, F1 score of 0.684, Brier score of 0.117, and AUC of 0.899; and the LGBM model exhibited an accuracy of 0.838, F1 score of 0.625, Brier score of 0.112, and AUC of 0.870. Conclusion:The SVM model demonstrated strong predictive performance, effectively assessing the risk of hypertension among steelworkers (Homo sapiens) and facilitating targeted health management interventions.

Objective:To evaluate the stability of percutaneous screw fixation for minimally invasive treatment of intra-articular calcaneal fractures using three-dimensional finite element analysis.Methods:CT scan was performed on the calcaneus of a normal adult for three-dimensional reconstruction. The DICOM data were imported into Mimics software to establish a model of a Sanders type IIB intra-articular calcaneal fracture. Based on the Essex-Lopresti classification of posterior facet morphology, the model was subdivided into two subtypes: tongue-type and depression-type. The calcaneus was divided into four fragments: sustentaculum tali, posterior tuberosity, anterior process (three points), and posterior articular surface (one surface). Two types of fixation methods, classical lateral anatomical plates and combinations of percutaneous screws, were simulated and performed. A three-dimensional finite element analysis was conducted by applying a stress combination of 420 N on the posterior subtalar articular surface, 200 N on the middle subtalar articular surface, and 300 N at the Achilles tendon insertion point. The maximum displacement and von Mises stress values of each bone fragment and implant were recorded to evaluate the biomechanical stability. For clinical validation, 34 patients with Sanders type IIB calcaneal fractures from Orthopedics Department of the Sixth Affiliated People's Hospital of Shanghai Jiao Tong University were treated with percutaneous reduction and screw fixation using the following configurations.Results:Under simulated stress, the A4 group with medial support screws in the tongue-type fracture subgroup demonstrated minimal overall calcaneal displacement (0.22 mm) and internal fixation displacement (0.14 mm). For the depression-type, the B2 group with medial support screws showed lower maximum stress in the calcaneus and internal fixation, at 22.04 MPa and 41.14 MPa, respectively, along with the lowest overall displacement (0.14 mm). The peak stress of all groups of implants remained below the material yield strength. The A4 and B2 protocols were applied to 15 cases of tongue-type calcaneal fractures and 19 cases of collapse-type calcaneal fractures. At the final follow-up The American Orthopaedic Foot & Ankle Society ankle-hindfoot score scale was 86.1±5.82 and 87.2±5.18, respectively, while the visual analog scale for pain was 1.60±1.24 and 1.58±1.02, respectively.Conclusions:Percutaneous screw fixation provided reliable stability for Sanders type IIB calcaneal fractures. The fixation configuration incorporating a medial support screw offers superior biomechanical performance in both tongue-type and depression-type fractures, representing an optimized minimally invasive technique with strong clinical applicability.

Objective:To analyze the factors influencing the lung function of coal miners, identify the optimal combination of indicators for evaluating lung function, develop a risk assessment model using machine learning, and offer personalized risk assessment for workers.Methods:In June 2023, through cluster sampling, male underground workers who participated in occupational health examinations at a coal mine in North China from July to August 2018 were selected as the research subjects. Their health examination results and occupational environmental data were collected. A total of 3, 320 coal miners were included. Randomly divide the research subjects into a training set (2324 people) and a validation set (996 people) in a ratio of 7∶3, and the balance of the two sets was tested. Perform LASSO regression analysis using R 4.2.2 software to select relevant important variables, and determine the model's input variables by combining them with relevant literature. Utilize Python 3.8 to construct logistic regression, random forest, support vector machine, and XG Boost models, assess the models' discriminative ability using metrics like accuracy, sensitivity, specificity, F1 score, ROC curve, and AUC, evaluate the models' calibration using Brier score, Log loss score, and calibration curve, and further analyze the clinical performance of the developed models through DCA decision curve analysis.Results:Among the 3 320 coal miners, 856 had abnormal lung function (25.78%). The XG Boost model was identified as the optimal model, achieving a training set accuracy of 87.39%, sensitivity of 86.60%, specificity of 87.67%, F1 score of 0.779, AUC of 0.945, Brier score of 0.071, Log loss of 0.267 and demonstrated good calibration curve consistency.Conclusion:The XG Boost model exhibits superior predictive performance compared to other models, and the model has high application value. The Shapley Additive Explanation (SHAP) method is employed for interpretation, making it a reliable basis for preventing abnormal lung function in coal miners.

Objective:To investigate the occurrence of work-related musculoskeletal disorders (WMSDs) among underground coal mine workers, identify the risk factors for WMSDs, and provide a scientific evidence for the prevention and treatment of WMSDs.Methods:In March 2024, through cluster sampling, the on-the-job workers who underwent questionnaire surveys and health examinations at a certain coal mine from July to August 2018 were selected as the research subjects. Basic information of employees, ergonomics-related characteristics, and the occurrence status of WMSDs in each part were collected, and multivariate logistic regression was used for analysis.Results:The incidence rate of WMSDs in at least one site among underground coal mine workers within the past year was 62.22% (219/352). The top three sites in sequence were the lower back (44.32%, 156/352), neck (26.14%, 92/352), and knee (26.14%, 92/352). Multivariate logistic regression analysis showed that frequently exerting great force with arms or hands during work ( OR=2.223, 95% CI: 1.022-4.836), prolonged static forward bending ( OR=1.544, 95% CI: 1.305-1.972), and frequently exerting great effort to operate tools or machines ( OR=2.206, 95% CI: 1.011-4.813), absence of external support systems ( OR=1.589, 95% CI: 1.349-1.996), and repetitive full-body twisting ( OR=1.523, 95% CI: 1.298-1.916) were all risk factors for the occurrence of WMSDs in the lower back ( P<0.05). Both night shift work ( OR=1.564, 95% CI: 1.339-1.939) and frequent forward neck flexion ( OR=1.532, 95% CI: 1.312-1.907) were all risk factors for the occurrence of WMSDs in the neck ( P<0.05). Lifting heavy objects above the shoulder ( OR=1.333, 95% CI: 1.142-1.782), uncomfortable posture and inability to exert force ( OR=1.873, 95% CI: 1.104-2.712), the use of vibration tools ( OR=2.958, 95% CI: 1.255-6.972), and length of service >10 years ( OR=1.525, 95% CI: 1.105-1.967) were all risk factors for the occurrence of WMSDs in the knee ( P<0.05) . Conclusion:The incidence of WMSDs among underground coal miners is relatively high, mainly concentrated in the lower back, neck and knee, and is related to factors such as poor working postures, and work organization. Coal mining enterprises should strengthen work organization, provide appropriate working equipment, and ensure reasonable distribution of workloads.

Objective:To identify risk factors influencing the incidence of hypertension among steelworkers (Homo sapiens) and establish an effective and easily implementable hypertension prediction model.Methods:In September 2023, 2214 steelworkers (Homo sapiens) were selected as study subjects. Basic demographic information, lifestyle, and occupational exposure data were collected, along with physiological measurements such as height, weight, and blood pressure. Multivariate unconditional logistic regression analysis was employed based on relevant literature to determine influencing factors for hypertension among steelworkers (Homo sapiens). Python 3.9 software was used to construct and compare logistic regression, support vector machine (SVM), random forest, extreme gradient boosting tree (XGBoost), and LGBM models. Model performance was evaluated using metrics such as receiver operating characteristic (ROC) curves, accuracy, calibration curves, and F1 scores. The Shapley Additive Explanations (SHAP) model was introduced for feature importance analysis to enhance the interpretability of the prediction model.Results:A total of 432 cases of hypertension were detected among 2214 study subjects, with a detection rate of 19.51%. Age, smoking status, salt intake, use of cooling equipment, carbon monoxide exposure, family history of hypertension, fasting blood glucose, triglycerides, and hemoglobin were identified as independent risk factors for hypertension ( P<0.05). A comparison of the five models revealed the following performance metrics: logistic regression achieved an accuracy of 0.853, F1 score of 0.680, Brier score of 0.108, and AUC of 0.907; SVM demonstrated an accuracy of 0.863, F1 score of 0.687, Brier score of 0.081, and AUC of 0.910; random forest showed an accuracy of 0.857, F1 score of 0.603, Brier score of 0.105, and AUC of 0.861; XGBoost yielded an accuracy of 0.850, F1 score of 0.684, Brier score of 0.117, and AUC of 0.899; and the LGBM model exhibited an accuracy of 0.838, F1 score of 0.625, Brier score of 0.112, and AUC of 0.870. Conclusion:The SVM model demonstrated strong predictive performance, effectively assessing the risk of hypertension among steelworkers (Homo sapiens) and facilitating targeted health management interventions.

Objective To provide a reference basis for the establishment of ideal animal models of leukemia,the characteristics of animal models of leukemia was summarized.Methods The themes of"leukemia"and"animal"were searched in CNKI databases.The relevant literature on animal experiments of leukemia from January 2010 to August 2024 were screened.And a database by Excel 2017 tables was established by organizing their research animal strains,modeling methods and testing indexes to analyze the characteristics of leukemia animal models.Results After comparative analysis,it was found that the animal strains in modeling were BALB/C-nude mice(40 times,29.86%)and NOD/SCID mice(32 times,23.88%),respectively.The modeling method was transplanted cells(127 times,93.38%),and the detection indexes were pathological tissues(80 times,14.84%),blood smears(75 times.13.91%),general condition(73 times,13.54%),bone marrow smear(73 times,13.54%),flow cytometry(55 times,10.20%),blood routine(50 times,9.28%).Conclusion The existing animal models of leukemia are complex and diverse.

Objective:To investigate the impact of tumor origin (ventral pancreatic origin and dorsal pancreatic origin) on prognosis in patients with pancreatic head cancer.Methods:A retrospective analysis was performed on the clinical data of 150 patients with pancreatic head cancer who received surgical treatment at the First Affiliated Hospital of the Naval Medical University from October 2014 to December 2017. Among these patients, 92 were male and 58 were female, aged (61.2±8.8) years. The 150 patients were divided into two groups based on tumor origin: the ventral pancreatic cancer group ( n=72) and the dorsal pancreatic cancer group ( n=78). A comparative analysis of clinical, pathological, and imaging charac-teristics was conducted between the two groups. Univariate and multivariable Cox proportional hazards models were used to analyze the association between pancreatic head cancer origin and overall survival (OS). Results:Patients with pancreatic head carcinoma arising from the ventral and dorsal pancreas accounted for 48%(72/150) and 52%(78/150) of the study cohort, respectively. Pancreatic head carcinoma arising from the dorsal pancreas were more likely to show pathological features of pancreatic parenchymal atrophy [73.1%(57/78) vs. 47.2%(34/72), χ2=10.49, P=0.001] and pancreatitis [44.9%(35/78) vs. 29.2%(21/72), χ2=3.95, P=0.047]. In contrast, patients with pancreatic head carcinoma arising from the ventral pancreas was more frequently associated with contact with the superior mesenteric artery [25.0%(18/72) vs. 1.3%(1/78), χ2=19.04, P<0.001], perineural invasion [100%(72/72) vs. 88.5%(69/78), χ2=8.84, P=0.003], and positive surgical margins [15.3%(11/72) vs. 2.6%(2/78), χ2=7.65, P=0.006], with all differences statistically significant. The ventral pancreatic cancer group demonstrated cumulative survival rates of 33.2% and 0 at 1-year and 2-year postoperative intervals, respectively, while the dorsal pancreatic cancer group exhibited rates of 56.7% and 24.8% at the corresponding timepoints. Comparison of Kaplan-Meier survival curves between the two groups showed a statistically significant difference ( χ2=6.00, P=0.014). Multivariable Cox proportional hazards analysis identified dorsal pancreatic origin pancreatic head cancer as an independent predictor of increased mortality risk compared to ventral origin tumors ( HR=2.75, 95% CI: 1.52-4.98, P=0.001). Conclusion:The embryonic origin of pancreatic head cancer determines its clinical, pathological, and imaging heterogeneity, and pancreatic head cancer arising from the ventral pancreas demonstrates significantly worse prognostic outcomes compared to dorsal pancreatic origin.

To review the randomized controlled trials (RCTs) at home and abroad on digital intelligence (DI)-driven rehabilitation in patients of neuromuscular disease, compare the effects of DI-driven rehabilitation with traditional rehabilitation, summarize the special needs and challenges faced by patients in rehabilitation of rare neuromuscular diseases, and provide evidence for the development and quality improvement of rehabilitation for rare neuromuscular diseases.