The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

ObjectiveTo propose a prediction method combining transition-aware soft labels and dynamic weighting to mitigate transition-zone prediction jitter of hard labels in locomotion intent prediction for intelligent lower-limb prostheses. MethodsSeven healthy subjects were recruited from Tongling University from January to July, 2025, wearing simulated lower-limb-prosthesis braces. Six inertial measurement units were used to acquire acceleration and angular-velocity under 21 locomotion modes. Cosine-based soft labels were introduced to reconstruct the smooth evolutionary gradient of locomotion-mode transitions. Probability-distribution features derived from soft labels were used to build a transition-aware weighting mechanism, which drived the model to focus dynamically on transition boundaries during training. A temporal convolutional network served as the prediction backbone. ResultsThe proposed method achieved an overall prediction accuracy of 94.03% across all locomotion modes, which substantially improved prediction performance during mode transitions, and suppressed probability jitter near decision boundaries and yielded smooth continuous probability trajectories of intent evolution. ConclusionThe proposed transition-aware soft-labeling and dynamic-weighting approach alleviates transition-region prediction oscillation caused by traditional discrete labels. It can provide smooth, robust and anticipatory intent-decision signals for intelligent lower-limb prostheses.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Estrogen, through binding to its receptors in the female endometrium, promotes the proliferation, differentiation, and angiogenesis of endometrial stromal cells, thereby playing a critical role in endometrial injury repair, regeneration, and the prevention and treatment of intrauterine adhesions. Currently, various estrogen-based pharmaceutical formulations are applied in clinical practice, and the development of related products is progressively advancing. This review systematically outlines the etiology of endometrial injury and the mechanisms by which estrogen facilitates endometrial repair and regeneration. Furthermore, it highlights recent research progress and clinical applications of estrogen-based therapies in endometrial regeneration, aiming to provide a valuable reference for relevant scientific research and clinical practice.

PURPOSE: To investigate the incidence and influencing factors of bone loss in patients with spinal cord injury (SCI). METHODS: A retrospective case-control study was conducted. Patients with SCI in our hospital from January 2019 to March 2023 were collected. According to the correlation between bone mineral density (BMD) at different sites, the patients were divided into the lumbar spine group and the hip joint group. According to the BMD value, the patients were divided into the normal bone mass group (t > -1.0 standard deviation) and the osteopenia group (t ≤ -1.0 standard deviation). The influencing factors accumulated as follows: gender, age, height, weight, cause of injury, injury segment, injury degree, time after injury, start time of rehabilitation, motor score, sensory score, spasticity, serum value of alkaline phosphatase, calcium, and phosphorus. The trend chart was drawn and the influencing factors were analyzed. SPSS 26.0 was used for statistical analysis. Correlation analysis was used to test the correlation between the BMD values of the lumbar spine and bilateral hips. Binary logistic regression analysis was used to explore the influencing factors of osteoporosis after SCI. p < 0.05 was considered statistically significant. RESULTS: The incidence of bone loss in patients with SCI was 66.3%. There was a low concordance between bone loss in the lumbar spine and the hip, and the hip was particularly susceptible to bone loss after SCI, with an upward trend in incidence (36% - 82%). In this study, patients with SCI were divided into the lumbar spine group (n = 100) and the hip group (n = 185) according to the BMD values of different sites. Then, the lumbar spine group was divided into the normal bone mass group (n = 53) and the osteopenia group (n = 47); the hip joint group was divided into the normal bone mass group (n = 83) and the osteopenia group (n = 102). Of these, lumbar bone loss after SCI is correlated with gender and weight (p = 0.032 and < 0.001, respectively), and hip bone loss is correlated with gender, height, weight, and time since injury (p < 0.001, p = 0.015, 0.009, and 0.012, respectively). CONCLUSIONS The incidence of bone loss after SCI was high, especially in the hip. The incidence and influencing factors of bone loss in the lumbar spine and hip were different. Patients with SCI who are male, low height, lightweight, and long time after injury were more likely to have bone loss.
Humans ; Spinal Cord Injuries/complications* ; Male ; Female ; Retrospective Studies ; Incidence ; Adult ; Bone Density ; Middle Aged ; Case-Control Studies ; Osteoporosis/etiology* ; Lumbar Vertebrae ; Bone Diseases, Metabolic/etiology* ; Aged ; Risk Factors

For middle-aged and elderly patients with conditions such as spinal fractures and degenerative spinal diseases, spinal internal fixation is a core surgical procedure for reconstructing spinal stability, heavily relying on the biomechanical stability provided by pedicle screw systems. Whereas, these patients are often complicated by osteoporosis that can significantly compromise the stability of the bone-pedicle screw interface, leading to a marked increase in pedicle screw loosening and surgical failure rates. The bone cement-augmented pedicle screw technique, which involves injecting bone cement into the vertebral body or screw trajectory to optimize the mechanical properties of the bone-pedicle screw composite, has been proven to significantly enhance fixation strength and effectively prevent screw-related failures, thereby reducing the incidence of internal fixation failure in high-risk populations undergoing spinal fusion. However, the widespread clinical application of this technique has faced challenges such as inaccurate clinical decision-making (indication and contraindication selection), non-standardized operative practices, and insufficient awareness of complication prevention, resulting in considerable variability in clinical outcomes and even severe complications. To address this, Prof. Luo Fei from First Affiliated Hospital of Army Medical University initiated the project and the Chinese Association Orthopaedic Surgeons organized relevant experts to develop the Evidence-based clinical practice guideline for bone cement-augmented pedicle screw technique ( version 2025), based on current evidence. The guidelines put forward 8 recommendations regarding the clinical value, scope of application, and operational standards of the technique, aiming to provide evidence-based medical support and technical standardization for clinical decision-making.

Objective To explore the potential central mechanisms of functional electrical stimulation(FES)hand cycling in promot-ing upper limb motor recovery after stroke.Methods A total of 35 stroke patients hospitalized in the Rehabilitation Center of Changzhou De'an Hospital from May,2023 to December,2024 were enrolled.They sequentially completed 10-minute FES hand cycling and 10-minute sham stimulation(simple hand cycling)tasks.The task order was randomized via dice rolling.Functional near-in-frared spectroscopy(fNIRS)was used to monitor real-time cerebral hemodynamic changes during both tasks to calculate the overall functional connectivity(FC)strength and the FC strength within regions of interest.Results The overall FC strength was higher during the FES hand cycling task than during the sham task(t=2.591,P<0.05),as well as FC between the ipsilateral primary motor cortex(iM1)and ipsilateral somatosensory association cortex(iSAC),iM1 and contralateral somatosensory association cortex(cSAC),ipsilateral pre-motor and supple-mentary motor cortex(iPMC)and iSAC,iPMC and cSAC,iPMC and ipsilateral primary somatosensory cortex(iS1),iPMC and iM1,and contralateral pre-motor and supplementary motor cortex(cPMC)and iSAC(PFDR<0.05).Conclusion FES hand cycling can promote the cortical function remodeling between cerebral hemispheres and the af-fected hemisphere,to integrate sensory-motor function.

Objective To address the issue of motion misclassification caused by individual gait differences and fixed time window data extraction in motion intention recognition for intelligent lower limb prostheses,this study proposes a motion intention recognition method based on adaptive temporal alignment.Methods In lower limb motion analysis,for continuous gait cycle data,inter-class variability across different steady-state modes was utilized to detect gait pattern consistency through inter-cycle frame differencing.For samples identified as single steady-state modes,the dynamic time warping algorithm was introduced to align adjacent mo-tion sequences,thereby reducing individual variability.Haar wavelet 4-level decomposition was applied to ex-tract low-frequency coefficients for feature vector construction,and classification was performed using a support vector machine.The experimental protocol was designed as follows:three inertial measurement units were used to collect lower limb acceleration and angular velocity data from subjects performing thirteen locomotion modes.The test subjects included ten healthy participants and one transtibial amputee.The locomotion modes consisted of five steady-state modes(level walking,stair ascent,stair descent,ramp ascent,and ramp descent)and eight transition modes(mutual transitions between level walking and stair ascent/descent,as well as ramp ascent/de-scent).Results Simulation tests on ten healthy individuals and one amputee showed recognition accuracies of 99.24%and 100%for five steady-state modes,and 98.51%and 89.11%for all thirteen motion modes,respectively.Conclusion This study proposes an adaptive temporal alignment-based motion intention recognition method.The pro-posed approach effectively reduces the interference of individual gait variability on feature representation,en-hances the consistency and discriminability of gait features,and ultimately improves recognition performance.

BACKGROUND: Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated. METHODS: Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups. RESULTS: A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes. CONCLUSIONS The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.

Neuropsychiatric disorders arise from complex interactions between genetic and environmental factors. DNA methylation, a reversible and environmentally responsive epigenetic regulatory mechanism, serves as a crucial bridge linking environmental exposure, gene expression regulation, and neurobehavioral outcomes. During long-duration deep-space missions, astronauts face multiple stressors-including microgravity, cosmic radiation, circadian rhythm disruption, and social isolation, which can induce alterations in DNA methylation and increase the risk of neuropsychiatric disorders. Genome-wide DNA methylation research can be divided into 3 major methodological stages: Study design, sample preparation and detection, and data analysis, each of which can be applied to astronaut neuropsychiatric health monitoring. Systematic comparison of the Illumina MethylationEPIC array and whole-genome bisulfite sequencing reveals their complementary strengths in terms of genomic coverage, resolution, cost, and application scenarios: the array method is cost-effective and suitable for large-scale population studies and longitudinal monitoring, whereas sequencing provides higher resolution and coverage and is more suitable for constructing detailed methylation maps and characterizing individual variation. Furthermore, emerging technologies such as single-cell methylation sequencing, nanopore long-read sequencing, and machine-learning-based multi-omics integration are expected to greatly enhance the precision and interpretability of epigenetic studies. These methodological advances provide key support for establishing DNA-methylation-based monitoring systems for neuropsychiatric risk in astronauts and lay an epigenetic foundation for safeguarding neuropsychiatric health during future long-term deep-space missions.
DNA Methylation ; Humans ; Space Flight ; Mental Disorders/genetics* ; Epigenesis, Genetic ; Astronauts/psychology* ; Weightlessness/adverse effects* ; Epigenomics