OBJECTIVE To systematically evaluate the effects of supplementation with different doses of vitamin D on maternal and infant outcomes in vitamin D-deficient pregnant women. METHODS Related literature on the effects of supplementing different doses of vitamin D on maternal and infant outcomes was searched in databases including CNKI， Wanfang Data， VIP， PubMed， Medline， the Cochrane Library， Embase from their inception to June 30， 2025. The risk of bias assessment tool from the Cochrane Handbook 5.1 was used to evaluate the quality of included literature. Meta-analysis of outcome indicators was performed by using RevMan 5.4 software. RESULTS A total of 15 studies were included， involving 4 664 patients [2 129 in the experimental group （daily dose ＞2 000 IU）， 2 058 in control group 1 （daily dose ≤1 000 IU） and 477 in control group 2 （daily dose ＞1 000-≤2 000 IU） ] . Meta-analysis results showed that the incidence of preeclampsia （PE） [OR＝0.71， 95%CI （0.53， 0.96）， P ＝0.03 ] ， gestational diabetes mellitus （GDM） [OR＝0.60， 95%CI （0.43， 0.84）， P ＝0.003 ] ， low birth weight of newborn [OR＝0.72， 95%CI （0.53， 0.97）， P ＝0.03 ] and macrosomia [OR＝0.53， 95%CI （0.29， 0.98）， P ＝0.04 ] in the experimental group were significant lower than control group 1； but there was no significant difference in the incidence of premature delivery [OR＝0.86， 95%CI （0.65， 1.13）， P ＝0.28 ] ， cesarean delivery [OR＝0.92， 95%CI （0.74， 1.15）， P ＝0.48 ] or stillbirth rate [OR＝0.77， 95%CI （0.48， 1.24）， P ＝0.29 ] . The incidence of low birth weight of ne wborn [OR＝0.64， 95%CI （0.41， 0.98）， P ＝0.04 ] in the experimental group was significant lower than control group 2； but there was no significant difference in the incidence of PE [OR＝0.61， 95%CI （0.25， 1.49）， P ＝0.28 ] ， the incidence of GDM [OR＝0.73， 95%CI （0.42， 1.24）， P ＝0.24 ] ， premature delivery rate [OR＝0.90， 95%CI （0.59， 1.39）， P ＝0.63 ] ， cesarean delivery rate [OR＝0.92， 95%CI （0.64， 1.33）， P ＝0.66 ] ， or stillbirth rate [OR＝0.68， 95%CI （0.24， 1.94）， P ＝0.48 ] . CONCLUSIONS Different doses of vitamin D supplementation in early pregnancy have a significant impact on maternal and infant pregnancy outcomes in vitamin D-deficient pregnant women； daily doses ＞2 000 IU have significant advantages in reducing the incidence of PE and GDM and improving the outcome of premature delivery.

Objective To report the individualized rehabilitation protocol administered to a patient suffering from corpus callosum injury subsequent to cerebral infarction,manifesting clinically as alien hand syndrome,attention deficits,spatial neglect and gait apraxia;and to emphasize the application of functional near infrared spectroscopy(fNIRS)in the assessment of cerebral activation.Methods A 54-year-old male with corpus callosum damage following cerebral infarction was assessed by a comprehen-sive array of neuropsychological assessments,such as Mini-Mental State Examination,Montreal Cognitive As-sessment,Loewenstein Occupational Therapy Cognitive Assessment and Wisconsin Card Sorting Test,etc.Based on these assessments,a personalized rehabilitation program was devised,incorporating physical therapy,occupa-tional therapy,task-oriented training,mirror therapy,computer-assisted cognitive training,as well as Schulte's square attention training,bilateral limb coordination training and transcranial magnetic stimulation(TMS).fNIRS was used to evaluate changes in brain activation before and after rehabilitation.Results After more than a month of comprehensive rehabilitation,the patient experienced significant improvements in Alien hand syndrome,attention deficit and spatial neglect.Gait was normalized,and enhancements were ob-served in motor function,cognition and activity of daily living.fNIRS analysis revealed favorable alterations in cerebral activation patterns.Conclusion For the intricate symptoms associated with corpus callosum injury,a multidisciplinary rehabilitation ap-proach,particularly the incorporation of Schulte's square attention training,bilateral coordination exercises and TMS,alongside fNIRS for monitoring cerebral activation,showed significant rehabilitation effects.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

Objective To analyze the application trends and research hotspots of brain-computer interface(BCI)technology in stroke rehabilitation over the past five years.Methods Relevant literatures on the use of BCI in stroke rehabilitation published between January,2021 and August,2025 were retrieved from the Web of Science Core Collection database.CiteSpace 6.4.R1 was used for visualiza-tion analysis.Results A total of 458 papers were included.The annual number of publications remained at a high level.China was the leading country in publication output,with Fudan University and Aalborg University as the top institutions.The most prolific author was Mads R.Jochumsen,while G.Pfurtscheller had the highest citation frequency.The key-words and burst terms with the highest frequency in this field were brain-computer interface,motor imagery,up-per limb and deep learning.Conclusion Over the past five years,research on BCI in stroke rehabilitation has maintained a high publication volume.The research hotspots focus on innovations in BCI algorithm technology and multidimensional validation of neu-ral mechanisms and rehabilitation efficacy.

Objective To report the individualized rehabilitation protocol administered to a patient suffering from corpus callosum injury subsequent to cerebral infarction,manifesting clinically as alien hand syndrome,attention deficits,spatial neglect and gait apraxia;and to emphasize the application of functional near infrared spectroscopy(fNIRS)in the assessment of cerebral activation.Methods A 54-year-old male with corpus callosum damage following cerebral infarction was assessed by a comprehen-sive array of neuropsychological assessments,such as Mini-Mental State Examination,Montreal Cognitive As-sessment,Loewenstein Occupational Therapy Cognitive Assessment and Wisconsin Card Sorting Test,etc.Based on these assessments,a personalized rehabilitation program was devised,incorporating physical therapy,occupa-tional therapy,task-oriented training,mirror therapy,computer-assisted cognitive training,as well as Schulte's square attention training,bilateral limb coordination training and transcranial magnetic stimulation(TMS).fNIRS was used to evaluate changes in brain activation before and after rehabilitation.Results After more than a month of comprehensive rehabilitation,the patient experienced significant improvements in Alien hand syndrome,attention deficit and spatial neglect.Gait was normalized,and enhancements were ob-served in motor function,cognition and activity of daily living.fNIRS analysis revealed favorable alterations in cerebral activation patterns.Conclusion For the intricate symptoms associated with corpus callosum injury,a multidisciplinary rehabilitation ap-proach,particularly the incorporation of Schulte's square attention training,bilateral coordination exercises and TMS,alongside fNIRS for monitoring cerebral activation,showed significant rehabilitation effects.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

Objective To analyze the application trends and research hotspots of brain-computer interface(BCI)technology in stroke rehabilitation over the past five years.Methods Relevant literatures on the use of BCI in stroke rehabilitation published between January,2021 and August,2025 were retrieved from the Web of Science Core Collection database.CiteSpace 6.4.R1 was used for visualiza-tion analysis.Results A total of 458 papers were included.The annual number of publications remained at a high level.China was the leading country in publication output,with Fudan University and Aalborg University as the top institutions.The most prolific author was Mads R.Jochumsen,while G.Pfurtscheller had the highest citation frequency.The key-words and burst terms with the highest frequency in this field were brain-computer interface,motor imagery,up-per limb and deep learning.Conclusion Over the past five years,research on BCI in stroke rehabilitation has maintained a high publication volume.The research hotspots focus on innovations in BCI algorithm technology and multidimensional validation of neu-ral mechanisms and rehabilitation efficacy.

Objective To investigate the effect of unilateral or bilateral transcranial direct current stimulation(tDCS)on post-stroke dysphagia.Methods From February,2023 to March,2025,27 stroke patients with dysphagia and nasal feeding in Beijing Bo'ai Hos-pital were randomly divided into healthy side stimulation group,bilateral stimulation group and sham stimulation group,with nine cases in each group.All the groups received conventional swallowing training and tDCS,while the healthy side stimulation group stimulated on the healthy side of oropharyngeal cortex;and the bilateral stimu-lation group alternatively stimulated bilateral oropharyngeal cortex,with one hour interval between bilateral stim-ulation;the sham stimulation group stimulated the healthy side of oropharyngeal cortex for 30 seconds and then stop.The course lasted two weeks.The scores of Standard Swallowing Function Assessment Scale(SSA),Modi-fied Mann Assessment of Swallowing Ability(MMASA)and Rosenbek Penetration-Aspiration Scale(PAS)were compared before and after treatment.Results The intra-group effect(F=16.185,P<0.01)was significant in the scores of SSA,the intra-group effect(F=28.650,P<0.01)and interaction effect(F=3.453,P<0.01)were significant in the scores of MMASA,and there was no significant difference in the inter-group effect,intra-group effect and interaction effect in the scores of PAS(P>0.05).Post hoc test showed that there was no significant difference in the scores of SSA,MMASA and PAS among three groups(P>0.05).There was significant difference in the score difference of MMASA be-fore and after treatment among three groups(F=4.698,P<0.05).Post hoc test showed that the score difference of MMAS was more in the healthy side stimulation group than in the bilateral stimulation group and the sham stimulation group(P<0.05),with no significant difference between the bilateral stimulation group and the sham stimulation group(P>0.05).Conclusion tDCS can partly improve post-stroke dysphagia.The healthy side anode stimulation is superior to the alter-nating bilateral hemisphere anode stimulation.

OBJECTIVE To compare the efficacy and safety of different daily doses of aspirin in the prevention of preeclampsia （PE）. METHODS The case-control studies and prospective randomized controlled trials on aspirin with daily dose ≥ 100 mg （trial group） vs. ＜100 mg （control group） in the prevention of PE were retrieved from PubMed， Medline， Embase， the Cochrane Library， CNKI， China Biomedical Literatue Database and Wanfang Data from base-building to January 2025. After literature screening， data extraction and quality evaluation， meta-analysis was performed by using RevMan 5.3 software. RESULTS A total of 11 literatures were included， involving 3 052 pregnant women. Meta-analysis showed the incidence of PE [RR＝0.63， 95%CI （0.53，0.76）， P＜0.000 01]， gestational hypertension [RR＝0.69， 95%CI （0.50，0.94），P＝0.02]， preterm birth [RR＝0.56， 95%CI （0.47，0.66）， P＜0.000 01]， and intrauterine growth retardation [RR＝0.73，95%CI （0.61，0.87），P＝0.000 5] in trial groups were significantly lower than control group. The incidence of postpartum hemorrhage between the two groups had no statistically significant difference [RR＝1.17， 95%CI （0.90，1.53），P＝0.25]. Subgroup analysis showed that the incidence of PE in Chinese pregnant women taking 150 mg of aspirin was significantly higher than taking 100 mg of aspirin [RR＝3.40， 95%CI （1.29， 8.93）， P＝0.01]； but there was no significant difference between the two groups in the incidences of postpartum hemorrhage， preterm birth （P＞0.05）. CONCLUSIONS Aspirin with daily dose ≥100 mg is more effective in preventing PE than daily dose ＜100 mg， with lower rates of gestational hypertension， preterm birth， and intrauterine growth retardation. It does not increase the risk of postpartum hemorrhage. For pregnant women in China， daily dose 100 mg of aspirin may be more effective in preventing PE than 150 mg.

Objective:To investigate the value of a deep learning radiomics (DLR) nomogram model based on intra-tumoral and peri-tumoral MRI features for differentiating IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma.Methods:This was a case-control study. The clinical and imaging data of 233 patients pathologically confirmed with either IgG4-ROD or orbital MALT lymphoma were retrospective collected between January 2020 and December 2024 from the Second Hospital of Jilin University (Center 1) and Beijing Tongren Hospital, Capital Medical University (Center 2). Patients from Center 1 ( n=158) were used as the training cohort, while those from Center 2 ( n=75) served as the validation cohort. Among the cases, 102 were IgG4-ROD (70 in training, 32 in validation) and 131 were orbital MALT lymphoma (88 in training, 43 in validation). Univariate and multivariate logistic regression analyses were used to identify independent clinical imaging predictors and build a clinical imaging model. Based on T 1WI, T 2WI, and diffusion weighted images, intra-tumoral regions were manually delineated, a 2 mm peri-tumoral margin was automatically generated, and both regions were combined as a single region of interest for radiomics feature extraction. Deep learning features were extracted using a ResNet-50 backbone, and after feature selection and dimensionality reduction, a DLR model was constructed. The clinical imaging features and DLR features were integrated to build a combined nomogram model. Model performance in differentiating IgG4-ROD from orbital MALT lymphoma was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis. The area under the curve (AUC) were compared using the DeLong test. Results:Bilateral orbital involvement ( OR=1.983, 95% CI 1.166-2.843, P=0.046) and extraocular muscle involvement ( OR=1.246, 95% CI 1.079-1.764, P=0.015) were identified as independent predictors for distinguishing IgG4-ROD from orbital MALT lymphoma and were used to construct the clinical model. Fourteen features (9 radiomics and 5 deep learning features) were selected for the DLR model, and a nomogram was developed. In the training set, the AUCs for the clinical model, DLR model, and nomogram were 0.762 (95% CI 0.712-0.812), 0.865 (95% CI 0.822-0.908), and 0.943 (95% CI 0.909-0.953), respectively. In the validation set, the AUCs were 0.733 (95% CI 0.675-0.791), 0.823 (95% CI 0.762-0.884), and 0.924 (95% CI 0.902-0.958), respectively. The nomogram showed significantly higher AUCs than those of the clinical and DLR models alone (training set: Z=3.92, 2.87, P0.001, P=0.004; validation set: Z=3.25, 2.46, P=0.001, 0.014). Calibration curves indicated good agreement between predicted and actual IgG4-ROD incidence, and decision curve analysis demonstrated the highest net benefit for the nomogram. Conclusion:A nomogram that incorporates both intra-tumoral and peri-tumoral DLR features and clinical imaging characteristics demonstrates excellent performance in distinguishing IgG4-ROD from orbital MALT lymphoma.