ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

Objective:To investigate the changes of serum helper T cell 1 (Th1)/helper T cell 2 (Th2) cytokines in patients with idiopathic oligonasthenospermia (IO).Methods:A total of 136 patients with IO admitted to the Xianyang Central Hospital from January 2020 to June 2022 were prospectively selected and divided into mild to moderate group (86 cases) and severe group (50 cases) according to the severity of IO. Another 60 healthy males were selected as normal control group. Baseline data, serum Th1 and Th2 cytokines [interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α) and interleukin (IL)-2, IL-4, IL-6, IL-10, IL-21] of the three groups were compared. Spearman correlation analysis was used to evaluate the correlation between IFN-γ, TNF-α, IL-2, IL-4, IL-6, IL-10, IL-21 and the severity of IO disease.Results:The sperm concentration, total sperm and percentage of forward motile sperm in severe group were lower than those in mild to moderate group and normal control group (all P<0.05), and the sperm concentration, total sperm and percentage of forward motile sperm in mild to the moderate group were lower than those in the normal control group (all P<0.05). The serum levels of Th1 cytokines IFN-γ, TNF-α, IL-2 and IL-21 in severe group were higher than those in the mild-moderate group and the normal control group (all P<0.05), and the serum levels of Th1 cytokines IFN-γ, TNF-α, IL-2 and IL-21 in mild-moderate group were higher than those in the normal control group (all P<0.05). The serum levels of Th2 cytokines IL-4, IL-6 and IL-10 in severe group were higher than those in the mild-moderate group and the normal control group (all P<0.05), and the serum levels of Th2 cytokines IL-4, IL-6 and IL-10 in mild-moderate group were higher than those in the normal control group (all P<0.05). Spearman correlation analysis showed that the severity of IO disease was positively correlated with the levels of serum Th1 cytokines (IFN-γ, TNF-α, IL-2, IL-21) and serum Th2 cytokines (IL-4, IL-6, IL-10) (all P<0.001). Conclusions:The Th1/Th2 immune imbalance in patients with IO is aggravated with the increase of disease severity. Early diagnosis and treatment of IO should be strengthened to prevent disease progression.

Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

[Objective]To propose syndrome differentiation and treatment approach of acupuncture for migraine based on the relationship between"spleen and stomach disorders"and the etiology and pathogenesis of migraine,providing theoretical and practical references for the clinical management.[Methods]The connotation of"spleen and stomach disorders"and its association with migraine were systematically reviewed.Based on this perspective,treatment strategies and acupuncture prescriptions for migraine were formulated,and supported by a representative clinical case study.[Results]"Spleen and stomach disorders"serves as a critical pathological foundation for migraine.Its pathogenesis can be summarized into four patterns"impaired spleen transport leading to malnourishment of the brain""phlegm-dampness obstructing the orifices and impairing mental clarity""spleen-stomach disharmony causing Qi stagnation"and"spleen deficiency with blood stasis resulting in orifice obstruction".Acupuncture treatment should prioritize regulating spleen-stomach function,supplemented by dredging local meridians and harmonizing Qi and blood.Key acupoints include Zhongwan(CV12),Xiawan(CV10),Qihai(CV6),and Guanyuan(CV4)for reinforcing primordial Qi,consolidating the foundation,and harmonizing Qi and blood;Zusanli(ST36),Shangjuxu(ST37)and Xiajuxu(ST39)for regulating Qi movement and balancing Yin and Yang;Sizhukong(TE23)and Fengchi(GB20)for clearing the head,improving vision,and relieving pain.In the presented case,the patient was diagnosed as dual deficiency of Qi and blood,acupuncture therapy focusing on replenishing Qi,nourishing blood and unblocking collaterals achieved significant clinical efficacy.[Conclusion]"Spleen and stomach disorders"is a pivotal mechanism underlying recurrent migraine.Acupuncture treatment for migraine should emphasize restoring spleen-stomach function,prioritizing the reinforcement of primordial Qi and harmonization of Qi and blood,thereby addressing the root cause to reduce migraine recurrence.

Objective:To investigate the effects of different types and administration times of defoaming agents on the gastric vision clarity before magnetically controlled capsule endoscopy (MCE) in children.Methods:A retrospective analysis was conducted on children who underwent MCE in Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University from January 2017 to March 2023.Children were divided into three groups based on type of defoaming agents: the simethicone emulsion group (10 mL simethicone emulsion), the dimethicone powder group (5 g dimethicone powder dissolved in 30 mL warm water), and the dimethicone emulsion group (4 mL dimethicone emulsion dissolved in 10 mL water). Each group was further divided into 3 subgroups based on the time of administration before the examination: 30 minutes, 45 minutes, and 60 minutes, resulting in a total of 9 subgroups. The primary outcome measure was the gastric bubble score. Secondary outcomes included gastric cleanliness score, examination time, gastric transit time (GTT), diagnostic efficacy, and safety assessment.Results:A total of 180 children (20 per group) were included in the study. The gastric bubble score (0.89 ± 0.35) and gastric cleanliness score (0.99 ± 0.52) in the 45-minutes subgroup of the dimethicone powder group were significantly lower than those in other groups, indicating better view clarity, with significant differences ( P<0.05). There were no significant differences in examination time, GTT, or the positive detection rate of gastric diseases among the groups ( P>0.05). Conclusion:Administration of defoaming agents before MCE can significantly reduce gastric bubbles and improve the view clarity of the gastric mucosa. The optimal regimen for children is taking 5 g dimethicone powder dissolved in 30 mL warm water 45 minutes before the examination.

Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

Objective To observe the value of non-contrast CT radiomics extreme gradient boosting(XGBoost)model based on SHAP method for predicting acute necrotic collection(ANC)around acute pancreatitis(AP).Methods A total of 307 patients with initially clinically diagnosed AP were retrospectively enrolled.The optimal radiomics features of peripheral pancreatic tissue volume of interest(VOI)were extracted and screened based on automatic segmentation on the first non-contrast CT,and the evaluation results of modified CT severity index(MCTSI)score of AP severity based on first enhanced CT were recorded.The patients were divided into peripancreatic ANC group(ANC group)and acute peripancreatic fluid collection(APFC)group according to follow-up abdominal CT.XGBoost method was used to construct radiomics model,MCTSI model and combined model for predicting AP ANC based on the optimal radiomics features,MCTSI and their combination,respectively.The diagnostic efficacy of each model was evaluated using 5-fold cross-validation method,and the contribution of each variable to combined model was analyzed with SHAP method.Results Among 307 cases,there were 134 cases in ANC group and 173 in APFC group.Totally 6 optimal radiomics features were screened based on the first non-contrast CT.The area under the receiver operating characteristic curve(AUC)of radiomics model,MCTSI model and combined model was 0.936,0.693 and 0.917,respectively.The AUC of MCTSI model was lower than that of radiomics model and combined model(Z=-3.485,-2.824,both P＜0.01),while no significant difference of AUC was found between radiomics model and combined model(Z=-0.817,P=0.415).The contribution of optimal radiomics features to combined model were all higher than that of MCTSI score.Conclusion Non-contrast CT radiomics XGBoost model could effectively predict AP ANC.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.