ObjectiveTo systematically evaluate the methodological quality and measurement properties of traditional Chinese medicine （TCM） syndrome efficacy evaluation scales， and to provide evidence-based references for selecting high-quality assessment tools in TCM clinical practice. MethodsChina National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP Database， Chinese Biomedical Literature Database （CBM）， PubMed， the Cochrane Library， Embase， and Web of Science were searched from inception to April 2， 2025， for studies evaluating the measurement properties of TCM syndrome efficacy evaluation scales. Data were extracted， and the methodological quality and measurement properties of the included scales were assessed according to the consensus-based standards for the selection of health measurement instruments （COSMIN）. Recommendation levels were formulated based on the grading of evidence. ResultsA total of 46 studies were included， involving 22 generic syndrome efficacy evaluation scales and 24 disease-specific syndrome efficacy evaluation scales. None of the scales reported cross-cultural validity or measurement error. According to the recommendation grades， 2 scales met Grade A recommendations and are suggested for clinical use； 38 scales were classified as Grade B， indicating potential applicability but requiring further validation； and 6 scales were classified as Grade C， suggesting the need for further refinement. ConclusionExisting TCM syndrome efficacy evaluation scales exhibit substantial variability in methodological quality， incomplete reporting of measurement properties， and insufficient attention to scale revision. Future efforts should emphasize standardized design in the development of TCM syndrome scales， strengthen validation procedures for key measurement properties， and prioritize dynamic revision of scales， thereby providing high-quality tools to support the precise evaluation of syndrome efficacy.

Objective To construct a nomogram model for prediction of constipation risk in adult patients with cardiovascular disease (CVD). Methods A total of 780 adult patients with CVD admitted to Zhongshan Hospital, Fudan University from June 15, 2025 to August 15, 2025 were prospectively enrolled and randomly divided into a training set and a validation set at a 7∶3 ratio. Risk factors for constipation were identified using the least absolute shrinkage and selection operator (LASSO) and Firth logistic regressions, and a nomogram model was subsequently constructed in the training set. Receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the discrimination ability of the model. Calibration curve and decision curve analysis (DCA) were used to assess calibration and clinical applicability. The value of model was evaluated in the validation set. Results Among 780 adult patients with CVD, 391 patients (50.1%) developed constipation. Eleven variables were selected by LASSO regression and Firth logistic regression, and a nomogram model was constructed. The ROC curve showed that the AUCs of the model to predict constipation in adult patients with CVD in training and validation sets were 0.881 and 0.883, respectively. Calibration curves and DCA demonstrated good calibration and predictive performance of the model in training and validation sets. Conclusions The nomogram model based on LASSO-Firth logistic regression demonstrates good predictive performance and may serve as a useful tool for identifying adult patients with CVD at high risk of constipation.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.

Objective To study the effects of cognitive behavioral therapy(CBT)on nutritional status and postoperative recovery in patients after gastric cancer surgery.Methods Sixty patients diagnosed with gastric cancer between January 1,2023,and December 30,2023,at the hospital in the department of gastroin-testinal and gland surgery were included in this study.Patients were randomly divided by drawing lots into ei-ther the observation group(received routine care plus CBT—based nursing program)or the control group(received routine care),with 30 patients in each group.Nutritional indicators and postoperative recovery out-comes were compared between the two groups.Results After the intervention,patients in the observation group showed significantly higher levels of albumin and hemoglobin compared to the control group(P＜0.05).However,there was no statistically significant difference in prealbumin levels between the two groups(P＞0.05).Furthermore,patients in the observation group scored higher on the Quality of Recovery-40 scale in terms of emotional state,physical comfort,psychological support,and overall score compared to the control group(P＜0.05).After the intervention,the observation group showed significantly lower HADS scores com-pared to the control group,with a statistically significant difference(P＜0.05).Conclusion CBT demon-strates significant positive effects in improving nutritional status and postoperative recovery quality in gastric cancer patients.CBT improves patients'emotional state,thereby affecting appetite and nutritional status,and promotes postoperative physical function recovery.

Objective To analyze the application characteristics,trends,and special advantages of anti-infection research using the Bayesian method,and to provide methodological reference for the development of anti-infection research.Methods PubMed,CNKI and WanFang Data were electronically searched for the studies on anti-infection using Bayesian method published from January 1,2015 to November 21,2023.The relevant information of publication time,anti-infection type,sample size,Bayesian characteristics and Bayesian application pattern were analyzed descriptively and reviewed.Results A total of 86 studies were included,of which 41.9％were observational studies,only 7.0％were enterprise-initiated studies,and 48.8％were mentioning prior information studies.There was no domestic intervention study.The application characteristics and advantages of Bayesian method in intervention study,observational study and pharmacokinetic study are different.In intervention researches and observational researches,the application of Bayesian design decision and the application of Bayesian analysis and estimation accounts for 69.2％and 52.8％at most,respectively.Conclusions The Bayesian method is flexible,can be used for small sample sizes and complex model research,and can deal with uncertainty.In intervention studies in the field of anti-infection in China,the Bayesian method has not been applied widely.Only a handful of studies applying Bayesian method have been initiated by companies.In the future,it is still necessary to promote the advantages and application scenarios of Bayesian methods in the field of anti-infection research and strengthen the standardization of the application of Bayesian method.

Objective:To investigate the effects of backward walking observational training combined with Soundsory cognitive training based on PASS (P: planning; A: attention; S: simultaneous; S:successive) theory on cognitive function, balance function and self-efficacy in elderly patients with post-stroke mild cognitive impairment (MCI).Methods:A randomized controlled trial was conducted. Elderly patients with post-stroke MCI admitted to the Department of Neurology, Affiliated Hospital of Jiangnan University from December 2022 to November 2023 were selected as the study objects by convenience sampling method. The patients were divided into control group and observation group by random number table. The patients in the control group implemented routine care and rehabilitation training, while the observation group were implemented backward walking observational training combined with Soundsory cognitive training based on the PASS theory. The Montreal Cognitive Assessment (MoCA), the Brunel Balance, and the Stroke Self-Efficacy Questionnaire (SSEQ) were used to evaluate the intervention effect before intervention, 2 weeks and 4 weeks of intervention, respectively.Results:A total of 116 elderly patients with post-stroke MCI were included, comprising 58 patients in the observation group (32 males and 26 females), aged (73.47 ± 7.10) years, and 58 patients in the control group (33 males and 25 females), aged (72.72 ± 8.37) years. Before intervention, there were no significant differences in the total scores of MoCA, SSEQ and Brunel Balance Scale between the 2 groups (all P>0.05). At 2 and 4 weeks of intervention, the total score of MoCA in the observation group were (20.10 ± 2.73), (22.98 ± 2.98) points, which were higher than those of the control group (18.24 ± 2.84), (20.47 ± 3.29) points, the differences were statistically significant ( t=3.61, 4.32, both P<0.05). The total score of SSEQ in the observation group were (49.97 ± 7.73), (54.98 ± 7.88) points, which were higher than those in the control group (46.50 ± 8.69), (51.59 ± 6.10) points, the differences were statistically significant ( t=2.27, 2.60, both P<0.05). At 4 weeks of intervention, the Brunel Balance Scale score in the observation group was 11(9, 11) points, which was higher than 10(8, 11) points in the control group, the difference was statistically significant ( Z=-2.00, P<0.05). Conclusions:Backward walking observational training combined with Soundsory cognitive training based on PASS theory can effectively improve cognitive and balance functions, and enhance self-efficacy in elderly patients with post-stroke MCI.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.

Objective:To construct an artificial intelligence (AI)-based automatic grading model for neurological severity at acute phase of patients with traumatic cervical spinal cord injury (TCSCI) and evaluate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 315 patients with TCSCI admitted to the Second Affiliated Hospital of Naval Medical University from January 2019 to December 2023, including 243 males and 72 females, aged 30-75 years [(57.6±7.0)years]. Injured segments involved C 1-C 4 in 143 patients and C 5-C 8 in 172. According to the American Spinal Injury Association (ASIA) scale, the injuries were classified as Grade A in 15 patients, Grade B in 53, Grade C in 74, and Grade D in 173. The patients were randomly divided into training group ( n=252) and test group ( n=63) with a ratio of 8∶2. The patients′ sensory and motor functions were assessed according to the ASIA scale within 48 hours after injury. The cervical spine MRI instance segmentation model was used to extract injury severity features of TCSCI patients in sagittal T2-weighted images. The grading model consisted of a two-layer cascade network. The first layer involved gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier. In the training group, the 6 machine learning models were trained separately. In the second layer, the performance of the six models was optimized to obtain the corresponding optimal grading models, so as to match the models with the best grading performance for each feature. In the test group, the performance of each model was evaluated by calculating accuracy, recall, precision, average precision, and F1 score. Results:A total of 138 clinical and imaging features were included to construct an automatic grading model for neurological severity of TCSCI patients at acute phase, comprising 132 clinical neurological features (including 56 light touch sensory scores, 56 pinprick sensory scores, and 20 key muscle scores) and 6 MRI imaging features. In the test group, the accuracy, recall, precision, average precision and F1 score of the six models, including gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier in the first layer of the automatic grading model for neurological severity at acute phase of TCSCI patients, in the overall grading of light touch, pinprick sensory and key muscle motor function were all above 0.86. In terms of the overall light touch function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.90), gradient boosting (0.99), Gaussian naive bayes (0.98), random forest (0.96), and gradient boosting (0.96), respectively. In terms of the overall pinprick sensory function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were gradient boosting (0.98), Gaussian naive bayes (0.98), gradient boosting (0.99), decision tree (0.99), and gradient boosting (0.95), respectively. In terms of the overall key muscle motor function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.97), gradient boosting and support vector machine classifier (0.97), decision tree (0.95), random forest (0.95), and support vector machine classifier (0.96), respectively. In terms of sensory function, gradient boosting had the highest number of superior performances in the overall light touch and pinprick sensory function grading. In terms of motor function, the support vector machine classifier had the highest number of superior performances in the overall key muscle motor function grading.Conclusion:The automatic grading model for neurological severity at acute phase of patients with TCSCI that is constructed based on machine learning models and two-layer cascade networks can achieve the optimization of the grading performance of each feature and exhibit a strong grading ability for the sensory and motor function severity.

Objective:To explore the effect of combining intermittent θ pulse stimulation (iTBS) of the cerebellum with lower extremity exoskeleton robot support on the balance and walking function of stroke survivors.Methods:Seventy-five stroke survivors complicated with lower extremity dysfunction were divided into an iTBS group, an exoskeleton group and a combined group, each of 25, according to a random number table. In addition to conventional rehabilitation training, the iTBS group was given cerebellar iTBS combined with traditional walking training, the exoskeleton group received sham cerebellar iTBS combined with walking training assisted by a lower extremity exoskeleton robot. The combined group received both therapies. The schedule was once a day, 5 days a week for 3 weeks. Before and after the treatment, the 10-metre walking test (10MWT), the Berg Balance Scale (BBS) and the Fugl-Meyer lower extremity assessment (FMA-LE) were used to evaluate the subjects′ walking ability, balance and lower extremity motor ability. Gait and neuro-electrophysiological tests were also conducted in all three groups.Results:After the treatment, a significant improvement was observed in the 10MWT times, BBS scores, FMA-LE scores, stride frequency and stride speed of all three groups compared with before the treatment. On average, the results of the exoskeleton and combined groups were significantly better than those of the iTBS group, and those of the combined group were significantly better than among the exoskeleton group. Almost everyone′s MEP latency and amplitude had improved significantly compared with before the treatment, but the improvements in the exoskeleton group tended to be superior to those in the iTBS group ( P≤0.05). The latency in the combined group averaged (21.25±1.70)ms, and the amplitude averaged (184.17±6.54)μV, both significantly better than the exoskeleton group′s averages. Conclusions:Cerebellum iTBS combined with lower extremity exoskeleton walker training can significantly improve the motor functioning, balance and walking ability of stroke survivors.

Objective:To explore the effect of combining repeated peripheral (rPMS) and central transcranial magnetic stimulation (rTMS) in treating upper limb motor dysfunction after a stroke.Methods:Seventy-eight patients with upper limb motor dysfunction after a stroke were randomly divided into a control group, an rTMS group and a combined magnetic stimulation group, each of 26. All three groups underwent routine rehabilitation, while the rTMS group was repeatedly given low frequency transcranial magnetic stimulation of the M1 region on the unaffected side, and the combined group also received repeated peripheral magnetic stimulation at Erb′s point on the affected upper limb. There was one treatment session a day, 5 days a week for 3 weeks. Before and after the treatment, everyone′s upper limb motor function was quantified using the Fugl-Meyer upper extremity assessment (FMA-UE) and the Wolf motor function test (WMFT). Skill in the activities of daily living was quantified in terms of a Barthel index (BI). Motor recovery of the upper limbs and hands was assessed using Brunnstrom staging. The latency and amplitude of the motor evoked potentials (MEPs) in the subjects′ affected abductor pollicis brevis muscles were also recorded before and after the treatment. Pearson correlation coefficients quantified the correlation between the changes in FMA-UE scores and MEP amplitudes before and after the treatment in the three groups.Results:There were no significant differences among the three groups before the treatment. Afterward, however, the average FMA-UE, WMFT and BI scores, as well as the upper limb and hand Brunnstrom stages and the average MEP latencies and amplitudes of all the three groups had improved significantly. The combined group′s average results were then significantly better than the other two groups′ averages, except for the upper limb Brunnstrom stages. The increases in MEP amplitude were positively correlated with the increases in FMA-UE scores among the rTMS and the combined group, but there was no significant correlation between them in the control group.Conclusions:The combined application of rPMS and contralateral low frequency rTMS can effectively relieve motor dysfunction in the upper limbs in the early stages after a stroke.