OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Since Li Dongyuan formally proposed the concept of "wind medicinals" （Feng Yao），their clinical application has been highly valued by physicians throughout history. However，influenced by the evolution of the term and connotation of "wind medicinals" in modern times，its conceptual understanding，leading to a decline in clinical utilization. Since the new century，Professor Wang Mingjie has integrated LIU Wanxu's sweat pore （Xuanfu） theory into the reinterpretation of wind medicinals，proposing the "Xuanfu-dredging wind medicinal theory"， which has gained widespread recognition in academic circles，revitalizing their clinical application. This study traces the origin of the Xuan-dredging wind medicinals theory and reviews their current functions and clinical applications，finding that the theoretical framework is preliminarily established. Characterized by their pungent and dispersing properties，wind medicines act by opening the Xuanfu throughout the body，exerting therapeutic effects such as dispelling pathogens，resolving stagnation，and enhancing treatments like blood-activation，spleen-fortification，and heat-clearing. They are widely used，showing advantages in treating systemic diseases including ophthalmic and cardiovascular/cerebrovascular disorders. Modern pharmacological research indicates preliminary consensus on hypotheses of cerebral，intestinal，hepatic，and renal Xuanfu. studies on formulas （e.g.，Qufeng Tongqiao Fang），single herbs （e.g.，Mahuang and Gegen），and active constituents （e.g.，tetramethylpyrazine） provide evidence that wind medicines improve key mechanisms like blood-brain barrier function and cerebral microcirculation （material basis of cerebral Xuanfu），supporting their use in brain disorders （e.g.，cerebral ischemia，depression）. Despite clinical and pharmacological support，the clinical application system for wind medicines remains incomplete. Future efforts should focus on high-quality clinical research and mechanistic studies to establish personalized application systems，enhance Xuanfu opening practices，and ensure the effectiveness and safety of wind medicines.

OBJECTIVE: To observe the effects of Tiaoshu Anshen (regulating the hinge and calming the mind) acupuncture on sleep quality and serum levels of 5-hydroxytryptamine (5-HT) and dopamine (DA) in patients with chronic insomnia. METHODS: A total of 58 patients with chronic insomnia were randomly divided into an acupuncture group and a medication group, 29 cases in each group. Tiaoshu Anshen acupuncture was applied at Baihui (GV20) and bilateral Shenmen (HT7), Sanyinjiao (SP6), Benshen (GB13) in the acupuncture group, once a day, 1-day interval was taken after 6 consecutive days of treatment. Estazolam tablet was given orally before bed in the medication group, 1 mg each time. The 4-week treatment was required in both groups. Before and after treatment, the sleep quality was assessed by Pittsburgh sleep quality index (PSQI) and polysomnography (PSG), the serum levels of 5-HT and DA were detected by ELISA. RESULTS: After treatment, the item scores and total scores of PSQI were decreased compared with those before treatment in the two groups (P<0.05); in the acupuncture group, the scores of sleep quality, sleep latency, sleep time, sleep efficiency, sleep disorders and total score of PSQI were lower than those in the medication group (P<0.05). After treatment, the total sleep time (TST) was prolonged (P<0.05), the sleep latency (SL) and wake after sleep onset (WASO) were shortened (P<0.05), the sleep efficiency (SE%), percentage of non-rapid eye movement stage 3 (N3%), percentage of rapid eye movement stage (REM%) and serum levels of 5-HT were increased (P<0.05) compared with those before treatment; the percentage of non-rapid eye movement stage 1 (N1%), percentage of non-rapid eye movement stage 2 (N2%) and serum levels of DA were decreased (P<0.05) compared with those before treatment in the two groups. After treatment, in the acupuncture group, TST was longer, while SL and WASO were shorter than those in the medication group (P<0.05), SE%, N3%, REM% and serum level of 5-HT were higher, while N1%, N2% and serum level of DA were lower than those in the medication group (P<0.05). CONCLUSION Tiaoshu Anshen acupuncture may improve the sleep quality by regulating the serum neurotransmitter levels i.e. 5-HT and DA in patients with chronic insomnia.
Humans ; Sleep Initiation and Maintenance Disorders/physiopathology* ; Male ; Acupuncture Therapy ; Female ; Middle Aged ; Adult ; Serotonin/blood* ; Sleep Quality ; Acupuncture Points ; Dopamine/blood* ; Aged ; Neurotransmitter Agents/blood* ; Young Adult

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Objective: To explore the relationship of physical exercise behavior with mobile phone addiction and mental health of college students, so as to provide evidence for interventions to improve mobile phone addiction and mental health of college students. Methods: From October 8 to December 20 in 2024, 896 college students from 4 colleges in Beijing were selected using a combination of convenience sampling and stratified random cluster sampling method. Physical Activity Rating Scale (PARS-3), Mobile Phone Addiction Tendency Scale (MPATS), Adolescence Mental Health Diathesis Questionnaire (AMHDQ) and Symptom Checklist 90 (SCL-90) were used. The correlation of mobile phone addiction and mental health on physical exercise behavior of college students were analyzed by multivariable Logistic regression and linear regression. Results: Among the surveyed college students, 504 (56.25%) students had low exercise, 262 (29.24%) had moderate exercise, 130 (14.51%) had high exercise, and 392 (43.75%) had sufficient exercise. The total score of PARS-3 was 18.00 ( 15.00 , 33.00) points. Logistic regression analysis showed that the total score of MPATS ( OR=1.022, 95%CI =1.008-1.036), the total score of SCL-90 ( OR=1.010, 95%CI = 1.005 -1.015), the total AMHDQ score ( OR=0.995, 95%CI =0.992-0.998) were significantly associated with insufficient exercise among college students ( P <0.05). Linear regression analysis showed that the scores of MPATS, AMHDQS and SCL-90 were significantly correlated with physical exercise behavior ( B=-0.20, 0.04, -0.07, P <0.05). Conclusion The physical exercise behavior of college students is related to mobile phone addiction and mental health.

OBJECTIVE: To summarize the methods of ankle hinge position design in the correction of clubfoot deformity by Ilizarov method, and to explore its application value in the prevention of ankle dislocation. METHODS: A retrospective study was conducted including 28 patients with rigid clubfoot deformity (34 feet) who met the selection criteria and admitted between September 2021 and December 2024. There were 19 males and 9 females with an average age of 31.8 years (range, 19-47 years). According to Dimeglio classification, there were 21 feet of degree Ⅲ and 13 feet of degree Ⅳ. The causes were traumatic sequelae in 9 cases, congenital foot deformity in 15 cases, spina bifida sequelae in 1 case, peripheral neuropathy in 1 case, and cerebral palsy sequelae in 2 cases. The malformation lasted from 6 to 46 years, with an average of 29.3 years. All patients were treated with Ilizarov circular external fixator, and the hinge position of ankle joint was planned according to the standard lateral X-ray film of foot and ankle and the principle of Ilizarov limb deformity correction center of rotation angulation (CORA) before operation. The 2008 International Clubfoot Study Group (ICFSG) scoring system was used to evaluate the efficacy. RESULTS: The deformity of rigid clubfoot was completely corrected in all patients, and the patients could walk with plantar weight-bearing, and the ankle weight-bearing walking significantly improved when compared with that before operation. There was no complication such as ankle dislocation, talus impact or extrusion, local skin necrosis, needle tract infection, or numbness of extremities during the correction process. All patients were followed up 5-39 months, with an average of 18.1 months. At last follow-up, according to the ICFSG scoring system, 23 feet were excellent, 10 feet were good, and 1 foot was fair, and the excellent and good rate was 97%. CONCLUSION Designing the position of the ankle hinge according to the principle of CORA can effectively avoid ankle dislocation, talus impingement, tibiotalar joint extrusion, and other ankle adverse events in the process of correcting clubfoot deformity, which has good application value in clinical practice.
Humans ; Male ; Female ; Clubfoot/diagnostic imaging* ; Ilizarov Technique/instrumentation* ; Adult ; Retrospective Studies ; External Fixators ; Ankle Joint/diagnostic imaging* ; Middle Aged ; Joint Dislocations/prevention & control* ; Treatment Outcome ; Young Adult

OBJECTIVES: To study the effects of early respiratory training combined with swallowing function training on physical development and neurodevelopment at a corrected gestational age of 6 months in infants with bronchopulmonary dysplasia (BPD). METHODS: A total of 69 BPD infants who could not be fed completely orally were prospectively selected from the Department of Neonatology of Hunan Children's Hospital between January 2018 and January 2021. Based on a random number table, the infants were divided into a conventional group (35 cases) and a training group (34 cases) (with 8 cases lost to follow-up; the final follow-up included 31 cases in the training group and 30 cases in the conventional group). Both groups received routine clinical treatment and care, while the training group additionally received respiratory and swallowing function training until the infants could independently feed orally. The weight, length, Gesell Developmental Schedule (GDS) results, readmission rate, and multiple readmission rate (two or more admissions) were compared between the two groups at a corrected age of 6 months. RESULTS: At corrected gestational age of 6 months, the training group had higher weight, length, and GDS scores in personal-social, language, gross motor, fine motor, and adaptive development compared to the conventional group (P<0.05). The readmission rate and multiple readmission rate were lower in the training group compared to the conventional group (P<0.05). CONCLUSIONS Early respiratory training combined with swallowing function training for BPD infants in a neonatal intensive care unit setting helps improve physical and neurological development and reduces the readmission rate.
Humans ; Bronchopulmonary Dysplasia/physiopathology* ; Prospective Studies ; Male ; Female ; Infant ; Deglutition/physiology* ; Gestational Age ; Infant, Newborn ; Breathing Exercises ; Child Development

We aimed to explore the value of personalized narrative intervention in the nursing care of elderly patients with stroke hemiplegia(SH).A total of 68 elderly patients with SH were selected and divided into two groups using the ball-rolling method,with 34 cases in each group.The control group received routine nursing care,while the observation group received routine care and personalized narrative nursing.The nursing outcomes of the two groups were compared.Compared with the control group,the observation group had lower scores in anxiety and depression(P<0.05),and higher scores in the Functional Evaluation of Cognitive State-Short Screening(FECS-SS)(P<0.05).The rehabilitation effects of normal rate in the observation group was higher than that in the control group(P<0.05).Personalized narrative nursing for SH patients can effectively relieve the patients'negative emotions,enhance their compliance with rehabilitation,and improve the outcomes of rehabilitation.

Objective:To compare the clinical efficacy of Minimally Invasive Surgery (MIS) and traditional Posterior Spinal Fusion (PSF) in treating children with paralytic scoliosis with pelvic obliquity (PSPO) following spinal cord injury.Methods:A retrospective analysis was conducted on the data of 25 patients with PSPO who underwent surgical treatment at the Drum Tower Hospital affiliated with Nanjing University Medical School from January 2017 to June 2023. The cohort included 4 males and 21 females, aged 12.3±2.8 years (range 9-14 years). Patients were divided into the MIS group (12 cases) and the PSF group (13 cases). Radiological parameters were measured preoperatively, postoperatively, and at the last follow-up. Surgical time, intraoperative blood loss, intraoperative blood transfusion volume, length of hospital stay, total hospitalization costs, and complications were recorded. The Scoliosis Research Society questionnaires-22 (SRS-22) Chinese version were used to assess patient satisfaction and efficacy.Results:There were no statistically significant differences between the MIS and PSF groups in age, gender, Risser sign, preoperative Cobb angle for scoliosis, pelvic tilt angle, or local kyphosis angle ( P>0.05). The MIS group demonstrated surgical time of 176±30 minutes, intraoperative blood loss of 300±70 ml, blood transfusion volume of 280±175 ml, and total hospitalization costs of 87'800± 13'300 yuan, all of which were lower than PSF group, with values of 280±91 minutes, 1'433±116 ml, 1'351±996 ml, and 14'8400±26'100 yuan, respectively. These differences were statistically significant ( t=3.789, P=0.001; t=29.328, P<0.001; t=3.667, P=0.001; t=7.271, P<0.001). In the MIS group, preoperative, postoperative, and last follow-up Cobb angles were 79.11°±6.74°, 35.86°±4.98°, and 36.27°±4.84° respectively; pelvic tilt angles were 24.79°±5.58°, 9.18°±3.32°, and 8.79°±2.94°; local kyphosis angles were 38.84°±4.18°, 12.96°±4.87°, and 11.43°±6.08°, respectively. Postoperative and last follow-up angles were significantly reduced compared to preoperative values, with statistically significant differences ( P<0.05). In the PSF group, preoperative, postoperative, and last follow-up Cobb angles were 82.06°±9.26°, 34.75°±5.14°, and 35.15°±5.04° respectively; pelvic tilt angles were 26.60°±6.21°, 10.12°±3.21°, and 9.91°±2.97°; local kyphosis angles were 40.92°±7.04°, 10.92°±7.26°, and 14.02°±5.58°, respectively. Differences from preoperative to postoperative measurements were statistically significant ( P<0.05). At the last follow-up, both groups showed no significant loss of scoliosis correction, and there were no statistically significant differences between the groups postoperatively or at the last follow-up ( P>0.05). In the MIS group, one case of superficial surgical site infection and one case of postoperative atelectasis occurred. In the PSF group, two cases of deep surgical site infection, one case of poor screw placement, and two cases were transferred to the ICU postoperatively due to excessive intraoperative bleeding. Preoperative SRS-22 total scores were 2.0±0.6 for PSF and 2.1±0.4 for MIS. Postoperative SRS-22 total scores (excluding satisfaction) were 3.0±0.5 for PSF and 2.9±0.3 for MIS. The within-group differences from preoperative to postoperative were statistically significant ( P<0.05), while the between-group differences from preoperative to postoperative were not statistically significant ( P>0.05). Conclusion:Compared to the PSF technique, MIS can shorten surgery time, reduce intraoperative blood loss and perioperative complications, and decrease hospitalization costs. MIS can achieve similar early clinical efficacy.