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.

ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

Alzheimer’s disease （AD） is an age-related neurodegenerative disorder. Marrow-sea insufficiency serves as the fundamental basis for the onset of AD. Early syndrome differentiation-based intervention helps to delay disease progression， and improve patients’ cognitive function. Qifu yin is a representative specialized prescription for AD with marrow-sea insufficiency syndrome. Studies demonstrate that Qifu yin exerts neuroprotective effects through multiple pathways， including inhibiting the abnormal deposition of amyloid β -protein and hyperphosphorylation of tau protein， alleviating neuroinflammation， regulating oxidative stress and mitochondrial dysfunction， modulating the cholinergic system， and improving synaptic plasticity. Qifu yin combined with Western medicine such as donepezil， memantine， and butylphthalide， or combined with external therapies such as acupuncture， can effectively improve cognitive function and activities of daily living in AD patients with favorable safety. Future research should focus on the core pathogenesis and key targets of AD with marrow-sea insufficiency syndrome， provide in-depth elucidation of the scientific connotation of Qifu yin’s “tonifying the kidney to produce marrow”， and further conduct high-quality clinical studies to provide scientific evidence for the prevention and treatment of AD with marrow-sea insufficiency syndrome.

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.

Bone metastasis is one of the common complications of lung cancer, which seriously affects the quality of life and survival of patients. At present, the clinical diagnosis of bone metastasis of lung cancer mainly depends on imaging methods, but due to its lack of sensitivity and potential radiation risk, about half of patients have already had bone-related events when they are diagnosed clearly. The treatment of bone metastasis of lung cancer mainly depends on surgery, radiotherapy and chemotherapy, targeted therapy, immunotherapy, etc. Although the treatment of bone metastasis of lung cancer has made some progress in recent years, there are still some problems such as high risk of other distant metastasis. This article mainly reviews the pathogenesis, diagnostic biomarkers and treatment progress of bone metastasis of lung cancer, in order to provide reference for the diagnosis and treatment of bone metastasis of lung cancer.

Objective: To evaluate the practical effectiveness of confidential unit exclusion (CUE) in ensuring blood safety in Zhengzhou, analyze its application characteristics and existing problems, and provide a basis for optimizing blood safety management strategies. Methods: A retrospective analysis was conducted on CUE data handled by Henan Red Cross Blood Center from January 2019 to December 2024. Parameters such as the number of cases, demographic characteristics, reasons for exclusion, and time of report were statistically analyzed and compared with those of non-CUE. Results: From 2019 to 2024, the CUE reporting rate in Zhengzhou was 0.002 6% (40/1 547 666). CUE donors were predominantly male (65.00%, 26/40), aged 18-34 years (47.50%, 19/40), had college degree orabove (50.00%, 20/40), and were employees of enterprises or public institutions (32.50%, 13/40). Among the 40 CUE blood units, only one was reactive for anti-TP, while all others were qualified. The main reasons for CUE were recent vaccination (32.50%, 13/40), medical conditions unsuitable for donation (27.50%, 11/40), and high-risk sexual behavior (17.50%, 7/40). A total of 70.00% of reports occurred within 24 hours after donation, during which none of the corresponding blood units had been released; all units reported after more than 7 days had already been issued for clinical use, with no adverse transfusion reactions reported upon follow-up. Conclusion: The confidential unit exclusion program has played an active role in establishing a supplementary information feedback channel for blood donors. The procedure can be optimized by strengthening interactive communication and confirmation before donation, improving the accuracy of donors' self-assessment, and expanding convenient and rapid information-based reporting channels.

ObjectiveTo investigate the imaging features of calcium salt deposition and serological markers in patients with alveolar echinococcosis through a retrospective analysis， as well as independent risk factors for the degree of calcium salt deposition in lesions， and to provide a basis for assessing disease process. MethodsA retrospective analysis was performed for the imaging and clinical data of 107 patients with alveolar echinococcosis who were admitted to The First Affiliated Hospital of Shihezi University from December 2023 to June 2025， and according to the volume of calcium salt deposition， they were divided into non-deposition group with 16 patients， mild deposition group with 52 patients， moderate deposition group with 16 patients， and severe deposition group with 23 patients. A one-way analysis of variance or the Kruskal-Wallis H test was used for comparison of continuous data between groups， and the χ² test or Fisher’s exact test was used for comparison of categorical data between groups. The four groups were further combined into the low deposition group （no/mild deposition） and the high deposition group （moderate/severe deposition）. A binary logistic regression analysis was used to investigate the independent influencing factors for calcium salt deposition， and a predictive model was established. The receiver operating characteristic （ROC） curve was used to assess the predictive performance of the model， and the Bootstrap method was used for internal validation. ResultsThere were significant differences between the four groups in sex distribution， involvement of other sites， white blood cell count， lymphocyte percentage， fibrinogen， uric acid， sodium ion， chloride ion， and calcium ion （all P<0.05）. The univariate analysis showed that there were significant differences between the four groups in sex， involvement of other sites， white blood cell count， lymphocyte percentage， fibrinogen， alanine aminotransferase， albumin， creatinine， uric acid， sodium ion， chloride ion， and calcium ion （all P<0.1）. The multi-collinearity diagnosis showed that the VIF values for all continuous variables ranged from 1.104 to 1.760， suggesting that collinearity did not affect modeling. An ordinal logistic regression model was established based on sex， involvement of other sites， calcium ion， lymphocyte percentage， and uric acid. The multivariate analysis showed that lymphocyte percentage （odds ratio ［OR］=1.106， 95% confidence interval ［CI］： 1.041 — 1.174， P=0.001） and blood calcium level （OR=0.005， 95%CI： 0.000 —0.230， P=0.007） were independent influencing factors for the degree of calcium salt deposition. The regression equation was established as Logit（P）=8.231 + 0.100 × lymphocyte percentage -5.344 × calcium ion. The ROC curve analysis showed that the model had an area under the ROC curve of 0.716， with a Youden index of 0.353， a sensitivity of 1.000， and a specificity of 0.353. The Hosmer-Lemeshow test showed that the model had poor calibration （χ²=20.688， P=0.008）. The Bootstrap method with 1000 repeated samples showed that the estimated values of lymphocyte percentage （OR=1.106， 95%CI： 1.049 — 1.186， P=0.002） and calcium ion （OR=0.005， 95%CI： 0.000 — 0.214， P=0.010） were consistent with the original model， and the confidence intervals did not include 1， which further supported the reliability of the model. ConclusionBoth lymphocyte percentage and blood calcium level are independent influencing factors for calcium salt deposition in alveolar echinococcosis， and the degree of calcium salt deposition in alveolar echinococcosis lesions increases with the reduction in blood calcium level and the increase in lymphocyte percentage.

Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

Short stature(SS) and disorder of sex development(DSD) are two types of conditions characterized by high clinical heterogeneity and complex etiology. There is interplay and mutual influence between the pathways regulated by growth hormone and sex hormones in skeletal and gonadal development. Causing co-occurrence of SS and DSD, as seen in conditions such as Turner syndrome, mixed gonadal dysgenesis, Noonan syndrome, and Prader-Willi syndrome. Patients with these disorders are often accompanied by distinctive facial features, endocrine and metabolic disturbances, cardiovascular disease, and other systemic complications. Genetic factors involved include chromosomal numerical and structural abnormalities; mutations in genes such as SHOX, CHD7, SOX8, and PTPN11, dysregulation of the RAS/mitogen activated protein kinase signaling pathway, and defects in imprinted genes. This article aims to systematically review the relevant research progress, in order to provide a reference for the clinical diagnosis and treatment strategies of patients with coexisting SS and DSD.

OBJECTIVE: To explore the prenatal and postnatal phenotypes of 22q11.2 microdeletion syndrome (22q11.2DS) and enhance clinical understanding of this condition. METHODS: Data were collected from 86 fetuses diagnosed with 22q11.2DS at four prenatal diagnostic centers across China between January 2014 and August 2025. Prenatal imaging findings, pregnancy outcomes, and postnatal conditions were analyzed. RESULTS: Among the 86 fetuses, complete ultrasound data were available for 65 cases. Cardiovascular abnormalities were observed in 42 cases, thymic hypoplasia or aplasia in 7 cases, urinary system anomalies in 6 cases, nuchal translucency (NT) thickening in 7 cases, butterfly vertebrae, clubfoot, omphalocele and diaphragmatic hernia in 1 case each, cleft lip and palate in 2 cases, and ultrasound soft markers in 13 cases. The parents of 9 fetuses opted to continue with the pregnancy. Among these, 6 showed no significant ultrasound abnormalities and no related phenotypes postnatally, while the remaining 3 exhibited ultrasound anomalies with postnatal manifestations including developmental delay, immunodeficiency, and cardiac defects. CONCLUSION Fetuses with 22q11.2DS may exhibit various ultrasound abnormalities in multiple systems before and after birth. In addition to cardiovascular anomalies, they may also present with thymic hypoplasia or aplasia, thickened NT, and urinary abnormalities. Fetuses with thickened NT or thymic anomalies should be closely monitored, and thymic assessment should be included in routine prenatal imaging evaluations. For fetuses with 22q11.2DS who show no ultrasound abnormalities, the risk of developing severe phenotypes after birth is relatively low, but occult palate clefts and psychiatric disorders cannot be ruled out. Due to limitations in sample size and follow-up duration, above conclusions require further validation through large-scale prospective studies.
Humans ; Female ; Pregnancy ; Ultrasonography, Prenatal ; DiGeorge Syndrome/genetics* ; Adult ; Male ; Follow-Up Studies ; Fetus/diagnostic imaging* ; Phenotype ; Infant, Newborn