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.

ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Osteoarthritis(OA)is a common joint disease in clinical practice,and cartilage damage is a typical pathological change.The pathogenesis of OA is complex,and various adverse factors can lead to the occurrence of OA.Mitochondria are im-portant organelles within cells and play important roles in cellular physiological and pathological activ-ities.Mitochondrial quality control is an important regulatory mechanism in the body to maintain nor-mal mitochondrial structure and function,mainly including mitochondrial biogenesis,mitochondrial dynamics,mitochondrial autophagy,mitochondrial oxidative stress,and other forms.The imbalance of mitochondrial quality control in chondrocytes is closely related to the occurrence and development of osteoarthritis,and regulating the balance of mi-tochondrial quality control is a potential therapeu-tic point for osteoarthritis.The author reviewed rel-evant research literature in recent years to provide a review of the relationship between mitochondrial quality control and the occurrence and develop-ment of osteoarthritis,in order to provide new ideas and directions for the research and diagnosis and treatment strategies of osteoarthritis.

Objective:To investigate the methods and skills required for the safe and swift removal of primary or secondary sacral tumors located between the second (inclusive) and fourth sacral vertebrae.Methods:The clinical images, pathology reports, surgical procedures, operation durations, intraoperative bleeding volumes, and postoperative functional follow-up data of 26 patients undergoing sacral tumor resection at the First Affiliated Hospital of Zhengzhou University and Xinjiang Production and Construction Corps Hospital between May 2020 and February 2025 were retrospectively examined. Additionally, the safety measures for sacral tumor resection and techniques for expedited specimen removal were evaluated.Results:According to magnetic resonance imaging (MRI) findings, all 26 patients presented with sacral tumors located between the second (inclusive) and fourth sacral vertebrae. Specifically, 9 patients were diagnosed with primary sacral tumors, pathologically confirmed as chordomas, while 17 patients had secondary sacral tumors. Among the secondary tumor cases, 12 were attributed to recurrent rectal cancer invading the sacrum, and 5 were due to malignant teratomas invading the sacrum. The 26 patients underwent a treatment strategy that began with managing the relationship between the internal iliac artery, vein branches, and the tumor, followed by the resection of the sacrum. During surgery, the bilateral sciatic foramina were accurately positioned, and the presacral fascia was dissected subsequent to the fracture of the sacrum. Among the 26 patients, 9 underwent sacral tumor resection directly through the posterior sacral approach. The average operation time for these patients was (71.1±4.9) minutes, with average blood loss of (186.7±72.8) milliliters. On the other hand, 17 patients underwent sacral tumor resection by transitioning from the supine position to the prone knife position through a combined abdominal and sacral approach. The average operation time for this group was (213.5±19.3) minutes, with average blood loss of (480.0±93.0) milliliters, significantly longer than that of the posterior sacral approach. The follow-up period ranged from 1 to 48 months, with a median of 20 months, ending on March 31, 2025. During this time, 26 patients achieved autonomous defecation with the aid of medication. None of the patients reported any functional movement disorders or pain in their lower limbs. It was observed that two out of the 26 patients developed distant metastasis, while the remaining 24 patients survived without any tumors.Conclusion:By pretreated the relationship between the internal iliac vessels and sacral tumors prior to resecting sacral tumors, utilizing the approach of initially fracturing the sacrum followed by rupturing the presacral fascia, the tumor can be entirely eliminated, resulting in a brief surgical procedure, reduced intraoperative bleeding, and minimal postoperative complications.

The development status of the key technologies of first-aid robotics was introduced in autonomous system,embo-died intelligence,digital twins,large artificial intelligence model and autonomous unmanned medical treatment.The present situation in first-aid robotics equipment was reviewed for first-aid diagnosis,treatment,assistance and transportation.The development trends of the key technology and equipment of first-aid robotics were analyzed.It was pointed out the involve-ment of big model-based embodied intelligence technology and digital twins technology in first aid might provide new pers-pectives for the application and advancement of specialized first-aid robotics.[Chinese Medical Equipment Journal,2025,46(3):96-114]

Objective:To compare the efficacy and safety of crisaborole ointment 2% versus pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged 2 years or older.Methods:A multicenter, randomized, open-label, controlled clinical trial was conducted. A total of 120 pediatric patients aged 2 - 17 years with mild to moderate atopic dermatitis were enrolled from departments of dermatology of 8 hospitals in China between March 2022 and February 2023. The participants were randomly assigned in a 1∶1 ratio to the crisaborole group and the pimecrolimus group, and received the treatment with crisaborole ointment 2% and pimecrolimus cream 1% respectively, twice a day for 4 weeks. Visits were scheduled at baseline/on day 1, as well as on days 8, 15, and 29. The primary efficacy outcome was the percentage of patients achieving the Investigator's Static Global Assessment (ISGA) success (defined as clear [0] or almost clear [1] on the ISGA scale, combined with ≥ 2‐grade improvement from baseline) on day 29. The secondary efficacy outcomes included changes in the Eczema Area and Severity Index (EASI) total scores from baseline to day 29, percentages of patients achieving ISGA improvement (defined as clear [0] or almost clear [1] on the ISGA scale), as well as changes in the Peak Pruritus Numerical Rating Scale (NRS) scores, Dermatology Life Quality Index (DLQI) /Infants' Dermatology Life Quality Index (IDLQI) /Children's Dermatology Life Quality Index (CDLQI) scores, and in the Dermatitis Family Impact (DFI) scores. Drug safety was evaluated according to the incidence of adverse events. Categorical data were compared using the chi-square test. Since measurement data did not follow a normal distribution, the rank sum test was used for comparisons of measurement data between groups.Results:A total of 106 children with mild to moderate atopic dermatitis were included in the per-protocol analysis set, with 52 in the crisaborole group (26 males and 26 females) and 54 in the pimecrolimus group (27 males and 27 females). There were no significant differences in age, disease duration, ISGA and EASI scores at baseline between the two groups (all P > 0.05). On day 29, 22 patients (42.31%) in the crisaborole group and 25 (46.30%) in the pimecrolimus group achieved ISGA success, with no significant difference between the two groups ( χ2 = 0.17, P = 0.68) ; 35 patients (67.31%) in the crisaborole group and 45 (83.33%) in the pimecrolimus group achieved ISGA improvement, also with no significant difference between the two groups ( χ2 = 3.68, P = 0.06) ; additionally, there were no significant differences in the EASI, pruritus NRS, DLQI/IDLQI/CDLQI, or DFI scores between the two groups (all P > 0.05). Adverse reactions to the two topical agents were mainly local reactions such as mild to moderate pain, itching, or worsening of itching, and no obvious systemic adverse reactions occurred. The incidence of drug-related adverse reactions was 46.15% (24 cases) in the crisaborole group and 37.04% (20 cases) in the pimecrolimus group, with no significant difference between the two groups ( χ2 = 0.91, P = 0.34) . Conclusion:The efficacy of crisaborole ointment 2% was comparable to that of pimecrolimus cream 1% in the treatment of mild to moderate atopic dermatitis in children aged ≥ 2 years, and it yielded early and rapid improvement in the quality of life of patients and their families, with good safety and tolerability profiles.

Objective To evaluate the effectiveness of active screening in improving the detection rate of carbape-nem-resistant Enterobacterales(CRE)in the intensive care units(ICUs).Methods From July 2023 to June 2024,active screening of rectal swab CRE was conducted on ICU patients in 10 hospitals.ICU patients who underwent ac-tive screening from July 2023 to June 2024 were selected as the study group,while those who did not undergo active screening from July 2022 to June 2023 were selected as the control group.Difference in CRE detection rates between the two groups of patients was compared.Results A total of 7 803 ICU patients were included in the study group,744 CRE strains were detected,with a detection rate of 9.53％,out of which 304 CRE strains were detected through routine detection(detection rate 3.90％),3 707 patients underwent active screen,440 CRE strains were detected(detection rate 11.87％).7 561 ICU patients were included in the control group,out of which 250 CRE strains were detected through routine detection,with a detection rate of 3.31％.There was a statistically significant difference in the overall detection rate of CRE between two groups of patients(x2=246.18,P＜0.001).In the study group,CRE detection rate of active screening(11.87％)was higher than that of routine detection(3.90％),with statistically significant difference(x2=264.26,P＜0.001).A total of 17 CRE strains were detected from the study group.The proportions of Klebsiella pneumoniae(80.92％vs 73.41％)and Serratia marcescens(2.30％vs0.23％)in the routine detection group were both higher than in the active screening group,while the proportion of Escherichia coli in the routine detection group was lower(8.22％vs 19.55％),all with statistically significant differences(all P＜0.05).Conclusion The prevalence of CRE in ICUs is relatively high,with a wide range of bac-terial species.Active screening can improve the detection rate of CRE.

A throat swab sample from a pediatric case in Tongzhou District, Beijing was identified as enterovirus; the patient was a 1-year-and-8-month-old male sporadic case. Whole-genome sequencing revealed a viral genome length of 7 436 bp. BLAST alignment confirmed the serotype as EV-D68. Phylogenetic analysis of the whole genome indicated that this strain belongs to the B3 clade, showing closer genetic proximity to the 2018 Shanghai strain MW697453 with 99.53% whole-genome nucleotide homology. Genetic and amino acid variation analysis demonstrated that the B3 subclade to which this strain belongs exhibits a nucleotide deletion at positions 718–726, differing from deletion sites observed in other B3 clade strains. A key neuropathogenic amino acid site, T650A, was found to have undergone mutation. Recombination analysis confirmed no cross-clade recombination events in this strain. This study conducted genetic characterization of the strain's evolutionary relationship with EV-D68 strains from different regions and years in China, providing data support for formulating prevention and control measures against EV-D68 infection.