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.

Objective To explore the application value of different Trocar layouts in retroperitoneal laparoscopic partial nephrectomy for localized renal cell carcinoma, so as to provide reference for the layout of Trocar during operation. Methods The data of 137 patients with localized renal cell carcinoma who underwent retroperitoneal laparoscopic partial nephrectomy in our hospital during Sep. 2021 and Sep. 2024 were retrospectively collected, including 68 patients with umbrella layout(umbrella group)and 69 with trapezoidal layout(trapezoidal group). The operation-related indexes, renal function(glomerular filtration rate, serum creatinine), traumatic stress indexes including C-reactive protein(CRP), cortisol, and interleukin-6(IL-6), complications and recurrence rate were compared between the two groups. Results There were no significant differences in pathological results, negative rate of surgical margins, hospitalization time, complications, 1-year recurrence rate, 6-month glomerular filtration rate and serum creatinine between the two groups(P＞0.05). Compared with the trapezoidal group, the umbrella group had less intraoperative blood loss [(72.11±18.30)mL vs.(87.59±20.63)mL], shorter operation time [(88.15±16.92)min vs.(99.40±20.78)min], renal warm ischemia time [(14.68±4.60)vs.(19.27±6.11)min], bowel sound recovery time [(21.00±5.75)h vs.(25.78±4.89)h] and ambulation time [(1.72±0.48)d vs.(2.04±0.55)d](P＜0.05). The umbrella group had lower CRP [(10.02±2.78)mg/ L vs.(12.00±3.15)mg/ L] on the first day after surgery, lower CRP [(7.89±0.76)vs.(8.77±1.25)mg/L] on the third day after surgery, lower cortisol [(0.67±0.20)μmol/L vs.(0.89±0.28)μmol/L] on the first day after surgery, lower cortisol [(0.58±0.15)μmol/L vs.(0.69±0.12)μmol/L] on the third day after surgery, lower IL-6 [(28.20±6.74)ng/L vs.(35.16±7.30)ng/L] on the first day after surgery, and lower IL-6[(21.25±5.39)ng/L vs.(29.78±4.26)ng/L] on the third day after surgery(P＜0.05). Conclusion Compared with the trapezoidal Trocar layout, the umbrella Trocar layout for retroperitoneal laparoscopic partial nephrectomy in patients with localized renal cell carcinoma offers advantages such as minimal surgical trauma, shorter operation and renal warm ischemia time, and faster postoperative recovery.

Indocyanine green has been a popular near-infrared fluorescent reagent in recent years, and indocyanine green fluorescence imaging navigation surgery technology has been widely used in hepatobiliary surgery. Many factors affect imaging in various clinical scenarios, which are roughly divided into liver background fluorescence interference and penetrating tissue thickness, including the dose and timing of the drug, liver lesions of the patient, and individualization of the patient. Under the concept of precise surgery, we should individualise the treatment of patients to minimise the influence of unfavourable factors and further improve the safety and efficacy of surgery. Currently, there is no reliable research evidence to guide the use of indocyanine green in terms of influencing factors, and clinical studies are urgently needed to optimise the use of indocyanine green in hepatobiliary surgery.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.

Objective:To investigate the anemia conditions and characteristics of iron metabolism during different stages of pregnancy in women with different genotypes of thalassemia.Methods:This cohort study selected 3 303 singleton pregnant women who underwent regular prenatal examinations and genetic tests of thalassemia and were delivered at Maternal & Child Health Hospital of Guangxi Zhuang Autonomous Region from January 2019 to December 2023. According to the results of thalassemia gene testing, the women were divided into groups: those without thalassemia genes served as the control group (1 539 cases), and those with thalassemia genes (1 764 cases) were further divided based on genotype into the -α/αα group (326 cases), --/αα or -α/-α group (649 cases), point mutation α-thalassemia group (201 cases), β 0-thalassemia group (368 cases), β +-thalassemia group (91 cases), and α combined with β-thalassemia group (129 cases). Hemoglobin (Hb) and serum ferritin (SF) levels were measured in the first, second, and third trimester of pregnancy. Differences in anemia and iron reserves among the groups at different pregnancy stages were compared using repeated measures analysis of variance, LSD test, Kruskal-Wallis rank-sum test, and Bonferroni correction. Results:Compared to the first trimester, Hb levels decreased in the second and third trimester across all groups (LSD test, all P<0.05), and the severity of anemia increased (Bonferroni correction, all P<0.017). The severity of anemia varied among the groups at the same pregnancy stage ( Hfirst trimester=918.20, Hsecond trimester=1 224.50, Hthird trimester=980.19; all P<0.001), and Hb levels also differed ( Ffirst trimester=282.54, Fsecond trimester=352.31, Fthird trimester=239.02; all P<0.001). The β 0-thalassemia group had higher rates of moderate anemia in the first, second, and third trimester of pregnancy [38.6% (142/368), 85.3% (314/368), and 73.6% (271/368)] compared to other groups (Bonferroni correction, all P<0.002), and lower Hb levels [(102.1±8.9), (92.0±7.3), and (94.6±7.7) g/L] than other groups (LSD test, all P<0.05). As pregnancy progresses, SF levels in each group of pregnant women gradually decreased (LSD test, all P<0.05), and the degree of iron deficiency worsened (Bonferroni correction, all P<0.05). The iron deficiency rate in thalassemia pregnant women during the third trimester ranges from 21.5% (79/368) to 46.0% (150/326). The degree of iron deficiency varies among groups within the same gestational period ( Hfirst trimester=79.13, Hsecond trimester=203.98, Hthird trimester=130.55; all P<0.001), and SF levels also differ ( Ffirst trimester=17.28, Fsecond trimester=44.60, Fthird trimester=31.87; all P<0.001). Among them, the β 0-thalassemia group had the lowest iron deficiency rates in the second, and third trimesters [9.8% (36/368), and 21.5% (79/368)] (Bonferroni correction, all P<0.002). SF levels in the β 0-thalassemia and β +-thalassemia groups were higher than those in other groups during each gestational period (LSD test, all P<0.05). Conclusions:Pregnant women with thalassemia may experience varying degrees of iron deficiency during pregnancy, with the severity of iron deficiency and anemia increasing with gestational age. The degree of iron deficiency and anemia during pregnancy varies among pregnant women with different genotypes of thalassemia. Clinically, individualized management should be provided for pregnant women with thalassemia based on their genotypes, with dynamic monitoring of anemia and iron metabolism changes.

Objective:To investigate the therapeutic value of visual endoscopic retrograde appendicitis therapy (vERAT) in pediatric patients with acute suppurative appendicitis (ASA).Methods:This was a retrospective cohort study. A total of 55 ASA patients who underwent vERAT at the Pediatric Department of the Tangdu Hospital of Air Force Medical University between November 2023 and January 2025 were selected and divided into groups based on the presence or absence of fecaliths: fecalith group and non-fecalith group. The baseline characteristics, initial treatment success rates, treatment costs, hospital stay duration, procedure time, and recurrence rates between two groups were compared. Mann-Whitney U test and χ2 test were used to evaluate group differences. Results:A total of 55 ASA patients were enrolled, including 38 males and 17 females, with the age of 11.2 (9.2, 13.1) years. Based on the presence of fecaliths, patients were divided into two groups: fecalith group (32 cases) and non-fecalith group (23 cases). No statistically significant differences were observed between the two groups in terms of age, gender, duration of abdominal pain, white blood cell count, neutrophil percentage, diameter of appendix, thickness of appendix clinical symptoms or signs (all P>0.05). The initial treatment success rates were 91% (29/32) in fecalith group and 96% (22/23) in non-fecalith group, with no statistically significant difference ( P=0.632). However, significant differences were noted in stent placement ( χ2=5.85, P=0.026) and procedure time ( Z=4.75, P<0.001). The follow-up duration time was 6.0 (2.0, 12.0) and 7.0 (2.0, 8.5) months for the fecalith and non-fecalith groups, respectively, with no significant difference ( Z=0.05, P=0.962). The recurrence rates were 14% (4/29) in fecalith group and 5% (1/22) in non-fecalith group, with no statistically significant difference ( P=0.375). Conclusions:vERAT can safely and effectively treat pediatric ASA, regardless of the presence or absence of fecaliths. It can provide a new treatment option for ASA.

Objective:To understand the development status of maternal and child health care institutions in China from 2012 to 2022, identify the challenges they face, and provide references for further promoting the high-quality development of these institutions.Methods:Data from the China Health Statistics Yearbook (2013—2015), China Health and Family Planning Statistics Yearbook (2016—2017), and China Health and Wellness Statistics Yearbook (2018—2023) were used. Descriptive analysis was conducted on the data related to resource allocation and utilization efficiency, service provision, income and expenditure structure, and operational status of maternal and child health care institutions in China from 2012 to 2022, using methods such as fixed-base growth rate, year-on-year growth rate, and average annual growth rate. Results:From 2012 to 2022, the number of maternal and child health care institutions in China decreased from 3 044 to 3 031. In terms of resource allocation, the average annual growth rates of bed numbers and business-use floor area were 5.404% and 10.923%, respectively, while the average annual growth rate of health professionals was 7.183%. Regarding service provision, the average annual growth rates of outpatient visits and inpatient admissions were 3.954% and 1.572%, respectively. In terms of service efficiency, the bed occupancy rate decreased from 76.9% to 53.9%, and the average number of patients seen per physician per day decreased from 8.85 to 7.30. In terms of income and expenditure and operations, the income-expenditure surplus rate decreased from 9.16% to 5.41%, and the debt-to-asset ratio increased from 27.88% to 33.60%. During the same period, the average annual growth rates of bed numbers and business-use floor area in grassroots maternal and child health care institutions were 4.545% and 10.091%, respectively, lower than the national average. The number of outpatient visits increased from 89.03 million to 126.93 million, with an average annual growth rate of 3.610%, while the number of inpatient admissions decreased from 4.19 million to 3.91 million, with an average annual decline of 0.689%. The income-expenditure surplus rate of grassroots institutions decreased from 7.76% to 4.05%, 1.36 percentage points lower than the national level, and the debt-to-asset ratio increased from 27.53% to 36.37%, higher than the overall level.Conclusions:From 2012 to 2022, maternal and child health care institutions in China achieved certain developments in resource allocation and service scale. However, several challenges remain, including unbalanced resource allocation, decreased utilization efficiency, slowed growth in medical service volume, imbalanced income and expenditure structure, increased asset operation risks, and restricted development of grassroots institutions. It is recommended that relevant management departments and maternal and child health care institutions optimize resource allocation, plan for service transformation and upgrading, expand income sources, strengthen internal financial control, and reinforce the construction of high-quality and efficient maternal and child health care systems to promote the high-quality development of maternal and child health care institutions in China.