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.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of pulmonary adenocarcinoma, accounting for approximately 2%-10% of all pulmonary adenocarcinoma cases. IMA is characterized by a high frequency of intrapulmonary metastasis and a generally poor prognosis. Clinically, IMA often presents with nonspecific pneumonia-like symptoms. Radiologically, it manifests as multilobar and multifocal consolidations and ground-glass opacities, which significantly overlap with the radiologic features of pneumonia and other benign or malignant tumors. Consequently, IMA is frequently misdiagnosed in clinical practice, and a definitive diagnosis is nearly impossible upon initial presentation, leading to delayed treatment and a poor prognosis. Currently, there is a lack of systematic reviews correlating the clinical manifestations, pathological characteristics, and radiologic features of IMA. This review aims to systematically elucidate the clinical, pathological, and radiologic features of IMA, and thoroughly analyze its diagnostic challenges and key points for differential diagnosis, in order to provide a theoretical basis and practical guidance for the early diagnosis of this disease.

Objective:To investigate the relationship between oxygen consumption (VO 2), carbon dioxide production (VCO 2), and Oxygen Consumption/lactate (VO 2/Lac) with risk of death in patients with severe ARDS. Methods:A retrospective cohort study method was used, and the study subjects were hospitalized for >5 days adult patients with severe ARDS in the central intensive care unit of Henan Provincial People's Hospital from 1 March 2020 to 30 June 2023. The following patients were excluded: IC test was not completed on the 4th day of ICU admission, IC test results were unreliable, mechanical ventilation duration had exceeded 48 h at the time of ICU transfer or admission, palliative care patients and pregnant and parturient women. Using indirect calorimetry to determine VO 2 and VCO 2 values on the 4th day of admission, reviewing medical records to obtain general condition, disease information, blood gas analysis (including lactate value), diagnostic and therapeutic measures, and following up deaths by telephone and time of death. The primary outcome measure was death at 90 days, and the secondary outcome measure was death at 28 days, length of stay in ICU, total length of stay, and total hospitalization cost. Cox regression analysis and linear regression analysis were used to investigate the relationship between VO 2, VCO 2, VO 2/Lac and primary and secondary outcome indexes. Results:A total of 216 patients were enrolled, 78 patients (36.1%) died and 138 patients (63.9%) survived at 90 days. After correction for confounders, the results of multifactorial Cox regression analysis suggested that compared with the Q4 group, HR (95% CI) for 90-day risk of death in the VO 2 Q1 and Q2 groups was 3.21 (1.38, 7.49) and 3.24 (1.42, 7.38), and HR (95% CI) for 90-day risk of death in the VCO 2 Q1, Q2 and Q3 groups was 5.88 (2.33, 14.84), 4.26 (1. 60, 11.34) and 3.54 (1.34, 9.35), respectively, and the HR (95% CI) for 90-day risk of death in the VO 2/Lac Q1, Q2 and Q3 groups were 8.72 (3.01, 25.25), 8.43 (2.91, 24.47) and 4.04 (1.34, 12.17) respectively. P-trends were all <0.05, indicating that VO 2, VCO 2 and VO 2/Lac were linearly and negatively associated with the risk of 90-day mortality. In addition, VO 2, VCO 2, and VO 2/Lac were negatively associated with 28-day risk of death and higher VO 2/Lac was negatively associated with length of ICU stay. Conclusions:VO 2, VCO 2 and VO 2/Lac were negatively associated with 90-day mortality risk and 28-day mortality risk in patients with severe ARDS and may be independent risk factors predicting mortality risk of such patients.

Objective:To investigate the impact of the number of examined lymph nodes (ELN) on survival outcomes in gastric cancer patients with postoperative pathological stage pN3b.Methods:This retrospective cohort study included 279 pN3b gastric cancer patients who underwent D2 gastrectomy at Nanfang Hospital, Southern Medical University (September 2008 to April 2023), with 35 patients receiving combination chemotherapy and anti-PD-1 therapy (immunotherapy group) and 244 receiving adjuvant chemotherapy alone (nonimmunotherapy group). Additionally, 422 patients with pN3b from the SEER database (2005 to 2020) were collected as an external validation cohort to determine the optimal cutoff value for the number of lymph nodes examined in the nonimmunotherapy group. The primary endpoints were overall survival (OS) and recurrence-free survival (RFS) in the nonimmunotherapy group of the Nanfang Hospital cohort, stratified by whether the number of examined lymph nodes was above or below the ELN optimal cutoff value. These findings were subsequently validated in the SEER cohort.Results:The optimal ELN cutoff value (34 nodes) was determined using X-tile software and by constructing an ELN-HR fitting model with inflection point identification. In the nonimmunotherapy group, patients with ELN >34 exhibited significantly prolonged survival compared to ELN ≤34 (median OS: 25.0 (95%CI:20.5-29.5) to 17.0 (95%CI:12.7-21.3) months, P=0.004; median RFS: 19.0 (95%CI:15.6-22.4) to 13.0 (95%CI:9.5-16.5) months, P=0.048). Multivariate Cox analysis also showed ELN >34 to be an independent protective factor for both OS (HR=0.576, 95%CI: 0.397-0.836) and RFS (HR=0.701, 95%CI: 0.492-0.998). In the SEER cohort, ELN >34 was associated with a 5-month OS extension (19 to 14 months, P=0.065), with multivariate analysis supporting its independent prognostic significance (HR=0.729, 95%CI: 0.580-0.915, P=0.006). Notably, in the immunotherapy group, patients with ELN >34 ( n=30) achieved a median OS of 41 months, but the median OS had not been reached in the ELN ≤34 group ( n=5) (1 death at 48 months). Conclusion:Higher ELN (>34) correlates with improved survival in nonimmunotherapy-treated pN3b gastric cancer patients. However, in pN3b gastric cancer patients treated with immunotherapy, the optimal ELN threshold requires further exploration to determine.

This study was analyzed the spatial-temporal distribution and aggregation characteristics of Yersinia pestispositive host animals and vector pathogens in marmot natural foci in the Qilian-Altun mountains,Gansu Province,to provide a scientific basis for precise plague prevention and control.Y.pestissurveillance data for marmot natural foci in Qilian-Altun Mountains of Gansu Province from 2014 to 2023 were obtained from the Disease Control and Prevention Center of Gansu Province.Origin 2024 software was used for data visualization and presentation.Global and local spatial autocorrelation analyses and trend analyses were conducted in ArcGIS 10.8 software,with townships as the spatial scale.Cumulatively,440 strains of Y.pestis were isolated from the natural marmot foci in the Qilian-Altun mountainsof Gansu Province from 2014 to 2023.Most strains was isolated from marmots(345 strains,78.41%),and the remainder were isolated from vectors.Temporal distribution analysis indicated that the highest number of detected bacteria was reported in July and August(both 121 strains,27.50%).Regional distribution analysis revealed that Aksai County reported the highest number of detected bacteria(255 strains,57.95%).Global spatial autocorrelation analysis showed a spatially clustered distribution of the number of bacteria detected annually in the townships containing natural foci,except in2014,2016,and 2021-2023.The strongest spatial clustering was observed in 2020(Moran's I=0.521 2,Z=14.397 0,P<0.001).Local spatial autocorrelation analysis indicated a"high-high"aggregation area in the natural foci every year from 2014 to 2023,primarily in Hongliuwan Town of Aksai County and Dangchengwan Town of Subei County.The distribution of the"low-low"aggregation area was essentially consistent with the low activity area of the Yersinia pestisepidemic.The trend in annual total bacterial count gradually increased from east to west,and peaked in the western part of the epidemic focus.Clear spatial aggregation characteristics of the number of Y.pestis were detected in the marmot natural foci in the Qilian-Altun mountains at the townshiplevel as a whole in Gansu Province from 2014 to 2023.The aggregation area was mainly in the western section of Qilian Mountain to the Altun mountain section of the epidemic source area.Monitoring and prevention and control efforts should be focused in this key area,with prevention and control measures tailored to the local conditions,and classified guidance to decrease the risk of plague occurrence and spread.

Mitochondrial autophagy is a unique mechanism that selectively clears dysfunctional or excess mitochondria,closely related to Alzheimer's disease(AD),which is characterized by aggregated neurotoxic proteins and dysfunctional mitochondria.Numerous recent studies have confirmed that traditional Chinese medicine can have significant therapeutic effects against AD,and its advantages including multi-target,multi-pathway,and multi-action mechanisms have become an important component of AD research.Chinese medicine formulas and monomeric active ingredients,such as Jiannaoyizhi Fang,Danggui Shaoyao Tang,alkaloids,and flavonoids,can regulate mitochondrial autophagy-related signaling pathways and targets,inhibit neuronal mitochondrial autophagy defects,and play a neuroprotective role.This review elaborates on the basic process of mitochondrial autophagy and its related signaling pathways and molecular mechanisms in the pathogenesis of AD,and considers the latest research progress on the use of traditional Chinese medicine to improve AD by regulating mitochondrial autophagy,to provide ideas and references for future basic research and clinical treatment.

BACKGROUND: Children's respiratory health demonstrates particular sensitivity to air pollution. Existing evidence investigating the association between short-term ozone (O₃) exposure and childhood pneumonia remains insufficient and inconsistent, especially in low- and middle-income countries (LMICs). METHOD: To provide more reliable and persuasive evidence, we implemented a multi-city, time-stratified case-crossover design with a large sample size, using data from seven representative children's hospitals across major geographical regions in China. To avoid the impact of the COVID-19 pandemic, individual-level medical records of inpatient children under 6 years of age diagnosed with pneumonia during 2016-2019 were collected. Conditional logistic regression models were fitted for each city, and city-specific estimates were pooled through a meta-analysis using a random-effects model. RESULTS: In total, the study included 137,470 pediatric pneumonia hospital admissions. The highest pooled estimate for O₃ occurred at lag0-1, with a 10 µg/m³ increase in O₃ associated with a 1.57% (95% CI: 0.67%-2.48%) higher risk of pediatric pneumonia hospital admissions. Stratified analyses indicated that the effects of O₃ were robust across different sexes, age groups, and admission seasons. We also observed a statistically significant increase in risk associated with O₃ concentrations exceeding the World Health Organization Air Quality Guidelines (WHO-AQGs). CONCLUSIONS This study revealed a significant positive association between O₃ and pediatric pneumonia hospital admissions. Our findings substantially strengthen the evidence base for the adverse health impacts of O₃, underscoring the importance of O₃ pollution control and management in reducing the public health burden of pediatric pneumonia.
Humans ; Ozone/analysis* ; China/epidemiology* ; Pneumonia/chemically induced* ; Child, Preschool ; Male ; Female ; Infant ; Cross-Over Studies ; Air Pollutants/analysis* ; Hospitalization/statistics & numerical data* ; Child ; Cities/epidemiology* ; Air Pollution/adverse effects* ; Infant, Newborn ; Environmental Exposure/adverse effects*