Objective To systematically analyze the spatiotemporal distribution characteristics and epidemiological trends of tracheal, bronchus, and lung cancer (TBL) disease burden attributed to air pollution globally and in China and the United States from 1990 to 2021, and to assess the patterns of disease burden changes from 2022 to 2031 based on predictive models, providing a scientific basis for formulating targeted TBL prevention and control strategies. Methods Based on the Global Burden of Disease (GBD) 2021 database, we analyzed the disease burden data of TBL attributed to air pollution globally and in China and the United States from 1990 to 2021. R Studio 4.3.2 software was used to analyze the corresponding trends and the Bayesian age-period-cohort (BAPC) prediction model was used to predict the status of the disease burden of TBL attributed to air pollution in the world and in China and the United States from 2022 to 2031. Results In 2021, China had the highest number of deaths and disability-adjusted life years attributed to air pollution (211 400 patients and 4.8947 million person-years), followed by the United States (6 000 patients and 124 300 person-years). The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) of TBL due to air pollution in the world and in China and the United States showed a decreasing trend. From 1990 to 2021, the ASMR and ASDR of TBL in China due to air pollution were much higher than those in the United States and the global average. In terms of gender, from 1990 to 2021, the disease burden of male patients with TBL attributed to air pollution was much higher than that of female patients. The BAPC prediction model showed that from 2022 to 2031, the ASMR and ASDR of TBL attributed to air pollution showed an upward trend globally, while they showed a downward trend in China and the United States. Conclusion Over the past 30 years, the air pollution-related TBL disease burden in the world and in China and the United States has continued to decline, but China's disease burden is still significantly higher than the global average. The disease burden in men far exceeds that in women, with men and the population aged ≥50 years being high-risk groups. In the future, the global disease trend may reverse and rise, while China and the United States are expected to continuously decline. However, precise prevention and control for high-risk groups remains a key challenge.

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 analyze the disease burden and its changing trends of tracheal, bronchus, and lung cancer (TBL) attributable to high fasting plasma glucose (HFPG) in China from 1990 to 2021, and to provide key strategic evidence for the prevention and treatment of TBL. Methods Data related to TBL attributable to HFPG in China from 1990 to 2021 were collected from the Global Burden of Disease Study 2021 database, including mortality rate, disability-adjusted life years (DALYs), age-standardized rates, and other indicators. The Joinpoint regression model was applied to analyze the temporal trends of these indicators. Furthermore, the grey prediction model GM (1, 1) was used to forecast the disease burden of TBL attributable to HFPG in China from 2022 to 2031. Results From 1990 to 2021, the overall disease burden of TBL attributable to HFPG in China showed an upward trend. The total number of deaths, DALYs, crude mortality rate, age-standardized mortality rate, crude DALYs rate, and age-standardized DALYs rate increased from 4700, 121300 person-years, 0.40/100000, 0.61/100000, 10.31/100000, and 14.10/100000 in 1990 to 17400, 386500 person-years, 1.22/100000, 0.82/100000, 27.16/100000, and 17.72/100000 in 2021, with growth rates of 270.21%, 218.63%, 205.00%, 34.43%, 163.43%, and 25.67%, respectively. The increase rates among females were higher than those among males. Analysis using the Joinpoint regression model indicated that both the age-standardized mortality rate and age-standardized DALYs rate exhibited a significant upward trend, with average annual percentage changes (AAPC) of 1.01% and 0.82%, respectively, during 1990-2021 (P<0.05). The disease burden across different age and gender groups generally increased from 1990 to 2021. Mortality and DALYs rates for both males and females rose with advancing age, with elderly individuals and males constituting the primary affected populations. The GM (1, 1) grey prediction model projected continued increases in mortality rate, age-standardized mortality rate, DALYs rate, and age-standardized DALYs rate of TBL attributable to HFPG in China from 2022 to 2031, reaching 1.64/100000, 1.06/100000, 36.45/100000, and 21.81/100000, respectively, by 2031. Conclusion The disease burden of TBL attributable to HFPG remains substantial in China from 1990 to 2021, with males and elderly populations bearing the highest burden. However, the growth rate of disease burden is faster among females compared to males. It is predicted that the disease burden will continue to rise over the next decade, necessitating enhanced focus on early diagnosis and treatment for women and older adults.

OBJECTIVE To explore the changes and correlations of color， components and anti-tumor activity of Gelsemium elegans during sand-frying processing， so as to provide a reference for the toxicity-attenuating and efficacy-preserving processing as well as quality control of sand-fried G. elegans decoction pieces.METHODS Raw G.elegans and its processed products prepared by sand-frying for 15， 30， 75， 105 and 120 s were taken as test samples. The chromatic values， contents of total alkaloids and seven main active components （gelsemine， humantenine， koumine， gelsenicine， etc.） were determined. Their in vitro anti-tumor activity was investigated. The correlations between color and component contents， as well as between total alkaloid content and anti-tumor activity were analyzed.RESULTS The color of G.elegans samples generally darkened after sand-frying， and the total color difference （Δ E * ） of samples sand-fried for 75 s was greater than 20. At the initial stage of sand-frying （15-30 s）， the total alkaloid content in processed products decreased sharply compared with the raw material； at the later-stage of sand-frying （75-105 s）， the declining rate of total alkaloid content slowed down and tended to be stable. The contents of gelsenicine and humantenidine in processed products dropped drastically at 30 s of sand-frying， while the contents of koumine and sempervirine decreased substantially （by about 80%） after 75 s of sand-frying and then gradually tended to be stable. The anti-HepG2 cell proliferation activity of each processed product decreased with the extension of frying time. Correlation analysis demonstrated that color brightness and yellow-blue chromaticity were significantly positively correlated with the contents of total alkaloids and all determined active components except gelsemine （ P ＜0.05 or P ＜0.01）； red-green chromaticity was significantly positively correlated with the contents of koumine， sempervirine， gelsemivirine and humantenidine （ P ＜0.05 or P ＜0.01）； Δ E * was significantly negatively correlated with the contents of total alkaloids and all determined active components except gelsemine. Besides， total alkaloid content was significantly negatively correlated with the half maximal inhibitory concentration of the samples on HepG2 cells （ P ＜0.05 or P ＜0.01）.CONCLUSIONS With the prolongation of sand-frying time， the color of sand-fried G.elegans decoction pieces gradually turned brownish-brown， the contents of seven active components and total alkaloids decreased progressively， and its anti-HepG2 cell activity showed an overall downward trend. Sand-frying for 75 s serves as a critical turning-point for the above-mentioned changes.

Respiratory syncytial virus(RSV), an RNA virus, is one of the leading pathogens causing respiratory tract infections in infants, the elderly and immunocompromised people, resulting in a huge medical burden of disease worldwide every year. The fusion protein(F protein) on the surface of RSV is the main target of neutralizing antibodies, especially the discovery of its pre-fusion conformation(pre-F), which has laid a key theoretical foundation for the design of a new generation of vaccines. In view of the current limited effective prevention and treatment methods for RSV-related diseases, it is necessary to develop effective vaccines, and vaccines of various technical routes, including subunit vaccines, mRNA vaccines,live attenuated vaccines and viral vector vaccines, are under the development stage. Among them, some RSV vaccine prouducts for the elderly and pregnant women have been approved for marketing. This paper mainly outlines the problems encountered in the early development of RSV vaccines, the current status and progress of RSV vaccine research, in order to provide reference for the follow-up development, clinical evaluation and immunization strategy formulation of RSV vaccines.

Objective:To investigate altered neurovascular coupling in patients with depression(DEP)using resting-state functional magnetic resonance imaging(MRI)and arterial spin labeling perfusion MRI,as well as its association with depressive symptoms.Methods:Neuropsychological assessment and multimodal MRI scans were performed for 25 DEP patients and 35 healthy controls(HCs).Arterial spin labeling perfusion MRI was used to calculate cerebral blood flow(CBF),and functional MRI was used to calculate regional homogeneity(ReHo).The Pearson correlation coefficient between CBF and ReHo was calculated to obtain neurovascular cou-pling.Results:At the whole-brain level,CBF-ReHo coupling was reduced in DEP patients compared with HCs.At the brain region level,CBF-ReHo coupling was reduced in 26 brain regions in DEP patients,which were mainly located in the visual network,the default network,and the auditory network.The correlation analysis showed that the coupling values of the left suboccipital gyrus,the left angular gyrus,and the left thalamus were negatively correlated with Hamilton Depression Scale score.Conclusion:There is a sig-nificant reduction in neurovascular coupling in DEP patients,which is correlated with the severity of DEP.

Objective:To explore the role of fibroblast growth factor 21 (FGF21) in rats with severe acute pancreatitis-associated acute lung injury (SAP-ALI) and its related molecular mechanisms.Methods:Twenty-four healthy male SD rats were randomly divided into 4 groups (random number, n=6 per group): Control group, SAP group, FGF21 intervention group (SAP+FGF21 group), and autophagy inhibitor group (SAP+FGF21+3-MA group). The SAP model was established by retrograde injection of 3.5% sodium taurocholate into the pancreatic duct. In SAP+FGF21 group, FGF21 10 mg/kg was intraperitoneally injected at 1 hour before modeling. In SAP+FGF21+3-MA group, FGF21 10 mg/kg and 3-MA 20 mg/kg were intraperitoneally injected at 1 h before modeling. Serum amylase activity was detected by biochemical kit. Plasma levels of tumor necrosis factor alpha (TNF-α) and FGF21 were detected by ELISA. HE staining was used to observe the pathological changes of pancreas and lung tissues. Immunofluorescence was used to detect the protein level of FGF21 in lung tissue. Western blot was used to detect the expression levels of autophagy-related proteins in lung tissue. Autophagosomes in lung tissue were observed by electron microscopy. Results:Compared with the Control group, the plasma and lung tissue FGF21 levels in SAP group were significantly decreased (both P<0.001) , severe pancreatic and lung tissue damage, and elevated plasma TNF-α levels ( P<0.001). Western Blot and transmission electron microscopy showed that: The expression of LC3Ⅱ/Ⅰ in lung tissue of SAP group was down-regulated [(0.912±0.052) vs. (0.700±0.135), P<0.001], and P62 protein level was up-regulated [(0.475±0.068) vs. (0.687±0.070), P<0.001] , and reduced autophagosome counts in the SAP group. In contrast, the SAP+FGF21 group showed elevated FGF21 levels (both P<0.01), attenuated pancreatic and lung injury ( P<0.001), decreased TNF-α levels [(280.10±49.36) pg/mL vs. (86.32±66.00) pg/mL, P<0.001]. Lung tissue of LC3 Ⅱ/Ⅰ levels increase [(0.700±0.135) vs. (0.853±0.073), P<0.01], P62 protein levels cut [(0.687±0.070) vs. (0.538±0.030), P<0.01] ], and increased autophagosomes and autolysosomes under electron microscopy. Compared with SAP+FGF21 group, the expression levels of FGF21 in plasma and lung tissue in SAP+FGF21+3-MA group were not significantly changed, and the level of autophagy was decreased. Pancreas and lung tissue injury was severe ( P<0.001), Plasma TNF-α level obviously higher [(86.32±66.00) pg/mL vs. (212.90±11.56) pg/mL, P<0.05]. Conclusion:FGF21 may play a protective role in SAP-ALI by up-regulating the level of autophagy.

Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.

Objective To integrate and analyze the disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019, along with the differences between genders, and predict the trends in disease burden changes from 2020 to 2029 to improve prevention and treatment strategies. Methods The disease burden of esophageal cancer caused by alcohol consumption in China from 1990 to 2019 was extracted and integrated from the 2019 Global Burden of Disease (GBD) database, and the corresponding trend was analyzed using the Joinpoint regression model with Joinpoint 4.9.1.0 software. The gray prediction model [GM (1, 1) ] was used to forecast the disease burden of alcohol-related esophageal cancer in China from 2020 to 2029. Results In 2019, the leading causes of esophageal cancer in China were tobacco, alcohol, high body mass index, and insufficient fruit and vegetable intake, accounting for the first to fifth positions in esophageal cancer deaths. From a gender perspective, in 2019, the death number and standardized mortality rate for males were 18.97 times and 20.00 times higher than for females, respectively. The disability-adjusted life years (DALYs) and standardized DALYs rate for males were 33.08 times and 24.78 times higher than those for females, respectively, indicating a heavier disease burden of alcohol-related esophageal cancer among Chinese males. From 1990 to 2019, the average annual percentage change (AAPC) in deaths and DALYs due to alcohol-related esophageal cancer in China was 2.08% and 1.63%, respectively, showing a continuous upward trend with statistical significance (P<0.05). The AAPC values for standardized mortality rate and standardized DALYs rate from 1990 to 2019 were –0.92% and –1.23%, respectively, showing a continuous downward trend with statistical significance (P<0.05). The population aged ≥55 years was the main group bearing the disease burden among all age groups from 1990 to 2019. The gray prediction model predicted that by 2029, the overall standardized mortality rate and standardized DALYs rate would decrease to 2.94/100 000and 67.94/100 000, with a greater decline in females than in males. Conclusion Over the past 30 years, the disease burden of alcohol-related esophageal cancer in China has slightly decreased. However, the reduction in disease burden is still lower compared to the overall decline in esophageal cancer burden, and the disease burden for males is significantly higher than for females. Focusing on prevention and treatment for males and the elderly population remains a major issue in addressing alcohol-related esophageal cancer in China.