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.

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 the analgesic effect and potential mechanism of punicalagin on neuropathic pain （NP） rats based on the hypoxia-inducible factor-1α （HIF-1α）/nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 （NLRP3） signaling pathway. METHODS Male SD rats were randomly divided into sham operation group （18 rats） and modeling group （72 rats）. NP rat model was established by chronic constriction injury （CCI） of sciatic nerve. The successfully modeled rats were divided into NP group， 2-methoxyestradiol group （HIF-1α antagonist 10 mg/kg）， punicalagin group （300 mg/kg）， and punicalagin+dimethyloxaloglycine group （punicalagin 300 mg/kg+HIF-1α agonist 175 mg/kg）， with 18 rats in each group. Rats in each group were injected intraperitoneally and/or intragastrically with the corresponding solution or 1% dimethyl sulfoxide/normal saline， once a day， for 14 consecutive days. After the last administration， the mechanical withdrawal threshold （MWT）， thermal withdrawal latency （TWL）， the levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β） and IL-6 in spinal cord tissue were detected； the morphological changes in the spinal dorsal horn were observed. Apoptosis rate of spinal dorsal horn neurons， the co-localization of NLRP3/ionized calcium binding adapter molecule 1 （Iba-1） （calculated by the number of NLRP3+/Iba-1+ cells） and the protein expressions of HIF-1α， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC） and caspase-1 in spinal cord tissue were detected. RESULTS Compared with the sham operation group， neurofibril in spinal dorsal horn of rats in NP group was thickened and wound into knots， and vacuolar degeneration containing silver granules was observed； the MWT and TWL were reduced or shortened； the levels of TNF-α， IL-1β and IL-6 in spinal cord tissue， the apoptosis rate of spinal dorsal horn neurons， the number of NLRP3+/Iba-1+ cells， and protein expressions of HIF-1α， NLRP3， ASC and caspase-1 were significantly increased or up-regulated （P＜0.05）. Compared with the NP group， the above indexes were significantly improved in the 2-methoxyestradiol group and punicalagin group （P＜0.05）， while dimethyloxaloglycine could significantly reverse the improvement effect of punicalagin on the above indexes （P＜0.05）. CONCLUSIONS Punicalagin can relieve pain in NP rats， and its analgesic effect may be achieved by inhibiting HIF-1α/NLRP3 signaling pathway and blocking the activation of ma0o4e@163.com NLRP3 inflammasome in spinal dorsal horn microglia.

Objective To investigate the correlation between plasma vitamin D levels and a novel inflam-matory marker,the systemic immune-inflammatory index(SII),in patients with type 2 diabetes.Methods This study adopted a cross-sectional design,in which patients diagnosed with type 2 diabetes who were admitted to the First Affiliated Hospital of Guangxi Medical University were enrolled as study participants.Data on demographic characteristics,medical history,physical examination findings,and laboratory test results were systematically collected.Participants were categorized into three groups based on their serum vitamin D levels:deficient,insuffi-cient,and sufficient.The relationship between vitamin D levels and the SII was evaluated using a multivariate linear regression model.Additionally,a restricted cubic spline model was employed to assess the nonlinear dose-response association between vitamin D levels and SII.Results This study enrolled a total of 5,716 patients with type 2 diabetes.A statistically significant difference in the SII was observed across groups with varying vitamin D levels(P<0.05),with the highest SII value found in the vitamin D-deficient group.Multivariate linear regression analysis revealed that,after adjusting for potential confounding factors including gender,age,season of blood collection,body mass index,hypertension,dyslipidemia,and chronic kidney disease,vitamin D levels were negatively associ-ated with SII(β=-2.68,95%CI:-3.56 to-1.81,P<0.001).Compared with the vitamin D-deficient group,the vitamin D-sufficient group exhibited significantly lower SII levels(β=-78.42,95%CI:-137.90 to-18.93,P=0.01).Furthermore,the restricted cubic spline model indicated a nonlinear dose-response relationship between vita-min D levels and SII(P<0.001).Conclusion There is a significant inverse correlation between plasma vitamin D levels and the SII in patients with type 2 diabetes.

BACKGROUND:Previous studies have demonstrated that acupuncture at the governor meridian has precise efficacy in the treatment of ischemic stroke and can improve cerebral ischemia-reperfusion injury by attenuating pyroptosis,but the upstream regulatory mechanisms are not yet fully clarified.OBJECTIVE:To observe the neuroprotective effect of electroacupuncture in model rats of cerebral ischemia-reperfusion injury.METHODS:Twenty-seven Sprague-Dawley rats were randomly divided into sham surgery,model,and electroacupuncture groups,with nine rats in each group.Modified suture method was used to establish cerebral ischemia-reperfusion model rats in the model and electroacupuncture groups.The electroacupuncture group was subjected to electroacupuncture at"Baihui,""Fengfu,"and"Dazhui"acupoints,20 minutes each,once a day,for 7 consecutive days.After treatment,neurological deficit scoring and pole test were performed to assess behavioral changes.Tri-phenyl tetrazolium chloride staining was used to assess cerebral infarction size in rats.Hematoxylin-eosin staining was performed to observe morphological changes in cerebral cortex tissue on the infarcted side of rats.Immunofluorescence analysis was used to determine Iba-1 and reactive oxygen species levels in cerebral cortex tissue on the infarcted side of rats,ELISA method was used for measuring interleukin-1β,interleukin-6 and tumor necrosis factor α levels in cerebral cortex tissue on the infarcted side of rats.Real-time fluorescence quantitative PCR and western blot were used to detect mRNA and protein expression levels of thioredoxin interaction protein,nod-like receptor associated protein 3(NLRP3),Caspase-1 and interleukin-1β in cerebral cortex tissue on the infarcted side of rats respectively,and the interaction between thioredoxin interaction protein and NLRP3 was analyzed by immunoprecipitation.RESULTS AND CONCLUSION:(1)Compared with the sham surgery group,rats in the model group showed an increase in neurological deficit score,pole test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining in the model group showed neuronal degeneration and necrosis,with fragmented and dissolved nuclei and cellular vacuoles.(2)Compared with the model group,rats in the electroacupuncture group showed a reduction in neurological deficit score,pole climbing test score,cerebral infarction volume(P<0.05),the immunofluorescence expression of Iba-1 and reactive oxygen species(P<0.05),the levels of interleukin-1β,interleukin-6 and tumor necrosis factor α(P<0.05),and the mRNA and protein expression of thioredoxin interaction protein,NLRP3,Caspase-1 and interleukin-1β in cerebral cortex tissue(P<0.05).Hematoxylin-eosin staining showed that the pathological damage of neurons in cerebral cortex tissue on the infarcted side of rats in the electroacupuncture group was significantly attenuated,with significantly reduced cell necrosis and vacuolation.(3)Immunoprecipitation assay showed an interaction between thioredoxin interaction proteins and NLRP3 in the cerebral cortical tissues on the infarcted side of rats in the model group.To conclude,electroacupuncture has a significant therapeutic effect against cerebral ischemia-reperfusion injury,possibly by inhibiting the reactive oxygen species/thioredoxin interaction protein/NLRP3 cell pyroptosis signaling pathway and activation of microglia to reduce the release of inflammatory factors.

Objective To address the personalized differences in motion gait between stroke patients and healthy older adults,as well as the issue of abnormal gait recognition,a deep learning fusion-based approach is proposed to effectively improve the accuracy of abnormal gait recognition.Methods A model fusing convolutional neural networks(CNN)and bidirectional long short-term memory networks(BiLSTM)was adopted,with the introduction of a residual network(ResNet).Unilateral ankle joint movement data at different walking speeds within a comfortable range were collected from healthy older adults and stroke patients.Signals from inertial sensors and electromyography sensors were used as inputs,while gait features were analyzed and gait differences between the two groups were compared.The effectiveness of the model was validated by comparing the classification performance of traditional deep learning models and CNN-ResNet-BiLSTM models with different layer combinations in terms of abnormal gait recognition accuracy.Results The CNN-ResNet-BiLSTM model,which introduced residual connectivity,performed excellently in abnormal gait recognition.Compared with traditional deep learning models such as the gated recurrent unit(GRU)and long short-term memory network(LSTM),its prediction accuracy was improved by 13.6％and 8.36％,respectively.Additionally,compared with other model combinations,this model achieved an overall accuracy of 97.78％.Conclusions The algorithm proposed in this study can be applied to stroke-related abnormal gait detection,providing technique support for the early diagnosis and precise monitoring of such diseases.

Objective To discuss the clinical efficacy of catheter-directed thrombolysis(CDT)in treating acute pulmonary embolism(APE).Methods A total of 215 patients with APE,who were admitted to the Second Affiliated Hospital of Shandong First Medical University of China,were enrolled in this study.Pulmonary angiography was performed in all the patients.After the location of the thrombus was identified,the pigtail catheter was rotated so as to break the thrombus into small pieces,which was followed by local infusion of thrombolytic agent urokinase to make recanalization of the occluded pulmonary artery.The postoperative clinical symptoms,blood oxygen saturation,mean pulmonary artery pressure,BNP,D-dimer,RV/LV diameter ratio were compared with their preoperative values.PESI scoring was used to evaluate the severity of the pulmonary embolism.Patients with PESI grade-Ⅲ and PESI grade-Ⅳ were classified into medium-risk group,and patients with PESI grade-V were classified into higher-risk group.Results Symptom relief immediately after surgery was observed in 210 patients,complete recanalization of pulmonary artery was achieved in 200 patients,and partial recanalization of pulmonary artery was seen in 15 patients.The preoperative mean pulmonary artery pressure,blood oxygen saturation,BNP,D-dimer,RV/LV diameter ratio were(46.24±5.32)mmHg,(90.36±3.23)％,(8 000.12±750.56)pg/mL,(7.5±2.3)mg/L and(1.63±0.22)respectively;at one week after surgery the above indicators were(26.12±3.36)mmHg,(98.74±2.12)％,(240.35±33.52)pg/mL,(1.75±0.36)mg/L and(1.11±0.13)respectively;the differences were statistically significant(all P＜0.05).In the patients who had symptoms of hemoptysis,shock and syncope before surgery,all these symptoms were completely disappeared in one week after CDT,and the symptoms of dyspnea,chest pain,and palpitations were significantly relieved after CDT,the differences were statistically significant(all P＜0.05).The difference in survival time between different PESI grade groups was statistically significant(P＜0.05).No serious postoperative complications such as severe arrhythmia,cerebral hemorrhage,or gastrointestinal bleeding occurred.Postoperative 3-month CT pulmonary angiography(CTPA)showed that the main pulmonary artery was well visualized and no thrombus-produced filling defect shadow was detected.Conclusion For the treatment of APE,CDT can promptly and rapidly open the obstructed pulmonary artery lumen,restore pulmonary artery hemodynamics,and correct hypoxemia.Therefore,CDT is a safe,effective and quick treatment for APE.

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.