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 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 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.

BACKGROUND:Psoralen has a strong anti-osteoporotic activity and may have a restorative effect on chemotherapy-induced osteoporosis. OBJECTIVE:To explore the restorative effect of psoralen on the osteogenic differentiation of bone marrow mesenchymal stem cells in mice inhibited by cyclophosphamide and its mechanism. METHODS:C57BL/6 mouse bone marrow mesenchymal stem cells were isolated and cultured.Effect of psoralen on viability of bone marrow mesenchymal stem cells was detected by MTT assay.Osteogenic induction combined with alkaline phosphatase staining was used to determine the optimal dose of psoralen to restore the osteogenic differentiation of bone marrow mesenchymal stem cells inhibited by cyclophosphamide.The mRNA expression levels of Runx2,alkaline phosphatase,Osteocalcin,osteoprotegerin,and Wnt/β-catenin signaling pathway-related genes Wnt1,Wnt4,Wnt10b,β-catenin,and c-MYC were measured by RT-qPCR at different time points under the intervention with psoralen.The protein expression of osteogenic specific transcription factor Runx2 and Wnt/β-catenin signaling pathway related genes Active β-catenin,DKK1,c-MYC,and Cyclin D1 was determined by western blot assay at different time points under the intervention with psoralen. RESULTS AND CONCLUSION:(1)There was no significant effect of different concentrations of psoralen on the viability of bone marrow mesenchymal stem cells.The best recovery of the inhibition of osteogenic differentiation of bone marrow mesenchymal stem cells caused by cyclophosphamide was under the intervention of psoralen at a concentration of 200 μmol/L.(2)Psoralen reversed the reduction in osteogenic differentiation marker genes Runx2,alkaline phosphatase,Osteocalcin and osteoprotegerin mRNA expression and Runx2 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(3)Psoralen reversed the decrease in Wnt/β-catenin pathway-related genes Wnt4,β-catenin,c-MYC mRNA and Active β-catenin,c-MYC,and Cyclin D1 protein expression and the increase in DKK1 protein expression in bone marrow mesenchymal stem cells caused by cyclophosphamide conditioned medium.(4)The results showed that cyclophosphamide inhibited osteogenic differentiation of bone marrow mesenchymal stem cells in mice,and psoralen had a restorative effect on it.The best intervention effect was achieved at a concentration of 200 μmol/L psoralen,and this protective effect might be related to the activation of Wnt4/β-catenin signaling pathway by psoralen.

Objective:To investigate the application and efficacy of personalized expander placement in the single expanded flap auricular reconstruction for microtia.Methods:This study was a prospective cohort study that included patients with microtia who underwent single expanded flap auricular reconstruction in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences between February 2023 and March 2024, according to specific inclusion and exclusion criteria. During the first-stage surgery, the tension and thickness of the skin in the postauricular area were evaluated using a pinch test. The anatomical layer of the expander placement was personalized as follows: (1) for thicker skin, the expander was placed in the subcutaneous layer; (2) for thinner skin, the expander was placed in the subcutaneous layer in the scalp region and in the subfascial layer in the hairless region behind the ear; (3) for areas of thin skin behind the residual ear, the expander was placed in the subfascial layer, with the remainder in the subcutaneous layer. In the second-stage surgery, autologous costal cartilage scaffolds were implanted for ear reconstruction, followed by a third-stage revision surgery. Postoperative follow-up was conducted to record complications. Before the third-stage surgery, two plastic surgeons, who did not participate in the operations, evaluated the aesthetic outcomes of the reconstructed ear using the Likert 4-point scale (1-4 points, with higher scores indicating better aesthetic outcomes).Results:A total of 152 children were included, with 97 males and 55 females; ages ranged from 5 to 13 years old, with a mean age of 6.8 years old. Of these, 89 cases were right-sided microtia, 53 left-sided microtia, and 10 bilateral microtia. In terms of skin characteristics, 35 cases had thick skin, 69 thin skin, and 48 thin skin behind the residual ear. During the first-stage surgery, complications included 15 cases of expander hematoma and 3 cases of expander infection. Both were controlled with symptomatic treatment. No cases of expander exposure occurred. The second-stage follow-up ranged from 6 to 12 months, with a mean of 7.9 months. The thickness of the reconstructed ear skin was appropriate, with well-defined subunits and no exposure of the cartilage scaffold. The aesthetic score for the reconstructed ear was (3.3 ± 0.5) points.Conclusion:The personalized placement of expanders effectively ensured appropriate thickness of the expanded flap in single expanded flap auricular reconstruction, providing good coverage for the rib cartilage framework and significantly enhancing the aesthetic outcomes of the reconstructed ears.

Magnetic resonance angiography(MRA)is a non-invasive imaging technique used to observe blood vessels.Quantitative analysis of MRA images enables visualization of vascular pathways,condition,and blood flow dynamics,which is essential for diagnosing vascular diseases such as vascular lesions,stenosis,and occlusions.Vessel segmentation serves as the fundamental basis for quantitative vascular analysis.However,the complex morphology of vessels,difficulties in labeling,and scarcity of accurate 3D vascular annotations pose significant challenges for MRA-based vessel segmentation.A strategy of selectively occluding vessels during model training is proposed to enhance the algorithm's capacity to capture the topological structure of blood vessels,thereby improving the continuity of vessel segmentation results.Additionally,a Refine network is incorporated to refine the binary segmentation results of the segmentation network,thereby further improving segmentation accuracy.Model training and testing are carried out using 42 cases of 3D MRA data from the public MIDAS dataset.For the test set,the 3D U-Net baseline model with vessel occlusion strategy shows a β0 Error of 1.2742±0.2103 and a β1 Error of 0.3393±0.0818,respectively,which are 0.1136 and 0.0280 lower than the baseline.The model integrating vessel occlusion strategy and Refine network achieves an average Dice score of 0.7105±0.0125,which is 0.0028 higher than the baseline.These results demonstrate that the proposed method effectively improves both vascular connectivity and segmentation accuracy.

Objective:To explore the influencing factors for pathological upgrading in prostate cancer patients with a Gleason score of 3 + 3 undergoing targeted biopsy，and to establish a nomogram prediction model.Methods:A retrospective analysis was conducted on 191 patients with localized prostate cancer diagnosed with a Gleason score of 3 + 3 through targeted biopsies at the First Affiliated Hospital of Nanjing Medical University from January 2020 to June 2024. The age of the patients was 67（61，73）years，with prostate-specific antigen（PSA）level of 7.44（5.53，10.19）ng/ml，prostate volume of 35.64（26.59，48.97）ml，and PSA density（PSAD）of 0.20（0.14，0.31）ng/ml 2. Among them，61 cases（31.94%）had a Prostate Imaging Reporting and Data System（PI-RADS）score of 3，104 cases（54.45%）had a score of 4，and 26 cases（13.61%）had a score of 5. The diameter of the main lesion was 10.75（7.86，14.00）mm. The lesions were located in the peripheral zone in 78 cases（40.84%），the transition zone in 99 cases（51.83%），and the anterior fibromuscular stroma in 14 cases（7.33%）. The lesions were found at the apex in 56 cases（29.32%），in the body in 120 cases（62.83%），and at the base in 15 cases（7.85%）. MRI revealed only one lesion with a PI-RADS score ≥ 3 in 131 cases，two suspected lesions in 43 cases，three suspected lesions in 12 cases，and four suspected lesions in 5 cases. Systematic biopsy was positive in 121 cases（63.4%）and negative in 70 cases（36.6%）. The lesions were confined to the left lobe in 63 cases（32.98%），right lobe in 68 cases（35.60%），and involved both lobes in 60 cases（31.41%）. The interval between biopsy and surgery was 9.0（7.0，14.0）days. Univariate analyses were performed using Mann-Whitney U tests or χ2 tests，and multivariate logistic regression was used to identify independent predictors of pathological upgrading. A nomogram model was constructed based on these independent predictors. The model’s discriminative ability was assessed using the area under the receiver operating characteristic（ROC）curve（AUC），and internal validation of the model’s consistency was conducted using the bootstrap resampling method. Decision curve analysis（DCA）was performed to assess clinical utility. Results:Among the 191 cases，60（31.4%）had no pathological upgrading after surgery，while 131（68.6%）showed upgrading. Univariate analysis showed that the maximum diameter of the main lesion［9.0（6.0，13.2）mm vs. 11.0（8.4，14.0）mm］，number of suspicious lesions on MRI［1.0（1.0，1.0）vs. 1.0（1.0，2.0）］，number of positive systematic biopsy cores［1.0（0，2.0）vs. 1.0（0，3.0）］，percentage of positive systematic biopsy cores［0.08（0，0.17）vs. 0.12（0，0.25）］，number of positive targeted biopsy cores［2.0（1.0，3.0）vs. 3.0（1.0，4.0）］，percentage of positive targeted biopsy cores［0.37（0.24，0.75）vs. 0.50（0.38，0.85）］，level of the index lesion，location of the index lesion，and PI-RADS score were associated with pathological upgrading（ P < 0.05）. Multivariate logistic regression analysis showed that PI-RADS score 4（ OR = 5.88，95% CI 2.41 - 14.35），number of suspicious lesions on MRI（ OR = 4.15，95% CI 1.88 - 9.17），location of the index lesion in the transition zone（ OR = 6.86，95% CI 2.81 - 16.73），and percentage of positive targeted biopsy cores（ OR = 4.37，95% CI 1.38 - 14.90）were independent risk factors for pathological upgrading（ P < 0.05）. The nomogram model constructed using these predictors had an AUC of 0.845. Internal validation using the Bootstrap method yielded an AUC value of 0.812，indicating high predictive accuracy of the model. The calibration curve indicated good calibration. Decision curve analysis showed that the threshold range for net benefit in the model was between 12% - 100%. Conclusions:The PI-RADS score 4，the number of lesions with PI-RADS ≥ 3，the location of the main lesion in the transition zone，and the percentage of positive needles in targeted biopsy are independent risk factors for pathological upgrading from Gleason score 3 + 3. The nomogram model constructed from these factors demonstrates good predictive performance and provides a reference for clinical decision-making.