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.

Piezo1, a mechanosensitive nonselective cation channel characterized by multiple transmembrane domains, plays a critical role intransducing mechanical stimuli at the cellular membrane and participates in various physiological and pathological processes. Recent studies have established a significant association between Piezo1 and the occurrence and development of multiple ophthalmic disorders. Substantial evidence demonstrates that Piezo1 contributes to ocular disease progression by regulating fundamental cellular processes including proliferation, differentiation, apoptosis, and inflammatory responses, with particular relevance to glaucoma, corneal diseases, retinal disorders, and dry eye syndrome. Piezo1 has made rapid progress in ophthalmology, and has been established as an important mechanosensor in the eye, widely involved in intraocular pressure regulation, retinal function maintenance, corneal homeostasis and repair, and ocular development, and its dysfunction is closely related to the pathological mechanisms of many important blinding eye diseases. Consequently, Piezo1 is not only a key molecule for understanding ocular mechanobiology, but also represents a highly promising therapeutic target. Its study offers new perspectives for the development of novel therapeutic strategies against ocular diseases. This review systematically summarizes current research advances regarding Piezo1 channels in ophthalmology, analyzes their mechanistic involvement in disease processes, and evaluates their potential therapeutic value, thereby offering innovative perspectives for the clinical management of ocular diseases.

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: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.

AIM:To observe the changes of Th17 related cytokines in tears of conjunctivochalasis(CCH)patients with liver-kidney yin deficiency treated with traditional Chinese medicine Qi Jing Mingmu decoction combined with artificial tears.METHODS:A total of 56 CCH patients(56 eyes)with liver-kidney yin deficiency of grade Ⅱ to Ⅲ were collected and randomly divided into treatment group(treated with Qi Jing Mingmu decoction combined with artificial tears)of 26 cases(26 eyes)and control group(treated with pure artificial tears)of 30 cases(30 eyes). The treatment course was 1 mo, and international ocular surface disease index(OSDI), tear film break-up time(BUT), tear meniscus height(TMH)and conjunctival congestion index of the patients were observed before and after treatment. The patients' tears were collected before and after treatment, and Th17 related cytokines in tears were detected using flow cytometry immunofluorescence luminescence method.RESULTS:After treatment, the OSDI, BUT and conjunctival congestion index of CCH patients in the treatment group and control group were significantly improved(all P<0.01). After treatment, the TMH of CCH patients in the treatment group was significantly reduced(P<0.01), while there was no statistically significant difference in TMH of the control group before and after treatment(P=0.41). After treatment, the levels of Th17 related cytokines IL-17A, IL-22, IFN-γ, IL-17F, and IL-1β in tears of CCH patients in the treatment group were significantly reduced after treatment(all P<0.01), and the changes in the treatment group were more significant(all P<0.05). There was no significant difference in the control group before and after treatment(all P>0.05). After treatment, the levels of IL-6 and TNF-α in the tears of both groups of CCH patients decreased compared to those before treatment(both P<0.05), but the changes in the treatment group were more significant(both P<0.01).CONCLUSION:Qi Jing Mingmu decoction combined with artificial tears can effectively improve the ocular surface microenvironment, enhance tear film stability, and inhibit ocular surface inflammation in CCH patients with liver-kidney yin deficiency. This may be related to its reduction in the secretion of Th17 related cytokines in tears.

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

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 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.

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.