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.

PURPOSE: To investigate the regulatory role of nerve injury-induced protein 1 (NINJ1) in the anti-inflammatory function of human umbilical cord mesenchymal stem cells (hUC-MSCs) co-stimulated by interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). METHODS: hUC-MSCs were expanded in vitro using standard protocols, with stem cell characteristics confirmed by flow cytometry and multilineage differentiation assays. The immunomodulatory properties and cellular activity of cytokine-co-pretreated hUC-MSCs were systematically evaluated via quantitative reverse transcription RT-qPCR, lymphocyte proliferation suppression assays, and Cell Counting Kit-8 viability tests. Transcriptome sequencing, Western blotting and small interfering RNA interference were integrated to analyze the regulatory mechanisms of NINJ1 expression. Functional roles of NINJ1 in pretreated hUC-MSCs were elucidated through gene silencing combined with lactate dehydrogenase release assays, Annexin V/Propidium Iodide apoptosis analysis, macrophage co-culture models, and cytokine Enzyme-Linked Immunosorbent Assay. Therapeutic efficacy was validated in a cecal ligation and puncture-induced septic mouse model: 80 mice were randomly allocated into 4 experimental groups (n=20/group): sham group (laparotomy without cecal ligation); phosphate-buffered saline-treated group (cecal ligation and puncture (CLP) + 0.1 mL phosphate-buffered saline); hUC-MSCs (small interfering RNA (siRNA)-interferon-gamma and tumor necrosis factor-alpha co-stimulation (IT))-treated group (CLP + hUC-MSCs transfected with scrambled siRNA); and hUC-MSCs (siNINJ1-IT)-treated group (CLP + hUC-MSCs with NINJ1-targeting siRNA). RESULTS: hUC-MSCs demonstrated compliance with International Society for Cellular Therapy criteria, confirming their stem cell identity. IFN-γ/TNF-α co-pretreatment enhanced the immunosuppressive capacity of hUC-MSCs, accompanied by the reduction of cellular viability, while concurrently upregulating pro-inflammatory cytokines such as interleukin-6 and interleukin-1β. This co-stimulation significantly elevated NINJ1 expression in hUC-MSCs, whereas genetic silencing of NINJ1 effectively suppressed pro-inflammatory cytokine production and attenuated damage-associated molecular patterns release through inhibition of programmed plasma membrane rupture. Furthermore, the NINJ1 interference potentiated the ability of cytokine-pretreated hUC-MSCs to suppress LPS-induced pro-inflammatory responses in RAW264.7 macrophages. In cecal ligation and puncture-induced sepsis model, NINJ1-silenced hUC-MSCs exhibited enhanced therapeutic efficacy, manifested by reduced systemic inflammation and multi-organ damage. CONCLUSION Our findings shed new light on the immunomodulatory functions of cytokine-primed MSCs, offering groundbreaking insights for developing MSC-based therapies against inflammatory diseases via interfering the expression of NINJ1.
Mesenchymal Stem Cells/drug effects* ; Animals ; Interferon-gamma/pharmacology* ; Tumor Necrosis Factor-alpha/pharmacology* ; Humans ; Mice ; Umbilical Cord/cytology* ; Cells, Cultured ; Apoptosis ; Male

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors

The neonatal mammalian heart has a remarkable regenerative capacity, while the adult heart has difficulty to regenerate. A metabolic reprogramming from glycolysis to fatty acid oxidation occurs along with the loss of cardiomyocyte proliferative capacity shortly after birth. In this study, we sought to determine if and how metabolic reprogramming regulates cardiomyocyte proliferation. Reversing metabolic reprogramming by carnitine palmitoyltransferase 1 (CPT1) inhibition, using cardiac-specific Cpt1a and Cpt1b knockout mice promoted cardiomyocyte proliferation and improved cardiac function post-myocardial infarction. The inhibition of CPT1 is of pharmacological significance because those protective effects were replicated by etomoxir, a CPT1 inhibitor. CPT1 inhibition, by decreasing poly(ADP-ribose) polymerase 1 expression, reduced ADP-ribosylation of dual-specificity phosphatase 1 in cardiomyocytes, leading to decreased p38 MAPK phosphorylation, and stimulation of cardiomyocyte proliferation. Our present study indicates that reversing metabolic reprogramming is an effective strategy to stimulate adult cardiomyocyte proliferation. CPT1 is a potential therapeutic target for promoting heart regeneration and myocardial infarction treatment.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

To explore the differences in biological gas production in the waterlogged zone of a coal seam fire-affected area, in this study the in-situ gas production experiment was conducted with the mine water from aquitard layers in coal seam fire zones in Xinjiang. The results showed that the biogas production first increased and then decreased with the increase in distance, and the highest gas production reached 216.55 mL. The changes in key metabolic pathways during the anaerobic fermentation of coal were analyzed, which showed that as the distance from the aquitard layer in the coal seam fire zone increased, the methanogenesis pathways gradually shifted from acetic acid decarboxylation and carbon dioxide reduction to acetic acid decarboxylation and methylamine methanogenesis. The significant variability in the in-situ mine water reservoir conditions contributed to the differences. In addition, the reservoir pressure and temperature increased as the distance from the fire zone became longer, and the salinity of the farthest mine water in the reverse fault was the highest due to the lack of groundwater supply. Pearson correlation analysis revealed significant correlations of microbial communities with key functional genes and the types and concentrations of ions. The ions significantly influencing microbial enzymatic metabolic activities included Al³⁺, Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Mg²⁺, PO₄^3-, and Mo⁶⁺. The differences in metabolic pathways were attributed to the integrated effects of a co-occurring environment with multiple ions. The gas production simulation experiments and metagenomic analyses provide data support for the practical application of in-situ biogas experiments, laying a foundation for engineering applications.
Biofuels ; Coal ; Methane/biosynthesis* ; Fires ; Groundwater ; Coal Mining ; Fermentation ; China ; Anaerobiosis

Objective To investigate the effect of interactions between hypoxia-inducing factor 1α(HIF-1α)and transforming growth faction-β2(TGF-β2)on the development of primary open-angle glaucoma(POAG)in high myopia(HM)and their early prediction value.Methods A prospective cohort study was conducted on 265 patients(421 eyes)with HM who were admitted to our hospital from February 2021 to June 2023.All patients underwent comprehensive oph-thalmological examinations,including optical coherence tomography to measure the total Bruch's membrane opening-disc edge minimum width(BMO)parameter,and the Humphrey field analyzer to measure the visual field index(VFI)and mean defect(MD)values.After a follow-up of 1 year(2 cases lost to follow up),the patients were divided into an occurrence group(51 cases,82 eyes)and a non-occurrence group(212 cases,337 eyes)according to whether POAG occurred.The clinical data[including age,gender,body mass index(BMI),central corneal thickness(CCT),axial length(AL),refrac-tive power,VFI,visual field MD,optic disc horizontal diameter,optic disc vertical diameter,elliptical index,optic disc ar-ea,optic cup area,disc rim area,cup-to-disc area ratio,and total BMO],tear HIF-1α and TGF-β2 were compared be-tween the two groups.The receiver operating characteristic(ROC)curves of tear HIF-1α and TGF-β2 for predicting HM with POAG were plotted to obtain the optimal cutoff value.The effects of tear HIF-1α,TGF-β2 and their interactions on the development of POAG in HM were analyzed.The Pearson method was used to analyze the correlation of tear HIF-1α and TGF-β2 with the optic disc structure of HM patients.The ROC analysis was used to evaluate the value of HIF-1α combined with TGF-β2 for predicting POAG.Results Compared with those in the non-occurrence group,the VFI and BMO were lower while the visual field MD,disc cup area,cup-to-disc area ratio,tear HIF-1α and TGF-β2 were higher in the occur-rence group(all P＜0.05).The interaction analysis showed that the interaction between tear HIF-1 α and TGF-β2 was a su-per-multiplication model,with a positive interaction effect on the occurrence of POAG in HM(P＜0.05).The Pearson analysis showed that tear HIF-1α and TGF-β2 were negatively correlated with VFI and total BMO,and positively correlated with their visual field MD,disc cup area,and cup-to-disc area ratio in patients with HM(all P＜0.05).The ROC curve analysis showed that the area under the ROC curve(AUC)of the effect of tear HIF-1α combined with TGF-β2 for predic-ting POAG in HM was 0.925(95％CI:0.896-0.949).The predictive value of tear HIF-1α combined with TGF-β2 was bet-ter than that of the two indicators alone.Conclusion HIF-1α and TGF-β2 are up-regulated in tears of HM patients com-plicated with POAG,and they interact positively during the occurrence of POAG.The combined detection of HIF-1α and TGF-β2 has predictive value for POAG.They can be used as an auxiliary clinical predictor of POAG,and can guide its clini-cal prevention and treatment.