The c-Jun N-terminal kinase （JNK） signaling pathway， a key member of the mitogen-activated protein kinase （MAPK） family， plays a central role in the pathogenesis and progression of central nervous system （CNS） diseases by regulating core biological processes such as apoptosis， inflammatory responses， synaptic plasticity， and autophagy. This article sorts out and analyzes relevant literature published domestically and internationally in recent years， summarizing the mechanisms of action of the JNK signaling pathway in common CNS diseases and the research progress in traditional Chinese medicine （TCM） interventions in CNS diseases through the regulation of the JNK signaling pathway. Studies have shown that active components of TCM， such as berberine， paeoniflorin， and astragaloside Ⅳ， as well as compound formulations like Heixiaoyao san， Ditan tang， and Buyang huanwu tang， can exert neuroprotective effects in various CNS disorders， including Alzheimer’s disease， Parkinson’s disease， cerebral ischemia-reperfusion injury， and epilepsy， by inhibiting the aberrant activation of the JNK signaling pathway， thereby alleviating neuroinflammation， oxidative stress， and neuronal apoptosis， while improving synaptic function and cognitive behavioral deficits， regulating autophagy， and maintaining blood-brain barrier integrity.

Alzheimer’s disease （AD） is an age-related neurodegenerative disorder. Marrow-sea insufficiency serves as the fundamental basis for the onset of AD. Early syndrome differentiation-based intervention helps to delay disease progression， and improve patients’ cognitive function. Qifu yin is a representative specialized prescription for AD with marrow-sea insufficiency syndrome. Studies demonstrate that Qifu yin exerts neuroprotective effects through multiple pathways， including inhibiting the abnormal deposition of amyloid β -protein and hyperphosphorylation of tau protein， alleviating neuroinflammation， regulating oxidative stress and mitochondrial dysfunction， modulating the cholinergic system， and improving synaptic plasticity. Qifu yin combined with Western medicine such as donepezil， memantine， and butylphthalide， or combined with external therapies such as acupuncture， can effectively improve cognitive function and activities of daily living in AD patients with favorable safety. Future research should focus on the core pathogenesis and key targets of AD with marrow-sea insufficiency syndrome， provide in-depth elucidation of the scientific connotation of Qifu yin’s “tonifying the kidney to produce marrow”， and further conduct high-quality clinical studies to provide scientific evidence for the prevention and treatment of AD with marrow-sea insufficiency syndrome.

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.

OBJECTIVES: Increasing detection of low-risk papillary thyroid carcinoma (PTC) is associated with overdiagnosis and overtreatment. N6-methyladenosine (m⁶A)-mediated microRNA (miRNA) dysregulation plays a critical role in tumor metastasis and progression. However, the functional role of m⁶A-miRNAs in PTC remains unclear. This study aims to elucidate the regulatory mechanism of m⁶A-miR-139-5p expression in PTC, determine its association with PTC metastasis, and evaluate its potential as a diagnostic biomarker for PTC metastasis, thereby providing experimental evidence for precision diagnosis and therapy. METHODS: Expression profiles of m⁶A-miRNAs were compared between the The Cancer Genome Atlas (TCGA) and GSE130512 cohorts to identify metastasis-associated candidates. Clinical specimens from 13 metastasis and 18 non-metastasis PTC patients were analyzed to assess m⁶A-miR-139-5p expression and its correlation with metastasis. Functional experiments were conducted to investigate the effect of fat mass and obesity-associated protein (FTO) on pri-miR-139 methylation and processing, clarifying its regulatory role in miR-139-5p expression. In TPC-1 cells, MTT assays were performed to evaluate whether miR-139-5p overexpression could counteract FTO-mediated cell proliferation. Transwell invasion assays were used to determine the impact of miR-139-5p on PTC cell invasion, exploring whether it functions through the ZEB1/E-cadherin axis. RESULTS: By comparing TCGA and GSE130512 cohorts, it was found that circulating m⁶A-miR-139-5p could serve as a biological indicator for detecting PTC metastasis. Detection of 13 metastatic and 18 non-metastatic clinical specimens showed that FTO inhibited the processing of pri-miR-139 by reducing its methylation level, leading to the dysregulation of miR-139-5p in PTC (P<0.05). In TPC-1 cells, MTT assay showed that overexpression of miR-139-5p could partially reverse FTO overexpression-mediated cell proliferation (P<0.05). In addition, miR-139-5p inhibited the invasive ability of PTC cells by targeting the ZEB1/E-cadherin axis, while FTO overexpression could partially weaken this inhibitory effect. CONCLUSIONS Circulating miR-139-5p can be a potential marker for evaluating PTC metastasis. FTO affects the expression and function of miR-139-5p by regulating m⁶A modification of pri-miR-139, but its clinical value needs further verification.
Humans ; MicroRNAs/metabolism* ; Thyroid Cancer, Papillary/metabolism* ; Alpha-Ketoglutarate-Dependent Dioxygenase FTO/metabolism* ; Thyroid Neoplasms/metabolism* ; Cell Line, Tumor ; Neoplasm Metastasis ; Adenosine/genetics* ; Gene Expression Regulation, Neoplastic ; Female ; Male ; Cadherins/metabolism* ; Cell Proliferation ; Zinc Finger E-box-Binding Homeobox 1/genetics*

This study established the mouse podocyte clone-5 (MPC5) with Smad3 knockout and studied the effect of transforming growth factor-beta 1 (TGF-β1) on the dedifferentiation of the MPC5 cells with Smad3 knockout, aiming to provide a cell tool for studying the role of Smad3 in mouse podocytes. The single-guide RNA (sgRNA) sequence targeting Smad3 was designed according to the principles of CRISPR/Cas9 design. The pX458-Smad3 vector was constructed and introduced into competent cells, and then the vector was extracted and used to transfect MPC5 cells. The successfully transfected cells were sorted by a flow cytometer. After single-cell clone expansion, PCR amplification of sequences adjacent to the edition site of Smad3 and sequencing were performed to identify potential cells with gene knockout. Western blotting was employed to verify the knockout efficiency of Smad3. Finally, the effect of Smad3 knockout on TGF-β1-induced dedifferentiation of MPC5 cells was analyzed by reverse transcription-polymerase chain reacting (RT-PCR), Western blotting, and the immunofluorescence method. The sgRNA was designed to target the fifth exon of Smad3. EGFP expression was observed 24 h after transfection of the pX458-Smad3 plasmid into MPC5 cells, with the transfection efficiency of 0.1% as determined by flow cytometry. From the transfected cells, 21 cell clones were obtained through flow cytometric sorting and single-cell clone expansion. PCR amplification and sequencing of the region around the sgRNA target site in Smad3 identified two cell clones with biallelic frameshift mutations. Western blotting results confirmed the absence of Smad3 expression in these clones, indicating successful establishment of the MPC5 cell line with Smad3 knockout. In normal MPC5 cells, TGF-β1 stimulation promoted the expression of fibrosis-related genes fibronectin and Col1a1 (collagen I) and inhibited the expression of the podocyte marker proteins synaptopodin and podocin, which suggested epithelial-mesenchymal transition and podocyte injury. However, in the two MPC5 cell lines with Smad3 knockout, TGF-β1-induced expression of epithelial-mesenchymal transition markers was significantly suppressed. The MPC5 cell lines with Smad3 knockout that were constructed by CRISPR/Cas9 provide a valuable cell model for functional studies of Smad3 protein and highlight the critical role of Smad3 in cell dedifferentiation.
Animals ; Smad3 Protein/genetics* ; CRISPR-Cas Systems/genetics* ; Mice ; Podocytes/metabolism* ; Transforming Growth Factor beta1/pharmacology* ; Cell Line ; Gene Knockout Techniques ; RNA, Guide, CRISPR-Cas Systems/genetics*

OBJECTIVE To investigate the relationship between M2-type macrophages and immunotherapy prognosis in head and neck squamous cell carcinoma.METHODS 165 patients with head and neck squamous cell carcinoma who received immunotherapy in Tangshan People's Hospital from April 2021 to May 2022 were selected as the study objects.According to the number of CD163 positive cells,the patients were divided into high expression group(n=75)and low expression group(n=90),and the relationship between the expression level of M2 macrophages and clinical characteristics was analyzed.Patients were divided into death group(n=62)and survival group(n=103)according to the prognosis.Hierarchical regression model was used to analyze the relationship between different clinical features and M2 macrophages,and Logistic regression was used to analyze the relationship between M2 macrophages and prognosis.The association and dose-response relationship between M2-type macrophages and death were analyzed by unconditional Logistic regression and restricted cubic spline model.The influence of M2-type macrophages on survival was verified by Kaplan-meier survival curve.REULTS The expression of M2 macrophages was compared in patients with different tumor diameters,T grades,N grades,different degrees of tumor differentiation,lymph node metastasis,and distant metastasis,and the differences were statistically significant(P<0.05).The differences in tumor diameter,T grade,N grade,degree of tumor differentiation,lymph node metastasis,distant metastasis,and expression level of M2 macrophages between the death group and survival group were statistically significant(P<0.05).Hierarchical regression analysis showed that tumor diameter,clinical T stage,clinical N stage,degree of tumor differentiation,lymph node metastasis and distant metastasis had significant effects on M2-type macrophages(P<0.05).After the collinear confounding factors were excluded,the expression level of M2-type macrophage was independently correlated with death(P<0.05).The 1-year survival rate was 82.64%,the 2-year survival rate was 66.77%,and the 3-year survival rate was 60.28%in patients with low expression of M2 macrophages.The 1-year survival rate was 60.11%,2-year survival rate was 42.53%,and 3-year survival rate was 41.82%in patients with high expression of M2 macrophages.There was statistical difference between the two groups(P<0.05).CONCLUSION There is an independent correlation between M2-type macrophages and the prognosis of patients with head and neck squamous cell carcinoma.The high expression of M2-type macrophages will promote the occurrence and development of head and neck squamous cell carcinoma.

BACKGROUND:Osteoporotic fracture is the most serious complication of osteoporosis.Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture,but the causal relationship between the two is unclear. OBJECTIVE:To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS:The genome-wide association study(GWAS)datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9,respectively.Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,Mendelian randomization analyses with random-effects inverse variance weighted,MR-Egger regression,weighted median,simple model,and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture.Sensitivity analyses were performed to test the reliability and robustness of the results.Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION:The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture.Elevated abundance of Actinomycetales[odds ratio(OR)=1.562,95%confidence interval(CI):1.027-2.375,P=0.037),Actinomycetaceae(OR=1.561,95%CI:1.027-2.374,P=0.037),Actinomyces(OR=1.544,95%CI:1.130-2.110,P=0.006),Butyricicoccus(OR=1.781,95%CI:1.194-2.657,P=0.005),Coprococcus 2(OR=1.550,95%CI:1.068-2.251,P=0.021),Family ⅩⅢ UCG-001(OR=1.473,95%CI:1.001-2.168,P=0.049),Methanobrevibacter(OR=1.274,95%CI:1.001-1.621,P=0.049),and Roseburia(OR=1.429,95%CI:1.015-2.013,P=0.041)would increase the risk of osteoporotic fractures in patients.Elevated abundance of Bacteroidia(OR=0.660,95%CI:0.455-0.959,P=0.029),Bacteroidales(OR=0.660,95%CI:0.455-0.959,P=0.029),Christensenellacea(OR=0.725,95%CI:0.529-0.995,P=0.047),Ruminococcaceae(OR=0.643,95%CI:0.443-0.933,P=0.020),Enterorhabdus(OR=0.558,95%CI:0.395-0.788,P=0.001),Eubacterium rectale group(OR=0.631,95%CI:0.435-0.916,P=0.016),Lachnospiraceae UCG008(OR=0.738,95%CI:0.546-0.998,P=0.048),and Ruminiclostridium 9(OR=0.492,95%CI:0.324-0.746,P=0.001)would reduce the risk of osteoporotic fractures in patients.We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method.That is,using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture.The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice,but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering.

Objective:To improve the isolation and culture method of seasonal influenza virus in embryonated chicken eggs (ECEs), and evaluate their isolation efficiency.Methods:We randomly selected 80 positive samples of H1N1 (H1N1pdm09) and seasonal H3N2 (H3N2snl) influenza virus nucleic acid, and inoculated them into the amniotic and urinary sac cavities of 10-day-old (traditional method) and 14-day-old (improved method) ECEs respectively to adapt the virus to the ECEs (E1-E2). Both method were used to inoculate 10-day-old urinary sac amplification virus (E2-E3), and the final virus isolation positive rates of the two method were compared; using fluorescence quantitative PCR method to detect viral nucleic acids in the improved amniotic and urinary sac cultures, and evaluate the viral proliferation at different inoculation sites; we analyzed the correlation between virus content and isolation positivity rate in the original specimen based on the CT value of nucleic acid testing and the final virus isolation positivity rate using the improved method.Results:The improved method obtained 42 strains of H1N1pdm09 strain, with a positive rate of 52.5% ( χ2=38.571, P<0.01); obtained 54 strains of H3N2snl strain, with a positive rate of 67.5% ( χ2=40.921, P<0.01). Significant differences were observed in the isolation efficiency of H1N1pdm09 samples when the improved method was applied to different inoculation sites of chicken embryos ( χ2=30.476, P<0.01), and similar differences were noted for H3N2snl samples ( χ2=4.928, P=0.026). There was no significant difference in the isolation rate of different CT value intervals of the original samples ( χH1N1pdm092=10.226, χH3N2snl2=3.764, P>0.05). Conclusions:The improved method of inoculating 14-day old ECEs adapted the virus, and the final number of strains obtained was significantly higher than the traditional method of inoculating 10 day old ECEs, which can significantly improve the positive isolation rate of H1N1pdm09 and H3N2snl influenza virus in ECEs. The amniotic cavity is more sensitive to H1N1pdm09 and H3N2snl influenza viruses, which helps the virus adapt in ECEs. There was no significant difference in the sample isolation rate and total positive rate of virus isolation among different CT value ranges, and further verification is needed.