ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

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.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

Glucose metabolic reprogramming is important in promoting the occurrence and development of malignant tumors and meeting the high demands of the malignant proliferation of tumor cells. An in-depth investigation of the mechanism of glucose metabolic reprogramming in lung cancer is important for the development of effective therapeutic strategies for lung cancer. Traditional Chinese medicine (TCM) is characterized by multiple components, targets, and pathways and can exert antitumor effects through multiple mechanisms. This article illustrates systematically the mechanism of action of Chinese herbal monomers and compound formulas regulating glucose metabolic reprogramming in lung cancer and explores deeply their regulatory effects on key enzymes of glycolysis and metabolism-related signaling pathways, and their potential in overcoming the drug resistance of lung cancer. It aims to provide a reference basis for the study of therapeutic targets and mechanisms of action of TCM in prevention and treatment of lung cancer and theoretical sources for the research and development of new medicines.

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

The study aimed to investigate the effect of the CD200R1 gene deletion on microglia activation and nigrostriatal dopamine neuron loss in the Parkinson's disease (PD) process. The CRISPR-Cas9 technology was applied to construct the CD200R1^-/- mice. The primary microglia cells of wild-type and CD200R1^-/- mice were cultured and treated with bacterial lipopolysaccharide (LPS). Microglia phagocytosis level was assessed by a fluorescent microsphere phagocytosis assay. PD mouse model was prepared by nigral stereotaxic injection of recombinant adeno-associated virus vector carrying human α-synuclein (α-syn). The changes in the motor behavior of the mice with both genotypes were evaluated by cylinder test, open field test, and rotarod test. Immunohistochemical staining was used to assess the loss of dopamine neurons in substantia nigra. Immunofluorescence staining was used to detect the expression level of CD68 (a key molecule involved in phagocytosis) in microglia. The results showed that CD200R1 deletion markedly enhanced LPS-induced phagocytosis in vitro by the microglial cells. In the mouse model of PD, CD200R1 deletion exacerbated motor behavior impairment and dopamine neuron loss in substantia nigra. Fluorescence intensity analysis results revealed a significant increase in CD68 expression in microglia located in the substantia nigra of CD200R1^-/- mice. The above results suggest that CD200R1 deletion may further activates microglia by promoting microglial phagocytosis, leading to increased loss of the nigrostriatal dopamine neurons in the PD model mice. Therefore, targeting CD200R1 could potentially serve as a novel therapeutic target for the treatment of early-stage PD.
Animals ; Microglia/physiology* ; Mice ; Phagocytosis ; Parkinson Disease/genetics* ; Disease Models, Animal ; Receptors, Cell Surface/physiology* ; Dopaminergic Neurons/pathology* ; Antigens, CD/metabolism* ; Gene Deletion ; Substantia Nigra ; Mice, Inbred C57BL ; Mice, Knockout ; Cells, Cultured ; Male ; alpha-Synuclein ; CD68 Molecule ; Orexin Receptors

OBJECTIVE: To observe the effect of electroacupuncture (EA) pretreatment of "biaoben acupoint combination" on cardiomyocyte mitochondrial fission in the rats with myocardial ischemia-reperfusion injury (MIRI) and explore its mechanism. METHODS: Fifty male SD rats were randomly divided into a sham-operation group, a model group, an EA pretreatment group, an EA pretreatment + Compound C group and an EA pretreatment+ML385 group, 10 rats in each group. In the EA pretreatment, the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, EA was delivered at bilateral "Neiguan" (PC6), "Zusanli" (ST36) and "Guanyuan" (CV4) for 20 min, with continuous wave and 2 Hz of frequency, 1 mA of current, once daily for consecutive 7 days. On day 8, in the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, 30 min before model preparation, the intraperitoneal injection with Compound C (0.3 mg/kg) and ML385 (30 mg/kg) was administered respectively. Except in the sham-operation group, the ligation of the left anterior descending coronary artery was performed to prepare MIRI rat model in the rest groups. In the sham-operation group, the thread was not ligated. After modeling, the content of reactive oxygen species (ROS) in the ischemic area was measured by flow cytometry, superoxide dismutase (SOD) was detected using xanthine oxidase method, and malondialdelyde (MDA) was detected using thiobarbituric acid (TBA) chromatometry. The morphology of myocardial tissue in the ischemic area was observed with HE staining, and the mitochondria ultrastructure of cardiomyocytes observed under transmission electron microscopy. Using immunofluorescence analysis, the positive expression of mitochondrial fission factor (MFF), mitochondrial fission 1 protein antibody (Fis1) and dynamin-related protein 1 (Drp1) was detected; and with immunohistochemical method used, the protein expression of adenosine monophosphate-activated protein kinase (AMPK), nuclear factor E2-associated factor2 (Nrf2) and Drp1 in the ischemic area was detected. RESULTS: Compared with the sham-operation group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 increased in the model group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 decreased (P<0.01), and the protein expression of Drp1 elevated (P<0.01). Compared with the model group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 were dropped in the EA pretreatment group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 rose (P<0.01), and the protein expression of Drp1 declined (P<0.01); and in the EA pretreatment+Compound C group and the EA pretreatment+ML385 group, the positive expression of MFF, Fis1 and Drp1, and the protein expression of Drp1 were all reduced (P<0.01). When compared with the EA pretreatment + Compound C group and the EA pretreatment+ML385 group, the content of ROS and MDA in the myocardial tissue of the ischemic area, and the positive expression of MFF, Fis1 and Drp1 were dropped in the EA pretreatment group (P<0.01); the content of SOD and the protein expression of AMRK and Nrf2 rose (P<0.01, P<0.05), and the protein expression of Drp1 decreased (P<0.05). In comparison with the model group, the EA pretreatment+Compound C group and the EA pretreatment+ML385 group, the cardiac muscle fiber rupture, cell swelling and mitochondrial disorders were obviously alleviated in the EA pretreatment group. The morphological changes were similar among the model group, the EA pretreatment+Compound C group and the EA pretreatment+ML385 group. CONCLUSION Electroacupuncture pretreatment of "biaoben acupoint combination" attenuates myocardial injury in MIRI rats, probably through promoting the phosphorylation of AMPK and Nrf2, inhibiting the excessive mitochondrial fission induced by Drp1, and reducing mitochondrial dysfunction caused by mitochondrial fragmentation and vacuolation.
Animals ; Electroacupuncture ; Male ; Rats, Sprague-Dawley ; Myocardial Reperfusion Injury/physiopathology* ; Myocytes, Cardiac/cytology* ; Rats ; Acupuncture Points ; Mitochondrial Dynamics ; Humans ; Reactive Oxygen Species/metabolism* ; NF-E2-Related Factor 2/genetics* ; Superoxide Dismutase/metabolism*

Neurological diseases are a major health concern, and brain injury is a typical pathological process in various neurological disorders. Different biomarkers in the blood or the cerebrospinal fluid are associated with specific physiological and pathological processes. They are vital in identifying, diagnosing, and treating brain injuries. In this review, we described biomarkers for neuronal cell body injury (neuron-specific enolase, ubiquitin C-terminal hydrolase-L1, αII-spectrin), axonal injury (neurofilament proteins, tau), astrocyte injury (S100β, glial fibrillary acidic protein), demyelination (myelin basic protein), autoantibodies, and other emerging biomarkers (extracellular vesicles, microRNAs). We aimed to summarize the applications of these biomarkers and their related interests and limits in the diagnosis and prognosis for neurological diseases, including traumatic brain injury, status epilepticus, stroke, Alzheimer's disease, and infection. In addition, a reasonable outlook for brain injury biomarkers as ideal detection tools for neurological diseases is presented.
Humans ; Biomarkers/cerebrospinal fluid* ; Nervous System Diseases/diagnosis* ; Brain Injuries/metabolism* ; Phosphopyruvate Hydratase/cerebrospinal fluid* ; Glial Fibrillary Acidic Protein/blood* ; S100 Calcium Binding Protein beta Subunit/blood* ; tau Proteins/cerebrospinal fluid* ; Ubiquitin Thiolesterase/blood* ; Myelin Basic Protein/cerebrospinal fluid* ; Neurofilament Proteins/blood* ; MicroRNAs/blood* ; Brain Injuries, Traumatic/metabolism*