OBJECTIVE To investigate the ameliorative effects and mechanisms of Miao medicine Euphorbia humifusa on hepatic fibrosis （HF） model rats. METHODS Thirty-eight rats were randomly assigned according to a random number table into control group （normal saline， n＝7）， model group （normal saline， n＝7）， E. humifusa low-， medium- and high-dose groups （0.675， 1.35， 2.70 g/kg， n＝6）， and silybin group （positive control， 18.9 mg/kg， n＝6）. All groups except the control group were subjected to HF induction via intraperitoneal injection of carbon tetrachloride. After modeling， rats were administered their respective drugs/normal saline by gavage， once a day， for 30 days. At the last medication， the liver index was calculated， and serum levels of tumor necrosis factor-α （TNF-α）， interleukin-17 （IL-17）， IL-1β， IL-6， alanine transaminase （ALT）， aspartate transaminase （AST）， and hydroxyproline （HYP） were measured. Liver morphology and HF changes were observed. Levels of transforming growth factor- β1 （TGF- β1） and α-smooth muscle actin （α-SMA）， as well as the expressions of α -SMA， proteins related to TNF- α/NF- κB signaling pathway， mRNA expressions of TNF-α and NF-κB p65 in liver tissue were determined. RESULTS Compared with model group， liver index， serum levels of TNF-α， IL-17， IL-1β， IL-6， ALT， AST and HYP， relative expression of NF-κB p65 mRNA， and the levels of TNF-α and α-SMA proteins and phosphorylated NF-κB p65 in liver tissues of rats from administration groups， the expressions of α-SMA and TGF-β1 in liver tissue of rats from E. humifusa medium-dose and high-dose groups， as well as positive staining percentage and mRNA expression of TNF- α in liver tissue of rats from E. humifusa high-dose group were all decreased significantly （P＜0.05 or P＜0.01）； IκBα protein expression from administration groups was significantly increased （P＜0.05 or P＜0.01）. Pathological changes and the degree of HF in the liver tissues of rats from administration groups were ameliorated to various extents. CONCLUSIONS E. humifusa may alleviate HF in rats by inhibiting the activation of the TNF-α/NF-κB signaling pathway.

Cardiovascular disease (CVD) remains one of the leading causes of mortality among adults globally, with continuously rising morbidity and mortality rates. Metabolic disorders are closely linked to various cardiovascular diseases and play a critical role in their pathogenesis and progression, involving multifaceted mechanisms such as altered substrate utilization, mitochondrial structural and functional dysfunction, and impaired ATP synthesis and transport. In recent years, the potential role of peroxisome proliferator-activated receptors (PPARs) in cardiovascular diseases has garnered significant attention, particularly peroxisome proliferator-activated receptor alpha (PPARα), which is recognized as a highly promising therapeutic target for CVD. PPARα regulates cardiovascular physiological and pathological processes through fatty acid metabolism. As a ligand-activated receptor within the nuclear hormone receptor family, PPARα is highly expressed in multiple organs, including skeletal muscle, liver, intestine, kidney, and heart, where it governs the metabolism of diverse substrates. Functioning as a key transcription factor in maintaining metabolic homeostasis and catalyzing or regulating biochemical reactions, PPARα exerts its cardioprotective effects through multiple pathways: modulating lipid metabolism, participating in cardiac energy metabolism, enhancing insulin sensitivity, suppressing inflammatory responses, improving vascular endothelial function, and inhibiting smooth muscle cell proliferation and migration. These mechanisms collectively reduce the risk of cardiovascular disease development. Thus, PPARα plays a pivotal role in various pathological processes via mechanisms such as lipid metabolism regulation, anti-inflammatory actions, and anti-apoptotic effects. PPARα is activated by binding to natural or synthetic lipophilic ligands, including endogenous fatty acids and their derivatives (e.g., linoleic acid, oleic acid, and arachidonic acid) as well as synthetic peroxisome proliferators. Upon ligand binding, PPARα activates the nuclear receptor retinoid X receptor (RXR), forming a PPARα-RXR heterodimer. This heterodimer, in conjunction with coactivators, undergoes further activation and subsequently binds to peroxisome proliferator response elements (PPREs), thereby regulating the transcription of target genes critical for lipid and glucose homeostasis. Key genes include fatty acid translocase (FAT/CD36), diacylglycerol acyltransferase (DGAT), carnitine palmitoyltransferase I (CPT1), and glucose transporter (GLUT), which are primarily involved in fatty acid uptake, storage, oxidation, and glucose utilization processes. Advancing research on PPARα as a therapeutic target for cardiovascular diseases has underscored its growing clinical significance. Currently, PPARα activators/agonists, such as fibrates (e.g., fenofibrate and bezafibrate) and thiazolidinediones, have been extensively studied in clinical trials for CVD prevention. Traditional PPARα agonists, including fenofibrate and bezafibrate, are widely used in clinical practice to treat hypertriglyceridemia and low high-density lipoprotein cholesterol (HDL-C) levels. These fibrates enhance fatty acid metabolism in the liver and skeletal muscle by activating PPARα, and their cardioprotective effects have been validated in numerous clinical studies. Recent research highlights that fibrates improve insulin resistance, regulate lipid metabolism, correct energy metabolism imbalances, and inhibit the proliferation and migration of vascular smooth muscle and endothelial cells, thereby ameliorating pathological remodeling of the cardiovascular system and reducing blood pressure. Given the substantial attention to PPARα-targeted interventions in both basic research and clinical applications, activating PPARα may serve as a key therapeutic strategy for managing cardiovascular conditions such as myocardial hypertrophy, atherosclerosis, ischemic cardiomyopathy, myocardial infarction, diabetic cardiomyopathy, and heart failure. This review comprehensively examines the regulatory roles of PPARα in cardiovascular diseases and evaluates its clinical application value, aiming to provide a theoretical foundation for further development and utilization of PPARα-related therapies in CVD treatment.

Objective To investigate the effect of rats'injuries and its mechanism caused by specific dose of radiation combined with decompression exposure.Methods 81 male SD rats were randomly divided into control group(n=9),radiation group(n=18),radiation+low-load decompression group(n=18),radiation+medium-load decompression group(n=18),and radiation+high-load decompression group(n=18).In addition to control group,the rats were irradiated with 60Co γ rays at 4 Gy and then underwent rapid escape experiments.The high-pressure exposure schemes were to stay underwater 57 m for 30 min,45 min or 60 min and reduce to normal pressure within(30±5)s,respectively.The high-pressure exposure was not carried out in radiation group.The behavior and death of rats in each group were observed 0.5 h after leaving the cabin.Blood(abdominal aorta)and lung tissues were collected at 3 h and 72 h,respectively.The changes of lung wet-dry weight ratio(W/D),lung pathology and serum levels of interleukin(IL)-1β,IL-6,tumor necrosis factor-α(TNF-α),superoxide dismutase(SOD),malondialdehyde(MDA),nitric oxide(NO),intercellular adhesion molecule-1(ICAM-1)and thromboxane B2(TXB2)were analyzed.Results Compared with control group and radiation group,radiation+low-load decompression group showed no significant difference in the injury and death rate of rats(P＞0.05),while radiation+medium-load decompression group and radiation+high-load decompression group showed significantly increase of the injury and death rate of rats(P＜0.05).Compared with control group,other groups showed no significant change in pulmonary W/D at 3 h(P＞0.05),and increased at 72 h(P＜0.05).HE staining showed that compared with control group,radiation group showed mild lung interstitial edema,while radiation+low-load decompression group showed obvious pulmonary tissue edema and a small number of red blood cells exudated in the alveolar cavity.The edema,congestion and inflammatory cell infiltration of lung tissue were more serious in radiation+medium-load decompression group and radiation+high-load decompression group.Compared with control group and radiation group,all radiation+decompression groups showed an increase in serum levels of IL-1β,IL-6,TNF-α,MDA,NO,ICAM-1 and TXB2(P＜0.05),and a decrease in SOD activity(P＜0.05).Compared with radiation+low-load decompression group,radiation+medium-load decompression group and radiation+high-load decompression group showed increase in serum levels of IL-1β,IL-6,MDA,ICAM-1 and TXB2(P＜0.05),and decrease in activity of SOD(P＜0.05).Except for control group,serum levels of IL-1β,IL-6,TNF-α,MDA,NO,ICAM-1 and TXB2 were decreased at 72 h compared with 3 h(P＜0.05),and SOD activity was increased at 72 h in all groups(P＜0.05).Conclusions High-load decompression can increase the injury and death rate of rats exposed to radiation and high pressure.The potential mechanism of the combined injury effect of radiation and decompression was related to inflammation,immune stress,oxidative damage,vasomotor activity and coagulation mechanism.

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

Lung cancer is the leading type of cancer death, and most patients with lung cancer are diagnosed at an advanced stage and have a very poor prognosis. Although low-dose computed tomography (LDCT) has entered the clinic as a screening tool for lung cancer, its false-positive rate is more than 90%. As one of the epigenetic modifications of research hotspots, DNA methylation plays a key role in a variety of diseases, including cancer.Hypermethylation of tumor suppressor genes and hypomethylation of proto-oncogenes are important events in tumorigenesis and development. Therefore, DNA methylation analysis can provide some useful information for the early screening, diagnosis, treatment and prognosis of lung cancer. Although invasive methods such as tissue biopsy remain the gold standard for tumor diagnosis and monitoring, they also have limitations such as inconvenience in sampling. In recent years, there has been a rapid development of liquid biopsy, which can detect primary or metastatic malignancies and reflect the heterogeneity of tumors. In addition, the blood sample can be collected in a minimally invasive or non-invasive format and is well tolerated in older and frail patients. This article explores some of the emerging technologies for DNA methylation analysis and provides an overview of the application of DNA methylation in the diagnosis and treatment of lung cancer.

Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To establish a machine learning radiomics model that can accurately predict MRI enhancement patterns of glioma based on T2 fluid attenuated inversion recovery(T2-FLAIR)images for optimizing the workflow of magnetic resonance imaging(MRI)examinations of glioma patients.Methods We retrospectively collected preoperative MR T2-FLAIR images from 385 patients with pathologically confirmed glioma,who were divided into enhancing and non-enhancing groups according to the enhancement pattern.Predictive radiomics models were established using Gaussian Process,Linear Regression,Linear Regression-Least absolute shrinkage and selection operator,Support Vector Machine,Linear Discriminant Analysis or Naive Bayes as the classifiers in the training cohort(n=201)and tested both in the internal(n=85)and external validation cohorts(n=99).The receiver-operating characteristic curve was used to assess the predictive performance of the models.Results The predictive model constructed based on 15 radiomics features using Gaussian Process as the classifier had the best predictive performance in both the training cohort and the internal validation cohort,with areas under the curve(AUC)of 0.88(95%CI:0.81-0.94)and 0.80(95%CI:0.71-0.88),respectively.In the external validation cohort,the model showed an AUC of 0.81(95%CI:0.71-0.90)with sensitivity,specificity,positive predictive value and negative predictive value of 0.98,0.61,0.76 and 0.96,respectively.Conclusion The T2-FLAIR-based machine learning radiomics model can accurately predict the enhancement pattern of gliomas on MRI.

Objective To summarize the reasons and management strategies of reoperation after oblique lateral interbody fusion(OLIF),and put forward preventive measures.Methods From October 2015 to December 2019,23 patients who under-went reoperation after OLIF in four spine surgery centers were retrospectively analyzed.There were 9 males and 14 females with an average age of(61.89±8.80)years old ranging from 44 to 81 years old.The index diagnosis was degenerative lumbar intervertebral dics diseases in 3 cases,discogenie low back pain in 1 case,degenerative lumbar spondylolisthesis in 6 cases,lumbar spinal stenosis in 9 cases and degenerative lumbar spinal kyphoscoliosis in 4 cases.Sixteen patients were primarily treated with Stand-alone OLIF procedures and 7 cases were primarily treated with OLIF combined with posterior pedicle screw fixation.There were 17 cases of single fusion segment,2 of 2 fusion segments,4 of 3 fusion segments.All the cases underwent reoperation within 3 months after the initial surgery.The strategies of reoperation included supplementary posterior pedicle screw instrumentation in 16 cases;posterior laminectomy,cage adjustment and neurolysis in 2 cases,arthroplasty and neuroly-sis under endoscope in 1 case,posterior laminectomy and neurolysis in 1 case,pedicle screw adjustment in 1 case,exploration and decompression under percutaneous endoscopic in 1 case,interbody fusion cage and pedicle screw revision in 1 case.Visu-al analogue scale(VAS)and Oswestry disability index(ODI)index were used to evaluate and compare the recovery of low back pain and lumbar function before reoperation and at the last follow-up.During the follow-up process,the phenomenon of fusion cage settlement or re-displacement,as well as the condition of intervertebral fusion,were observed.The changes in in-tervertebral space height before the first operation,after the first operation,before the second operation,3 to 5 days after the second operation,6 months after the second operation,and at the latest follow-up were measured and compared.Results There was no skin necrosis and infection.All patients were followed up from 12 to 48 months with an average of(28.1±7.3)months.Nerve root injury symptoms were relieved within 3 to 6 months.No cage transverse shifting and no dislodgement,loosening or breakage of the instrumentation was observed in any patient during the follow-up period.Though the intervertebral disc height was obviously increased at the first postoperative,there was a rapid loss in the early stage,and still partially lost after reopera-tion.The VAS for back pain recovered from(6.20±1.69)points preoperatively to(1.60±0.71)points postoperatively(P＜0.05).The ODI recovered from(40.60±7.01)％preoperatively to(9.14±2.66)％postoperatively(P＜0.05).Conclusion There is a risk of reoperation due to failure after OLIF surgery.The reasons for reoperation include preoperative bone loss or osteoporosis the initial surgery was performed by Stand-alone,intraoperative endplate injury,significant subsidence of the fusion cage after surgery,postoperative fusion cage displacement,nerve damage,etc.As long as it is discovered in a timely manner and handled properly,further surgery after OLIF surgery can achieve better clinical results,but prevention still needs to be strengthened.

Objective To develop a risk prediction model based on the influencing factors of chemotherapy-in-duced oral mucositis in patients with hematologic tumors,and to verify the effect.Methods A case-control study was conducted to retrospectively collect the data of 444 adult patients with hematological malignancies who under-went chemotherapy in the hematology department of a tertiary hospital in Wuhan from March 2021 to January 2022.Univariate analysis,collinearity diagnosis and binary logistic regression analysis were used to explore influenc-ing factors for chemotherapy-induced oral mucositis in patients with hematologic tumors,and then a prediction mod-el which was presented with nomogram was constructed.Receiver operating characteristic,Hosmer Lemeshow and calibration charts were used to verify the predictive effect of the model,and Bootstrap resampling method was used for internal verification of the prediction model.The clinical data of 171 patients with hematologic malignancies who underwent chemotherapy in the hematology department of 2 tertiary hospitals in Wuhan from February to Oc-tober 2022 were prospectively collected for external verification.Results History of oral lesions,treatment methods,high-dose chemotherapy,vomiting,combined chemo-therapy,platelet count,and hemoglobin were independent predic-tive factors of chemotherapy-induced oral mucositis in patients with blood tumors.The area under the curve of the model was 0.822,and Hosmer-Lemeshow test P=0.602.After internal verification and external verification,the area under the curve of the model was 0.813 and 0.735,respectively.Hosmer-Lemeshow test were 0.115 and 0.820,re-spectively.These calibration charts showed that the observed results of the model were consistent with the predicted results.Conclusion The risk prediction model of chemotherapy-induced oral mucositis in hematologic tumor pa-tients has good predictive performance and extrapolation,which is an effective screening tool to help medical staff identify the high-risk patients with chemotherapy-induced oral mucositis early.