BACKGROUND:The exact pathogenesis of temporomandibular joint osteoarthritis is currently unclear.Traditional clinical treatment strategies for temporomandibular joint osteoarthritis are symptomatic treatments such as pain relief and reduction of inflammation,which can stop the progression of the disease to a certain degree but cannot reverse the destruction of the cartilage.Cartilage degeneration,as one of the most prominent pathologic features in the development of temporomandibular joint osteoarthritis,has been the subject of an increasing number of studies that focus on its pathogenesis.Consequently,we hope to provide an ideal radical solution for the regeneration of the temporomandibular joint.OBJECTIVE:To review the progress of research on cartilage degeneration in temporomandibular joint osteoarthritis.METHODS:The search terms were"temporomandibular joint osteoarthritis,degradation of cartilage matrix,synovitis,oxidative stress,chondrocyte hypertrophy,chondrocyte apoptosis,ferroptosis,autophagy,angiogenesis,extracellular vesicles"in Chinese and English.Literature search was conducted in PubMed database and CNKI,and the time limit for the search was from January 2004 to October 2024.Screening was performed by analyzing and reading the literature,and according to the inclusion and exclusion criteria,81 papers were finally included for review.RESULTS AND CONCLUSION:(1)Increased secretion of cartilage matrix degrading enzymes causes degradation of the cartilage matrix,leading to cartilage degeneration.(2)Synovitis promotes cartilage degeneration through macrophage M1-type polarization and production of inflammatory mediators.(3)Oxidative stress promotes cartilage degeneration by exacerbating the inflammatory response through overproduction of reactive oxygen species.(4)Chondrocyte phenotypic changes and death lead to the decrease of cartilage matrix synthesis,resulting in cartilage degeneration.(5)Blood vessels of subchondral bone penetrate the calcified cartilage layer to reach the superficial cartilage layer,which destroys the cartilage structure and leads to cartilage degeneration.(6)Bioactive substances carried by serum-derived extracellular vesicles in inflammatory states also promote cartilage degeneration in temporomandibular joint osteoarthritis.

BACKGROUND:The exact pathogenesis of temporomandibular joint osteoarthritis is currently unclear.Traditional clinical treatment strategies for temporomandibular joint osteoarthritis are symptomatic treatments such as pain relief and reduction of inflammation,which can stop the progression of the disease to a certain degree but cannot reverse the destruction of the cartilage.Cartilage degeneration,as one of the most prominent pathologic features in the development of temporomandibular joint osteoarthritis,has been the subject of an increasing number of studies that focus on its pathogenesis.Consequently,we hope to provide an ideal radical solution for the regeneration of the temporomandibular joint.OBJECTIVE:To review the progress of research on cartilage degeneration in temporomandibular joint osteoarthritis.METHODS:The search terms were"temporomandibular joint osteoarthritis,degradation of cartilage matrix,synovitis,oxidative stress,chondrocyte hypertrophy,chondrocyte apoptosis,ferroptosis,autophagy,angiogenesis,extracellular vesicles"in Chinese and English.Literature search was conducted in PubMed database and CNKI,and the time limit for the search was from January 2004 to October 2024.Screening was performed by analyzing and reading the literature,and according to the inclusion and exclusion criteria,81 papers were finally included for review.RESULTS AND CONCLUSION:(1)Increased secretion of cartilage matrix degrading enzymes causes degradation of the cartilage matrix,leading to cartilage degeneration.(2)Synovitis promotes cartilage degeneration through macrophage M1-type polarization and production of inflammatory mediators.(3)Oxidative stress promotes cartilage degeneration by exacerbating the inflammatory response through overproduction of reactive oxygen species.(4)Chondrocyte phenotypic changes and death lead to the decrease of cartilage matrix synthesis,resulting in cartilage degeneration.(5)Blood vessels of subchondral bone penetrate the calcified cartilage layer to reach the superficial cartilage layer,which destroys the cartilage structure and leads to cartilage degeneration.(6)Bioactive substances carried by serum-derived extracellular vesicles in inflammatory states also promote cartilage degeneration in temporomandibular joint osteoarthritis.

ObjectiveTo investigate the relationship between the vasodilation and hydrogen sulfide （H₂S） mechanism of total flavonoids of chrysanthemum （TFCC） and the large conductance Ca²⁺- activated K⁺ （BK_Ca） channels on vascular smooth muscle cells （VSMCs） of the middle cerebral artery in rats. In addition， this study will also investigate the effect of TFCC on the expression of BK_Ca channel alpha protein in rat middle cerebral artery VSMCs. MethodsThe primary method employed was acute digestion to isolate VSMCs from the middle cerebral artery of rats； whole-cell patch-clamp techniques were used to measure BK_Ca channel currents； primary tissue adherence culture was utilized to cultivate VSMCs from the middle cerebral artery of rats； Western blot were employed to determine protein expression levels. ResultsIn whole-cell patch-clamp experiments， both the H₂S donor NaHS （100 μmol/L） and endogenous H₂S enhanced BK_Ca channel currents. TFCC （30， 90， and 270 mg/L） also activatedBK_Ca channels and exhibited a certain concentration-dependent effect. Even after blocking endogenous H₂S production， TFCC （270 mg/L） still activated BK_Ca channels in VSMCs of the middle cerebral artery in rats. In Western blot experiments， the α-subunit of BK_Ca channel proteins was expressed in all groups of cells， but TFCC （30， 90， and 270 mg/L） and inhibitor IBTX group did not affect the expression of channel protein content.Conclusion TFCC can promote the opening of BK_Ca channels by promoting the generation of endogenous H₂S， or directly activate BK_Ca channels， thereby playing a role in relaxing cerebral blood vessels. However， TFCC had no significant effect on the expression of BK_Ca channel proteins.

ObjectiveTo investigate the associations of multiple glycolipid metabolic indicators and their cumulative abnormality burden with incident colorectal cancer risk in a general population-based prospective cohort, and to examine the mediating role of glycolipid metabolic abnormalities in the relationships between smoking, alcohol consumption, physical activity, and colorectal cancer incidence. MethodsA total of 17 897 eligible participants recruited from the Taizhou Cohort between 2011 and 2014 were included. Cox proportional hazards regression models were used to assess the associations of conventional and derived glycolipid indicators, as well as a glycolipid abnormality index constructed from total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, fasting plasma glucose, and insulin, with incident colorectal cancer risk. Restricted cubic spline models were applied to evaluate dose-response relationships for major indicators. Receiver operating characteristic curves were generated to compare predictive performance across indicators. Mediation analyses were conducted to assess the mediating effects of the glycolipid abnormality index on the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence. ResultsDuring a median follow-up of 11.2 years, 102 incident colorectal cancer cases were identified, with an incidence density of 51.2 per 100 000 person-years. After adjustment for potential confounders, Cox proportional hazards regression analyses showed that decreased high-density lipoprotein cholesterol was associated with a 1.74-fold higher risk of colorectal cancer (HR=1.74, 95%CI: 1.03‒2.95), elevated low-density lipoprotein cholesterol was associated with a 1.78-fold higher risk (HR=1.78, 95%CI: 1.05‒3.02), and abnormal fasting plasma glucose was associated with a 1.86-fold higher risk (HR=1.86, 95%CI: 1.15‒3.02). Triglycerides and fasting plasma glucose showed an approximately linear increasing association with colorectal cancer risk in multivariable restricted cubic spline models. The glycolipid abnormality index showed a clear gradient association with colorectal cancer risk; participants with three or more abnormal indicators had a 3.08-fold higher risk than those without abnormalities (HR=3.08, 95%CI: 1.60‒5.92). The area under the curve was 0.795, higher than that of individual glycolipid indicators and other combined indices, and gender-stratified analyses showed generally consistent patterns. Glycolipid metabolic abnormalities partially mediated the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence, with mediation proportions of 8.33%, 9.45%, and 9.04%, respectively. ConclusionGlycolipid metabolic abnormalities are associated with an increased risk of incident colorectal cancer. The glycolipid abnormality index shows an increasing relationship with colorectal cancer risk and demonstrates better discrimination, and it partially mediates the associations of smoking, alcohol consumption, and physical activity with colorectal cancer incidence in the overall population.

The plants of the genus Caragana Fabr. are desert plants of the Leguminosae family, which are widely used in traditional ethnomedicine, with the effects of nourishing Yin and nourishing blood, dispelling wind and removing dampness, clearing heat and detoxifying toxins. The anti-inflammatory effect of Caragana Fabr. has attracted much attention, the research on the related mechanism has made some progress, but it lacks systematic collation. By summarising the anti-inflammatory effects of the active ingredients of Caragana Fabr., the authors found that they have good anti-inflammatory activities on different inflammatory cell models in vitro, and have good improvement effects on rheumatoid arthritis, colitis, complex nephritis, and acute lung injury and other disease models. The anti-inflammatory effects of the active components of this genus mainly include the reduction of the levels of a variety of pro-inflammatory factors, and the signalling pathways involved mainly include TLR4/NF-κB, TLR4/MAPK, TLR4/NF-κB/IRF3, JAK/STAT-1, ERK/STAT-1 and so on. Therefore, the paper mainly reviews the research progress on the anti-inflammatory effects and mechanisms of the Caragana Fabr. plants and their active components according to disease types, with a view to providing reference for the in-depth study of their anti-inflammatory activities and the development of new products with related functions.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective:To analyze the risk factors for in-hospital malignant ventricular arrhythmia (MVA) in acute myocardial infarction (AMI) and to construct and validate a risk prediction model.Methods:This study was a retrospective cohort study. Patients aged≥18 years who were admitted to Qilu Hospital of Shandong University with a diagnosis of AMI and underwent coronary angiography (CAG) from May 2016 to March 2023 were selected, and the patients' clinical routine test indicators and CAG results were collected. Univariate and bidirectional stepwise logistic regression were used to screen out the risk factors for constructing the best prediction model. The prediction model was constructed by combining the results of multivariate logistic regression. The Hosmer-Lemeshow test and ROC curve, calibration curve, and decision curve were drawn to evaluate the model. The nomogram was drawn to visualize the model, and the Bootstrap self-sampling method was used for internal validation. The ROC curve was drawn to evaluate the predictive performance of each risk factor and prediction model. Finally, a multicollinearity test was performed.Results:Among the 4 205 patients finally included in the study, 115 patients (2.735%) developed MVA during hospitalization. The predictive factors screened out included age (X1), diastolic blood pressure (X2), respiratory rate (X3), blood glucose (X4), serum potassium (X5), logarithmic NT-proBNP (X6), myocardial infarction type (NSTEMI=X7, unclassified=X8), J wave (X9), Killip grade (Ⅱ=X10, Ⅲ=X11, Ⅳ=X12), and the regression equation was ln(p/1-p)=-4.699+0.029×X1-0.012×X2+0.059×X3+0.148×X4-1.175×X5+0.866×X6-1.427×X7-0.475×X8+0.758×X9+0.294×X10+0.902×X11+1.815×X12. The area under the ROC curve (AUC) of the model was 0.855 (95% CI: 0.816-0.894), and the Hosmer-Lemeshow test ( χ2=14.178, P=0.077) and the calibration curve showed that the predicted probability was consistent with the actual probability. The probability threshold of 0% to 65% had a better clinical net benefit. The area under the internal validation ROC curve (AUC) was 0.855, 95% CI: 0.813-0.891. The prediction performance of the nine variables was stronger than that of any single variable. There was no multicollinearity between the variables. Conclusions:Age, diastolic blood pressure, respiratory rate, blood glucose, serum potassium, NT-proBNP, type of AMI, J wave, and Killip class are forecasting indicator for in-hospital MVA in AMI. The risk prediction model based on the above factors has good predictive performance.