BACKGROUND:As a common disease in middle-aged and elderly,osteoarthritis is difficult to cure,and the pathogenesis is not clear.Long non-coding RNA participates in the pathogenesis of osteoarthritis through many ways,such as regulating translation,promoting or inhibiting mRNA,and adsorbing miRNAs. OBJECTIVE:To review the types of common long non-coding RNA in osteoarthritis,and the influence of multiple long non-coding RNAs on the pathological factors related to osteoarthritis,to analyze the future application of long non-coding RNAs in osteoarthritis. METHODS:Literature retrieval was conducted in CNKI,WanFang Data,VIP database,PubMed,Web of Science and Sciencedirect databases,using the search terms of"osteoarthritis,degenerative joint disease,degenerative arthritis,OA,LncRNA,long non-coding RNA,long noncoding RNA,long intergenic non-coding RNA"in Chinese and English.All relevant literature published from 1976 and May 2024 was retrieved.After literature screening,induction,analysis and summary,93 articles were finally included for review. RESULTS AND CONCLUSION:This review collected 25 long non-coding RNAs that are well studied with osteoarthritis.Long non-coding RNAs,as a molecular sponge for miRNA,are competing endogenous RNAs to competitively adsorb miRNAs and then affect downstream targets.Long non-coding RNAs can regulate physiopathological processes such as chondrocyte apoptosis and proliferation,cartilage extracellular matrix degradation,and inflammatory responses.Long non-coding RNAs are expected to become a biomarker and potential therapeutic target for the clinical diagnosis and therapeutic prognosis of osteoarthritis,and it may become a new strategy for the clinical treatment of osteoarthritis in the future.

Objective:Exploring the association between Life's Crucial 9 (LC9) and the risk of thyroid dysfunction (TD), as well as its potential predictive capacity.Methods:A total of 247 600 TD-free participants from the UK Biobank were enrolled in the study. The LC9 score was divided into three CVH groups: low (0-), medium (50-), and high (80-100). Cox proportional hazards regression models were used to calculate the HRs and 95% CIs of the risk of TD with LC9 CVH status. Calculate Harrell's concordance index ( C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to evaluate the predictive ability of the LC9 score and Life's Essential 8 (LE8) score. Results:During a median follow-up of 12.3 years, 5 515, 911, and 4 869 new cases of TD, hyperthyroidism, and hypothyroidism were documented, respectively. Participants with a high LE8 CVH group had 57.00% ( HR=0.43, 95% CI: 0.38-0.49), 55.00% ( HR=0.45, 95% CI: 0.34-0.60), and 58.00% ( HR=0.42, 95% CI: 0.37-0.47) lower risk of TD, hyperthyroidism, and hypothyroidism, respectively, than those with low CVH group. Compared with the LE8 score, the improvement in C-index for the LC9 score predicted TD risk was 0.004 (95% CI: 0.001-0.007), the NRI was 0.101 (95% CI: 0.021-0.103), and the IDI was 0.001 (95% CI: 0.000-0.001). Conclusions:The better CVH status, defined by LC9, was associated with a lower risk of TD. Compared to the LE8 score, the LC9 score demonstrated a significant enhancement in both risk discrimination and reclassification capability for TD risk.

Objective:To explore the clinical application value of serum N-glycan profiles for evaluating the severity of liver tissue inflammation in patients with chronic hepatitis B (CHB).Methods:A total of 221 CHB patients who underwent liver biopsy at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2018 to December 2020 were retrospectively enrolled. The Scheuer scoring system was used to assess the histological inflammation grade of the liver tissue. Serum N-glycan levels were measured using DNA sequencer-assisted N-glycan fingerprinting (NGFP). Using the upper limit of the alanine aminotransferase (ALT) reference value (40 U/L) as a cutoff, logistic regression models were developed to construct diagnostic models under two scenarios: normal ALT or abnormal ALT. Models based on serum N-glycan levels and serum N-glycan levels combined with routine laboratory indicators, were used to non-invasively evaluation of various pathological grades of liver tissue inflammation in CHB patients. The DeLong test was used to compare the diagnostic efficacy of the models by analyzing the areas under the receiver operating characteristic curve (AUC). Glycosylation-related gene expression differences associated with varying degrees of liver inflammation were analyzed using the Gene Expression Omnibus (GEO) database.Results:In CHB patients with normal ALT level, the relative abundances of N-glycan structure peak 1 (NGA2F) and peak 2 (NGA2FB) increased with higher liver inflammation grades, while the relative abundance of peak 5 (NA2) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G A) and its enhanced version (HIS-G A Plus) for identifying significant inflammation and necrosis (≥G2, indicating the initiation of antiviral therapy) were 0.805 (95% CI 0.690-0.899) and 0.904 (95% CI 0.821-0.960), respectively. In CHB patients with ALT>40 U/L, the relative abundances of peaks 1 (NGA2F), 2 (NGA2FB), and 3 (NG1A2F) increased with higher liver inflammation grades, while the relative abundances of peaks 8 (NA3) and 11 (NA4) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G B) and its enhanced version (HIS-G B Plus) for identifying significant inflammation (≥G2) were 0.810 (95% CI 0.727-0.889) and 0.838 (95% CI 0.754-0.901), respectively. With increasing liver inflammation grades, the expression levels of four glycosyltransferase genes (CHST4, FUT8, SLC51B, and ST8SIA4) were significantly upregulated ( P<0.05). Conclusions:Serum N-glycan biomarker models can be used to assist in evaluating the severity of liver tissue inflammation in CHB patients with both normal and abnormal ALT levels.

Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

Objective:Exploring the association between Life's Crucial 9 (LC9) and the risk of thyroid dysfunction (TD), as well as its potential predictive capacity.Methods:A total of 247 600 TD-free participants from the UK Biobank were enrolled in the study. The LC9 score was divided into three CVH groups: low (0-), medium (50-), and high (80-100). Cox proportional hazards regression models were used to calculate the HRs and 95% CIs of the risk of TD with LC9 CVH status. Calculate Harrell's concordance index ( C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to evaluate the predictive ability of the LC9 score and Life's Essential 8 (LE8) score. Results:During a median follow-up of 12.3 years, 5 515, 911, and 4 869 new cases of TD, hyperthyroidism, and hypothyroidism were documented, respectively. Participants with a high LE8 CVH group had 57.00% ( HR=0.43, 95% CI: 0.38-0.49), 55.00% ( HR=0.45, 95% CI: 0.34-0.60), and 58.00% ( HR=0.42, 95% CI: 0.37-0.47) lower risk of TD, hyperthyroidism, and hypothyroidism, respectively, than those with low CVH group. Compared with the LE8 score, the improvement in C-index for the LC9 score predicted TD risk was 0.004 (95% CI: 0.001-0.007), the NRI was 0.101 (95% CI: 0.021-0.103), and the IDI was 0.001 (95% CI: 0.000-0.001). Conclusions:The better CVH status, defined by LC9, was associated with a lower risk of TD. Compared to the LE8 score, the LC9 score demonstrated a significant enhancement in both risk discrimination and reclassification capability for TD risk.

Objective To explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

Objective:To explore the clinical application value of serum N-glycan profiles for evaluating the severity of liver tissue inflammation in patients with chronic hepatitis B (CHB).Methods:A total of 221 CHB patients who underwent liver biopsy at Mengchao Hepatobiliary Hospital of Fujian Medical University from January 2018 to December 2020 were retrospectively enrolled. The Scheuer scoring system was used to assess the histological inflammation grade of the liver tissue. Serum N-glycan levels were measured using DNA sequencer-assisted N-glycan fingerprinting (NGFP). Using the upper limit of the alanine aminotransferase (ALT) reference value (40 U/L) as a cutoff, logistic regression models were developed to construct diagnostic models under two scenarios: normal ALT or abnormal ALT. Models based on serum N-glycan levels and serum N-glycan levels combined with routine laboratory indicators, were used to non-invasively evaluation of various pathological grades of liver tissue inflammation in CHB patients. The DeLong test was used to compare the diagnostic efficacy of the models by analyzing the areas under the receiver operating characteristic curve (AUC). Glycosylation-related gene expression differences associated with varying degrees of liver inflammation were analyzed using the Gene Expression Omnibus (GEO) database.Results:In CHB patients with normal ALT level, the relative abundances of N-glycan structure peak 1 (NGA2F) and peak 2 (NGA2FB) increased with higher liver inflammation grades, while the relative abundance of peak 5 (NA2) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G A) and its enhanced version (HIS-G A Plus) for identifying significant inflammation and necrosis (≥G2, indicating the initiation of antiviral therapy) were 0.805 (95% CI 0.690-0.899) and 0.904 (95% CI 0.821-0.960), respectively. In CHB patients with ALT>40 U/L, the relative abundances of peaks 1 (NGA2F), 2 (NGA2FB), and 3 (NG1A2F) increased with higher liver inflammation grades, while the relative abundances of peaks 8 (NA3) and 11 (NA4) decreased ( P<0.05). The AUCs of the HIS-G model (HIS-G B) and its enhanced version (HIS-G B Plus) for identifying significant inflammation (≥G2) were 0.810 (95% CI 0.727-0.889) and 0.838 (95% CI 0.754-0.901), respectively. With increasing liver inflammation grades, the expression levels of four glycosyltransferase genes (CHST4, FUT8, SLC51B, and ST8SIA4) were significantly upregulated ( P<0.05). Conclusions:Serum N-glycan biomarker models can be used to assist in evaluating the severity of liver tissue inflammation in CHB patients with both normal and abnormal ALT levels.

Objective: To explore the neuroprotective effects of the Shaoyao Gancao decoction (SGD) against excitatory damage in PC12 cells and the role of the Src-NR2-nNOS pathway mediation by SGD in regulating γ-aminobutyric acid (GABA)-glutamate (Glu) homeostasis. Methods: N-Methyl-d-aspartic acid (NMDA) was used to establish a PC12 cell excitability injury model. To investigate the neuroprotective effect of SGD, a cell counting kit-8 (CCK-8) assay was used to determine PC12 cell viability, Annexin V/Propidium Iodide (Annexin V/PI) double staining was used to determine PC12 cell apoptosis, and Ca2+ concentration was observed using laser confocal microscopy. GABA receptor agonists and antagonists were used to analyze the neuroprotective interactions between γ-aminobutyric acid (GABA) and NMDA receptors. Additionally, molecular biology techniques were used to determine mRNA and protein expression in the Src-NR2-nNOS pathway. We analyzed the correlations between the regulatory sites of GABA and NMDA interactions, excitatory neurotoxicity, and brain damage at the molecular level. Results: NMDA excitotoxic injury manifested as a significant decrease in cell activity, increased apoptosis and caspase-3 protein expression, and a significant increase in intracellular Ca2+ concentration. Administration of SGD, a GABAA receptor agonist (muscimol), or a GABAB receptor agonist (baclofen) decreased intracellular Ca2+ concentrations, attenuated apoptosis, and reversed NMDA-induced upregulation of caspase-3, Src, NMDAR2A, NMDAR2B, and nNOS. Unexpectedly, a GABAA receptor antagonist (bicuculline) and a GABAB receptor antagonist (saclofen) failed to significantly increase excitatory neurotoxicity. Conclusions Taken together, these results not only provide an experimental basis for SGD administration in the clinical treatment of central nervous system injury diseases, but also suggest that the Src-NR2A-nNOS pathway may be a valuable target in excitotoxicity treatment.

Objective: To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine (TCM) pulse conditions through voice signals. Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning. Audio features were extracted from voice recordings in the TCM pulse condition dataset. The obtained features were combined with information from tongue and facial diagnoses. A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation, and the modeling methods were validated using publicly available datasets. Results: The analysis showed that the proposed method achieved an accuracy of 92.59% on the public dataset. The accuracies of the three single-label pulse manifestation models in the test set were 94.27%, 96.35%, and 95.39%. The absolute accuracy of the multi-label model was 92.74%. Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Objective To investigate the clinical significance of serum S100 calcium binding protein(S100 A4)and S100A12 in patients with severe traumatic brain injury(sTBI).Methods A total of 147 sTBI pa-tients admitted to Handan Central Hospital from March 2021 to March 2023 were selected as the sTBI group,and 50 healthy subjects who underwent physical examination in Handan Central Hospital during the same pe-riod were selected as the control group.The correlation between S100A4,S100A12 levels and brain injury markers and the influencing factors of early death in sTBI patients were analyzed,and the predictive value of serum S100A4 and S100A12 for early death in sTBI patients was studied.Results The serum levels of S100A4,S100A12,myelin basic protein(MBP),S100B and neuron specific enolase(NSE)in sTBI group were significantly higher than those in control group(P＜0.05).The serum levels of S100A4 and S100A12 were positively correlated with MBP,NSE and S100B in sTBI patients(P＜0.05).Multivariate Logistic regression analysis showed that decreased Glasgow coma scale(GCS)score on admission and increased serum levels of S100A4,S100A12,MBP,NSE and S100B were risk factors for early death in sTBI patients(P＜0.05).Receiv-er operating characteristic curve showed that the combination of serum S100A4 and S100A12 with GCS score,MBP,NSE and S100B was superior to any single detection in predicting early death in sTBI patients.Conclu-sion The serum levels of S100A4 and S100A12 are increased in sTBI patients,which are related to the aggra-vations of brain injury and early death.The combined detection of S100A4 and S100A12 has a good predictive value for early death in sTBI patients.