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Rheumatoid arthritis (RA) is a chronic inflammatory disease which is closely related to genetic background. Single‐nucleotide polymorphisms (SNPs) have been found to play an important role in the development of RA. This study intends to investigate the links between gene polymorphisms in the interleukin‐23 receptor (IL23R) and interleukin 17A (IL17A) and susceptibility to RA in the Western Chinese Han population. Four SNPs (rs6693831 T > C, rs1884444 G > T, and rs7517847 T > G in IL23R gene, and rs2275913 G > A in IL17A gene) were genotyped in 246 RA patients and 362 healthy controls by high resolution melting analysis. The comparative analyses among genotype distributions, clinical indicators, and IL‐17A and IL‐23R levels in RA patients were also performed. The study revealed that the SNP rs6693831 and rs1884444 of IL23R had a significant association with RA susceptibility. The frequencies of rs6693831 genotype CC and allele C were significantly higher in the RA group and associated with higher RA risk compared with genotype TT and allele T (OR = 7.797, 95% confidence interval [CI] = 4.072–14.932 and OR = 5.984, 95%CI = 3.190–11.224, respectively). The TT genotype of rs1884444 appeared to decrease the RA risk compared with the GG genotype (OR =.251, 95%CI =.118–.536). The genotype CC and allele C of rs6693831 and the genotype GG and allele G of rs1884444 may be risk factors for RA. IL23R gene polymorphisms may be involved in the risk of RA susceptibility in the Western Chinese Han population. [ABSTRACT FROM AUTHOR]

T cells are key drivers of the pathogenesis of autoimmune diseases by producing cytokines, stimulating the generation of autoantibodies, and mediating tissue and cell damage. Distinct mitochondrial metabolic pathways govern the direction of T-cell differentiation and function and rely on specific nutrients and metabolic enzymes. Metabolic substrate uptake and mitochondrial metabolism form the foundational elements for T-cell activation, proliferation, differentiation, and effector function, contributing to the dynamic interplay between immunological signals and mitochondrial metabolism in coordinating adaptive immunity. Perturbations in substrate availability and enzyme activity may impair T-cell immunosuppressive function, fostering autoreactive responses and disrupting immune homeostasis, ultimately contributing to autoimmune disease pathogenesis. A growing body of studies has explored how metabolic processes regulate the function of diverse T-cell subsets in autoimmune diseases such as systemic lupus erythematosus (SLE), multiple sclerosis (MS), autoimmune hepatitis (AIH), inflammatory bowel disease (IBD), and psoriasis. This review describes the coordination of T-cell biology by mitochondrial metabolism, including the electron transport chain (ETC), oxidative phosphorylation, amino acid metabolism, fatty acid metabolism, and one‑carbon metabolism. This study elucidated the intricate crosstalk between mitochondrial metabolic programs, signal transduction pathways, and transcription factors. This review summarizes potential therapeutic targets for T-cell mitochondrial metabolism and signaling in autoimmune diseases, providing insights for future studies. • Mitochondrial metabolism emerged as potential modulators of T-cell differentiation. • T cells in autoimmune disorders exhibit a disease-specific metabolic signature. • Targeting mitochondrial metabolism may restore dysfunctional immunity. • Novel drug delivery strategies targeting precise T-cell subsets are needed. • T-cell mitochondrial metabolism is promising to treat autoimmunity. [ABSTRACT FROM AUTHOR]

• The rs73620203 locus is correlated with RA susceptibility, and the genotypes carrying the T allele are protective factors of RA. The same findings were illustrated in female RA patients. • Among the three genotypes CC, CT, and TT of rs73620203, serum osteocalcin and BALP levels were significantly lower in the TT genotype than in the CC and CT genotypes, while serum β-CTX levels were not significantly different. • It is inferred that T allele at rs73620203 in TGIF gene may be related to the role of the rs73620203 locus in regulating the TGIF gene expression and thus regulating the function of the Smad signaling pathway. • It is suggested that TGIF gene polymorphisms perhaps can be regarded as novel markers for predicting the risk of developing RA. To explore the association of single nucleotide polymorphisms (SNPs) in the transforming growth interacting factor (TGIF) gene with bone metabolism markers and rheumatoid arthritis (RA) susceptibility. Three SNPs were genotyped in 155 RA patients and 168 healthy controls using high-resolution melting (HRM) analysis. The serum levels of osteocalcin, bone alkaline phosphatase (BALP), and β type I collagen-crosslinked C telopeptide (β-CTX) were detected using electrochemical luminescence in 108 patients randomly selected from the RA group. Genotype and allele frequency analysis showed that rs73620203 was associated with bone erosion in RA (P = 0.012 and P = 0.003, respectively), and individuals carrying the T allele for rs73620203 showed a decreased RA risk (OR = 0.59, 95% CI = 0.42–0.84; P = 0.003). In sex-specific analysis, the rs73620203 polymorphism was associated with susceptibility to RA in women (P = 0.022 and P = 0.006, respectively). In addition, RA patients with three genotypes at the rs73620203 locus showed significant differences in serum osteocalcin and BALP (P = 0.006 and P = 0.037, respectively). Haplotype analysis revealed that the haploid ATG and GCA frequencies were significantly lower in the RA group (P = 0.036, OR = 0.693; P = 0.002, OR = 0.189, respectively), while the haploid ACA frequency of the RA group was enhanced (P < 0.01, OR = 5.058). Our study provides the first evidence that rs73620203 is associated with RA susceptibility and the relationship between TGIF gene SNPs and the regulation of bone metabolism in RA patients. [ABSTRACT FROM AUTHOR]

Portal hypertension is a common manifestation in late-to-end-stage liver diseases and can cause severe complications such as ascites, hepatic encephalopathy, etc. However, an early diagnosis of portal hypertension is often difficult as it can be asymptomatic. Though the gold standard to diagnose portal hypertension is hepatic vein catheterization, ultrasound elastography is regarded as a noninvasive alternative that can be used to accurately predict portal hypertension and a few further complications such as gastro-esophageal varices. Since ultrasound elastography is available in most medical centers, and is cheaper and noninvasive, studying its function in predicting portal hypertension is of paramount importance. Therefore, this review generalized the results of recently published articles in order to establish the indicators that were related to diagnostic and prediction efficiency. Our study found that various technologies of ultrasound elastography could be used to predict portal hypertension with satisfactory diagnostic sensitivity, specificity, accuracy, and AUC. Meanwhile, we also recognized similar diagnostic efficiency of ultrasound elastography in gastro-esophageal varices. [ABSTRACT FROM AUTHOR]

Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54–0.9), and five studies reported sensitivity (range, 60– 90%) and specificity (range, 64 − 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting. [ABSTRACT FROM AUTHOR]