The efficacy and safety of different Janus kinase inhibitors as monotherapy in rheumatoid arthritis: A Bayesian network meta-analysis.

OBJECTIVE: This study aims to evaluate the efficacy and safety of JAK inhibitors in the treatment of patients with RA. METHODS: The databases CNKI, VIP, Wanfang, CBM, and PubMed, Embase, Cochrane Library and Web of Science were searched to identify relevant randomized controlled trials (RCTs), all from the time of database creation to April 2024. Screening, data extraction, and risk of bias assessment (using Review Manager-5.3 software) were independently performed by at least two authors. The network meta-analysis was conducted using R 4.1.3 software. PROSPERO registration number: CRD42022370444. RESULTS: Thirty-three RCTs included 15,961 patients The experimental groups involved six JAK inhibitors (filgotinib, tofacitinib, decernotinib, baricitinib, upadacitinib and peficitinib) and 12 interventions (different doses of the six JAK inhibitors), and the control group involved adalimumab (ADA) and placebo. Compared with placebo, all JAK inhibitors showed a significant increase in efficacy measures (ACR20/50/70). Compared with ADA, only tofacitinib, low-dose decernotinib, and high-dose peficitinib showed a significant increase in ACR20/50/70. Decernotinib ranked first in the SUCRA ranking of ACR20/50/70. In terms of safety indicators, only those differences between low-dose filgotinib and high-dose upadacitinib, low-dose tofacitinib and high-dose upadacitinib were statistically significant. Low-dose filgotinib ranked first in the SUCRA ranking with adverse events as safety indicators. Only the efficacy and safety of tofacitinib ranked higher among different SUCRA rankings. CONCLUSION: Six JAK inhibitors have better efficacy than placebo. The superior efficacy of decernotinib and safety of low-dose filgotinib can be found in the SUCRA. However, there are no significant differences in safety between the different JAK inhibitors. Head-to-head trials, directly comparing one against each other, are required to provide more certain evidence.

Sema4D as a biomarker for Predicting rheumatoid arthritis disease activity.

OBJECTIVE: The semaphorins are membrane or secreted proteins first identified in neural development. Semaphorin 4D (Sema4D) is the first family member found to have immune properties. We evaluated the potential of Sema4D as a marker for rheumatoid arthritis (RA) disease activity, singly and in combination with other known biomarkers including rheumatoid factor (RF) and C-reactive protein (CRP). METHODS: Three hundred and eleven RA patients were enrolled. The patients were divided into three groups based on their disease activity in 28 joints (DAS28): mild, moderate, and severe. The healthy group included 40 healthy individuals. SerumSema4D was measured by quantitative ELISA and the specificity and sensitivity of biomarkers were evaluated by generating a receiver operating characteristic (ROC) curve to analyze their diagnostic accuracy. RESULTS: Serum Sema4D levels in the moderate and severe RA groups were elevated significantly above those of the controls (P < 0.01), while levels in the mild RA and control groups did not differ significantly (P > 0.05). The Sema4D cutoff threshold was 15.7 ng/ml when the DAS28 was applied as a reference. Compared to the erythrocyte sedimentation rate (ESR and CRP, Sema4D had the highest specificity (96.8%) and area under the curve (0.80) for diagnosing RA activity. The highest specificity (100%) for the biomarker combinations was obtained when Sema4D was combined with CRP and anti-CCP, the combination of the Sema4D combined with ESR and anti-CCP had the highest sensitivity (99.35%). According to this result, a new model for jointly calculating RA activity of Sema4D,anti-CCP and CRP was constructed. Meanwhile another model is established by using the method of multivariate analysis.Model comparison results showed the the multiple regression algorithm method fitted the patients' disease activity better. CONCLUSION: The serum Sema 4D level effectively reflects moderate to severe RA activity. Sema4D levels can be used together with conventional RA biomarkers to increase the diagnostic power of RA activity. The multiple regression algorithm method is promising in disease activity calculation.

The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis.

Objective: Although Leflunomide (LEF) is effective in treating rheumatoid arthritis (RA), there are still a considerable number of patients who respond poorly to LEF treatment. Till date, few LEF efficacy-predicting biomarkers have been identified. Herein, we explored and developed a DNA methylation-based predictive model for LEF-treated RA patient prognosis. Methods: Two hundred forty-five RA patients were prospectively enrolled from four participating study centers. A whole-genome DNA methylation profiling was conducted to identify LEF-related response signatures via comparison of 40 samples using Illumina 850k methylation arrays. Furthermore, differentially methylated positions (DMPs) were validated in the 245 RA patients using a targeted bisulfite sequencing assay. Lastly, prognostic models were developed, which included clinical characteristics and DMPs scores, for the prediction of LEF treatment response using machine learning algorithms. Results: We recognized a seven-DMP signature consisting of cg17330251, cg19814518, cg20124410, cg21109666, cg22572476, cg23403192, and cg24432675, which was effective in predicting RA patient's LEF response status. In the five machine learning algorithms, the support vector machine (SVM) algorithm provided the best predictive model, with the largest discriminative ability, accuracy, and stability. Lastly, the AUC of the complex model(the 7-DMP scores with the lymphocyte and the diagnostic age) was higher than the simple model (the seven-DMP signature, AUC:0.74 vs 0.73 in the test set). Conclusion: In conclusion, we constructed a prognostic model integrating a 7-DMP scores with the clinical patient profile to predict responses to LEF treatment. Our model will be able to effectively guide clinicians in determining whether a patient is LEF treatment sensitive or not.

Photoexcited wireless electrical stimulation elevates nerve cell growth.

Electrical stimulation was restrained by an external power supply and wires, despite its ability to promote nerve cell growth. Bismuth sulfide (Bi2S3) offered a novel prospect for achieving wireless electrical stimulation due to its photoelectric effect. Herein, silver nanoparticles (Ag NPs) were in-situ grown on Bi2S3 surface (Ag/Bi2S3) and then mixed with poly-L-lactic acid (PLLA) powders to fabricate PLLA-Ag/Bi2S3 conduits. On the one hand, Bi2S3 would generate photocurrent under light excitation, forming a wireless electrical stimulation. On the other hand, Ag NPs would form localized electrical fields under light excitation to inhibit rapid electron-hole recombination of Bi2S3. Moreover, Ag NPs would act as electron mediators to accelerate electron transfer, further elevating photocurrent. Electrochemical tests and FDTD simulations revealed the localized electrical fields generated by Ag NPs acted on Bi2S3, resulting in a boosted electron-hole separation evidenced by a reduction in photoluminescence intensity. EIS measurements demonstrated a faster electron transfer occurred on Ag/Bi2S3. As a result, the photocurrent of PLLA-Ag/Bi2S3 increased from 0.26 to 1.03 µA as compared with PLLA-Bi2S3. The enhanced photocurrent effectively promoted cell differentiation by up-regulating Ca2+ influx and nerve growth-related protein SYN1 expression. This work suggested a promising countermeasure in the design of photocurrent stimulation conduits for nerve repair.

Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network.

OBJECTIVES: This study was designed to identify the potential diagnostic biomarkers of rheumatoid arthritis (RA) and to explore the potential pathological relevance of immune cell infiltration in this disease. METHODS: Three previously published datasets containing gene expression data from 35 RA patients and 29 controls (GSE55235, GSE55457, and GSE12021) were downloaded from the GEO database, after which a weighted correlation network analysis (WGCNA) approach was utilized to clarify differentially abundant genes. Candidate biomarkers of RA were then identified via the use of a LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. Data were validated based upon the area under the receiver operating characteristic curve (AUC) values, with hub genes being identified as those with an AUC > 85% and a P value < 0.05. Lastly, the CIBERSORT algorithm was used to assess immune cell infiltration of RA tissues, and correlations between immune cell infiltration and disease-related diagnostic biomarkers were assessed. RESULTS: The green-yellow module containing 87 genes was found to be highly correlated with RA positivity. FADD, CXCL2, and CXCL8 were identified as potential RA diagnostic biomarkers (AUC > 0.85), and these results were validated using the GSE77298 dataset. Immune cell infiltration analyses revealed the expression of hub genes to be correlated with mast cells, monocytes, activated NK cells, CD8 T cells, resting dendritic cells, and plasma cells. CONCLUSION: These data indicate that FADD, CXCL2, and CXCL8 are valuable diagnostic biomarkers of RA, offering new insight that can guide future studies of RA incidence and progression.