👤 Samin Abed

The melanocortin-4 receptor (MC4R) agonists have emerged as potential treatments for obesity, particularly in patients with rare genetic syndromes. However, their overall effects on obesity and cardiometabolic risk factors remain uncertain. To systematically evaluate the efficacy of MC4R agonists on weight-related outcomes and cardiometabolic risk factors. We conducted this study following PRISMA 2020 guidelines. Eligible studies included clinical trials ((RCTs and single-arm trials) of the effects of MC4R agonist drugs on anthropometric factors and cardiovascular risk factors. Random model effects meta-analyses were performed for this meta-analysis, with heterogeneity and small-study effects explored through sensitivity and publication bias analyses. A total of 12 studies were included. Treatment with MC4R agonists significantly reduced body weight compared with placebo in RCTs (WMD − 5.07 kg; 95% CI − 8.13 to − 2.02), with even larger reductions in single-arm studies (–11.23%; 95% CI − 15.43 to − 7.04). MC4R agonists also lowered BMI by − 13.67% (95% CI − 17.21 to − 10.12), waist circumference by − 11.75 cm, BMI Z-score by − 0.98, and hunger scores by − 3.38. These agents reduced triglyceride levels by − 35.53 mg/dL and LDL-C levels by − 9.14 mg/dL, while HDL-C levels showed a nonsignificant increase of + 2.37 mg/dL. Systolic blood pressure declined by − 4.38 mmHg, while diastolic pressure showed no meaningful change. MC4R agonists produce clinically meaningful weight reduction and improvements in several cardiometabolic risk factors. These findings support MC4R agonists as a promising therapy for genetic forms of obesity, while their role in nonspecific obesity requires confirmation in large, long-term randomized trials. The online version contains supplementary material available at 10.1186/s13098-025-02071-2. Show less

Many cancer patients who initially respond to chemotherapy eventually develop chemoresistance, and to address this, we previously conducted a RNAi screen to identify genes contributing to resistance. One of the hits from the screen was branched-chain α-keto acid dehydrogenase kinase (BCKDK). BCKDK controls the metabolism of branched-chain amino acids (BCAAs) through phosphorylation and inactivation of the branched-chain α-keto acid dehydrogenase complex (BCKDH), thereby inhibiting catabolism of BCAAs. We measured the impact on paclitaxel sensitivity of inhibiting BCKDK in ovarian and breast cancer cell lines. Inhibition of BCKDK using siRNA or two chemical inhibitors (BCKDKi) was synergistic with paclitaxel in both breast and ovarian cancer cells. BCKDKi reduced levels of BCAA and the addition of exogenous BCAA suppressed this synergy. BCKDKi inactivated the mTORC1-Aurora pathway, allowing cells to overcame M-phase arrest induced by paclitaxel. In some cases, cells almost completed cytokinesis, then reverted to a single cell, resulting in multinucleate cells. BCKDK is an attractive target to augment the sensitivity of cancer cells to paclitaxel. Show less

Schizophrenia (SCZ) is a serious mental condition with an unknown cause. According to the reports, Brodmann Area 10 (BA10) is linked to the pathology and cortical dysfunction of SCZ, which demonstrates a number of replicated findings related to research on SCZ and the dysfunction in tasks requiring cognitive control in particular. Genetics' role in the pathophysiology of SCZ is still unclear. Therefore, it may be helpful to understand the effects of these changes on the onset and progression of SCZ to find novel mechanisms involved in the regulation of gene transcription. In order to determine the molecular regulatory mechanisms affecting the SCZ, the long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) axes in the BA10 area were determined using a bioinformatics approach in the present work. A microarray dataset (GSE17612) consisted of brain post-mortem tissues of the BA10 area from SCZ patients and matched healthy subjects was downloaded from the Gene Expression Omnibus (GEO) database. This dataset included probes for both lncRNAs and mRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also discovered using the DIANA-LncBase and miRTarBase databases. In the ceRNA network, positive correlations between DEmRNAs and DElncRNAs were evaluated using the Pearson correlation coefficient. Finally, lncRNA-associated ceRNA axes were built by using the co-expression and DElncRNA-miRNA-DEmRNA connections. We identified the DElncRNA-miRNA-DEmRNA axes, which included two key lncRNAs ( Show less