👤 Asiyeh Sadat Zahedi

Depression is a widespread neuropsychiatric disorder that significantly impacts emotional and cognitive function. Antidepressant medications are frequently accompanied by various adverse effects. C-phycocyanin has been previously shown to exert potent anti-inflammatory, and neuroprotective properties. Therefore, this study evaluated the therapeutic effects of C-phycocyanin against anxiety and depressive-like behaviors, and memory dysfunction in an animal model of chronic unpredictable mild stress (CUMS)-induced depression and explored the underlying mechanisms. Rats were daily exposed for six weeks to CUMS, during which phycocyanin (100 mg/kg, orally) was administered in the final three weeks of the study. Following the assessment of anxiety/ depressive-like behaviors, and memory dysfunction by the open field test (OFT), tail suspension test (TST), elevated plus maze (EPM), and passive avoidance test (PAT), rats were euthanized by decapitation. Then, hippocampal TNF-α and IL-1β concentrations, and hippocampal protein expressions (Iba-1, CD86, NF-κβ, CREB, and BDNF) were determined by an ELISA assay, and western blots, respectively. C-phycocyanin significantly decreased immobility time in OFT and TST, increased open arm time in EPM, and step-through latency time in PAT. Furthermore, C-phycocyanin suppressed CUMS-induced the M1 microglia polarization and neuroinflammation by reducing hippocampal TNF-α and IL-1β concentrations, and the protein expression of Iba-1, CD86, and NF-κβ in the hippocampus of CUMS-exposed rats. It also increased the hippocampal protein expression of CREB and BDNF. C-phycocyanin improved CUMS-induced anxiety and depressive-like behaviors, and memory dysfunction, which could be explained, at least in part, by inhibition of M1 microglial polarization and neuroinflammation, and enhancement of CREB/BDNF signaling. Show less

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors. Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS. Show less

This population-based longitudinal study is the first investigation that assesses the association of common MC4R SNPs with the obesity-related parameters over time and determines the effect of risk alleles during the three adulthood life periods (early, middle, and late) in a large Iranian cohort, a population with a unique genetic make-up that has been understudied and relatively unexplored. We obtained the genotype of 5370 unrelated adults who participated in the ongoing Tehran Cardiometabolic Genetic Study (TCGS) cohort project for the common MC4R SNPs. Linear regression and linear mixed model analyses were performed to examine the effect of MC4R polymorphisms on maximum BMI and other obesity-related factors over time. We recognized that several SNPs associated with the maximum BMI and the increased BMI, waist circumference, and waist-hip ratio across Iranian adults over a lifetime. Interestingly, we found that rs9954571-A has a yet unreported protective role against obesity-related factors, including BMI, waist circumference, waist-hip ratio, and triglyceride level. Additionally, a survey of the impact of the MC4R risk score throughout the adulthood life periods indicated that the MC4R risk score is influenced both the elevated BMI and waist circumference only during the early adulthood period. Our findings can expand our knowledge about the MC4R genetic variant's contributions to adulthood obesity and highlight the importance of evaluating the genetic components affecting obesity over a lifetime, which could be considered for obesity clinical screening and treatment. Show less

Central nervous system demyelination is the main feature of multiple sclerosis (MS). The most important unmet need in MS is use of treatments that delay the progression of the disease. Leucine-rich repeat and Immunoglobulin-like domain containing NOGO receptor-interacting protein 1(LINGO-1) have been known as inhibitors of oligodendrocyte differentiation and myelination. We investigated LINGO-1 antibody effects on remyelination and neurobehavioral deficit using cuprizone-induced demyelination. Animals were randomly divided into three groups (n = 10): (1) Control group; received the regular diet, (2) CPZ group; normal saline was injected intraperitoneally, and (3) Treatment group; LINGO-1 antibody (10 mg/kg) was injected IP once every six days for 3 weeks. We assessed the level of myelin basic protein (MBP), neurofilament heavy chain (NF200), and Brain-derived neuroprotective factor (BDNF) in the corpus callosum (CC) by immunostaining against MBP, NF200, and BDNF. We found decreased levels of MBP, NF200, and BDNF in demyelinated CC, and anti-LINGO-1 treatment improved demyelinated structures. Furthermore, motor impairment was measured by Open-field (OFT) and Balance beam tests. In the treatment group, motor impairment was significantly improved. These results provide evidence that LINGO-1 antibody can improve remyelination and neurobehavioral deficit. Show less