Shahid Beheshti University of Medical Sciences and Health Services

About: Shahid Beheshti University of Medical Sciences and Health Services is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Medicine. The organization has 19456 authors who have published 33659 publications receiving 365676 citations.

Macronutrients, vitamins and minerals intake and risk of esophageal squamous cell carcinoma: a case-control study in Iran

Abstract: Background Although Iran is a high-risk region for esophageal squamous cell carcinoma (ESCC), dietary factors that may contribute to this high incidence have not been thoroughly studied. The aim of this study was to evaluate the effect of macronutrients, vitamins and minerals on the risk of ESCC.

Sodium butyrate promotes apoptosis in breast cancer cells through reactive oxygen species (ROS) formation and mitochondrial impairment

Abstract: Sodium butyrate (NaBu) is a short-chain fatty acid which serves as a histon deacetylase inhibitor and has received considerable interest as a possible regulator of cancer cell death. The regulatory effect of NaBu on cancer cell growth or death has yet to be illustrated in many cancers including breast cancer. This study is aimed to elucidate the possible effect of NaBu on regulation of breast cancer growth and apoptosis. The cytotoxic effect of NaBu on the growth of breast cancer cells (MCF-7 and MDA-MB-468) and normal breast cells (MCF-10A) was determined using MTT assay. Annexin-V-FITC staining and PI staining were performed to detect apoptosis and cell cycle distribution using Flow cytometry, the level of mitochondrial membrane potential (Δψm), Reactive oxygen species (ROS)formation and caspase activity were determined accordingly. Based on our data, NaBu induced a dose and time-dependent cell toxicity in breast cancer cells which was related to the cell cycle arrest and induction of apoptosis. The impact of NaBu on MCF-10A cell toxicity, cell cycle distribution and apoptosis was inconsiderable. NaBu-elicited apoptosis was accompanied by the elevated level of ROS, increased caspase activity and reduced mitochondrial membrane potential (Δψm) in MCF-7 and MDA-MB-468 cells and with no effect on the above mentioned factors in MCF-10A cells. Our study provided insight in to the role of NaBu on the regulation of breast cancer cell growth and lighten up the pro-apoptotic activity of NaBu.

Associations of autozygosity with a broad range of human phenotypes

Abstract: In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

Genetic characterization of ESBL producing strains of Klebsiella pneumoniae from Tehran hospitals

Abstract: Introduction: This study was conducted to determine the genetic characterization of extended-spectrum beta-lactamase (ESBL) producing strains of Klebsiella pneumoniae isolated from Iranian patients in hospitals in Tehran. Methodology: Antibiotic susceptibility of 104 isolates was determined using the disk diffusion test. The Minimum Inhibitory Concentrations (MICs) of imipenem and meropenem were determined for isolates showing reduced susceptibility to carbapenems. The phenotypic confirmatory test (PCT) was used to screen the isolates for ESBL production. PCR was used to detect bla SHV , bla TEM and bla CTX-M and the amplicons from selected clones were sequenced. Isolates producing ESBLs were analyzed by pulsed-field gel electrophoresis (PFGE). Results: One isolate showed resistance to imipenem (MIC = 16 µg/ml). Resistance to amikacin and ciprofloxacin was 44.2% and 25.0%, respectively. ESBL production was detected in 72.1% (n = 75) of isolates. The prevalence of bla SHV , bla TEM and bla CTX-M genes among the isolates was 55.7% (n = 58), 30.7% (n = 32) and 45.2% (n = 47), respectively. The sequencing revealed the amplicons corresponding to bla (TEM-1, TEM-79, SHV-1, SHV-12, SHV-31, CTX-M-15) genes. While the bla CTX-M-15 is the dominant gene among the Iranian isolates, we detected the bla SHV-31 and bla TEM-79 genes for the first time in the country. PFGE differentiated the 71 ESBL-producing isolates into 62 different genotypes. Clonal dissemination of ESBLs was found in the neonatal intensive care unit and intensive care unit of one hospital. Conclusion: The findings are evidence of the spread of multi-resistant clones of ESBL producers in Tehran hospitals.

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Abstract: Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.