Association between the flap endonuclease 1 gene polymorphisms and cancer susceptibility: An updated meta-analysis

Flap endonuclease 1 (FEN1) has emerged as an important enzyme in the maintenance of genomic instability and preventing carcinogenesis. The relationship between FEN1 −69G>A (rs174538)+4150G>T (rs4246215) polymorphisms and cancer susceptibility has been reported; however, results were inconclusive. In the present study, a meta-analysis of data from eligible reports was carried out to summarize the possible relationship between FEN1 polymorphisms and cancer risk. A total of 11 articles, including 20 studies with 7366 cases and 9028 controls and 18 studies with 6649 cases and 8325 controls for FEN1 rs174538 and FEN1 rs4246215 polymorphisms, respectively, were recruited for meta-analysis. Overall, meta-analyses showed that FEN1 rs174538 and rs4246215 polymorphisms are significantly associated with the decreased risk of cancer. The stratified analysis proposed that both variants were associated with protection against gastrointestinal cancer, breast cancer, hepatocellular cancer, esophageal cancer, gastric cancer, colorectal cancer, and lung cancer. In conclusion, this meta-analysis revealed an association between FEN1 polymorphisms and cancer risk. Additional studies in a larger study population that include subjects from a variety of ethnicities are warranted to further verify our findings.     

Pierce into the Native Structure of Ata,a Trimeric Autotransporter of Acinetobacter baumannii ATCC 17978

International Journal of Peptide Research and Therapeutics - Acinetobacter baumannii is an important pathogen responsible for nosocomial infections worldwide. Trimeric autotransporters, the...     

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

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.     

Genotyping of –374A/T, –429A/G,And 63 bp Ins/Del Polymorphisms of Rage By Rapid One-Step Hexaprimer Amplification Refractory Mutation System Polymerase Chain Reaction in Breast Cancer Patients

Several studies have focused on the RAGE genetic background and have demonstrated that its polymorphisms affect the receptor's activity, expression, and downstream signaling. However, there is only little information regarding RAGE polymorphism in breast cancer. In the present study, the authors studied RAGE polymorphisms in 71 patients with breast cancer and 93 healthy women. RAGE –374T/A, –429T/C, and 63 bp Ins/del polymorphisms were analyzed using a hexaprimer amplification refractory mutation system PCR (H-ARMS-PCR). The results showed that RAGE polymorphisms are not associated with breast cancer in the current study population. Larger studies are required to confirm these data in other populations.     

Assessment of Heavy Metal Pollution and Human Health Risks Assessment in Soils Around an Industrial Zone in Neyshabur,Iran

Biological Trace Element Research - Heavy metal pollution of soils in industrial zones continues to attract attention because of its potential human health risks. The present research is an attempt...     

Covalent vs. Non‐Covalent Inhibition: Tackling Drug Resistance in EGFR – A Thorough Dynamic Perspective

A persistent challenge in the treatment of non‐small cell lung cancer (NSCLC) with EGFR is the emergence of drug‐resistant caused by somatic mutations. The EGFR L858R/T790 M double mutant (EGFR^DM) was found to be the most alarming variant. Despite the development of a wide range of inhibitors, none of them could inhibit EGFR^DM effectively. Recently, 11h and 45a , have been found to be potent inhibitors against EGFR^DM through two distinctive mechanisms, non‐covalent and covalent binding, respectively. However, the structural and dynamic implications of the two modes of inhibitions remain unexplored. Herein, two molecular dynamics simulation protocols, coupled with free‐energy calculations, were applied to gain insight into the atomistic nature of each binding mode. The comparative analysis confirmed that there is a significant difference in the binding free energy between 11h and 45a (ΔΔG_bind=?21.17 kcal/mol). The main binding force that governs the binding of both inhibitors is vdW, with a higher contribution for 45a . Two residues ARG841 and THR854 were found to have curtailed role in the binding of 45a to EGFR^DM by stabilizing its flexible alcohol chain. The 45a binding to EGFR^DM induces structural rearrangement in the active site to allow easier accessibility of 45a to target residue CYS797. The findings of this work can substantially shed light on new strategies for developing novel classes of covalent and non‐covalent inhibitors with increased specificity and potency.     

Investigating the Structural Stability of RADA16-I Peptide Conjugated to Gold Nanoparticles

International Journal of Peptide Research and Therapeutics - RADA 16-I is an amphiphilic peptide which can form macroscopic scaffolds through self-assembly and has found many applications in tissue...     

Biofilm formation of Salmonella enterica serovar Enteritidis cocultured with Acanthamoeba castellanii responds to nutrient availability

International Microbiology - Acanthamoeba spp. and Salmonella share common habitats, and their interaction may influence the biofilm-forming ability of Salmonella. In this study, biofilm formation...     

Exploring dengue proteome to design an effective epitope-based vaccine against dengue virus

Dengue, a mosquito-borne disease, is caused by four known dengue serotypes. This infection causes a range of symptoms from a mild fever to a sever homorganic fever and death. It is a serious public health problem in subtropical and tropical countries. There is no specific vaccine currently available for clinical use and study on this issue is ongoing. In this study, bioinformatics approaches were used to predict antigenic, immunogenic, non-allergenic, and conserved B and T-cell epitopes as promising targets to design an effective peptide-based vaccine against dengue virus. Molecular docking analysis indicated the deep binding of the identified epitopes in the binding groove of the most popular human MHC I allele (human leukocyte antigens [HLA] A*0201). The final vaccine construct was created by conjugating the B and T-cell identified epitopes using proper linkers and adding an appropriate adjuvant at the N-terminal. The characteristics of the new subunit vaccine demonstrated that the epitope-based vaccine was antigenic, non-toxic, stable, and soluble. Other physicochemical properties of the new designed construct including isoelectric point value, aliphatic index, and grand average of hydropathicity were biologically considerable. Molecular docking of the engineered vaccine with Toll-like receptor 2 (TLR2) model revealed the hydrophobic interaction between the adjuvant and the ligand binding regions in the hydrophobic channel of TLR2. The study results indicated the high potential capability of the new multi-epitope vaccine to induce cellular and humoral immune responses against the dengue virus. Further experimental tests are required to investigate the immune protection capacity of the new vaccine construct in animal models.

Communicated by Ramaswamy H. Sarma