CSF inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

Background: A growing body of evidence suggests that neuroinflammation contributes actively to pathophysiology of Alzheimer's disease (AD) and promotes AD progression. The predictive value of neuroinflammatory biomarkers for disease-staging or estimating disease progression is not well understood. In this study, we investigate the diagnostic and prognostic utility of inflammatory biomarkers in combination with conventional AD biomarkers.

Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data.

Purpose: Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortality rate of 40 ​%, accurate diagnosis and prompt initiation of appropriate antibiotic therapy are crucial for optimizing patient outcomes and preventing life-threatening complications. This study aimed to expand the use of computational models to improve the diagnostic accuracy of SBP in cirrhotic patients by incorporating a broader range of data, including clinical variables and laboratory values.

PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery.

Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities.

Analyzing the performance of short-read classification tools on metagenomic samples toward proper diagnosis of diseases.

Accurate knowledge of the genome, virus and bacteria that have invaded our bodies is crucial for diagnosing many human diseases. The field of bioinformatics encompasses the complex computational methods required for this purpose. Metagenomics employs next-generation sequencing (NGS) technology to study and identify microbial communities in environmental samples.

A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, posing a growing clinical challenge. PTC exhibits two age-related peaks, with established risk factors including family history and radiation exposure. Managing even low-risk, localized PTC cases remain complex, with growing interest in active surveillance as an alternative to immediate surgery.

Metabolite signature of human malignant thyroid tissue: A systematic review and meta-analysis.

Background: Thyroid cancer (TC) is the predominant malignancy within the endocrine system. However, the standard method for TC diagnosis lacks the capability to identify the pathological condition of all thyroid lesions. The metabolomics approach has the potential to manage this problem by identifying differential metabolites.

Engineering chimeric autoantibody receptor T cells for targeted B cell depletion in multiple sclerosis model: An study.

Background: Recent evidence suggests that B cells and autoantibodies have a substantial role in the pathogenesis of Multiple sclerosis. T cells could be engineered to express chimeric autoantibody receptors (CAARs), which have an epitope of autoantigens in their extracellular domain acting as bait for trapping autoreactive B cells. This study aims to assess the function of designed CAAR T cells against B cell clones reactive to the myelin basic protein (MBP) autoantigen.

High prevalence of Mucosa-Associated extended-spectrum β-Lactamase-producing Escherichia coli and Klebsiella pneumoniae among Iranain patients with inflammatory bowel disease (IBD).

Background: Several pieces of evidence suggest that certain pathobionts belonging to Enterobacterales are associated with the development and progression of inflammatory bowel diseases (IBD). Extended-spectrum β-lactamases (ESBLs) ESBLs are frequently found in the Enterobacterales members, particularly in Escherichia coli and Klebsiella spp., and might trigger antibiotic-induced perturbations of the intestinal microbiota and led to more severe disease activity in IBD.

Various concentrations of hesperetin induce different types of programmed cell death in human breast cancerous and normal cell lines in a ROS-dependent manner.

The polyphenolic component of citrus fruits, hesperetin (Hst), is a metabolite of hesperidin. In this study, we examined the effect of varying doses and exposure times of hesperetin on MCF-7 and MDA-MB-231 cancer cells, as well as MCF-10A normal cells. By using MTT assay, real-time PCR, western blot, and flow cytometry, we determined the effects of Hst on cell viability, ROS levels, and markers of cell death.

Systematic Review and Dose-Response Meta-Analysis on the Relationship between Different Gluten Doses and Risk of Coeliac Disease Relapse.

Gluten proteins are known as immunological triggers for inflammation resulting in mucosal lesions in patients with coeliac disease (CD). Adherence to a strict gluten-free diet (GFD) is currently known as the only effective treatment for CD. In this study, we performed a systematic review and dose-response meta-analysis on data from previous studies to investigate the association between different gluten doses administered and the risk of CD relapse.

Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach.

Background: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance.

The use of integrated text mining and protein-protein interaction approach to evaluate the effects of combined chemotherapeutic and chemopreventive agents in cancer therapy.

Combining chemotherapeutic (CT) and chemopreventive (CP) agents for cancer treatment is controversial, and the issue has not yet been conclusively resolved. In this study, by integrating text mining and protein-protein interaction (PPI), the combined effects of these two kinds of agents in cancer treatment were investigated. First, text mining was performed by the Pathway Studio database to study the effects of various agents (CP and CT) on cancer-related processes.

Overrepresentation of Enterobacteriaceae and is the major gut microbiome signature in Crohn's disease and ulcerative colitis; a comprehensive metagenomic analysis of IBDMDB datasets.

Objectives: A number of converging strands of research suggest that the intestinal Enterobacteriaceae plays a crucial role in the development and progression of inflammatory bowel disease (IBD), however, the changes in the abundance of Enterobacteriaceae species and their related metabolic pathways in Crohn's disease (CD) and ulcerative colitis (UC) compared to healthy people are not fully explained by comprehensive comparative metagenomics analysis. In the current study, we investigated the alternations of the Enterobacterales population in the gut microbiome of patients with CD and UC compared to healthy subjects.

Methods: Metagenomic datasets were selected from the Integrative Human Microbiome Project (HMP2) through the Inflammatory Bowel Disease Multi'omics Database (IBDMDB).

Proteomic profiling of the rat hippocampus from the kindling and pilocarpine models of epilepsy: potential targets in calcium regulatory network.

Herein proteomic profiling of the rat hippocampus from the kindling and pilocarpine models of epilepsy was performed to achieve new potential targets for treating epileptic seizures. A total of 144 differently expressed proteins in both left and right hippocampi by two-dimensional electrophoresis coupled to matrix-assisted laser desorption-mass spectrometry were identified across the rat models of epilepsy. Based on network analysis, the majority of differentially expressed proteins were associated with Ca homeostasis.

Alpha influenza virus infiltration prediction using virus-human protein-protein interaction network.

More than ten million deaths make influenza virus one of the deadliest of history. About half a million sever illnesses are annually reported consequent of influenza. Influenza is a parasite which needs the host cellular machinery to replicate its genome.

Comprehensive host-pathogen protein-protein interaction network analysis.

Background: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network.

Parallelizing Assignment Problem with DNA Strands.

Background: Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the effective solutions for NP-hard problems. However, solving these problems in polynomial time needs massive parallel machines and is not applicable up to now.

SARS-CoV-2-human protein-protein interaction network.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments.

Digging deeper into volatile organic compounds associated with cancer.

Volatile organic compounds (VOCs), produced and emitted through the metabolism of cancer cells or the body's immune system, are considered novel cancer biomarkers for diagnostic purposes. Of late, a large number of work has been done to find a relationship between VOCs' signature of body and cancer. Cancer-related VOCs can be used to detect several types of cancers at the earlier stages which in turn provide a significantly higher chance of survival.

AntAngioCOOL: computational detection of anti-angiogenic peptides.

Background: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment.

Methods: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study.

OligoCOOL: A mobile application for nucleotide sequence analysis.

Today smartphones are inseparable parts of modern life and are capable of performing many desktop computers' tasks such as scientific analysis with greater convenience. Here, we present OligoCOOL, which is an Android application for analyzing nucleic sequences. This application enables users to perform several common biomedical analyses for a given nucleotide sequence.

Cancer Odor Database (COD): a critical databank for cancer diagnosis research.

http://bioinf.modares.ac.