Signal execution modes emerge in biochemical reaction networks calibrated to experimental data.

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations.

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated.

Hyperpolarized C metabolic imaging detects long-lasting metabolic alterations following mild repetitive traumatic brain injury.

Career athletes, active military, and head trauma victims are at increased risk for mild repetitive traumatic brain injury (rTBI), a condition that contributes to the development of epilepsy and neurodegenerative diseases. Standard clinical imaging fails to identify rTBI-induced lesions, and novel non-invasive methods are needed. Here, we evaluated if hyperpolarized C magnetic resonance spectroscopic imaging (HP C MRSI) could detect long-lasting changes in brain metabolism 3.

Nano-gelcoat application of glass fiber reinforced polymer composites for marine application: Structural, mechanical, and thermal analysis.

To address the critical issue of environmental sustainability, the application of nano-gelcoat has emerged as a promising approach to extend the service life and enhance the durability of glass fiber-reinforced polymer composites (GFRPs), thereby protecting the marine environment. Despite the extensive use of GFRPs in marine structures, their performance is significantly influenced by marine environmental factors. However, despite its potential, there is a lack of research on the experimental application of nano-gelcoat.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated.

Learning feedback molecular network models using integer linear programming.

Analysis of intracellular molecular networks has many applications in understanding of the molecular bases of some complex diseases and finding effective therapeutic targets for drug development. To perform such analyses, the molecular networks need to be converted into computational models. In general, network models constructed using literature and pathway databases may not accurately predict experimental network data.

A hybrid model to study how late long-term potentiation is affected by faulty molecules in an intraneuronal signaling network regulating transcription factor CREB.

Systems biology analysis of intracellular signaling networks has tremendously expanded our understanding of normal and diseased cell behaviors and has revealed paths to finding proper therapeutic molecular targets. When it comes to neurons in the human brain, analysis of intraneuronal signaling networks provides invaluable information on learning, memory and cognition-related disorders, as well as potential therapeutic targets. However, neurons in the human brain form a highly complex neural network that, among its many roles, is also responsible for learning, memory formation and cognition.

Exploring extreme signaling failures in intracellular molecular networks.

Developing novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network.

Kallikrein gene family as biomarkers for recurrent prostate cancer.

Aim: To assess kallikrein (KLK) expression in recurrent and non-recurrent prostate tumors and adjacent healthy prostate tissues.

Methods: The expression levels of 15 KLK genes in 34 recurrent and 36 non-recurrent prostate cancer samples and 19 adjacent healthy prostate tissue samples was assessed with quantitative reverse-transcription polymerase chain reaction. The samples were obtained from Baylor College of Medicine, Houston, TX, USA between 2013 and 2016.

Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data.

Oxidative stress and apoptosis in electromagnetic waves exposed Zebrafish embryos and protective effects of conductive nonwoven fabric.

The amount of technological products including television, radio transmitters, and mobile phone that have entered our daily life has increased in recent years. But these devices may cause adverse effects on human health. Electromagnetic shielding fabrics may limit and inhibit electromagnetic waves.

Revealing the functions of novel mutations in RAB3GAP1 in Martsolf and Warburg micro syndromes.

Purpose: Martsolf (MS) and Warburg micro syndromes (WARBM) are rare autosomal recessive inherited allelic disorders, which share similar clinical features including microcephaly, intellectual disability, brain malformations, ocular abnormalities, and spasticity. Here, we revealed the functions of novel mutations in RAB3GAP1 in a Turkish female patient with MS and two siblings with WARBM. We also present a review of MS patients as well as all reported RAB3GAP1 pathogenic mutations in the literature.

MicroRNAs as prognostic markers in prostate cancer.

Background: Prostate cancer (PCa) is the most commonly diagnosed malignancy in men who are especially over the age of 50 years in the western countries. Currently used therapeutic modalities mostly fail to give positive clinical outcomes and nearly 30% of the PCa patients eventually develop clinical recurrence. Therefore, understanding the underlying mechanisms of PCa progression is of paramount importance to help determining the course of disease.

miR-33a is a tumor suppressor microRNA that is decreased in prostate cancer.

Prostate cancer is one of the most frequently diagnosed neoplasms among men worldwide. MicroRNAs (miRNAs) are involved in numerous important cellular processes including proliferation, differentiation and apoptosis. They have been found to be aberrantly expressed in many types of human cancers.

Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy.

Background: Prostate cancer (PCa) is a leading reason of death in men and the most diagnosed malignancies in the western countries at the present time. After radical prostatectomy (RP), nearly 30% of men develop clinical recurrence with high serum prostate-specific antigen levels. An important challenge in PCa research is to identify effective predictors of tumor recurrence.

Critical flicker frequency test for diagnosing minimal hepatic encephalopathy in patients with cirrhosis.

Background/aims: The critical flicker frequency (CFF) and psychometric hepatic encephalopathy score (PHES) are commonly proposed tests for detecting minimal hepatic encephalopathy (MHE); however, no studies have examined their value for detecting MHE in Turkey.

Materials And Methods: A total of 70 patients with cirrhosis without overt HE, 205 controls for PHES, and 100 controls for the CFF test were included. All the patients underwent the PHES and CFF tests during the same session.

Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification.

In this paper, a new approach based on Binary Black Hole Algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach, BBHA is used to perform gene selection and AdaboostM1 with 10-fold cross validation is adopted as the classifier. Also, to find the relation between the biomarkers for biological point of view, decision tree algorithm (C4.

A novel method for splice sites prediction using sequence component and hidden Markov model.

With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.

Assessing posterior ocular structures in β-thalassemia minor.

Purpose: The aim of this study was to investigate the effect of β-thalassemia minor on choroidal, macular, and peripapillary retinal nerve fiber layer thickness.

Methods: To form the sample, we recruited 40 patients with β-thalassemia minor and 44 healthy participants. We used spectral-domain optical coherence tomography to take all measurements of ocular thickness, as well as measured intraocular pressure, axial length, and central corneal thickness.

Identification of microRNA profile specific to cancer stem-like cells directly isolated from human larynx cancer specimens.

Background: Emerging evidences proposed that microRNAs are associated with regulation of distinct physio-pathological processes including development of normal stem cells and carcinogenesis. In this study we aimed to investigate microRNA profile of cancer stem-like cells (CSLCs) isolated form freshly resected larynx cancer (LCa) tissue samples.

Methods: CD133 positive (CD133) stem-like cells were isolated from freshly resected LCa tumor specimens.

A Meta-Analysis: Identification of Common Mir-145 Target Genes that have Similar Behavior in Different GEO Datasets.

Background: MicroRNAs, which are small regulatory RNAs, post-transcriptionally regulate gene expression by binding 3'-UTR of their mRNA targets. Their deregulation has been shown to cause increased proliferation, migration, invasion, and apoptosis. miR-145, an important tumor supressor microRNA, has shown to be downregulated in many cancer types and has crucial roles in tumor initiation, progression, metastasis, invasion, recurrence, and chemo-radioresistance.

The altered promoter methylation of oxytocin receptor gene in autism.

Autism spectrum disorder (ASD) is one of the lifelong existing disorders. Abnormal methylation status of gene promoters of oxytonergic system has been implicated as among the etiologic factors of ASDs. We, therefore, investigated the methylation frequency of oxytocin receptor gene (OXTR) promoter from peripheral blood samples of children with autistic features.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.