New Therapeutic Targets for Hepatic Fibrosis in the Integrin Family, α8β1 and α11β1, Induced Specifically on Activated Stellate Cells

Huge effort has been devoted to developing drugs targeting integrins over 30 years, because of the primary roles of integrins in the cell-matrix milieu. Five αv-containing integrins, in the 24 family members, have been a central target of fibrosis. Currently, a small molecule against αvβ1 is undergoing a clinical trial for NASH-associated fibrosis as a rare reagent aiming at fibrogenesis. Latent TGFβ activation, a distinct talent of αv-integrins, has been intriguing as therapeutic target. None of the αv-integrin inhibitors, however, has been in the clinical market. αv-integrins commonly recognize an Arg-Gly-Asp (RGD) sequence, and thus the pharmacophore of inhibitors for the 5-integrins is based on the same RGD structure. The RGD preference of the integrins, at the same time, dilutes ligand specificity, as the 5-integrins share ligands containing RGD sequence such as fibronectin. With the inherent little specificity in both drugs and targets, “disease specificity” has become less important for the inhibitors than blocking as many αv-integrins. In fact, an almighty inhibitor for αv-integrins, pan-αv, was in a clinical trial. On the contrary, approved integrin inhibitors are all specific to target integrins, which are expressed in cell-type specific manner: αIIbβ3 on platelets, α4β1, α4β7 and αLβ2 on leukocytes. Herein, “disease specific” integrins would serve as attractive targets. α8β1 and α11β1 are selectively expressed in hepatic stellate cells (HSCs) and distinctively induced upon culture activation. The exceptional specificity to activated HSCs reflects rather “pathology specific” nature of these new integrins. The monoclonal antibodies against α8β1 and α11β1 in preclinical examinations may illuminate the road to the first medical reagents.

Mobile applications for anxiety and stress reduction: potential for preventing noncommunicable diseases

The article discusses using mobile applications for smartphones as tools to reduce anxiety and stress. The modern views on pathogenesis of the relationship between stress, anxiety disorders and cardiovascular disease are considered. Based on a review of some representative studies, a methodology for evaluating the characteristics and functions of mobile applications for managing anxiety and stress symptoms is proposed. The applications selected for analysis were tested according to this methodology. There are following key features of the Russianlanguage field of mobile applications for anxiety and stress reduction: a small number of applications, high prevalence of using breathing exercises, meditations and sound therapy, an extremely low disease specificity and focus mainly on helping with general symptoms of anxiety.

Predicting the Diagnosis of Prostate Cancer with a Scoring System Based on Novel Biomarkers

Objective: To predict prostate cancer using novel biomarker ratios and create a predictive scoring system.Materials and Methods: Data of a total of 703 patients who consulted Urology Department of Selayang Hospital between January 2013 and December 2017 and underwent prostate biopsy were screened retrospectively. Prostate specific antigen (PSA) levels, prostate volumes (PV), neutrophil and lymphocyte counts, neutrophil-to-lymphocyte ratio (NLR), Prostate specific antigen density (PSAD)and histopathology were evaluated. Results: Ages ranged from43-89 years, divided into 2 groups as per biopsy results; positive for prostate cancer (n=290, 41.3%) and negative for malignancy (n=413; 58.7%). Intergroup comparative evaluations were performed. Independent variables with p<0.001 in the univariate analysis were age, DRE, PV, NLR, PSAD.A scoring system was modelled using NLR <0.9, PSAD >0.4, Age >70 and DRE. A score of 2 or more predicted prostate cancer with a Sensitivity of 83.8% and Specificityof 86.4%Conclusions: NLR is shown to be good predictor for prostate cancer its usage in this scoring system affords more disease specificity as compared to PSA alone.

Islet Autoantibodies, Assay Specificity and Disease Specificity

Type 1 Diabetes (T1D) is one of the most common chronic diseases in childhood, which is caused by destruction of insulinproducing pancreatic beta cells. Its incidence increases 3-5% annually and doubles every 20 years [1,2]. On one hand, acute and chronic complications of T1D seriously affect the quality of life and even life span of patients. On the other hand, prognosis can greatly be improved when the disease prediction and closely monitoring are applied, leading to earlier diagnosis and treatment [3]. Islet Autoantibodies (IAbs), as most reliable biomarkers at present for islet autoimmunity, precede clinical T1D by years and play an essential role in prediction and clinical diagnosis of T1D [4,5].

Joint disease-specificity at the regulatory base-pair level

Given the pleiotropic nature of coding sequences and that many loci exhibit multiple disease associations, it is within non-coding sequence that disease-specificity likely exists. Here, we focus on joint disorders, finding among replicated loci, that GDF5 exhibits over twenty distinct associations, and we identify causal variants for two of its strongest associations, hip dysplasia and knee osteoarthritis. By mapping regulatory regions in joint chondrocytes, we pinpoint two variants (rs4911178; rs6060369), on the same risk haplotype, which reside in anatomical site-specific enhancers. We show that both variants have clinical relevance, impacting disease by altering morphology. By modeling each variant in humanized mice, we observe joint-specific response, correlating with GDF5 expression. Thus, we uncouple separate regulatory variants on a common risk haplotype that cause joint-specific disease. By broadening our perspective, we finally find that patterns of modularity at GDF5 are also found at over three-quarters of loci with multiple GWAS disease associations.

The Emerging Roles of circFOXO3 in Cancer

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs which are mainly formed by reverse splicing of precursor mRNAs. They are relatively stable and resistant to RNase R because of their covalently closed structure without 5’ caps or 3’ poly-adenylated tails. CircRNAs are widely expressed in eukaryotic cells and show tissue, timing, and disease specificity. Recent studies have found that circRNAs play an important role in many diseases. In particular, they affect the proliferation, invasion and prognosis of cancer by regulating gene expression. CircRNA Forkhead box O3 (circFOXO3) is a circRNA confirmed to be abnormally expressed in a variety of cancers, including prostate cancer, hepatocellular carcinoma, glioblastoma, bladder cancer, and breast cancer, etc. At present, the feature of circFOXO3 as a molecular sponge is widely studied to promote or inhibit the development of cancers. However, the diverse functions of circFOXO3 have not been fully understood. Hence, it is important to review the roles of circFOXO3 in cancers. This review has summarized and discussed the roles and molecular mechanism of circFOXO3 and its target genes in these cancers, which can help to enrich our understanding to the functions of circRNAs and carry out subsequent researches on circFOXO3.

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

A Hybrid Predictive Model Integrating C4.5 and Decision Table Classifiers for Medical Data Sets

This article describes how, recently, data mining has been in great use for extracting meaningful patterns from medical domain data sets, and these patterns are then applied for clinical diagnosis. Truly, any accurate, precise and reliable classification models significantly assist the medical practitioners to improve diagnosis, prognosis and treatment processes of individual diseases. However, numerous intelligent models have been proposed in this respect but still they have several drawbacks like, disease specificity, class imbalance, conflicting and lack adequacy for dimensionality of patient's data. The present study has attempted to design a hybrid prediction model for medical domain data sets by combining the decision tree based classifier (mainly C4.5) and the decision table based classifier (DT). The experimental results validate in favour of the claims.