HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) versus adult T-cell leukemia/lymphoma (ATLL)

ObjectivesHuman T cell leukemia virus-1 (HTLV-1) infection may lead to one or both diseases including HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) or adult T cell leukemia lymphoma (ATLL). The complete interactions of the virus with host cells in both diseases is yet to be determined. This study aims to construct an interaction network for distinct signaling pathways in these diseases based on finding differentially expressed genes (DEGs) between HAM/TSP and ATLL.ResultsWe identified 57 hub genes with higher criteria scores in the primary protein-protein interaction network (PPIN). The ontology-based enrichment analysis revealed following important terms: positive regulation of transcription from RNA polymerase II promoter, positive regulation of transcription from RNA polymerase II promoter involved in meiotic cell cycle and positive regulation of transcription from RNA polymerase II promoter by histone modification. The upregulated genes TNF, PIK3R1, HGF, NFKBIA, CTNNB1, ESR1, SMAD2, PPARG and downregulated genes VEGFA, TLR2, STAT3, TLR4, TP53, CHUK, SERPINE1, CREB1 and BRCA1 were commonly observed in all the three enriched terms in HAM/TSP vs. ATLL. The constructed interaction network was then visualized inside a mirrored map of signaling pathways for ATLL and HAM/TSP, so that the functions of hub genes were specified in both diseases.Peer reviewe

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe

Deciphering microRNA-mRNA regulatory network in adult T-cell leukemia/lymphoma; the battle between oncogenes and anti-oncogenes.

Adult T-cell leukemia/lymphoma (ATLL) is virus-caused cancer that originates from the infection by human T-cell leukemia virus type 1. ATLL dysregulates various biological pathways related to the viral infection and cancer progression through the dysexpression of miRNAs and mRNAs. In this study, the potential regulatory subnetworks were constructed aiming to shed light on the pathogenesis mechanism of ATLL. For this purpose, two mRNA and one miRNA expression datasets were firstly downloaded from the GEO database. Next, the differentially expressed genes and miRNAs (DEGs and DE-miRNAs, respectively), as well as differentially co-expressed gene pairs (DCGs), were determined. Afterward, common DEGs and DCGs targeted by experimentally validated DE-miRNAs were explored. The oncogenic and anti-oncogenic miRNA-mRNA regulatory subnetworks were then generated. The expression levels of four genes and two miRNAs were examined in the blood samples by qRT-PCR. The members of three oncogenic/anti-oncogenic subnetworks were generally enriched in immune, virus, and cancer-related pathways. Among them, FZD6, THBS4, SIRT1, CPNE3, miR-142-3p, and miR-451a were further validated by real-time PCR. The significant up-regulation of FZD6, THBS4, and miR-451a as well as down-regulation of CPNE3, SIRT1, and miR-142-3p were found in ATLL samples than normal samples. The identified oncogenic/anti-oncogenic subnetworks are pieces of the pathogenesis puzzle of ATLL. The ultimate winner is probably an oncogenic network that determines the final fate of the disease. The identified genes and miRNAs are proposed as novel prognostic biomarkers for ATLL

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Abstract Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability

Extractive Spectrophotometric Method for Determination of Pioglitazone Hydrochloride in Raw Material and Tablets Using Ion-Pair Formation

A simple, rapid and extractive spectrophotometric method was developed for the determination of pioglitazone hydrochloride in pure and pharmaceutical formulations. This method is based on the formation of yellow ion-pair complex between the basic nitrogen of the drug and bromocresol green (BCG) in phthalate buffer of pH 2.4. The formed complexes were extracted with chloroform and measured at 419 nm. The analytical parameters and their effects on the proposed systems are investigated. Beer’s law was obeyed in the range 2.5-14 μg/mL with correlation coefficient ≥ 0.995. The proposed method has been applied successfully for the determination of drug in commercial tablets dosage forms. No significant interference was observed from the excipients commonly used as pharmaceutical aids with the assay procedure. The validity of the proposed method was established by parallel determination against HPLC method and there was no significant difference between these two methods

A genosensor for detection of HTLV-I based on photoluminescence quenching of fluorescent carbon dots in presence of iron magnetic nanoparticle-capped Au

Abstract Carbon dots and Fe3O4@Au were synthesized to develop a new biosensor to detect DNA target. We investigated the photoluminescence property of carbon dots (CDs) in the presence of Fe3O4-capped Au (Fe3O4@Au). Firstly, we designed two dedicated probes for unique long sequence region of human T-lymphotropic virus type 1 genome. One of the probes was covalently bound to the CDs. In the absence of target, CDs-probe was adsorbed on the surface of Fe3O4@Au through two possible mechanisms, leading to quenching the fluorescence emission of CDs. The fluorescence emission of CDs was recovered in the presence of target since double-stranded DNA cannot adsorb on the Fe3O4@Au. Also, Fe3O4@Au can adsorb the unhybridized oligonucleotides and improves the accuracy of detection. The specificity of the proposed biosensor was confirmed by BLAST search and assessed by exposing the biosensor to other virus targets. The experimental detection limit of the biosensor was below 10 nM with linear range from 10 to 320 nM

Neurological complications after COVID-19: A narrative review

COVID-19 is primarily classified as a respiratory disorder; however, various neurological symptoms have been reported in COVID-19 patients. Neurological manifestations may be the initial signs of COVID-19 and can develop in patients of different age groups and with or without underlying disease. COVID-19 causes a broad range of complications in the central nervous system. These include headaches, altered mental status, dizziness, seizures, cerebrovascular events, encephalitis, and other encephalopathies. Moreover, a broad spectrum of peripheral nervous system symptoms such as olfactory and gustatory dysfunctions, neuropathy, visual impairments, neuralgia, cranial nerves palsy, and muscle involvement could manifest as symptoms. Despite various efforts, the exact pathogenesis of the COVID-19 neurological complications has not been clarified yet. Moreover, the reason for the development of neurological manifestation in only some COVID-19 patients has not been determined. This review focuses on the different neurological symptoms associated with COVID-19 and the possible pathological mechanisms hoping to provide new insights for diagnosis, therapies, or other forms of intervention

Rethink about electrolyte: Potassium fluoride as a promising additive to an electrolyte for the water oxidation by a nanolayered Mn oxide

Water oxidation is a bottleneck of the hydrogen production through the water-splitting reaction. Herein, the promising role of fluoride on the water-oxidizing activity of a nano layered Mn oxide under the electrochemical condition is reported. The experiments show the increase of the water-oxidizing activity of the nanolayered Mn oxide under an electro-water oxidation circumstance in the presence of potassium fluoride as a promising additive to an electrolyte. As a result, the required overpotential is decreased and the yield of oxygen evolution raised in the water-oxidation reaction. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved