Statistical Bioinformatics to Uncover the Underlying Biological Mechanisms That Linked Smoking with Type 2 Diabetes Patients Using Transcritpomic and GWAS Analysis.

Type 2 diabetes (T2D) is a chronic metabolic disease defined by insulin insensitivity corresponding to impaired insulin sensitivity, decreased insulin production, and eventually failure of beta cells in the pancreas. There is a 30-40 percent higher risk of developing T2D in active smokers. Moreover, T2D patients with active smoking may gradually develop many complications. However, there is still no significant research conducted to solve the issue. Hence, we have proposed a highthroughput network-based quantitative pipeline employing statistical methods. Transcriptomic and GWAS data were analysed and obtained from type 2 diabetes patients and active smokers. Differentially Expressed Genes (DEGs) resulted by comparing T2D patients' and smokers' tissue samples to those of healthy controls of gene expression transcriptomic datasets. We have found 55 dysregulated genes shared in people with type 2 diabetes and those who smoked, 27 of which were upregulated and 28 of which were downregulated. These identified DEGs were functionally annotated to reveal the involvement of cell-associated molecular pathways and GO terms. Moreover, protein-protein interaction analysis was conducted to discover hub proteins in the pathways. We have also identified transcriptional and post-transcriptional regulators associated with T2D and smoking. Moreover, we have analysed GWAS data and found 57 common biomarker genes between T2D and smokers. Then, Transcriptomic and GWAS analyses are compared for more robust outcomes and identified 1 significant common gene, 19 shared significant pathways and 12 shared significant GOs. Finally, we have discovered protein-drug interactions for our identified biomarkers.

Adsorption Studies of Volatile Organic Compound (Naphthalene) from Aqueous Effluents: Chemical Activation Process Using Weak Lewis Acid, Equilibrium Kinetics and Isotherm Modelling.

This study deals with the preparation of activated carbon (CDSP) from date seed powder (DSP) by chemical activation to eliminate polyaromatic hydrocarbon-PAHs (naphthalene-C10H8) from synthetic wastewater. The chemical activation process was carried out using a weak Lewis acid of zinc acetate dihydrate salt (Zn(CH3CO2)2·2H2O). The equilibrium isotherm and kinetics analysis was carried out using DSP and CDSP samples, and their performances were compared for the removal of a volatile organic compound-naphthalene (C10H8)-from synthetic aqueous effluents or wastewater. The equilibrium isotherm data was analyzed using the linear regression model of the Langmuir, Freundlich and Temkin equations. The R2 values for the Langmuir isotherm were 0.93 and 0.99 for naphthalene (C10H8) adsorption using DSP and CDSP, respectively. CDSP showed a higher equilibrium sorption capacity (qe) of 379.64 µg/g. DSP had an equilibrium sorption capacity of 369.06 µg/g for C10H8. The rate of reaction was estimated for C10H8 adsorption using a pseudo-first order, pseudo-second order and Elovich kinetic equation. The reaction mechanism for both the sorbents (CDSP and DSP) was studied using the intraparticle diffusion model. The equilibrium data was well-fitted with the pseudo-second order kinetics model showing the chemisorption nature of the equilibrium system. CDSP showed a higher sorption performance than DSP due to its higher BET surface area and carbon content. Physiochemical characterizations of the DSP and CDSP samples were carried out using the BET surface area analysis, Fourier-scanning microscopic analysis (FSEM), energy-dispersive X-ray (EDX) analysis and Fourier-transform spectroscopic analysis (FTIR). A thermogravimetric and ultimate analysis was also carried out to determine the carbon content in both the sorbents (DSP and CDSP) here. This study confirms the potential of DSP and CDSP to remove C10H8 from lab-scale synthetic wastewater.

Molecular Biomarker Identification Using a Network-Based Bioinformatics Approach That Links COVID-19 With Smoking.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to "Smoking and COVID-19: a scoping review," about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

Bioinformatics approach to analyse COVID-19 biomarkers accountable for generation of intracranial aneurysm in COVID-19 patients.

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer.

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery.

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

Exploring the anti-cancer potential of dietary phytochemicals for the patients with breast cancer: A comprehensive review.

BACKGROUND: The most common and deadly cancer in female is breast cancer (BC) and new incidence and deaths related to this cancer are rising. AIMS: Several issues, that is, high cost, toxicity, allergic reactions, less efficacy, multidrug resistance, and the economic cost of conventional anti-cancer therapies, has prompted scientists to discover innovative approaches and new chemo-preventive agents. MATERIALS: Numerous studies are being conducted on plant-based and dietary phytochemicals to discover new-fangled and more advanced therapeutic approaches for BC management. RESULT: We have identified that natural compounds modulated many molecular mechanisms and cellular phenomena, including apoptosis, cell cycle progression, cell proliferation, angiogenesis and metastasis, up-regulation of tumor-suppressive genes, and down-regulation of oncogenes, modulation of hypoxia, mammosphere formation, onco-inflammation, enzymatic regulation, and epigenetic modifications in BC. We found that a number of signaling networks and their components such as PI3K/Akt/mTOR, MMP-2 and 9, Wnt/-catenin, PARP, MAPK, NF-κB, Caspase-3/8/9, Bax, Bcl2, Smad4, Notch1, STAT3, Nrf2, and ROS signaling can be regulated in cancer cells by phytochemicals. They induce up-regulation of tumor inhibitor microRNAs, which have been highlighted as a key player for ani-BC treatments followed by phytochemical supplementation. CONCLUSION: Therefore, this collection offers a sound foundation for further investigation into phytochemicals as a potential route for the development of anti-cancer drugs in treating patients with BC.

Methodology for laboratory-based antimicrobial resistance surveillance in animals.

Antimicrobial resistance (AMR) is a crucial and emerging multifactorial "One Health" problem involving human and animal health, agriculture, aquaculture, and environment; and posing a potential public health hazard globally. The containment of AMR justifies effective surveillance programs to explicate the magnitude of the problem across the contributing sectors. Laboratory-based AMR testing and characterization is the key component of an AMR surveillance program. An AMR surveillance program should have a "top management" for fund mobilization, planning, formulating, and multilateral coordinating of the surveillance activities. The top management should identify competent participating laboratories to form a network comprising a reference laboratory and an adequate number of sentinel laboratories. The responsibilities of the reference laboratory include the development of standardized test methods for ensuring quality and homogeneity of surveillance activities, providing training to the laboratory personnel, and in-depth AMR characterization. The sentinel laboratories will take the responsibilities of receiving samples, isolation and identification of microbes, and initial AMR characterization. The sentinel laboratories will use simple antimicrobial susceptibility test (AST) methods such as disk diffusion tests, whereas the reference laboratories should use automated quantitative AST methods as well as advanced molecular methods to explicit AMR emergence mechanisms. Standard guidelines set by Clinical Laboratory Standards Institute or the European Committee on Antimicrobial Susceptibility Testing, should be followed to bring about conformity and harmonization in the AST procedures. AMR surveillance program in animals is eventually similar to that in human health with the exception is that veterinary antibiotics and veterinary pathogens should be given preference here. Hence, the review study was envisaged to look deep into the structure of the AMR surveillance program with significance on laboratory-based AMR testing and characterization methods.

Assessment of microplastics and nanoplastics released from a chopping board using Raman imaging in combination with three algorithms.

As contaminants of emerging concern, microplastics and nanoplastics are ubiquitous in not only aquatic and terrestrial environments but also household settings. While the characterisation of microplastics is still a challenge, the analysis of nanoplastics is even more difficult. In this study, we aim to examine several novel algorithmic methods intended for analysing complex Raman spectrum matrices towards visualisation of plastic particles released from a chopping board. Specifically, we compare and advance three decoding algorithms, including (i) a logic-based algorithm to merge and cross-check multiple Raman images that map the intensities of several characteristic peaks; (ii) a principal component analysis-based algorithm to generate intensity images from whole sets of spectra, not just from individual characteristic peaks; (iii) an algebra-based algorithm to merge and cross-check the loading matrix to enhance characterisation efficiency. Assisted with a scanning electron microscope, we estimate that 100-300 microplastics / nanoplastics per mm per cut along the groove formed on the chopping board, and ~3000 per mm2 per cut in the scratched area, may be released from a chopping board during food preparation and may be subsequently ingested by human. Overall, the Raman imaging combined with algorithms can provide effective characterisation of microplastics and nanoplastics.

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services.

The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the "HAM10000" dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model's classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Evidence of combined effect of amino acid substitutions within G-H and B-C loops of VP1 conferring serological heterogeneity in foot-and-mouth disease virus serotype A.

Foot-and-mouth disease virus (FMDV) serotype A exhibits a higher degree of genetic and antigenic diversity resulting in frequent vaccine failure due to serological mismatch between the vaccine and heterologous strains. Currently, knowledge on the molecular basis of antigenic relationships among the FMDVs is limited; nevertheless, intratype antigenic variation due to mutation(s) is widely considered as the main hurdle to appropriate FMD vaccine development. Here, we studied genetic and antigenic variations of four FMDV serotype A isolates, BAN/GA/Sa-197/2013 (BAN-197), BAN/CH/Sa-304/2016 (BAN-304), BAN/DH/Sa-307/2016 (BAN-307) and BAN/DH/Sa-310/2017 (BAN-310) circulating in Bangladesh during 2013-2017. Initially, antigenic relationships (r1 -values) of the field isolates were evaluated by the two-dimensional microneutralization test (2D-MNT) using the hyperimmune antisera raised in cattle against the vaccine strain, BAN-304. Interesingly, the results showed protective serological cross-reactivity (r1 -values > 0.4) between the vaccine strain and the field isolates, BAN-307 and BAN-310, except BAN-197 that substantially mismatched (r1  = 0.129 ± 0.043) with the BAN-304. Although VP1-based phylogeny grouped all the isolates within the same sublineage C (a subgroup of VP3Δ59 variant) under the lineage A/ASIA/G-VII, strikingly, computational analyses of the viral capsid proteins demonstrated significant deviation at the VP1 G-H loop of BAN-197 from the vaccine strain, while VP(2-4) of both isolates were structurally conserved. To bridge the gap of how the distortion of the G-H loop and consequent antigenic hetergeneity occurred in BAN-197, we performed in silico combinatorial substitutions of the VP1 mutant amino acids (aa) of BAN-197 with the respective residues in BAN-304. Remarkably, our analyses revealed that two substitutions of distantly located aa at B-C (T48I:threonine â†’ isoleucine) and G-H (A143V:alanine â†’ valine) loops, in combination, distorted the VP1 G-H loop. Overall, this work contributes to understanding the molecular basis of antigenic relationships operating in serotype A FMDVs and the selection of suitable vaccine strain(s) for effective prophylaxis of FMD based on VP1-based analyses.

Emergence of novel lineage of foot-and-mouth disease virus serotype Asia1 BD-18 (G-IX) in Bangladesh.

Foot-and-mouth disease virus (FMDV) is a highly evolutionary divergent pathogen causing great economic havoc in many countries. Among its seven existing serotypes, Asia1 is the least divergent with a single topotype both genetically and antigenically. It is reported sporadically in Indian subcontinent and was classified under lineage G-VIII. In 2018, serotype Asia1 re-emerged in Bangladesh after 2013, along with circulation of a novel serotype Asia1 BD-18 (G-IX) lineage. VP1 phylogeny and sequence variation clearly demonstrated the novel strains which was estimated to have at least >5% nucleotide divergence with distinct clade formation. Also, the Bayesian phylogeographic inferences traced back to the origin time of lineage G-IX in early 2017 and a possible origin in Bangladesh. Mutational analysis considering established eight lineages revealed that the virus strains belonged to lineage G-IX contained a unique mutation at 44 position in the B-C loop region of VP1. Inappropriate vaccination and inefficient outbreak surveillance possibly contributed to the current episode of emergence. Therefore, active surveillance and continued vigilance are essential to assess and timely detect the occurrence, extent and distribution of this novel Asia1 strains in Bangladesh and the neighbouring countries.

Multiplex PCR assay for the detection of five meat species forbidden in Islamic foods.

Food falsification has direct impact on public health, religious faith, fair-trades and wildlife. For the first time, here we described a multiplex polymerase chain reaction assay for the accurate identification of five meat species forbidden in Islamic foods in a single assay platform. Five pairs of species-specific primers were designed targeting mitochondrial ND5, ATPase 6, and cytochrome b genes to amplify 172, 163, 141, 129 and 108 bp DNA fragments from cat, dog, pig, monkey and rat meats, respectively. All PCR products were identified in gel-images and electrochromatograms obtained from Experion Bioanalyzer. Species-specificity checking against 15 important meat and fish and 5 plant species detected no cross-species amplification. Screening of target species in model and commercial meatballs reflected its application to detect target species in process foods. The assay was tested to detect 0.01-0.02 ng DNA under raw states and 1% suspected meats in meatball formulation.

A suitable method for the detection of a potential fraud of bringing macaque monkey meat into the food chain.

Being the third-largest primate population has not made macaque (Macaca fascicularis sp.) monkeys less exposed to threats and dangers. Despite wildlife protection, they have been widely hunted and consumed in several countries because of their purported nutritional values. In addition to trading as pure bush meats in several places, monkey meat has been sold in meatball and soup products in Indonesia. Thus the possibility of macaque meat trafficking under the label of common meats is quite high. This paper reports the development of a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay with the shortest amplicon length for the confirmed detection of monkey meat under compromised states which are known to degrade DNA. We amplified a 120-bp region of d-loop gene using a pair of macaque-specific primers and confirmed their specificity for the target species through cross-challenging against 17 different species using a 141-bp site of an 18 S rRNA gene as an endogenous control for eukaryotes. This eliminated the possibilities of any false-negative detection with complex matrices or degraded specimens. The detection limit was 0.00001 ng DNA in a pure state and 0.1% of meat in mixed matrices and commercial meatball products. RFLP analysis further authenticated the originality of the PCR product and distinctive restriction patterns were found upon AluI and CViKI-1 digestion. A micro-fluidic lab-on-a-chip automated electrophoretic system separated the fragments with high resolution. The assay was validated for screening commercial meatball products with sufficient internal control.