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Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%–15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

With the high pervasiveness of viral diseases, the battle against viruses has never ceased. Here we discuss five cellular processes, namely “autophagy”, “programmed cell death”, “immune response”, “cell cycle alteration”, and “lipid metabolic reprogramming”, that considerably guide viral replication after host infection in an orchestrated manner. On viral infection, “autophagy” and “programmed cell death” are two dynamically synchronized cell survival programs; “immune response” is a cell defense program typically suppressed by viruses; “cell cycle alteration” and “lipid metabolic reprogramming” are two altered cell housekeeping programs tunable in both directions. We emphasize on their functionalities in modulating viral replication, strategies viruses have evolved to tune these processes for their benefit, and how these processes orchestrate and govern cell fate upon viral infection. Understanding how viruses hijack host networks has both academic and industrial values in providing insights toward therapeutic strategy design for viral disease control, offering useful information in applications that aim to use viral vectors to improve human health such as gene therapy, and providing guidelines to maximize viral particle yield for improved vaccine production at a reduced cost.

The COVID-19 is an issue of international concern and threat to public health and there is an urgent need of drug/vaccine design. There is no vaccine or specific drug yet made as of July 23, 2020, for the coronavirus disease (COVID-19). Thus, the patients currently can only be treated symptomatically. A quick identification of the drugs for COVID-19 may act as a potential therapeutic medication which has been used earlier in patients to answer the present pandemic condition before it could get more worse. According to our view, an artificial intelligence (AI) based tool that may predict drugs/peptides directly from the sequences of infected patients and thereby, they might have better affinity with the target and contribute towards vaccine design against COVID-19. Researchers across the world proposed several vaccines/drugs for COVID-19 utilizing AI based approaches, however, testing of these proposed vaccines/drugs will be needed to verify the safety and feasibility for combating COVID-19.

GPR (G protein receptor) 139 and 142 are novel foundling GPCRs (G protein-coupled receptors) in the class “A” of the GPCRs family and are suitable targets for various biological conditions. To engage these targets, validated pharmacophores and 3D QSAR (Quantitative structure-activity relationship) models are widely used because of their direct fingerprinting capability of the target and an overall accuracy. The current work initially analyzes GPR139 and GPR142 for its genomic alteration via tumor samples. Next to that, the pharmacophore is developed to scan the 3D database for such compounds that can lead to potential agonists. As a result, several compounds have been considered, showing satisfactory performance and a strong association with the target. Additionally, it is gripping to know that the obtained compounds were observed to be responsible for triggering pan-cancer. This suggests the possible role of novel GPR139 and GPR142 as the substances for initiating a physiological response to handle the condition incurred as a result of cancer.

One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target’s deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer. We aim to identify a prognostic lncRNA signature for the TNBC. First, samples were filtered out with inadequate tumor purity and retrieved the lncRNA expression data stored in the TANRIC catalog. TNBC sufferers were divided into two prognostic classes which were dependent on their survival time (shorter or longer than 3 years). Random forest was utilized to select lncRNA features based on the lncRNAs differential expression between shorter and longer groups. The Stochastic gradient boosting method was used to construct the predictive model. As a whole, 353 lncRNAs were differentially transcribed amongst the shorter and longer groups. Using the recursive feature elimination, two lncRNAs were further selected. Trained by stochastic gradient boosting, we reached the highest accuracy of 69.69% and area under the curve of 0.6475. Our findings showed that the two-lncRNA signs can be proved as potential biomarkers for the prognostic grouping of TNBC’s sufferers. Many lncRNAs remained dysregulated in TNBC, while most of them are likely play a role in cancer biology. Some of these lncRNAs were linked to TNBC’s prediction, which makes them likely to be promising biomarkers.

Esophageal adenocarcinoma (EAC) is a deadly cancer with high mortality rate, especially in economically advanced countries, while Barrett's esophagus (BE) is reported to be a precursor that strongly increases the risk of EAC. Due to the complexity of these diseases, their molecular mechanisms have not been revealed clearly. This study aims to explore the gene signatures shared between BE and EAC based on integrated network analysis. We obtained EAC- and BE-associated microarray datasets GSE26886, GSE1420, GSE37200, and GSE37203 from the Gene Expression Omnibus and ArrayExpress using systematic meta-analysis. These data were accompanied by clinical data and RNAseq data from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were conducted to explore the relationship between gene sets and clinical traits as well as to discover the key relationships behind the co-expression modules. A differentially expressed gene-based protein–protein interaction (PPI) complex was used to extract hub genes through Cytoscape plugins. As a result, 403 DEGs were excavated, comprising 236 upregulated and 167 downregulated genes, which are involved in the cell cycle and replication pathways. Forty key genes were identified using modules of MCODE, CytoHubba, and CytoNCA with different algorithms. A dark-gray module with 207 genes was identified which having a high correlation with phenotype (gender) in the WGCNA. Furthermore, five shared hub gene signatures (SHGS), namely, pre-mRNA processing factor 4 (PRPF4), serine and arginine-rich splicing factor 1 (SRSF1), heterogeneous nuclear ribonucleoprotein M (HNRNPM), DExH-Box Helicase 9 (DHX9), and origin recognition complex subunit 2 (ORC2), were identified between BE and EAC. SHGS enrichment denotes that RNA metabolism and splicosomes play a key role in esophageal cancer development and progress. We conclude that the PPI complex and WGCNA co-expression network highlight the importance of phenotypic identifying hub gene signatures for BE and EAC.

This study aims to achieve a clearer and stronger understanding of all the mechanisms involved in the occurrence as well as in the progression of lung cancer along with discovering trustworthy prognostic markers. We combined four gene expression profiles (GSE19188, GSE19804, GSE101929, and GSE18842) from the GEO database and screened the commonly differentially expressed genes (CDEGs). We performed differentially expressed group analysis on CDEGs, alteration and mutational analysis, and expression level verification of core differential genes. Systems biology discoveries in our examination are predictable with past reports. Curiously, our examination revealed that screened biomarker adjustments, for the most part, coexist in lung cancer. After screening 952 CDEGs, we found that the up-regulation of neuromedin U (NMU) and GTSE1 in the case of lung cancer is related to poor prognosis. On the other hand, FOS CDKN1C expression is associated with poor prognosis and is responsible for the down-regulation of CDKN1C and FOS. Changes in these qualities are on free pathways to lung cancer and are not usually of combined quality variety. Even though biomarkers were related to both survival occasions in our examination, it gives us another point of view while playing out the investigation of hereditary changes and clinical highlights employing information mining. Based on our results, we found potential and prospective clinical applications in GTSE1, NMU, FOS, and CDKN1C to act as prognostic markers in case of lung cancer.

LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers’ signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins’ features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.

A central approach for better understanding the forces involved in maintaining protein structures is to investigate the protein folding and thermodynamic properties. The effect of the folding process is often disturbed in mutated states. To explore the dynamic properties behind mutations, molecular dynamic (MD) simulations have been widely performed, especially in unveiling the mechanism of drug failure behind mutation. When comparing wild type (WT) and mutants (MTs), the structural changes along with solvation free energy (SFE), and Gibbs free energy (GFE) are calculated after the MD simulation, to measure the effect of mutations on protein structure. Pyrazinamide (PZA) is one of the first-line drugs, effective against latent Mycobacterium tuberculosis isolates, affecting the global TB control program 2030. Resistance to this drug emerges due to mutations in pncA and rpsA genes, encoding pyrazinamidase (PZase) and ribosomal protein S1 (RpsA) respectively. The question of how the GFE may be a measure of PZase and RpsA stabilities, has been addressed in the current review. The GFE and SFE of MTs have been compared with WT, which were already found to be PZA-resistant. WT structures attained a more stable state in comparison with MTs. The physiological effect of a mutation in PZase and RpsA may be due to the difference in energies. This difference between WT and MTs, depicted through GFE plots, might be useful in predicting the stability and PZA-resistance behind mutation. This study provides useful information for better management of drug resistance, to control the global TB problem.

Nanotechnology utilizes engineered materials and devices which function with biological systems at the molecular level and could transform the management of neurodegenerative diseases (NDs) by provoking, reacting to, and intermingling with target sites to stimulate physiological responses while minimizing side effects. Blood–brain barrier (BBB) protects the brain from harmful agents, and transporting drugs across the BBB is a major challenge for diagnosis, targeting, and treatment of NDs. The BBB provides severe limitations for diagnosis and treatment of Alzheimer’s disease (AD), Parkinson’s disease (PD), and various other neurological diseases. Conventional drug delivery systems generally fail to cross the BBB, thus are inefficient in treatment. Although gradual development through research is ensuring the progress of nanotheranostic approaches from animal to human modeling, aspects of translational applicability and safety are a key concern. This demands a deep understanding of the interaction of body systems with nanomaterials. There are various plant-based nanobioactive compounds which are reported to have applicability in the diagnosis and treatment of these NDs. This review article provides an overview of applications of nanotheranostics in AD and PD. The review also discusses nano-enabled drug delivery systems and their current and potential applications for the treatment of various NDs.

Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.

Pyrazinamide (PZA) is one of the main FDA approved drugs to be used as the first line of defense against Mycobacterium Tuberculosis (MTB). It is activated into pyrazinoic acid (POA) via MTB's pncA gene-encoded pyrazinamidase (PZase). Mutations are most commonly responsible for PZA-resistance in nearly 70% of the resistant samples. In the present work, MTB positive samples were chosen for PZA drug susceptibility testing (DST) against critical concentration (100 ug/ml) of PZA. The resistant samples were subjected to pncA sequencing. As a result, 36 various mutations have been observed in the PZA resistant samples, uploaded to the NCBI (GeneBank accession no. MH461111). Here we report the mechanism of PZA resistance behind the three mutants (MTs), Asp12Ala, Pro54Leu, and His57Pro in comparison with the wild type (WT) through molecular dynamics simulation to unveil how these mutations affect the overall conformational stability. The post-simulation analyses revealed notable deviations as compared to the WT structure. Molecular docking studies of PZA with MTs and WT, pocket volume inspection and overall shape complementarity analysis confirmed the deleterious nature of these mutations and gave an insight into the mechanism behind PZA-resistance. These analyses provide vital information regarding MTB drug resistance and could be extremely useful in therapy management and overcoming its global burden.

The nurseries and plantations of medicinally and economically important plants are facing challenge on health front as they are being rapidly exposed to fungal pathogens thereby affecting their health and productivity adversely. The plant pathogenic fungi significantly damage and reduce the flora of subcontinent. Lack of knowledge and trouble in identification of fungal pathogens, are imposing a peril to both the flora and human health all over the world. Albeit routine pathological techniques can be used for the identification of plant fungal pathogens but these methods are time consuming and often need sound knowledge in mycotaxonomy. Recently, molecular (DNA sequence) data has emerged with the nuclear ribosomal internal transcribed spacer (ITS) region (DNA barcoding) as crucial biomarker to disclose the necessary information for the taxonomic identification of plant pathogenic fungi. However, development of nucleotide sequence database of plant pathogenic fungi will enable authentic pathogen identification easy and quickly even by a person not trained in fungal taxonomy. This study presents the development of a new plant fungal pathogen -Internal Transcribed Spacer Sequence database (PFP- ITSS Database) holding 1215 ITS sequences collected from various sources. It represents 1215 plant fungal pathogens (PFP) in relation to their respective economical and medicinal plants; and is available at http://shaktisahislab.com/include/ITSdb. PFP-ITSS Database will provide useful information for better understanding of plant pathogenic fungi, which cause disease in both economical and medicinal plants. Keywords: ITS, Wrapper, Integrity, Mining, BLAST, Primer

Alterations and personal variations of RNA interactions have been mechanistically coupled with disease etiology and phenotypical variations. RNA biomarkers, RNA mimics, and RNA antagonists have been developed for diagnostic, prognostic, and therapeutic uses. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two major types of RNA molecules with regulatory roles, deregulation of which has been implicated in the initiation and progression of many human malignancies. Accumulating evidence indicated the clinical roles of regulatory RNAs in cancer control, stimulating a surge in exploring the functionalities of regulatory RNAs for improved understanding on disease pathogenesis and management. In this review, we highlight the critical roles of lncRNAs and miRNAs played in tumorigenesis, scrutinize their potential functionalities as diagnostic/prognostic biomarkers and/or therapeutic targets in clinics, outline opportunities that ncRNAs may bring to complement current clinical practice for improved cancer management and identify challenges faced by translating frontier knowledge on non-coding RNAs (ncRNAs) to bedside clinics as well as possible solutions.

Background: The global burden of tuberculosis (TB) and antibiotic resistance is attracting the attention of researchers to develop some novel and rapid diagnostic tools. Although, the conventional methods like culture are considered as the gold standard, they are time consuming in diagnostic procedure, during which there are more chances in the transmission of disease. Further, the Xpert MTB/RIF assay offers a fast diagnostic facility within 2 h, but due to low sensitivity in some sample types may lead to more serious state of the disease. The role of computer technologies is now increasing in the diagnostic procedures. Here, in the current study we have applied the artificial neural network (ANN) that predicted the TB disease based on the TB suspect data.Methods: We developed an approach for prediction of TB, based on an ANN. The data was collected from the TB suspects, guardians or care takers along with samples, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12,636 records of TB patients, collected during the years 2016 and 2017 from the provincial TB reference laboratory, Khyber Pakhtunkhwa, Pakistan. The training and test set of the suspect data were kept as 70 and 30%, respectively, followed by validation and normalization. The ANN takes the TB suspect’s information such as gender, age, HIV-status, previous TB history, sample type, and signs and symptoms for TB prediction.Results: Based on TB patient data, ANN accurately predicted the Mycobacterium tuberculosis (MTB) positive or negative with an overall accuracy of >94%. Further, the accuracy of the test and validation were found to be >93%. This increased accuracy of ANN in the detection of TB suspected patients might be useful for early management of disease to adopt some control measures in further transmission and reduce the drug resistance burden.Conclusion: ANNs algorithms may play an effective role in the early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in diagnostics for rapid disease management. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in countries with a high TB burden.

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46-62 and 65-76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

Pyrazinamide (PZA) is the only drug for the elimination of latent Mycobacterium tuberculosis (MTB) isolates. However, due to the increased number of PZA-resistance, the chances of the success of global TB elimination seems to be more prolonged. Recently, marine natural products (MNPs) as an anti-TB agent have received much attention, where some compounds extracted from marine sponge, Haliclona sp. exhibited strong activity under aerobic and hypoxic conditions. In this study, we screened articles from 1994 to 2019 related to marine natural products (MNPs) active against latent MTB isolates. The literature was also mined for the major regulators to map them in the form of a pathway under the dormant stage. Five compounds were found to be more suitable that may be applied as an alternative to PZA for the better management of resistance under latent stage. However, the mechanism of actions behind these compounds is largely unknown. Here, we also applied synthetic biology to analyze the major regulatory pathway under latent TB that might be used for the screening of selective inhibitors among marine natural products (MNPs). We identified key regulators of MTB under latent TB through extensive literature mining and mapped them in the form of regulatory pathway, where SigH is negatively regulated by RshA. PknB, RshA, SigH, and RNA polymerase (RNA-pol) are the major regulators involved in MTB survival under latent stage. Further studies are needed to screen MNPs active against the main regulators of dormant MTB isolates. To reduce the PZA resistance burden, understanding the regulatory pathways may help in selective targets of MNPs from marine natural sources.

Abstract: An ecological optimization along with a detailed parametric study of an irreversible regenerative Brayton heat engine with isothermal heat addition have been carried out with external as well as internal irreversibilities. The ecological function is defined as the power output minus the power loss (irreversibility) which is ambient temperature times the entropy generation rate. The external irreversibility is due to finite temperature difference between the heat engine and the external reservoirs while the internal irreversibilities are due to nonisentropic compression and expansion processes in the compressor and the turbine respectively and the regenerative heat loss. The ecological function is found to be an increasing function of the isothermal-, sink- and regenerative-side effectiveness, isothermal-side inlet temperature, component efficiencies and sink-side temperature while it is found to be a decreasing function of the isobaric-side temperature and effectiveness and the working fluid heat capacitance rate. The effects of the isobaric-side effectiveness are found to be more than those of the other parameters and the effects of turbine efficiency are found to be more than those of the compressor efficiency on all the performance parameters of the cycle.