A Unified Computational Framework for a Robust, Reliable, and Reproducible Identification of Novel miRNAs From the RNA Sequencing Data.

In eukaryotic cells, miRNAs regulate a plethora of cellular functionalities ranging from cellular metabolisms, and development to the regulation of biological networks and pathways, both under homeostatic and pathological states like cancer.Despite their immense importance as key regulators of cellular processes, accurate and reliable estimation of miRNAs using Next Generation Sequencing is challenging, largely due to the limited availability of robust computational tools/methods/pipelines. Here, we introduce miRPipe, an end-to-end computational framework for the identification, characterization, and expression estimation of small RNAs, including the known and novel miRNAs and previously annotated pi-RNAs from small-RNA sequencing profiles.

MiR-330-5p and miR-1270 target essential components of RNA polymerase I transcription and exhibit a novel tumor suppressor role in lung adenocarcinoma.

Upregulation of RNA polymerase I (Pol I) transcription and the overexpression of Pol I transcriptional machinery are crucial molecular alterations favoring malignant transformation. However, the causal molecular mechanism(s) of this aberration remain largely unknown. Here, we found that Pol I transcription and its core machinery are upregulated in lung adenocarcinoma (LUAD).

Identification of a new member of Mortaparib class of inhibitors that target mortalin and PARP1.

Mortalin, a heat shock family protein enriched in cancer cells, is known to inactivate tumor suppressor protein p53. Abrogation of mortalin-p53 interaction and reactivation of p53 has been shown to trigger growth arrest/apoptosis in cancer cells and hence, suggested to be useful in cancer therapy. In this premise, we earlier screened a chemical library to identify potential disruptors of mortalin-p53 interaction, and reported two novel synthetic small molecules (5-[1-(4-methoxyphenyl) (1,2,3,4-tetraazol-5-yl)]-4-phenylpyrimidine-2-ylamine) and (4-[(1E)-2-(2-phenylindol-3-yl)-1-azavinyl]-1,2,4-triazole) called Mortaparib and Mortaparib, respectively.

Gene expression based inference of cancer drug sensitivity.

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data.

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study.

Background: Evidence from peer-reviewed literature is the cornerstone for designing responses to global threats such as COVID-19. In massive and rapidly growing corpuses, such as COVID-19 publications, assimilating and synthesizing information is challenging. Leveraging a robust computational pipeline that evaluates multiple aspects, such as network topological features, communities, and their temporal trends, can make this process more efficient.

Pea aphid winged and wingless males exhibit reproductive, gene expression, and lipid metabolism differences.

Alternative, intraspecific phenotypes offer an opportunity to identify the mechanistic basis of differences associated with distinctive life history strategies. Wing dimorphic insects, in which both flight-capable and flight-incapable individuals occur in the same population, are particularly well-studied in terms of why and how the morphs trade off flight for reproduction. Yet despite a wealth of studies examining the differences between female morphs, little is known about male differences, which could arise from different causes than those acting on females.

Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images.

Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling.

A Mobile Education and Social Support Group Intervention for Improving Postpartum Health in Northern India: Development and Usability Study.

Background: Structural and cultural barriers limit Indian women's access to adequate postnatal care and support despite their importance for maternal and neonatal health. Targeted postnatal education and support through a mobile health intervention may improve postnatal recovery, neonatal care practices, nutritional status, knowledge and care seeking, and mental health.

Objective: We sought to understand the feasibility and acceptability of our first pilot phase, a flexible 6-week postnatal mobile health intervention delivered to 3 groups of women in Punjab, India, and adapt our intervention for our next pilot phase, which will formally assess intervention feasibility, acceptability, and preliminary efficacy.

A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations.

Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Postinduction of carcinogenicity, mutations further propagate and drastically alter the cancer genomes. Although a subset of driver mutations has been identified and characterized to date, most cancer-related somatic mutations are indistinguishable from germline variants or other noncancerous somatic mutations.

: Learning effective strategies for COVID-19 vaccine distribution using reinforcement learning.

A vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread.

Characterizing the mutational landscape of MM and its precursor MGUS.

Mutational Signatures and Tumor mutational burden (TMB) have emerged as prognostic biomarkers in cancer genomics. However, the association of TMB with overall survival (OS) is still unknown in newly diagnosed multiple myeloma (NDMM) patients. Further, the change in the mutational spectrum involving both synonymous and non-synonymous mutations as MGUS progresses to MM is unexplored.

Data mining and structural analysis for multi-tissue regeneration potential of BMP-4 and activator drugs.

Despite substantial progress in surgery, managing multi-tissue injuries is strenuous to accomplish and requires a multi-staged serial treatment of individual tissues. Stimulated regeneration affects the complete structural and functional repair of both hard and soft tissues post-injury and thus serves as an attractive therapeutic option to target multi-tissue injuries. This study utilized data mining and structural analysis to identify a target that has the ability to evoke healing of the two most commonly injured tissues i.

Conserved and divergent chaperoning effects of Hsp60/10 chaperonins on protein folding landscapes.

The GroEL/ES chaperonin cavity surface charge properties, especially the negative charges, play an important role in its capacity to assist intracavity protein folding. Remarkably, the larger fraction of GroEL/ES negative charges are not conserved among different bacterial species, resulting in a large variation in negative-charge density in the GroEL/ES cavity across prokaryotes. Intriguingly, eukaryotic GroEL/ES homologs have the lowest negative-charge density in the chaperonin cavity.

Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning.

The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present , a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences.

Protein Structure Readouts of Cancer Drivers for Precision Medicine.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of cancer or a transformed cell, the initiation and progression are driven by only a few mutational events, viz., driver mutations that progressively govern and execute the functional impacts.

Working for your own folks: the microeconomics of social media.

This paper uses a comparative ethnographic approach to explore the ways in which social media enables new economic strategies that capitalize on women's traditional forms of reliance within their local communities. We use ethnographic examples from northern Chile, southeast Italy, and south India to show how women are successful in establishing small but prestigious entrepreneurial activities by using social media to respond to local social and cultural needs. Women use social media to transform both conventional work practices and individuals' notions of work in ways that overcome important structural constraints they face in their respective communities.

Cancer chemotherapy and beyond: Current status, drug candidates, associated risks and progress in targeted therapeutics.

Cancer is an abnormal state of cells where they undergo uncontrolled proliferation and produce aggressive malignancies that causes millions of deaths every year. With the new understanding of the molecular mechanism(s) of disease progression, our knowledge about the disease is snowballing, leading to the evolution of many new therapeutic regimes and their successive trials. In the past few decades, various combinations of therapies have been proposed and are presently employed in the treatment of diverse cancers.

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits.

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers.

Caffeic acid phenethyl ester (CAPE) confers wild type p53 function in p53 mutant: bioinformatics and experimental evidence.

Mutations in the tumor suppressor protein p53 is a prevalent feature in majority of cancers resulting in inactivation of its activities related to control of cell cycle progression and proliferation. p53 is one of the common hotspot mutations that causes decrease in its thermodynamic stability. Some small molecules have been shown to bind to the mutated site and restore its wild type thermodynamics and tumor suppressor function.

A Low Dose Combination of Withaferin A and Caffeic Acid Phenethyl Ester Possesses Anti-Metastatic Potential In Vitro: Molecular Targets and Mechanisms.

Withaferin A (Wi-A) and Caffeic Acid Phenethyl Ester (CAPE) are the bioactive ingredients of Ashwagandha () and propolis, respectively. Both of these natural compounds have been shown to possess anticancer activity. In the present study, we recruited a low dose of each of these compounds and developed a combination that exhibited remarkably potent anti-migratory and anti-angiogenic activities.