Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism.

Introduction: The objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML).

Methods: In this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%.

Effect of Withaferin-A, Withanone, and Caffeic Acid Phenethyl Ester on DNA Methyltransferases: Potential in Epigenetic Cancer Therapy.

Background: DNA methyltransferases (DNMTs) have been reported to be potential drug targets in various cancers. The major hurdle in inhibiting DNMTs is the lack of knowledge about different DNMTs and their role in the hypermethylation of gene promoters in cancer cells. Lack of information on specificity, stability, and higher toxicity of previously reported DNMT inhibitors is the major reason for inadequate epigenetic cancer therapy.

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges.

Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity.

BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.

Identification of the genomic features responsible for the progression of Multiple Myeloma (MM) cancer from its precancerous stage MGUS can improve the understanding of the disease pathogenesis and, in devising suitable preventive and treatment measures. We have designed an innovative AI-based model, namely, the Bio-inspired Deep Learning architecture for the identification of altered Signaling Pathways (BDL-SP) to discover pivotal genomic biomarkers that can potentially distinguish MM from MGUS. The proposed BDL-SP model comprehends gene-gene interactions using the PPI network and analyzes genomic features using a deep learning (DL) architecture to identify significantly altered genes and signaling pathways in MM and MGUS.

The supply is there. So why can't pregnant and breastfeeding women in rural India get the COVID-19 vaccine?

Despite COVID-19 vaccines being available to pregnant women in India since summer 2021, little is known about vaccine uptake among this high need population. We conducted mixed methods research with pregnant and recently delivered rural women in northern India, consisting of 300 phone surveys and 15 in-depth interviews, in November 2021. Only about a third of respondents were vaccinated, however, about half of unvaccinated respondents reported that they would get vaccinated now if they could.

A folded π-system with supramolecularly oriented dipoles: single-component piezoelectric relaxor with NLO activity.

Organic molecules with an active dipole moment have a natural propensity to align in an antiparallel fashion in the solid state, resulting in zero macroscopic polarization. This primary limitation makes the material unresponsive to switching with electric fields, mechanical forces, and to intense laser light. A single-component organic material that bestows macroscopic dipole-driven electro-mechanical and optical functions, , piezoelectric, ferroelectric and nonlinear optical (NLO) activity, is unprecedented due to the design challenges imparted by crystal symmetry and dipole orientations.

Human microbiome variance is underestimated.

Most of the variance in the human microbiome remains unexplained. Although an extensive list of individual lifestyles shaping the microbiome has been identified, important gaps in knowledge persist. Most human microbiome data are from individuals living in socioeconomically developed countries.

Salivary protein kinase C alpha and novel microRNAs as diagnostic and therapeutic resistance markers for oral squamous cell carcinoma in Indian cohorts.

Oral squamous cell carcinoma (OSCC) is the second leading cause of cancer-related morbidity and mortality in India. Tobacco, alcohol, poor oral hygiene, and socio-economic factors remain causative for this high prevalence. Identification of non-invasive diagnostic markers tailored for Indian population can facilitate mass screening to reduce overall disease burden.

Machine learning models to predict sweetness of molecules.

Sweetness is a vital taste to which humans are innately attracted. Given the increasing prevalence of type-2 diabetes, it is highly relevant to build computational models to predict the sweetness of small molecules. Such models are valuable for identifying sweeteners with low calorific value.

Transcriptional advantage influence odorant receptor gene choice.

Odorant receptors (ORs) obey mutual exclusivity and monoallelic mode of expression. Efforts are ongoing to decipher the molecular mechanism that drives the 'one-neuron-one-receptor' rule of olfaction. Recently, single-cell profiling of olfactory sensory neurons (OSNs) revealed the expression of multiple ORs in the immature neurons, suggesting that the OR gene choice mechanism is much more complex than previously described by the silence-all-and-activate-one model.

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome.

Trait Anxiety Influences Negative Affect-modulated Distribution of Visuospatial Attention.

Visuospatial attention allows humans to selectively gate and prioritize visual (including salient, emotional) information for efficiently navigating natural visual environments. As emotions have been known to influence attentional performance, we asked if emotions also modulate the spatial distribution of visual attention and whether any such effect was further associated with individual differences in anxiety. Participants (n = 28) discriminated the orientation of target Gabor patches co-presented with distractors, speedily and accurately.

Sigma70Pred: A highly accurate method for predicting sigma70 promoter in K-12 strains.

Sigma70 factor plays a crucial role in prokaryotes and regulates the transcription of most of the housekeeping genes. One of the major challenges is to predict the sigma70 promoter or sigma70 factor binding site with high precision. In this study, we trained and evaluate our models on a dataset consists of 741 sigma70 promoters and 1,400 non-promoters.

The nonindustrialised microbiome in a modern world.

The microbiome contributes to human development and maturation, and is essential for maintenance of health and prevention of disease. While the human genome encodes one's identity, the microbiome - also individually unique - provides a window on one's lifestyle and exposure to environmental variables. The microbiome thus serves as a biomarker of host health and a driver of certain diseases.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells.

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood.

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.