Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.

The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.

Key Anodic Interfacial Phenomena and their Control in Next-Generation Lithium and Sodium Metal Batteries.

Advancing next-generation battery technologies requires a thorough understanding of the intricate phenomena occurring at anodic interfaces. This focused review explores key interfacial processes, examining their thermodynamics and consequences in ion transport and charge transfer kinetics. It begins with a discussion on the formation of the electro chemical double layer, based on the GuoyChapman model, and explores how charge carriers achieve equilibrium at the interface.

The q-RASPR approach for predicting the property and fate of persistent organic pollutants.

This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.

Small Molecule with Big Impact: Metarrestin Targets the Perinucleolar Compartment in Cancer Metastasis.

Metarrestin (ML246) is a first-in-class pyrrole-pyrimidine-derived small molecule that selectively targets the perinucleolar compartment (PNC). PNC is a distinct subnuclear structure predominantly found in solid tumor cells. The occurrence of PNC demonstrates a positive correlation with malignancy, serving as an indicator of tumor aggressiveness, progression, and metastasis.

Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally active drugs. We initially developed ML models using nine different algorithms separately on topological descriptors (referred to as simply "descriptors" in the subsequent sections of the manuscript) and MACCS fingerprints (referred to as "fingerprints" in the subsequent sections of the manuscript), thus generating 18 different ML QSAR models. Using the chemical spaces defined by the modeling descriptors and fingerprints, the similarity and error-based RASAR descriptors were computed, and the most discriminating RASAR descriptors were used to develop another set of 18 different ML c-RASAR models.

Intelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210.

Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process.

A Novel Crosslinking Approach for Biomanufacturing of a Collagen-Based Skin Dermal Template.

Third-degree burns result in extensive damage to the skin's epidermal and dermal layers, with limited treatment options available. Currently, xenogeneic collagen-based skin grafts are used as scaffolds to integrate into the wound bed and provide a template for neodermis formation. Existing commercial products like Integra dermal templates rely on a time-consuming and variable dehydrothermal (DHT) crosslinking process.

Decoding KRAS dynamics: Exploring the impact of mutations and inhibitor binding.

KRAS (Kirsten rat sarcoma viral oncogene homologue), the most common mutated protein in human cancers, is the leading cause of morbidity and mortality. Before Sotorasib (AMG-510) was approved for non-small cell lung cancer treatment in 2020, the oncogenic KRAS mutations were believed to be non-druggable. High-resolution X-ray crystal structures of GDP-bound KRAS mutants with and without inhibitor are resolved and deposited in the Protein Data Bank (PDB).

Multi-niche human bone marrow on-a-chip for studying the interactions of adoptive CAR-T cell therapies with multiple myeloma.

Multiple myeloma (MM), a cancer of bone marrow plasma cells, is the second-most common hematological malignancy. However, despite immunotherapies like chimeric antigen receptor (CAR)-T cells, relapse is nearly universal. The bone marrow (BM) microenvironment influences how MM cells survive, proliferate, and resist treatment.

Exploring the role of carbon quantum dots as countermeasure for SARS-CoV-2 virus.

The world witnessed disturbingly rapid unfolding of COVID-19 pandemic with emergence of SARS-CoV-2 virus resulting in severe morbidity and mortality and it still persists through incessant transmission across the globe even after years. Since the last decade, carbon quantum dots (CQDs) have gained much attention due to their favourable aqueous solubility, nano size (<10 nm), inherent fluorescence, biocompatibility, and environment friendliness. In the wider search for effective strategies for treatment, prevention, and diagnosis of SARS-CoV-2 virus, nanotechnology-based formulation using CQDs have emerged as an interesting option.

Hybrid model development through the integration of quantitative read-across (qRA) hypothesis with the QSAR framework: An alternative risk assessment of acute inhalation toxicity testing in rats.

Regulatory authorities frequently need information on a chemical's capacity to produce acute systemic toxicity in humans. Due to concerns about animal welfare, human relevance, and reproducibility, numerous international initiatives have centered on finding a substitute for using animals in acute systemic lethality testing. These substitutes include the more current in-silico and in vitro techniques.

An unusual activity of mycobacterial MutT1 Nudix hydrolase domain as a protein phosphatase regulates nucleoside diphosphate kinase function.

MutT proteins are Nudix hydrolases characterized by the presence of a Nudix box, GX5EX7REUXEEXGU, where U is a bulky hydrophobic residue and X is any residue. Major MutT proteins hydrolyze 8-oxo-(d)GTP (8-oxo-GTP or 8-oxo-dGTP) to the corresponding 8-oxo-(d)GMP, preventing their incorporation into nucleic acids. Mycobacterial MutT1 comprises an N-terminal domain (NTD) harboring the Nudix box motif, and a C-terminal domain (CTD) harboring the RHG histidine phosphatase motif.

Glycyrrhizic Acid-Loaded Poloxamer and HPMC-Based Forming Gel of Acacia Honey for Improved Wound Dressing: Formulation Optimization and Characterization for Wound Treatment.

The present study aims to formulate a stimuli-responsive hydrogel system to codeliver acacia honey and glycyrrhizic acid for topical application that will aid in absorbing wound exudates, control microbial infestation, and produce angiogenic and antioxidant effects to accelerate wound healing. Therefore, both the natural active constituents were incorporated within an hydrogel composed of poloxamer and hydroxypropyl methylcellulose (HPMC), where the concentrations of the polymers were optimized using Design-Expert software considering optimum values of the dependent variables, gelation temperature (34-37 °C), gelation time (<10 min), and the viscosity (2000-3500 cPs). The optimized formulation showed improved physicochemical properties such as mucoadhesiveness, porosity, swelling, and spreadability, which makes it suitable for wound application.

Development of hybrid models by the integration of the read-across hypothesis with the QSAR framework for the assessment of developmental and reproductive toxicity (DART) tested according to OECD TG 414.

The governing laws mandate animal testing guidelines (TG) to assess the developmental and reproductive toxicity (DART) potential of new and current chemical compounds for the categorization, hazard identification, and labeling. modeling has evolved as a promising, economical, and animal-friendly technique for assessing a chemical's potential for DART testing. The complexity of the endpoint has presented a problem for Quantitative Structure-Activity Relationship (QSAR) model developers as various facets of the chemical have to be appropriately analyzed to predict the DART.

Tubercular arachnoiditis: A rare culprit of paraparesis in a young adult - A case report and review of literature.

Background: The prevalence of central nervous system tuberculosis (TB) is about 1-2% of all TB cases. Atypical cases like the present case, being interpreted as leptomeningeal metastasis in magnetic resonance imaging (MRI), can pose a dilemma, delaying or even leading to mistreatment.

Case Description: A 19-year-old male presented with acute onset paraparesis and bowel bladder involvement presented with an MRI lumbar spine suggesting leptomeningeal metastasis from D11-L5 levels who underwent decompression biopsy which on histopathological examination revealed to be tubercular granulomatous infection.

The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of classification-based machine learning models to predict the toxicity of chemicals associated with plastic packaging. Using an extensive dataset of chemical structures, we trained multiple machine learning models-Random Forest, Support Vector Machine, Linear Discriminant Analysis, and Logistic Regression-targeting endpoints such as Neurotoxicity, Hepatotoxicity, Dermatotoxicity, Carcinogenicity, Reproductive Toxicity, Skin Sensitization, and Toxic Pneumonitis.

A bibliometric analysis of the Cheminformatics/QSAR literature (2000-2023) for predictive modeling in data science using the SCOPUS database.

A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000-2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of different countries and Institutes in Cheminformatics/QSAR publications, the contributions of researchers based on the number of documents, appearance in the top-cited articles, h-index, composite c-score (ns), and the newly introduced q-score. Finally, a list of the top 50 Cheminformatics/QSAR researchers is presented.

Formulation and characterization of novel dairy-based dip utilizing heat-acid-coagulated milk gel and whey.

An attempt was made to develop a novel dairy-based dip-like product from heat-acid-induced milk gel and whey. Based upon preliminary trials, the combination of cream (15-35%), whey (60-70%) and common salt (0.8-1.

Reusable Iron-Copper Catalyzed Cross-Coupling of Primary Amides with Aryl and Alkyl Halides: Access to N-Arylamides as Potential Antibacterial and Anticancer Agents.

We present, for the first time, an efficient ligand-free iron-copper catalyzed cross-coupling reaction involving a variety of aryl, heteroaryl halides (including chlorides, bromides, and iodides), and alkyl bromides with diverse aryl and aliphatic primary amides, conducted under solvent-minimized conditions. This economically competitive protocol successfully yielded the corresponding cross-coupling products, N-arylamides and N-alkylamides, in good to excellent yields with broad substrate scope (65 examples) and tolerance to several sensitive functionalities (including heterocycles). No conventional work-up is required for this protocol, and the developed method is applicable for gram-scale synthesis.

Indications and Outcomes for Tracheostomies in Pediatric Oncology Patients-A Single Center Study.

Background: Children with cancer face a high risk of complications including prolonged mechanical ventilation requiring tracheostomies. While tracheostomies have been demonstrated to be a generally safe procedure, there remain significant rare complications and a paucity of literature addressing outcomes specifically for pediatric patients with cancer. The objective of this study was to characterize pediatric patients with cancer who underwent tracheostomies and describe their indications and outcomes for length of stay, decannulation, and complications.

Inverse correlation between Leishmania-induced TLR1/2 and TGF-β differentially regulates parasite persistence in bone marrow during the chronic phase of infection.

Host-tissue preference is a critical aspect of parasitic infections and is directly correlated with species diversity. Even the same species, Leishmania donovani, infects the host's bone marrow, spleen, and liver differentially. The tissue-specific persistence of Leishmania results from host-pathogen immune conflicts and arguments.

Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions-In Silico Studies of Drug-Likeness and Human Placental Transport.

A total of 16 organic sunscreens and over 160 products of their degradation in biotic and abiotic conditions were investigated in the context of their safety during pregnancy. Drug-likeness and the ability of the studied compounds to be absorbed from the gastrointestinal tract and cross the human placenta were predicted in silico using the SwissADME software (for drug-likeness and oral absorption) and multiple linear regression and "ARKA" models (for placenta permeability expressed as fetus-to-mother blood concentration in the state of equilibrium), with the latter outperforming the MLR models. It was established that most of the studied compounds can be absorbed from the gastrointestinal tract.