Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays

Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure

This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A total of 2000 hepatocyte nuclear regions of interest (ROIs) from mouse liver tissue were analyzed, and for each ROI, 5 different parameters were calculated: Long Run Emphasis, Short Run Emphasis, Run Length Nonuniformity, and 2 wavelet coefficient energies obtained after the discrete wavelet transform. These parameters served as input for supervised machine learning models, specifically random forest and gradient boosting classifiers. The models demonstrated relatively robust performance in distinguishing hepatocyte chromatin structures belonging to the group exposed to IONPs from the controls. The study's findings suggest that iron oxide nanoparticles induce substantial changes in hepatocyte chromatin distribution and underscore the potential of AI techniques in advancing hepatocyte evaluation in physiological and pathological conditions.

Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility

The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid's bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices.

Bioactivity and Bioavailability of Carotenoids Applied in Human Health: Technological Advances and Innovation

This article presents a groundbreaking perspective on carotenoids, focusing on their innovative applications and transformative potential in human health and medicine. Research jointly delves deeper into the bioactivity and bioavailability of carotenoids, revealing therapeutic uses and technological advances that have the potential to revolutionize medical treatments. We explore pioneering therapeutic applications in which carotenoids are used to treat chronic diseases such as cancer, cardiovascular disease, and age-related macular degeneration, offering novel protective mechanisms and innovative therapeutic benefits. Our study also shows cutting-edge technological innovations in carotenoid extraction and bioavailability, including the development of supramolecular carriers and advanced nanotechnology, which dramatically improve the absorption and efficacy of these compounds. These technological advances not only ensure consistent quality but also tailor carotenoid therapies to each patient's health needs, paving the way for personalized medicine. By integrating the latest scientific discoveries and innovative techniques, this research provides a prospective perspective on the clinical applications of carotenoids, establishing a new benchmark for future studies in this field. Our findings underscore the importance of optimizing carotenoid extraction, administration, bioactivity, and bioavailability methods to develop more effective, targeted, and personalized treatments, thus offering visionary insight into their potential in modern medical practices.

Novel and efficient techniques in the discovery of antioxidant peptides

As a research hotspot in food science and nutrition, antioxidant peptides can function by scavenging free radicals, inhibiting peroxides, and chelating metal ions. Therefore, how to efficiently discover and screen antioxidant peptides has become a key issue in research and production. Traditional discovery methods are time-consuming and costly, but also challenging to resolve the quantitative structure-activity relationship of antioxidant peptides. Several novel techniques, including artificial intelligence, molecular docking, bioinformatics, quantum chemistry, phage display, switchSENSE, surface plasmon resonance, and fluorescence polarization, are emerging rapidly as solutions. These techniques possess efficient capability for the discovery of antioxidant peptides, even with the potential for high-throughput screening. In addition, the quantitative structure-activity relationship can be resolved. Notably, combining these novel techniques can overcome the drawbacks of a single one, thus improving efficiency and expanding the discovery horizon. This review has summarized eight novel and efficient techniques for discovering antioxidant peptides and the combination of techniques. This review aims to provide scientific evidence and perspectives for antioxidant peptide research.

Artificial neural networks in contemporary toxicology research

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.

PARP-1-Associated Pathological Processes: Inhibition by Natural Polyphenols

Poly (ADP-ribose) polymerase-1 (PARP-1) is a nuclear enzyme involved in processes of cell cycle regulation, DNA repair, transcription, and replication. Hyperactivity of PARP-1 induced by changes in cell homeostasis promotes development of chronic pathological processes leading to cell death during various metabolic disorders, cardiovascular and neurodegenerative diseases. In contrast, tumor growth is accompanied by a moderate activation of PARP-1 that supports survival of tumor cells due to enhancement of DNA lesion repair and resistance to therapy by DNA damaging agents. That is why PARP inhibitors (PARPi) are promising agents for the therapy of tumor and metabolic diseases. A PARPi family is rapidly growing partly due to natural polyphenols discovered among plant secondary metabolites. This review describes mechanisms of PARP-1 participation in the development of various pathologies, analyzes multiple PARP-dependent pathways of cell degeneration and death, and discusses representative plant polyphenols, which can inhibit PARP-1 directly or suppress unwanted PARP-dependent cellular processes.