Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

Recently, there has been a growing interest in using natural products as treatment alternatives in several diseases. Nerolidol is a natural product which has been shown to have protective effects in several conditions. The low water solubility of nerolidol and many other natural products limits their delivery to the body. In this research, a drug delivery system composed of alginate and chitosan was fabricated and loaded with nerolidol to enhance its water solubility. The chitosan–alginate nanoparticles were fabricated using a new method including the tween 80 pre-gelation, followed by poly-ionic crosslinking between chitosan negative and alginate positive groups. Several characterization techniques were used to validate the fabricated nanoparticles. The molecular interactions between the chitosan, alginate, and nerolidol molecules were confirmed using the Fourier transform infrared spectroscopy. The ultraviolet spectroscopy showed an absorbance peak of the blank nanoparticles at 200 nm and for the pure nerolidol at 280 nm. Using both scanning and transmission electron microscopy, the nanoparticles were found to be spherical in shape with an average size of 12 nm and 35 nm for the blank chitosan–alginate nanoparticles and the nerolidol-loaded chitosan–alginate nanoparticles, respectively. The nanoparticles were also shown to have a loading capacity of 51.7% and an encapsulation efficiency of 87%. A controlled release profile of the loaded drug for up to 28 h using an in vitro model was also observed, which is more efficient than the free form of nerolidol. In conclusion, chitosan–alginate nanoparticles and nerolidol loaded chitosan–alginate nanoparticles were successfully fabricated and characterized to show potential encapsulation and delivery using an in vitro model.

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Efforts to decrease the deleterious effects of renal ischemia–reperfusion injury (IRI) are ongoing. Recently, there has been increasing interest in using natural phytochemical compounds as alternative remedies in several diseases. Nerolidol is a natural product extracted from plants with floral odors and has been proven to be effective for the treatment of some conditions. We investigated the effect of nerolidol in a rat model of renal IRI. Nerolidol was dissolved in a vehicle and administered orally as single daily dose of 200 mg/kg for 5 days prior to IRI and continued for 3 days post IRI. G-Sham (n = 10) underwent sham surgery, whereas G-IRI (n = 10) and G-IRI/NR (n = 10) underwent bilateral warm renal ischemia for 30 min and received the vehicle/nerolidol, respectively. Renal functions and histological changes were assessed before starting the medication, just prior to IRI and 3 days after IRI. Nerolidol significantly attenuated the alterations in serum creatinine and urea, creatinine clearance, urinary albumin and the urinary albumin–creatinine ratio. Nerolidol also significantly attenuated the alterations in markers of kidney injury; proinflammatory, profibrotic and apoptotic cytokines; oxidative stress markers; and histological changes. We conclude that nerolidol has a renoprotective effect on IRI-induced renal dysfunction. These findings might have clinical implications.

Accurate classification of genetic variants is critical for precision medicine, particularly hereditary diseases such as breast cancer. However, widely used tools like MutPred and Combined Annotation Dependent Depletion (CADD) offer genome-wide pathogenicity predictions that often overlook disease-specific variant behavior, limiting their clinical utility. This study addresses that gap by training and benchmarking nine machine learning (ML) models-including ensemble and baseline classifiers-on a breast cancer gene-specific dataset rich in conservation scores, functional annotations, and allele frequency features. Among all models, the Extra Trees model achieved the highest performance, with an accuracy of 0.999 and a 95% confidence interval of (0.998–1.000). recursive feature elimination identified the most informative genomic features, enhancing model efficiency. To ensure clinical transparency, we applied interpretability techniques including Local Interpretable Model-Agnostic Explanations and permutation feature importance, which highlighted the key drivers of each prediction. The calibration curve further confirmed the reliability of predicted probabilities, supporting their potential use in clinical decision-making. On an independent ClinGen dataset, Extra Trees achieved 99.1% accuracy and outperformed widely used predictors confirming its robustness and clinical applicability. This is the first comprehensive benchmarking study to apply ML models specifically to breast cancer-related missense variants using disease-gene-specific training data and integrated interpretability. Our results show that disease-specific ML approaches outperform general predictors, offering improved reliability, transparency, and relevance to clinical genomics. By bridging the gap between broad genome-wide tools and tailored clinical prediction, this study lays the foundation for implementing ML-driven pathogenicity prediction in breast cancer diagnostics and precision medicine, with potential expansion to other disease contexts.

The BioAI Hackathon at the Centre of New Technologies at the University of Warsaw convened 43 international researchers to collaboratively explore artificial intelligence (AI) approaches for solving complex challenges in computational biology. Nine interdisciplinary and multi-institutional teams addressed the following problems: disease-gene prioritization, microbiome analysis, drug-protein interactions, alternative splicing prediction, chromatin architecture study and toxicological profiling. Using cutting-edge tools such as graph neural networks (GNNs), large language models (LLMs), and multi-omics integration frameworks, participants developed scalable and reproducible analytical pipelines. The results include: a disease gene prioritization framework using GNNs, a microbiome dynamics analysis for poultry health prediction and the construction of chromatin structure-aware regulatory networks. All projects follow the open science principles and display translational potential. This hackathon underscores the transformative role of AI in biomedicine and the value of collaborative, time-bounded innovation for accelerating discovery in life sciences. All projects are publicly available on GitHub: https://github.com/SFGLab