Anti-Cancer Activity and Phenolic Content of Extracts Derived from Cypriot Carob (Ceratonia siliqua L.) Pods Using Different Solvents.

Extracts derived from the Ceratonia siliqua L. (carob) tree have been widely studied for their ability to prevent many diseases mainly due to the presence of polyphenolic compounds. In this study, we explored, for the first time, the anti-cancer properties of Cypriot carobs. We produced extracts from ripe and unripe whole carobs, pulp and seeds using solvents with different polarities. We measured the ability of the extracts to inhibit proliferation and induce apoptosis in cancer and normal immortalized breast cells, using the MTT assay, cell cycle analysis and Western Blotting. The extracts' total polyphenol content and anti-oxidant action was evaluated using the Folin-Ciocalteu method and the DPPH assay. Finally, we used LC-MS analysis to identify and quantify polyphenols in the most effective extracts. Our results demonstrate that the anti-proliferative capacity of carob extracts varied with the stage of carob maturity and the extraction solvent. The Diethyl-ether and Ethyl acetate extracts derived from the ripe whole fruit had high Myricetin content and also displayed specific activity against cancer cells. Their mechanism of action involved caspase-dependent and independent apoptosis. Our results indicate that extracts from Cypriot carobs may have potential uses in the development of nutritional supplements and pharmaceuticals.

Explainable AI Modeling in the Prediction of Cardiovascular Disease Risk.

The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability. Further work is needed to enhance the argumentation-based rule interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas.

Gynaecological Artificial Intelligence Diagnostics (GAID) GAID and Its Performance as a Tool for the Specialist Doctor.

BACKGROUND: Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is such a system. Based on artificial intelligence (AI) argumentation technology, it was developed to incorporate, as much as possible, a complete representation of the medical knowledge in gynaecology and to become a real-life tool that will practically enhance the quality of healthcare services and reduce stress for the clinician. Our study aimed to evaluate GAIDS' efficacy and accuracy in assisting the working expert gynaecologist during day-to-day clinical practice. METHODS: Knowledge-based systems utilize a knowledge base (theory) which holds evidence-based rules ("IF-THEN" statements) that are used to prove whether a conclusion (such as a disease, medication or treatment) is possible or not, given a set of input data. This approach uses argumentation frameworks, where rules act as claims that support a specific decision (arguments) and argue for its dominance over others. The result is a set of admissible arguments which support the final decision and explain its cause. RESULTS: Based on seven different subcategories of gynaecological presentations-bleeding, endocrinology, cancer, pelvic pain, urogynaecology, sexually transmitted infections and vulva pathology in fifty patients-GAID demonstrates an average overall closeness accuracy of zero point eighty-seven. Since the system provides explanations for supporting a diagnosis against other possible diseases, this evaluation process further allowed for a learning process of modular improvement in the system of the diagnostic discrepancies between the system and the specialist. CONCLUSIONS: GAID successfully demonstrates an average accuracy of zero point eighty-seven when measuring the closeness of the system's diagnosis to that of the senior consultant. The system further provides meaningful and helpful explanations for its diagnoses that can help clinicians to develop an increasing level of trust towards the system. It also provides a practical database, which can be used as a structured history-taking assistant and a friendly, patient record-keeper, while improving precision by providing a full list of differential diagnoses. Importantly, the design and implementation of the system facilitates its continuous development with a set methodology that allows minimal revision of the system in the face of new information. Further large-scale studies are required to evaluate GAID more thoroughly and to identify its limiting boundaries.

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient Boosting exhibited marginally superior predictive performance compared to Random Forest for LOS classification. Notably, variables like triage acuity and the Elixhauser Comorbidity Index (ECI) emerged as robust predictors. The extracted rules optimize LOS stratification and resource allocation, demonstrating the critical role of data-driven methodologies in improving ED workflow efficiency and patient care delivery.

Argumentation: A calculus for Human-Centric AI.

This paper aims to expose and analyze the potential foundational role of Argumentation for Human-Centric AI, and to present the main challenges for this foundational role to be realized in a way that will fit well with the wider requirements and challenges of Human-Centric AI. The central idea set forward is that by endowing machines with the ability to argue with forms of machine argumentation that are cognitively compatible with those of human argumentation, we will be able to support a naturally effective, enhancing and ethical human-machine cooperation and "social" integration.