Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management

Objective: The honeymoon phase in type 1 diabetes (T1D) represents a temporary improvement in glycemic control but may complicate insulin management. The aim was to develop and validate a machine learning (ML)-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes. Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring data, glucose management indicator (GMI) reports, hemoglobin A1c (HbA1c) values, and patient medical history, were used to train ML models including long short-term memory (LSTM) networks, transformer models, random forest, and gradient boosting machines (GBMs). These were designed to analyze glucose trends and identify the honeymoon phase in T1D patients. Results: The transformer model achieved the highest accuracy at 91%, followed by GBMs at 89%, LSTM at 88%, and random forest at 87%. Key features, such as glucose variability, insulin adjustments, GMI values, and HbA1c levels were critical to model performance. Accurate identification of the honeymoon phase enabled optimized insulin adjustments, enhancing glucose control and reducing hypoglycemia risk. Conclusion: The ML-driven approach provides a robust method for detecting the honeymoon phase in T1D patients, demonstrating potential for improved personalized insulin management. The findings suggest significant benefits in patient outcomes, with future research focused on further validation and clinical integration

Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning

Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experimental methods are often slow and expensive, driving the need for advanced AI-based approaches in PK modeling. This study compares cutting-edge machine learning models, including Graph Neural Networks (GNNs), Transformers, and Stacking Ensembles, against traditional models like Random Forest and XGBoost, using a dataset of over 10,000 bioactive compounds from the ChEMBL database. The Stacking Ensemble model achieved the highest accuracy (R2 of 0.92, MAE of 0.062), outperforming GNNs (R2 of 0.90) and Transformers (R2 of 0.89). These AI models excelled in capturing complex molecular interactions and long-range dependencies, significantly improving PK predictions. The high accuracy achieved (R2 ​= ​0.92) by the Stacking Ensemble method indicates that AI models can streamline the drug discovery process by reducing costly in vivo experiments, enabling faster go/no-go decisions during preclinical evaluations, and ultimately accelerating the development of new therapeutics. This reduction in time and cost could facilitate broader industry adoption of AI-driven PK modeling. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters, further enhancing the performance and robustness of these predictive models

AI-driven diagnostics and personalized treatment planning in oral oncology: Innovations and future directions

The increasing incidence and complexity of oral cancers demand advancements in both diagnostic precision and individualized treatment strategies. This study investigates the application of artificial intelligence (AI), particularly through deep learning and machine learning models, to enhance diagnostic accuracy and support personalized treatment planning in oral oncology. Recent advancements in AI-driven diagnostics, particularly using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have significantly improved early detection and treatment prediction for oral cancer. By integrating datasets from medical imaging, clinical records, and histopathological profiles, our AI-driven models achieved a diagnostic accuracy of 93 %, with a sensitivity of 91 % and specificity of 94 %, surpassing traditional diagnostic approaches. Furthermore, our treatment prediction models, employing patient-specific tumour characteristics and clinical variables, demonstrated an 87 % accuracy in forecasting optimal therapeutic responses, effectively tailoring treatment strategies to individual patients. These findings underscore AI's transformative potential in oral oncology, providing a foundation for improved patient outcomes and paving the way for future innovations in personalized medicine, as highlighted by recent studies in the field

Influence of Nano Silica on the Geotechnical Properties of Clayey Soil Stabilized with Lime

The soil must be able to withstand the load and transfer it to ground within the range of allowable deformations, for which it must possess good physical and geotechnical properties. The conventional method of stabilization such as removal and replacement of ill – suited soils incur higher cost and is time consuming. A new method of stabilization, designated as chemical stabilization can be adopted. This method initiate chemical reactions such as cation exchange, pozzolanic activity etc., which consequently enhance the geotechnical properties of soil. In this study, the mechanical behavior of soil is improved by addition of nanoparticles i.e., nano silica along with cementitious material, lime. Considering economic issues of nano silica usage and results of this research in soil stabilization projects, in this study 0, 1, 3 and 5 % weight of nano silica is used as well as 0, 2 and 4 % weight of lime is used. The effect of these replacements is studied by Atterberg limits test, compaction test and unconfined compressive strength (UCS) test. The effect of curing time on samples at 3, 7 and 28 days of age has also been investigated. The optimum percentage replacement of nano silica in lime stabilized soil is determined. The microstructure of the stabilized soil is studied by Scanning electron microscopy (SEM) test. Thus the results proved that there is a significant improvement in the plasticity, compaction and strength properties with slight addition of nano silica in clayey soil mixed with lime.</jats:p