Bayesian optimization driven strategy for detecting credit card fraud with Extremely Randomized Trees.

Credit card usage has surged, heightening concerns about fraud. To address this, advanced credit card fraud detection (CCFD) technology employs machine learning algorithms to analyze transaction behavior. Credit card data's complexity and imbalance can cause overfitting in conventional models.

Plasma neurofilament light outperforms glial fibrillary acidic protein in differentiating behavioural variant frontotemporal dementia from primary psychiatric disorders.

Objective: Timely, accurate distinction between behavioural variant frontotemporal dementia (bvFTD) and primary psychiatric disorders (PPD) is a clinical challenge. Blood biomarkers such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) have shown promise. Prior work has shown NfL helps distinguish FTD from PPD.

TNO155 is a selective SHP2 inhibitor to target -dependent oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is known to be driven by multiple intricated receptor tyrosine kinases (RTKs) including EGFR, PI3K/AKT and MAPK signaling pathways. However, whilst targeting EGFR with cetuximab has been approved for the treatment of OSCC, other single-agent inhibitors of the RTKs have shown modest effects in improving survival. From the genome-wide CRISPR/Cas9 screen on 21 OSCC cell lines, we have identified among the top essential genes in OSCC.

A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms.

Objective: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data.