Aluminum phthalocyanine tetrasulfonate conjugated to surface-modified Iron oxide nanoparticles as a magnetic targeting platform for photodynamic therapy of Ehrlich tumor-bearing mice.

Background: Photodynamic therapy (PDT) is a targeted treatment option for cancers that are non-responding to ordinary anticancer therapies. It involves activating a photosensitizer with a light source of a specific wavelength to destroy targeted cells and their surrounding vasculature. Aluminum phthalocyanine tetra sulfonate (AlPcS) has gained attention as a second-generation photosensitizer for its strong absorption in the red-light region.

A framework for sharing of clinical and genetic data for precision medicine applications.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues.

Item analysis: the impact of distractor efficiency on the difficulty index and discrimination power of multiple-choice items.

Background: Distractor efficiency (DE) of multiple-choice questions (MCQs) responses is a component of the psychometric analysis used by the examiners to evaluate the distractors' credibility and functionality. This study was conducted to evaluate the impact of the DE on the difficulty and discrimination indices.

Methods: This cross-sectional study was conducted from April to June 2023.

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stroke. It is a major predictor of poor outcomes and is detected late. Machine learning models are shown to be useful for early detection, however training such models suffers from small sample sizes due to rarity of the condition.