Increased Levels of Anti- Antibodies During Hospital Admission in Septic Patients.

Background/objectives: In a previous study, we described elevated anti- IgG levels in septic patients in relation to disease severity. In this study, our objective was to analyze the evolution of anti- immunoglobulins in septic patients during hospital admission and their association with αβ and γδ T cell subsets.

Methods: We recruited 80 subjects: 40 patients with sepsis and 40 controls.

I'm Getting a Migraine: A Comparative Evaluation of Patient and Clinician Interpretations of Migraine Symptoms.

Patients and providers vary in how they describe common otolaryngology-related complaints. These differences can lead to miscommunication and frustration that may affect patient outcomes and satisfaction. The aim of this cross-sectional survey-based study was to explore the differences in migraine symptom selection by otolaryngology patients and clinicians.

Knowledge, practices, and perceived barriers on breast self-examination of female college students: Experience from a local higher education institution.

Background: Breast self-examination (BSE) is an inexpensive, harmless screening tool for finding breast cancer. This study examines the knowledge, practices, and perceived barriers of female college students from a local higher education institution (HEI) regarding BSE, focusing on those in the reproductive age group.

Materials And Methods: Three hundred sixty (360) female college students, including 226 health sciences majors and 134 non-health sciences majors, were selected through criteria sampling.

Seabird biomonitoring indicates similar plastic pollution throughout the Canary Current.

Marine plastic pollution is an emerging global threat for biodiversity. Plastic ingestion is one of the most typical and studied consequences with petrels being a particularly vulnerable group. We studied the plastic ingestion by Cory's shearwater (Calonectris borealis) fledglings in three islands of the Canarian Archipelago (Tenerife, Gran Canaria and Lanzarote).

Implementation of deep reinforcement learning models for emotion detection and personalization of learning in hybrid educational environments.

The integration of artificial intelligence in education has shown great potential to improve student's learning experience through emotion detection and the personalization of learning. Many educational settings lack adequate mechanisms to dynamically adapt to students' emotions, which can negatively impact their academic performance and engagement. This study addresses this problem by implementing a deep reinforcement learning model to detect emotions in real-time and personalize teaching strategies in a hybrid educational environment.