GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.

Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures.

Impact of ultrasound settings on lung vertical artefacts: an observational study in mechanically ventilated patients.

Introduction: The number of vertical artefacts (VAs) in lung ultrasound (LUS) impacts patients' clinical management. This study aimed to demonstrate the influence of ultrasound settings on the number of VAs in patients under invasive mechanical ventilation (IMV).

Methods: Patients under IMV were recruited for LUS, including three breathing cycles with a motionless curvilinear probe on the thoracic region with the most VAs.

Do Unresectable Giant Cell Tumors of Bone Treated With Denosumab Progress After Discontinuation of Treatment?

Background: Denosumab represents a valuable treatment option for unresectable giant cell tumors of the bone (GCTBs). However, no standardized protocols exist determining the length of administration, with few studies having been published on patients who reached the end of treatment.

Aims: To analyze the outcomes of patients diagnosed with GCTB and who had finished single treatment with denosumab.

The Effect of Postoperative Sepsis on 1-Year Mortality and Cancer Recurrence Following Transhiatal Esophagectomy for Esophageal-Gastric Junction Adenocarcinomas: A Retrospective Observational Study.

Introduction: Transhiatal esophagectomy (THE) is used for specific gastroesophageal junction adenocarcinomas. THE is a high-risk surgical procedure. We aimed to assess the impact of postoperative sepsis (sepsis or septic shock) on the 1-year mortality after THE and to determine the risk factors associated with these outcomes.

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress.

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally.