The clinical assessment of lung involvement in patients with Still's disease, results from the multicentre international AIDA Network Still's Disease Registry.

Objectives: To assess the lung involvement in patients with Still's disease, an inflammatory disease assessing both children and adults. To exploit possible associated factors for parenchymal lung involvement in these patients.

Methods: A multicentre observational study was arranged assessing consecutive patients with Still's disease characterized by the lung involvement among those included in the AIDA (AutoInflammatory Disease Alliance) Network Still's Disease Registry.

Adaptation, not prediction, drives neuronal spiking responses in mammalian sensory cortex.

Unlabelled: Predictive coding (PC) hypothesizes that the brain computes internal models of predicted events and that unpredicted stimuli are signaled with prediction errors that feed forward. We tested this hypothesis using a visual oddball task. A repetitive sequence interrupted by a novel stimulus is a "local" oddball.

Characterizing biomarkers of ageing in Singaporeans: the ABIOS observational study protocol.

Ageing is the primary driver of age-associated chronic diseases and conditions. Asian populations have traditionally been underrepresented in studies understanding age-related diseases. Thus, the Ageing BIOmarker Study in Singaporeans (ABIOS) aims to characterise biomarkers of ageing in Singaporeans, exploring associations between molecular, physiological, and digital biomarkers of ageing.

High-Resolution OCT Reveals Age-Associated Variation in the Region Posterior to the External Limiting Membrane.

Purpose: To evaluate visibility of a sub-band posterior to the external limiting membrane (ELM) and assess its age-associated variation.

Methods: In a retrospective cross-sectional study, normal eyes were imaged using a high-resolution spectral-domain optical coherence tomography (SD-OCT) prototype (2.7-µm axial resolution).

Attention-guided erasing for enhanced transfer learning in breast abnormality classification.

Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.

Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.