Abstract
Eye diseases, such as glaucoma, diabetic retinopathy, and age-related macular degeneration, drive the growing need for reliable and scalable analyses of fundus and optical coherence tomography (OCT) images. Deep learning performs strongly in ocular structure segmentation. However, it typically relies on dense pixel-wise annotations, which are costly and difficult to obtain at scale. Weakly supervised learning (WSL) can reduce this burden by leveraging coarse labels, limited strong annotations, and unlabeled data. This systematic umbrella review synthesizes survey and review articles on weakly supervised deep learning for image segmentation, with a focus on ocular imaging (fundus and OCT/OCTA). After analyzing twenty-one secondary studies, the main finding reveals an empty intersection: WSL-focused segmentation surveys are often modality-agnostic. Conversely, ocular reviews are predominantly fully supervised and seldom offer quantitative evidence on annotation-effort savings or direct comparisons between weak and fully supervised methods on identical datasets. Across the included reviews, label-efficient strategies cluster around CAM/MIL formulations, sparse supervision (points/scribbles/boxes), pseudo-labelling/self-training, and semi-/self-supervised learning, implemented mainly with U-Net/DeepLab families and increasingly Transformer or hybrid backbones. These results provide a structured map of available WSL mechanisms and, critically, identify reproducible reporting gaps that currently prevent fair benchmarking in ocular segmentation. Therefore, this review supports the development of ocular-specific benchmarks and minimum reporting practices that link segmentation performance to annotation effort.

Abstract
Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Highlights What are the main findings? Envelope methods (convex hull and alpha shape) are generally more sensitive to point density loss than voxel-based grids, which maintain a relative stability, although they were not always the closest to field-based volume estimations. Methods parameters (alpha and voxel size) influence accuracy and should be adapted to point cloud density, canopy structure, and growth stage. What are the implications of the main findings? UAV photogrammetry provides dense, low-cost 3D canopy data suitable for vineyard monitoring at the row or plant level. Multi-temporal 3D measurements can support vineyard management and integration with decision support systems.Abstract Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.

Abstract

Abstract
Stroke affects over 100 million people worldwide, and over two-thirds of survivors experience lasting upper-limb impairments, which significantly impact their quality of life. The global shortage of rehabilitation providers, who cannot attend to all patients who need it, creates an urgent, not yet answered, need for reliable and accessible rehabilitation innovations. Robotic rehabilitation has been emerging as an effective alternative to traditional physical therapy. This paper presents the development and evaluation of 2 degree-of-freedom exoskeleton, coupled to a collaborative robotic manipulator, which performs upper-limb rehabilitation. The system targets elbow flexion/extension and forearm pronation/supination, using two direct current brushless actuators. To accommodate a wide range of users, the mechanical design is modular and adjustable, allowing the rehabilitation of a broad range of arm lengths, while mechanical barriers prevent unsafe joint motions. Furthermore, limit switches ensure the movements are performed within safe values and an emergency button is also available for emergency stop. Safety assessment confirmed the actuators' performance and the integrity of the physical barriers. Three different rehabilitation modes were implemented: passive assist, active assist and active resist. Passive assistance tests achieved consistent trajectory tracking with a root mean square error of 4.85(o)& strns; for pronation/supination and 0.87 & strns;(o) for elbow flexion/extension, while maintaining smooth motion profiles with spectral arc length values of-1.603 and-1.56, respectively. Active resistance generated stable bidirectional torque across the full range of motion, reaching up to 1 Nm for forearm pronation/supination and 7 Nm for elbow flexion/extension. The adaptive active assistance strategy modified the assistance torque in real time according to the detected user performance. These findings establish a foundation for future clinical evaluation and real-world applications, with the system's modular design and multiple therapy modes showing potential to support diverse rehabilitation needs.

Abstract
Feline chronic kidney disease is a leading cause of mortality in geriatric cats, characterized by a progressive and irreversible loss of renal function. Despite its high prevalence, early diagnosis remains challenging due to nephron compensatory mechanisms and the limited sensitivity of traditional biomarkers, creating a diagnostic gap that necessitates the exploration of novel biomarkers for earlier detection. This review examines the complex pathophysiology of the disease, including renin-angiotensin-aldosterone system activation, tubulointerstitial fibrosis, and mineral metabolism disturbances. By analyzing recent scientific literature, this work evaluates current diagnostic landscape and clinical relevance of emerging biomarkers. Evidence indicates that symmetric dimethylarginine and fibroblast growth factor-23 improve detection of early metabolic and filtration changes, while urinary biomarkers like cystatin B and retinol-binding protein provide specific insights into tubular injury. Bridging the diagnostic gap requires a transition from a reactive, azotemia-based framework to a multi-parametric diagnostic approach that integrates novel biomarkers with serial clinical and laboratory monitoring. Although financial constraints and limited availability restrict widespread clinical implementation, incorporating these advances is essential for earlier prognostic stratification and timely therapeutic decision-making. This integrated strategy has the potential to slow disease progression and improve survival and quality of life in cats with chronic kidney disease.

Abstract
Visible-Near-Infrared (Vis-NIR) spectroscopy, spanning approximately 400 to 2500 nm, is an innovative technology with growing relevance for diagnostics performed at the point of care (POC). This review explores the potential of Vis-NIR in veterinary medicine, highlighting its advantages over complex techniques like Raman and Fourier transform infrared spectroscopy (FTIR) by being rapid, non-invasive, reagent-free, and compatible with miniaturized, portable devices. The methodology involves directing a broadband light source, often using LEDs, toward the sample (e.g., blood, urine, faeces), collecting spectral information related to molecular vibrations, which is then analyzed using chemometric methods. Successful veterinary applications include hemogram analysis in dogs, cats, and Atlantic salmon, and quantifying blood in ovine faeces for parasite detection. Key limitations include spectral interference from strong absorbers like water and hemoglobin, and the limited penetration depth of light. However, combining Vis-NIR with Self-Learning Artificial Intelligence (SLAI) is shown to isolate and mitigate these multi-scale interferences. Vis-NIR spectroscopy serves as an important complement to centralized laboratory testing, holding significant potential to accelerate clinical decisions, minimize stress on animals during assessment, and improve diagnostic capabilities for both human and animal health, aligning with the One Health concept.