Search Results (4)

Lung cancer remains the leading cause of cancer-related mortality globally and is often diagnosed at advanced stages in Saudi Arabia. This cross-sectional study aimed to quantify public awareness and knowledge of lung cancer screening (LCS) using LDCT and identify barriers to its implementation in Riyadh. A validated 24-item questionnaire was administered to 452 participants to assess demographic factors, smoking history, and LCS knowledge. Results revealed that only 30.1% of participants had heard of LCS, and 50.2% demonstrated “poor” knowledge scores (mean score 11.0 ± 4.97). Higher knowledge scores were significantly associated with being female, having a bachelor’s degree or higher, and being a non-smoker. While 78.1% expressed willingness to undergo screening, the most significant barrier was a lack of knowledge about the test (44.1%), followed by concerns regarding radiation exposure (36.1%). Conversely, a healthcare provider’s recommendation was identified as the primary motivator for 53.3% of respondents. These findings highlight a critical “awareness–willingness” gap. While public willingness is high, this should not be misconstrued as systemic preparedness; substantial educational and structural gaps remain that must be bridged before national implementation can be considered feasible. We conclude that while public willingness is high, successful implementation requires a transition toward organized invitation systems and the use of multifactorial risk profiles. Integrating epidemiological evidence with proactive policy design is essential to ensure that the national program avoids systematic under- or over-inclusion and remains effective for all demographics. Full article

High-resolution NICFI-Planet image collection on Google Earth Engine (GEE) promises fine-scale tropical forest monitoring, but persistent cloud covers, shadows, and haze undermine its value. Here, we present two simple, fully reproducible cloud-masking algorithms. We introduce (A) a Blue and Near-Infrared threshold and (B) a Sentinel-2-derived statistical thresholding approach that sets per-band cutoffs. Both are implemented end-to-end in GEE for operational use. The algorithms were first developed, tuned, and evaluated in the Sundarbans (Bangladesh) using strongly contrasting dry- and monsoon-season scenes. To assess their broader utility, we additionally tested them in two independent deltaic mangrove systems, namely, the Bidyadhari Delta in West Bengal, India, and the Ayeyarwady Delta in Myanmar. Across all sites, Algorithm B consistently removes the largest share of cloud and bright-water pixels but tends to over-mask haze and low-contrast features. Algorithm A retains more usable pixels; however, its aggressiveness is region-dependent. It appears more conservative in the Sundarbans but noticeably more over-inclusive in the India and Myanmar scenes. A Random Forest classifier provided map offers a useful reference but the model is dependent on the quantity and quality of labeled samples. The novelty of the algorithms lies in their design specifically for NICFI-Planet basemaps and their ability to operate without labeled samples. Because they rely on simple, fully shareable GEE code, they can be readily applied in regions in a consistent manner. These two algorithms offer a pragmatic operational pathway: apply them as a first-pass filter keeping in mind that its behavior may vary across environments. Full article

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow based on the CoMMa (Confined Morphologies Mapping) toolbox to classify pockmarks in Flensburg Fjord, Germany–Denmark. Initial detection employed the Bathymetric Position Index (BPI) with intentionally permissive parameters to ensure high recall of morphologically diverse features. Morphometric descriptors were then extracted and used to train a Random Forest classifier, enabling noise reduction and refinement of overinclusive delineations. Validation against expert-derived mappings showed that the model achieved an overall classification accuracy of 86.16%, demonstrating strong performance across the validation area. These findings highlight how integrating a GIS-based geomorphometry toolbox with machine learning yields a reproducible, objective, and scalable approach to seabed mapping, supporting decision-making processes and advancing standardized methodologies in marine geomorphology. Full article

(1) Background: the investigation of how interpersonal functioning affects eating psychopathology has been receiving increasing attention in the last decade. This study evaluates the impact of virtual social inclusion or ostracism on emotions, perceived stress, eating psychopathology, and the drive to binge or restrict in patients across the eating disorder spectrum. (2) Methods: a group of 122 adolescent and adult females with different eating disorder diagnoses were compared to 50 healthy peers with regards to their performance on, and responses to the Cyberball task, a virtual ball-tossing game. Each participant was randomly assigned to playing a social inclusion or a social exclusion block of the Cyberball task and completed self-report assessments of emotions, perceived stress and urge to restrict/binge before and after the task. (3) Results: patients with anorexia nervosa showed a more negative impact on psychological well-being evaluated with the need threat scale after the excluding block, while patients with bulimia nervosa reported more negative effects after the overincluding condition. Patients with binge eating disorder showed a reduction in specific negative emotions after the overincluding block, unlike all other participants. (4) Conclusions: findings show significant correlations between restraint thoughts in patients with bulimia nervosa and binge thoughts in patients with binge eating disorder after being exposed to the inclusion condition. Different reactions in cognitive and emotional states of patients with eating disorders after different interpersonal scenarios confirm the impact of inclusive or exclusive relationships on eating psychopathology, with specific and different responses across the eating disorder spectrum, that have been discussed, linked to their eating behavioral cognition. Full article