Search Results (452)

This paper presents an analysis of the physicochemical and biological properties of the developed composite zinc phosphate cement modified with bismuth oxide and phosphorus slag additives. The powder phase was synthesized by sintering a frit with an optimal composition (ZnO, MgO, SiO₂, Bi₂O₃) using phosphorus slag as the active component. The study included an assessment of the microstructure, chemical resistance in aggressive environments (5% NaCl solution, 10% lactic acid, carbonated water), solubility in artificial saliva, and cytotoxicity in human fibroblasts. The addition of phosphorus slag was found to promote the formation of low-melting eutectics, which reduces the sintering temperature by 100 °C and increases the material’s whiteness to 97.8%. X-ray diffraction analysis confirmed the presence of zincite, quartz, and periclase phases, forming a dense microstructure without pronounced pores or cracks. The experimental cement demonstrated high acid resistance: the maximum weight loss in lactic acid was 8%, while the leaching of toxic elements (Pb, As, Cr, etc.) remained extremely low (10–67 ppm), confirming the material’s environmental safety. Testing of the composite zinc phosphate cement in artificial saliva revealed minimal weight loss compared to similar products. Biological testing showed that the cement’s cytotoxicity is dose-dependent; at a 0.3 g dose and a 1:4 dilution, the material loses its toxic properties and becomes safe for living tissue. The developed zinc phosphate composite cement composition offers improved aesthetic and mechanical properties, high chemical stability, and biocompatibility at working concentrations, making it promising for use in clinical dentistry. Full article

African migrants living in High-Income Countries are more vulnerable to socio-economic insecurity, as well as higher morbidity and mortality. In their cultural background, these populations, especially the Bamiléké ethnic group from Cameroon, tend to socially value stoutness as a symbol of health and prosperity, potentially obesogenic in urban areas. However, recent studies showed that urbanisation has led to an acculturation process toward the promotion of thinness in young African adults, exposing them to emerging eating disorders. From this perspective, we conducted an original mixed-methods anthropological study, through a repeated qualitative cross-sectional study (two time points in 2011 and 2021) and a quantitative survey (in 2012), in young Cameroonian Bamiléké [18–39 y] migrating to urban Cameroon and France to innovatively explore obesity- and eating disorder-related body weight perceptions. We observed that the qualitative rural–urban tendencies were similar between 2011 and 2021. While rural people tended to value stoutness as a symbol of prosperity, wellbeing and peacefulness, urban people, especially in Paris, tended to value thinness, even extreme thinness, as a symbol of beauty and health and reported a pronounced weight stigma. Quantitatively, we found that Parisian Bamiléké had a desired and ideal body size (DBS and IBS) in the normal-weight category with standard deviations reaching or close to the underweight category, whereas the rural and urban ones had DBS and IBS means between normal-weight and overweight categories. Moreover, we observed a high prevalence of overweight and obesity in both urban groups while the Parisian one tended to underestimate one’s weight and expected to lose it. Public health policies should consider body image as a persisting risk factor for obesity and also a risk factor for emerging eating disorders in young Cameroonian migrants experiencing this dual burden within the urban transition. Full article

Background and Objectives: Soft denture lining materials improve stress distribution and patient comfort but can lose mechanical stability under routine chemical cleansing. This study aimed to evaluate the time-dependent effects of two alkaline peroxide-based denture cleansing tablets (i.e., Efferdent and Protefix) on Shore A hardness and weight change of three soft lining materials (i.e., Ufi Gel P, Ufi Gel SC, and Visco-gel) at days 1, 7, and 30. Materials and Methods: Ninety specimens (n = 10/group) were assigned to a 3 × 3 factorial design. Specimens were immersed in cleansing solutions for 8 h daily and stored in artificial saliva for 16 h; controls remained solely in artificial saliva. Shore A hardness was measured using a durometer, and weight was recorded with a precision scale. Data were analyzed by mixed-design ANOVA and linear regression (α = 0.05). Results: Material type significantly affected hardness and weight change (p < 0.001). Visco-gel showed a marked increase in Shore A hardness (from about 15–16 to 26–27 HA) and greater weight loss (approximately 0.04–0.06 g), whereas silicone-based materials (Ufi Gel P and Ufi Gel SC) demonstrated more stable hardness values (from about 24–25 to 31–32 HA) with minimal weight variation (generally below about 0.02 g). The type of cleansing tablet had a smaller but significant effect (p = 0.004), with Protefix causing greater alterations. Weight change was negatively correlated with hardness increase (R² = 0.33, p < 0.001). Conclusions: Within the limitations of this in vitro study, material composition was identified as the main determinant of degradation resistance, with silicone-based liners demonstrating greater durability under the tested conditions, while Efferdent may be considered a milder option for long-term cleansing. Full article

Protein-losing enteropathy (PLE) is an uncommon and often underrecognized manifestation of lymphoproliferative disorders and may be difficult to diagnose when conventional gastrointestinal investigations are unrevealing. We present an 82-year-old woman with recurrent hospital admissions initially spanning six months for diarrhea, weight loss, peripheral edema, and persistent hypoalbuminemia. Initial upper gastrointestinal endoscopy was normal, and colonoscopy was deferred due to intercurrent infection. Despite extensive laboratory and radiologic evaluation, including routine biochemical testing and imaging, the etiology of PLE remained unclear. Peripheral blood flow cytometry subsequently identified a small kappa-restricted monoclonal B-cell population compatible with marginal zone lymphoma, later confirmed on bone marrow biopsy, raising suspicion for gastrointestinal involvement. Video capsule enteroscopy demonstrated diffuse erosive and ulcerative disease throughout the small intestine, providing an anatomical explanation for the patient’s protein loss. Following lymphoma-directed therapy, repeat capsule enteroscopy showed complete normalization of the small bowel mucosa. This case highlights the diagnostic value of combining peripheral blood flow cytometry and capsule endoscopy in unexplained protein-losing enteropathy, a rare and diagnostically challenging presentation of indolent lymphoma, and illustrates the role of capsule imaging in both disease localization and treatment monitoring. As a single-case report, these findings are not generalizable, and further studies are required to evaluate the broader applicability of this diagnostic approach. Full article

Lesser hedgehog tenrecs (Echinops telfairi) are small, nocturnal insectivores from Madagascar that exhibit hibernation and brief torpor bouts. While multiple studies have investigated torpor physiology, no studies have focused on torpor in tenrecs maintained under human care or their recommended husbandry. We surveyed 96 institutions (71.9% response rate) housing 172 tenrecs to assess husbandry, torpor, handling, and associated weight changes. Most institutions reported that torpor occurred annually, typically between October and April, and lasted approximately 5–6 months. Weight distributions differed significantly pre- versus post-torpor, with females and males losing 34.4 g and 20.9 g on average, respectively. Females were heavier than males before and after torpor, the first report of a sex-based weight difference in this species. Most institutions used tenrecs as ambassador animals, and approximately half continued educational programming during torpor. Tenrecs handled more frequently during torpor tended to gain more weight, likely due to increased energy expenditure and compensatory feeding. Tenrecs entered torpor regardless of daylight, temperature, or humidity, with no significant husbandry differences between torpor- and non-torpor-reporting institutions. Torpor is a critical physiological adaptation supporting energy conservation and species survival. Its expression should be supported through appropriate environmental conditions, diet, minimal disturbance, and monitoring. Full article

Infrared small target detection (IRSTD) aims to extract weak small targets from complex backgrounds, and has significant practical application value in safety-critical fields such as traffic monitoring and maritime search and rescue. However, due to the inherently weak features of infrared small targets and the difficulty in effectively separating target from complex backgrounds, existing methods are prone to losing detail during the process. To aid this situation, this study proposes an anisotropic enhanced dual-branch network (AEDNet), including two core modules: the anisotropic dual-branch enhancement module (ADBE) and the multi-scale receptive field and attention collaboration module (MSRF-AC). To better separate target features from the background, the ADBE module employs independent branches of anisotropic operators to enhance target edge contrast and employs an adaptive dual-branch feature fusion to dynamically adjust feature weights, thereby improving the discriminability between the target and the background. Furthermore, to better preserve the details of the target, the MSRF-AC module employs multi-branch separable convolutions and is equipped with dual attention to capture small target features, thereby enhancing the ability to preserve target details. We conducted experiments on the NUDT-SIRST and MDvsFA datasets. Experimental results show that AEDNet outperforms existing SOTA methods across key metrics. Particularly, on the MDvsFA dataset, AEDNet achieves an IoU of 90.10%, an nIoU of 89.29%, a PD of 91.59%, and an FA of 6.25 × 10⁻⁶. Full article

Background: Patients with single-ventricle physiology represent a high-risk group for heart transplantation. Due to complex anatomical and physiological challenges, including multiple prior sternotomies, pulmonary artery abnormalities, and systemic consequences of altered circulation, they represent both a surgical and a clinical challenge. We aimed to analyze perioperative challenges, as well as early and long-term complications, in this specific group of patients. Methods: We performed a retrospective data analysis of a high-volume heart transplant center, focusing on patients with single-ventricle physiology who were scheduled for heart transplantation due to end-stage heart failure. We retrospectively analyzed the period from the beginning of the transplant program in November 1985 to the end of November 2024. Results: Among 1553 transplanted patients (adults and children), 29 were transplanted due to congenital heart disease (congenital valvular disease not included). In this group, nine patients were transplanted due to end-stage heart failure in the course of single-ventricle physiology. Age at transplantation ranged from 7 to 31 years (median, 17 years), and body weight ranged from 15 to 69 kg (median, 47.9 kg). All nine patients referred for heart transplantation presented with single-ventricle physiology. Their underlying congenital heart defects were heterogeneous and included hypoplastic left heart syndrome (HLHS), double-outlet left ventricle (DOLV), transposition of the great arteries (TGA) with associated ventricular septal defects (VSDs), atrial septal defects (ASDs), valvular abnormalities such as tricuspid and or pulmonary valve atresia or stenosis, systemic or atrioventricular valve regurgitation, and vascular abnormalities, including right-sided aortic arch, aortic coarctation, and pulmonary artery hypoplasia, stenosis, or occlusion, as well as associated pulmonary vascular abnormalities such as left pulmonary artery stenosis and MAPCAs. All patients had previously undergone staged palliative procedures, including Norwood, Hemi-Fontan, Fontan, bidirectional Glenn, modified Blalock–Taussig shunts, Bjork–Fontan, or pulmonary artery banding, often with repeated interventions such as balloon angioplasty, stent placement, or MAPCA closure. Extracardiac comorbidities were common and included coagulopathies, protein-losing enteropathy, hepatic dysfunction, and chronic venous insufficiency. Preoperative functional status was markedly impaired in all patients (NYHA III-IV, INTERMACS 3-4), with severely reduced exercise capacity and thrombotic events in several individuals. Perioperative transplant surgical strategies included femoral cannulation in four cases and standard aortic and caval cannulation in five cases. Pulmonary artery reconstruction was required in all patients. Extended donor pulmonary arteries were applied in eight cases, while a bifurcated Dacron prosthesis was utilized in one patient. Perioperative mortality was 33%, with three deaths attributed to bleeding and hemodynamic instability, while overall mortality was 44% including one late death unrelated to transplantation. Protein-losing enteropathy, although persistent in the immediate postoperative period, resolved in all surviving patients, underscoring the transformative impact of transplantation. Conclusions: These findings emphasize the importance of individualized surgical planning, extended donor pulmonary artery harvesting, and careful preoperative coordination. Heart transplantation remains a viable and life-extending option for selected single-ventricle patients, despite the significant technical and clinical challenges involved. Full article

Large Language Models (LLMs) have acquired vast amounts of knowledge during pre-training. However, there are a lot of challenges when it is deployed in real-world applications, such as poor interpretability, hallucinations, and the inability to reference private data. To address these issues, Retrieval-Augmented Generation (RAG) has been proposed. Traditional RAG relying on text-based retrievers often converts documents using Optical Character Recognition (OCR) before retrieval. While testing has revealed that it tends to overlook tables and images contained within the documents. RAG, relying on vision-based retrievers, often loses information on text-dense pages. To address these limitations, we propose DRAG: Dual-channel Retrieval-Augmented Generation for Hybrid-Modal Document Understanding, a novel retrieval paradigm. The DRAG method proposed in this paper primarily comprises two core improvements: first, a parallel dual-channel processing architecture is adopted to separately extract and preserve the visual structural information and deep semantic information of documents, thereby effectively enhancing information integrity; second, a novel dynamic weighted fusion mechanism is proposed to integrate the retrieval results from both channels, enabling precise screening of the most relevant information segments. Empirical results demonstrate that our method achieves Competitive performance across multiple general benchmarks. Furthermore, performance on biomedical datasets (e.g., BioM) specifically highlights its potential in specialized, vertical domains such as elderly care and rehabilitation, where documents are characterized by dense hybrid-modal information. Full article

The cultivation of potatoes is essential for rural food security, and the use of Unmanned Aerial Vehicle Red-Green-Blue (UAV-RGB) imagery allows for precise and cost-effective estimation of yield and identification of varieties, overcoming the limitations of manual assessment. We evaluated four INIA varieties (Bicentenario, Canchán, Shulay and Tahuaqueña) by integrating agronomic measurements (height, number and weight of tubers, leaf health) with color and textural indices derived from RGB orthomosaics. Yield prediction was modeled using Random Forest (RF) and Gradient Boosting (GB); varietal identification was approached with (i) a Convolutional Neural Network (CNN) that classifies RGB images and (ii) classical models such as Random Forest, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Decision Trees and Logistic Regression trained on EfficientNetB0 embeddings. The results showed significant genotypic differences in yield (p < 0.001): Tahuaqueña 13.86 ± 0.27 t ha⁻¹ and Bicentenario 6.65 ± 0.27 t ha⁻¹. The number of tubers (r = 0.52) and plant height (r = 0.23) correlated with yield; RGB indices showed low correlations (r < 0.3) and high redundancy (r > 0.9). RF achieved a better fit (Coefficient of determination, R² = 0.54; Root Mean Square Error, RMSE = 2.72 t ha⁻¹), excelling in stolon development (R² = 0.66) and losing precision in maturation due to foliar senescence. In classification, the CNN and RF on embeddings achieved F1-macro ≈ 0.69 and 0.66 (Receiver Operating Characteristic—Area Under the Curve, ROC AUC RF = 0.89), with better identification of Bicentenario and Shulay. We conclude that UAV-RGB is a cost-effective alternative for phenotypic monitoring and varietal selection in high Andean contexts. These findings support the integration of UAV-RGB imagery into breeding and monitoring pipelines in resource-limited Andean systems. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

The growing global production of plastic, which reached 460 million tonnes in 2022 and has projections of 5.4 million tonnes of waste by 2050 without intervention, has created a severe environmental crisis that demands the development of sustainable alternatives. In this context, this study aims to characterise biodegradable films based on cassava starch and gellan gum, combining microstructural and mechanical properties with the evaluation of thermo-optical parameters. An important advance was the pioneering application of a self-normalised photoacoustic technique, used for the first time to measure thermal diffusivity (0.0013 ± 0.0002 cm²/s) and optical absorption coefficients (at 660 nm) as a function of different concentrations of aniline blue. The results validate the material, which showed high solubility (89.23 ± 1.03%) and crystallinity of 27.40 ± 1.68%. The film demonstrated remarkable biodegradability, losing almost all of its weight (98.30 ± 1.01%) in just 15 days. The measurement of the optical absorption coefficients (at 660 nm) confirmed a linear relationship with the concentration of aniline, validating Beer–Lambert’s law and providing the absorptivity of the dye within the solid matrix—something inaccessible with conventional methods. In conclusion, these films offer significant potential as a viable ecological substitute for single-use plastics, contributing significantly to mitigating the global impact of plastic waste. Full article

Protein-losing enteropathy (PLE) is a spectrum of gastrointestinal disorders in which protein loss occurs through the gastrointestinal tract. One of the underlying causes is chronic inflammatory enteropathy (CIE). Conventional therapies for CIE often include diet, immunosuppressives, anti-microbials, probiotics, and, recently, fecal microbial transplantation (FMT). This case report highlights the use of lyophilized material-based FMT through oral capsules and enema in a dog with PLE and concurrent protein-losing nephropathy (PLN). The patient initially had a significantly increased dysbiosis index (DI) and required repeated FMT treatments, resulting in a positive clinical response through improvement in body weight, serum albumin concentrations, fecal scores, and normalization of the DI over time. To maintain clinical responses, FMT had to be performed monthly. Approximately 1 year after starting FMT therapy, the patient then developed an episode of acute hemorrhagic diarrhea syndrome (AHDS) associated with netF-gene-encoding Clostridium perfringens strains, after which the DI became abnormal again. The patient responded clinically well to monthly FMT treatments again, but it took several months for normalization of the DI after the AHDS episode. In summary, this case report highlights the continued use of adjunct lyophilized FMT in a dog with PLE resulting in improved clinical control over time. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Link prediction is a fundamental problem for graphs, which can reveal the potential relationships between users. Graph embedding can easily encode graph structural relations, and heterogeneous attribute features in a continuous vector space, which is effective in link prediction. However, graph embedding methods for large-scale graphs suffer high computation and space costs, and sampling enclosing subgraphs is a practical yet efficient way to obtain the most features at the least cost. Nevertheless, the existing sampling techniques may lose essential features when the random sampling number of nodes is not large, as node features are assumed to follow a uniform distribution. In this paper, we propose a novel large-scale graph sampling strategy for link prediction named Weighted Sampling Enclosing subgraphs-based Link prediction (WSEL) to resolve this issue, which maximumly preserves the structural and attribute features of enclosing subgraphs with less sampling. More specifically, we first extract the feature importance of each node in an enclosing subgraph and then take the node importance as node weight. Then, random walk node sequences are obtained by multiple weighted random walks from a target pair of nodes, generating a weighted sampling of enclosing subgraphs. By leveraging the weighted sampling enclosing subgraphs, WSEL can scale to larger graphs with much less overhead while maintaining some essential information of the original graph. Experiments on real-world datasets demonstrate that our model can scale to larger graphs while maintaining competitive link prediction performance under substantially reduced computational cost. Full article

Overweight and obesity represent a significant global health challenge, requiring comprehensive, long-term approaches. Digital weight-loss services (DWLSs) have emerged as promising obesity care models, as they facilitate access to continuous multidisciplinary care. This study aimed to evaluate 12-month weight-loss and adherence patterns in a large unsubsidized DWLS in the UK, which combined lifestyle therapy with semaglutide treatment. A retrospective cohort design was used to analyze data from 7279 patients who initiated treatment between 1 January 2023, and 1 May 2024. Of these patients, 1678 (23.05%) met all criteria for inclusion in the efficacy estimand, which included receiving a minimum of 8 medication orders and submitting weight data within 341–379 days after program initiation. The efficacy estimand achieved a mean weight loss of 15.67%, with 92.49% losing a clinically meaningful amount of weight (≥5%). A strong positive association was found between weight tracking frequency and weight loss, to the extent that a percentage discrepancy of 8.41 points was observed between patients who tracked on less than 20 occasions (Median = 11.83%) and those who tracked at least 100 times (Median = 20.24%). A significant association between weight loss and semaglutide orders was also observed, with a clear distinction existing between patients who received less than 12 orders, and those who received 12 or more orders. Patients whose DWLS experience was supplemented with Wegovy recorded significantly higher mean weight loss than those who were treated with Ozempic (17.68% vs. 14.72%). The findings highlight the importance of program engagement in DWLS outcomes and suggest the need for a comparative analysis of unsubsidized and subsidized services. The study is limited by its real-world observational design and reliance on self-reported data; future research should compare outcomes between unsubsidized and subsidized DWLS cohorts. Full article