Linde (United States)

To evaluate the effectiveness of zinc oxide (ZnO) ointments versus non-irritant barrier films (NIBFs) in the prevention of Incontinence-Associated Dermatitis (IAD). Specific objectives included analyzing the effects of treatment over time, establishing when IAD appears in each group, and determining the safety and cost-effectiveness of both treatments. A multicenter prospective cohort study was carried out in 10 social health centers in Spain. The final sample included 164 older institutionalized patients with urinary and/or mixed incontinence, divided into two cohorts (79 with ZnO and 85 with NIBF). Follow-up lasted for six weeks. Validated scales were used for data collection, such as the Categorization of Moisture-Associated Skin Damage (MASD) of the GNEAUPP (National [Spain] group for the assessment of pressure ulcers and chronic wounds) and the Visual Erythema Scale (VES). The overall incidence of IAD was 20.7% in the sample during follow-up. No statistically significant difference in effectiveness was found between ZnO (27.8% incidence of IAD) and NIBF (32.9% incidence of IAD) in preventing IAD (p = 0.479). However, survival analysis suggested that the onset of IAD is delayed more in the NIBF group. No adverse events or side effects were reported attributable solely to the use of the products. When considering the total cost per process (including staff application time), ZnO ointment was EUR 0.02 more expensive per patient per day than NIBFs. Although both products have similar efficacy and safety, NIBFs delay the onset of IAD more than ZnO. In addition, despite a higher unit price, NIBFs are more cost-effective per procedure because of savings in nursing time during application and removal.

Abstract Large language models (LLMs) are primarily built on the Transformer architecture, in which all hidden layers share a fixed-dimensional representation spaces. This homogeneity constrains representational capacity, impedes interpretability, and induces computational redundancy. We propose EPMORE (Explainable Process Mixture-of-Experts), a novel architecture that models inference as a process of dimensional elevation, Ensure the entire inference/training process, intermediate states observable and explainable, and ensure the whole process is traceable end-to-end. EPMORE decomposes the entire inference/training process into a hierarchical sequence of representation spaces — from a semantic space (128 dimensions), to one or more logical spaces (512 dimensions each), and finally to fact-expert representation spaces (1024 dimensions) — allowing deeper network stages to encode progressively richer and more abstract features. A core component, Middle Output Reuse (MOR), enables each layer to produce interpretable intermediate predictions. Theoretically, forward propagation can be interpreted as representation-space expansion, while backward propagation corresponds to a dimensional contraction process. Experiments show that, compared with dense and conventional mixture-of-experts (MoE, Deepseek) baselines, EPMORE improves interpretability, activation sparsity, parameter independence, and inference performance while reducing computational cost. These findings suggest that hierarchical dimensional elevation is a promising alternative to standard Transformer design.

The determination of kinetic binding properties of DNA-tagged compounds binding to a target protein is an important step in the development of nucleic acid-based proteolysis targeting chimeras (PROTACs). Focal molography (FM) is a recent addition to the toolbox of instruments that allow for measuring kinetic binding data of drugs binding to a target protein with high experimental throughput. Here, we applied focal molography to characterize the binding of DNA-VHL ligand conjugates-these are "DNA-PROTAC" compounds-to VHL. We synthesized two libraries of 20 such compounds with diverse amino acid linkers by solid-phase amide coupling and used FM to measure their affinity to VHL in both singleplex and 20-plex multiplexed measurement formats. Systematic comparison of equilibrium and kinetic fitting approaches reveals strong within-format correlations that preserve compound rankings despite systematic offsets in absolute K D values. The multiplexed format achieves 20fold higher throughput while yielding stronger structureactivity correlations with lipophilicity compared to singleplex measurements. Our analysis identifies hydrophobicity as the primary driver of the contribution of the linker part to overall linker-VHL ligand affinity for VHL in the case of DNA-PROTACs. These findings validate multiplexed FM as a robust platform for PROTAC structureactivity relationship studies and provide design principles for optimizing linker-E3 ligase interactions.

Quantifying biomolecular interactions under near-physiological conditions is essential for understanding biological processes. However, this remains a challenge in established techniques: for surface plasmon resonance, bulk refractive-index shifts and non-specific adsorption in hydrogel matrices can dominate the signal in serum, whereas for isothermal titration calorimetry, complex media primarily introduce large, composition-dependent background heats and run-to-run variability that obscure the binding enthalpy. Here, we demonstrate that focal molography, a label-free optical biosensing method, can reliably measure the kinetics and thermodynamics of the protein kinase A regulatory subunit binding to cyclic AMP derivatives in both buffer and 50% human serum. By performing the kinetic measurement over a range of temperatures, we were able to reveal that binding remains highly favorable in both environments, yet the results suggest that the balance of driving forces shifts toward enthalpic contributions in serum. This hints at the substantial difference of interaction mechanisms in a complex biological media compared to buffer systems. Our findings show that focal molography reliably quantifies protein-ligand interactions in complex media, providing consistent kinetic and thermodynamic data while overcoming limitations of existing methods. This highlights the potential of focal molography as a valuable tool for studying interactions under near-native conditions.

تهدف هذه الدراسة إلى تحليل أثر استخدام منصات الذكاء الاصطناعي وأدوات التفاعل المباشر على تحسين الإنتاجية والتحصيل الدراسي لطلاب المرحلة الإعدادية أثناء الجلسات التعليمية عبر الإنترنت. مع التقدم التكنولوجي السريع وتزايد استخدام التعليم الإلكتروني، يوفر الذكاء الاصطناعي إمكانيات واسعة لتحسين تجربة التعلم من خلال تخصيص المحتوى التعليمي وتقديم التغذية الراجعة الفورية. تعتمد الدراسة على أدوات مثل Mentimeter وPadlet لتقييم التفاعل، ومنصات الذكاء الاصطناعي مثل Read AI التي تساعد في متابعة الأداء وتحليل التفاعل في الوقت الفعلي. تشير النتائج إلى أن تطبيقات الذكاء الاصطناعي تسهم في زيادة التفاعل والإنتاجية،

Targeting the issues of "shortcuts" and insufficient contextual understanding in complex cross-modal reasoning of multimodal large models, this paper proposes a zero-shot multimodal reasoning component guided by human-like cognitive strategies centered on an "intent sketch". The component comprises a plug-and-play three-module pipeline-Intent Perceiver, Strategy Generator, and Strategy Selector-that explicitly constructs a "understand-plan-select" cognitive process. By generating and filtering "intent sketch" strategies to guide the final reasoning, it requires no parameter fine-tuning and achieves cross-model transfer solely through in-context engineering. Information-theoretic analysis shows that this process can reduce conditional entropy and improve information utilization efficiency, thereby suppressing unintended shortcut reasoning. Experiments on IntentBench, WorldSense, and Daily-Omni validate the method's generality and robust gains; compared with their respective baselines, the complete "three-module" scheme yields consistent improvements across different reasoning engines and pipeline combinations, with gains up to approximately 9.51 percentage points, demonstrating the practical value and portability of the "intent sketch" reasoning component in zero-shot scenarios.

This article presents a comprehensive analysis of artificial intelligence and its impact on various aspects of sustainable development. AI is actively utilized to enhance decision-making, automate processes, and optimize numerous fields of activity. However, its integration into critical domains such as sustainable development, public administration, and cultural innovation raises concerns regarding accessibility, inclusivity, and resource redistribution. The study examines key aspects of artificial intelligence, its classification—including narrow and general AI—and the technologies employed, such as machine learning, natural language processing, and computer vision. Special attention is given to AI’s relationship with sustainable development goals and its role in advancing innovative solutions in social and environmental spheres. The article also explores the ethical, social, and cultural consequences of AI implementation, emphasizing the necessity of developing responsible, transparent, and sustainable systems that align with international standards and ensure long-term societal well-being. This research may be of interest to a broad audience of specialists and researchers engaged in fields related to AI development, its applications, and its influence on societal processes.

Abstract Metabolic dysfunction‐associated steatotic liver disease (MASLD) has become an important liver disease worldwide due to the rising rate of type 2 diabetes, obesity, and other metabolic disorders. The early detection of MASLD is vital to slow progression to cirrhosis and hepatocellular carcinoma; yet, existing research tools for studying hepatic steatosis are limited in terms of their effectiveness and invasiveness. Herein, a near‐infrared (NIR) fluorescent probe, TCF‐FBN, for the selective imaging of lipid droplets (LDs) in MASLD is reported. TCF‐FBN exhibits bright NIR fluorescence and a large Stokes shift in nonpolar solvents, and minimal fluorescence in polar solvents, making it suitable for biological imaging. The probe is bound to bovine serum albumin (BSA) through supramolecular forces to enhance its biocompatibility, and the resulting TCF‐FBN@BSA ensemble is used for fluorescence imaging of LDs generated in hepatocytes. Notably, the modification of TCF‐FBN@BSA with galactosyl ligands producing TCF‐FBN@Gal‐BSA enables the targeted imaging of the liver of mice with MASLD via asialoglycoprotein receptor (ASGPR)‐mediated biorecognition.