Search Results (459)

Background/Objectives: Sensorineural hearing loss (SNHL) is a chronic condition with no established pharmacological treatment. Recent advances in drug-based therapies offer promising opportunities to prevent or treat SNHL. This scoping review summarizes the current landscape of pharmacotherapeutics for SNHL. Methods: This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A literature search of PubMed, Google Scholar, Embase, Scopus, and Web of Science was conducted in 2024 using keywords related to SNHL and pharmacotherapeutics. A review protocol was preregistered on the Open Science Framework. A systematic search of five electronic databases identified published studies from 2004 to 2024 on pharmacological treatments for SNHL in human participants, as well as ongoing clinical trials. Interventions were categorized by mechanism of action: antioxidant therapy, steroid-based combination therapy, hematologic-based therapy, pathway modulator therapy, regenerative therapy, and gene therapy. A narrative synthesis approach was used to map key trends across treatment types, study designs, and outcomes. Results: Sixty-six records met the inclusion criteria, including 48 published studies and 18 ongoing or recently completed clinical trial records. Antioxidants, corticosteroids, hematologic agents, and pathway modulators have demonstrated potential in preventing or treating SNHL caused by cisplatin, aminoglycosides, noise-induced ototoxicity, and intraoperative cochlear implantation trauma. Emerging regenerative and gene therapies show promise as future pharmacologic treatment options. Conclusions: Pharmacologic therapies for SNHL are promising but remain constrained by small sample sizes, heterogeneous study designs, and drug delivery challenges across the blood–labyrinth barrier. Future research should prioritize multicenter randomized trials, optimized delivery strategies, and integration of precision medicine approaches. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

Conventional full-space heating systems waste massive fossil-derived energy on unoccupied indoor areas and cause uncomfortable “warm head, cold feet” issues against sustainable building targets. To fill this gap and advance low-carbon indoor heating solutions for sustainable office development, this study proposes an innovative localized heating terminal combining radiant panels and downward laminar air supply. An experimental platform was established, with twelve testing cases covering varied supply air velocity, supply air temperature and radiant panel temperature to explore its thermal comfort and energy-saving sustainability performance. Experimental results demonstrate that, under the optimal operating condition (0.55 m/s airflow, 23.5 °C supply air, 36 °C radiant panel), the vertical head–foot temperature difference reduces to merely 1.2 °C, far below the 3–5 °C threshold of conventional heating equipment; the draught rate approaches zero to eliminate cold draft discomfort. Critically, 65–75% of total supplied heat concentrates within human-occupied zones, drastically cutting redundant heat loss and advancing building heating sustainability. The terminal features dual working modes: convection contributes 78.7–94.4% of total heat for rapid warm-up while radiant heat maintains stable long-term comfortable surroundings. Such flexible dual-mode design supports sustainable part-load operation matching intermittent office occupancy, making this terminal a feasible low-carbon option for modern sustainable office buildings prioritizing energy efficiency and a healthy indoor environment. Full article

Background: Monoclonal antibodies (mAbs) initially played a major role in outpatient COVID-19 management by providing rapid passive immunity and reducing progression to severe disease. However, continuous SARS-CoV-2 evolution progressively compromised the effectiveness of several anti-spike products. This narrative review summarizes the trajectory of COVID-19 mAbs across three phases: early clinical efficacy, loss of efficacy due to immune escape, and future directions. Methods: We conducted a narrative review focusing on mechanisms of action, pivotal clinical trials, and real-world effectiveness of neutralizing anti-spike mAbs and host-directed immunomodulatory mAbs. Emphasis was placed on the impact of variants—especially Omicron—on susceptibility and clinical use, as well as on emerging next-generation platforms. Results: First-generation neutralizing mAbs substantially reduced the hospitalization rates during the Alpha and Delta waves, while immunomodulatory mAbs became standard options for the hyperinflammatory phase in hospitalized patients. With the emergence of Omicron and its sub-lineages, extensive immune escape led to marked reductions in neutralization for many earlier anti-spike agents and consequent restrictions in use. Later-generation approaches targeting more conserved epitopes provided temporary solutions but were also challenged by ongoing antigenic drift. Host-directed immunomodulators retained clinical relevance because their mechanism is independent of viral spike mutations. Conclusions: The clinical role of monoclonal antibodies in COVID-19 has been dynamic and increasingly constrained by viral evolution. Future strategies should prioritize broadly neutralizing antibodies targeting conserved epitopes, innovative delivery platforms, and integration with real-time surveillance to preserve clinical utility in the endemic phase and improve preparedness for future outbreaks. Full article

Complex conflict environments often involve composite conflicting parties composed of multiple individuals with divergent assessments, posing significant challenges for preference elicitation and conflict analysis in the Graph Model for Conflict Resolution (GMCR). Existing option prioritization methods in GMCR rely on ordinal rankings of statement importance and binary evaluations of state support, which limits their ability to capture nuanced and heterogeneous preferences. Moreover, current GMCR frameworks lack a systematic mechanism to reconcile divergent individual assessments and construct collective preferences. To address these gaps, this study integrates consensus-based group decision making into the GMCR framework through flexible assessments. Specifically, pairwise comparisons are employed to represent the relative importance of statements, while continuous values in [0, 1] are used to characterize the degree of statement support for states. To reconcile heterogeneous assessments, a minimum adjustment-based consensus reaching process is developed. Furthermore, grounded in inequity aversion theory from behavioral economics, fairness concern is incorporated to model individuals’ sensitivity to adjustment differences between themselves and others. Based on this mechanism, two minimum adjustment consensus models with fairness concern are proposed. The resulting consensus-based preferences are subsequently integrated into GMCR stability analysis to identify equilibrium solutions. A supply chain carbon reduction case is used to illustrate the implementation process and applicability of the proposed framework. Full article

Background/Objectives: Osteoporosis remains a clinically important metabolic bone disorder with limited bone-forming therapeutic options. SET domain bifurcated protein 1 (SETDB1) is involved in osteogenic epigenetic regulation, but small-molecule discovery guided by SETDB1-associated structural regions remains limited. This study aimed to identify a candidate compound with in silico relevance to a SETDB1-associated ligand-bound pocket and assess its association with early osteogenic readouts. Methods: A computational–experimental workflow was used, including hierarchical molecular docking, MM-GBSA rescoring, ADMET-based prioritization, redocking validation, molecular dynamics simulations, and preliminary in vitro evaluation in MC3T3-E1 cells. Compound 271 (C271) was selected based on structure-based screening results and predicted developability-related properties. Cytocompatibility, alkaline phosphatase (ALP) activity and staining, selected molecular markers, and SETDB1–H3 molecular dynamics behavior were evaluated. Results: Redocking reproduced the reference binding mode, and molecular dynamics simulations indicated that C271 maintained a relatively persistent conformation around the predicted SETDB1-associated pocket. Comparative SETDB1–H3 simulations showed altered H3 dynamics and SETDB1–H3 contact patterns in the C271-containing system. In cell-based assays, C271 showed no appreciable cytotoxicity within the tested concentration range and was associated with increased ALP activity and staining. C271 treatment was accompanied by higher global H3K9me3 and Runx2 levels, whereas SETDB1 protein abundance remained largely unchanged. Conclusions: C271 was identified as a computationally prioritized SETDB1-related candidate compound associated with early osteogenic-associated cellular responses. The evidence supports computational plausibility and cell-level association, but does not establish direct SETDB1 engagement, SETDB1 enzymatic modulation, SETDB1-dependent causality, or late-stage osteogenic maturation/mineralization. Given the single-compound evaluation, further target-engagement, enzymatic, and functional studies are needed. Full article

Early identification of at-risk students is crucial for timely pedagogical intervention. Determining which assessments instructors should prioritize is complicated by the fact that different eXplainable-AI (XAI) methods can produce conflicting rankings for the same predictive model. We develop a framework combining a sequential GRU model with two complementary XAI techniques, Gradient SHAP (attribution) and DiCE (counterfactuals), and evaluate it in a foundational Data Structures and Algorithms course. The framework produces predictions and explanations for every prefix length throughout the semester and quantifies inter-method agreement and intra-method stability using three disagreement metrics. Intersecting the top-k features identified by both methods isolates a compact subset of assessments whose predictive role is confirmed across two fundamentally different explanation mechanisms. We interpret this cross-method agreement as a heuristic that increases confidence in identified features relative to single-method results, though not as evidence of causal validity. For individual students, the framework uses the intersection of the two types of explanations when it is non-empty; otherwise, the instructor chooses between SHAP’s diagnostic view and DiCE’s prescriptive view, with an optional check against the top-k list. The resulting guidance is less susceptible to method-specific biases than analyses relying on a single method. Full article

Due to multiple anthropogenic drivers, coastal wetlands have lost roughly 50% of their historical coverage, and deterioration is accelerating with rising sea levels. Thin-layer placement (TLP), the spreading of sediment dredged from nearby water bodies across existing wetlands or shallow mudflats to raise surface elevation, has emerged as a viable approach to sustain and restore these habitats. Strategies for the prioritization of site selection and design elements for TLP interventions remain unclear; a gap that must be closed to coordinate dredging with wetland restoration efficiently, given time, financial, and sediment constraints. Here, we present a transferable workflow to plan TLP projects, including systematic assessment of restoration needs, development of sediment application options, and prioritization of project sites that leverage publicly available remote-sensing data products and stakeholder input. We demonstrate its applicability in a rapidly deteriorating salt marsh–mangrove co-dominated system on the Atlantic coast of Florida. Guided by stakeholder priorities for storm-surge mitigation and habitat improvement, we tracked long-term (1952–2023) changes in vegetated wetland coverage to quantify loss trends and establish historic habitat borders as restoration targets. We then summarized short-term (2010–2023) habitat-mosaic shifts to resolve plant-species composition changes. In our focal system, long-term analyses revealed hotspots (zones 1 and 7) of >35% vegetation loss, while short-term analyses showed a 180% mangrove expansion and cordgrass degradation across all zones, suggesting a nuanced, tailored approach to sediment application. Taken together, this workflow provides a data-driven, stakeholder-informed process for TLP site prioritization to restore threatened wetlands, bolster coastal resilience, and maximize stakeholder benefits in our demonstration system in northeast Florida and, more broadly, to other dynamic coastlines. Full article

Detecting the presence of carbonation is critical for monitoring structural safety and durability. Identifying the presence of carbonation reveals the risk of chemical changes within the concrete and the potential for reinforcement corrosion. This detection allows for a reliable and prioritized assessment of the structure’s current condition. Therefore, checking for the presence or absence of carbonation is a critical indicator in determining structural safety and maintenance priorities. This study explicitly addresses a critical gap in the literature, where existing carbonation research predominantly focuses on regression-based estimation of carbonation depth, while the problem of direct visual classification of carbonation presence for rapid decision-making currently remains underexplored. In this context, the study aims to fill this research gap through developing a robust and field-applicable deep learning-based classification framework for the automated detection of carbonation presence on concrete surfaces using images, while systematically comparing the performance of different YOLO architectures and assessing the suitability of a previously unused dataset (ConcreteCARB) for carbonation classification tasks. In this context, YOLOv8m, YOLOv11m, YOLOv12m, and YOLOv26m were compared for concrete carbonation classification, aiming to find the most suitable model. The results show that YOLOv8m and YOLOv11m achieve perfect accuracy (Accuracy = 0.9981, Precision = 1, Recall = 0.9964, Specificity = 1, AUC-ROC = 1). In inference efficiency analyses, the YOLOv11m model was identified as the fastest model with the lowest latency and highest FPS. While YOLOv8m and YOLOv26m offered balanced speed-performance results, YOLOv12m showed a relatively lower processing speed. The findings indicate that YOLOv11m is the most suitable option for real-time applications. Full article

Restless legs syndrome (RLS) is a common sensorimotor disorder with limited treatment options and incompletely understood pathophysiology. Genome-wide association studies have identified numerous risk loci, but translating these findings into causal genes and therapeutic targets remains challenging. We performed a proteome-wide association study (PWAS) integrating RLS genome-wide association study (GWAS) data from FinnGen with two brain pQTL datasets (ROSMAP and Banner). We validated the identified proteins using TWAS, SMR, and colocalization analyses using brain pQTL and eQTL datasets. To further investigate peripheral protein associations, we performed SMR using plasma pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP). We also conducted a phenome-wide association study (PheWAS) to screen for potential off-target effects of the prioritized genes, followed by drug prediction using DSigDB and molecular docking. PWAS identified GLO1, along with GRWD1 and MAP2K5, as significantly associated with RLS. GLO1 was identified by brain-based SMR (p = 0.0001), colocalization (PP.H4 = 0.96), TWAS (p = 0.048), and was confirmed by plasma-based SMR (p = 3.16 × 10⁻⁹) as the only protein associated with RLS. PheWAS analysis, without associations for 783 non-RLS phenotypes, confirmed the specificity of GLO1. Among 27 predicted GLO1-targeting compounds, Gambierol had the strongest binding affinity (−8.3 kcal/mol). This proteogenomic study identifies GLO1 as a prioritized causal gene and promising drug target for RLS, combining brain and plasma data to provide new insights into pathogenesis and candidate drug development. Full article

Background/Objectives: Lean metabolic dysfunction-associated steatotic liver disease (lean MASLD) is an increasingly recognized phenotype occurring in individuals with normal body mass index (BMI), despite clinically important hepatic and cardiometabolic risk. This narrative review summarizes current evidence on its epidemiology, pathophysiology, diagnostic challenges, clinical outcomes, and management. Methods: A narrative literature review was conducted using PubMed, Embase, and Cochrane Library from database inception to March 2026. Relevant studies on lean MASLD/lean NAFLD, including cohort studies, meta-analyses, clinical trials, consensus statements, and practice guidelines, were prioritized. Results: Lean MASLD reflects interactions between visceral adiposity, insulin resistance, genetic susceptibility, sarcopenia, dietary and lifestyle factors, vitamin D deficiency, and gut microbiome alterations. Diagnosis is challenging because BMI and aminotransferase levels may underestimate metabolic vulnerability, MASH, or clinically significant fibrosis. Available data suggest increased liver-related events, liver-related mortality, and all-cause mortality compared with individuals without steatotic liver disease, although comparisons with non-lean MASLD remain heterogeneous. Resmetirom and semaglutide have expanded treatment options for noncirrhotic MASH with moderate to advanced fibrosis, but lean patients are underrepresented in pivotal trials. Conclusions: Lean MASLD is an underrecognized but clinically important phenotype. Earlier recognition, fibrosis risk stratification, sarcopenia assessment, cardiometabolic optimization, and lean-specific therapeutic research are needed to improve outcomes. Full article

OLIVIA (OperationaL Intentions adVIser for Aviation) was developed in the HAIKU project. It is a flight deck tool providing support to mission-level decisions in complex situations by assessing and prioritizing route options according to operational intentions. It uses Artificial Intelligence to translate (1) operational intentions from pilots to route generation and optimization inputs and (2) route proposal KPIs into operational intention assessments. This paper reports on the final development of OLIVIA, the results from the human-in-the-loop experiments, and insights and recommendations regarding the development of similar assistants for the flight deck. Full article

Fish-derived products are extensively acknowledged for their substantial role in fostering balanced diets and supporting a healthy way of life. This research is aimed at formulating, analyzing and evaluating a fish-based food product. The methodology adopted in this study adheres to contemporary food safety standards, prioritizing the utilization of minimal technological processes and natural ingredients, a focus that is gaining prominence within contemporary industrial practices. Thus, the proposal for a formulation obtained by integrating powders and extracts from plant byproducts (Citrus) represents a concrete application direction with real potential for commercialization. The product has been enriched with biocomponents derived from orange peel, namely orange extract (OE) and orange peel powder (PPO). The research focused on product development and the in situ evaluation of the effects of OE and PPO. The physicochemical composition, bioactive compound content, and antioxidant activity were evaluated, along with the microbiological status under post-opening refrigeration conditions, in order to simulate actual consumer use. In addition, the product’s color parameters and sensory attributes were analyzed. The results highlight significant potential for the development of a clean-label fish-based product, characterized by a simplified and easily implementable formulation, aligned with current production and consumption requirements. Compared to the control sample, both OE and PPO significantly influenced the analyzed parameters. Differences in physicochemical composition were observed in the experimental samples. In addition, PPO increased the antioxidant activity of the samples and the profile of bioactive compounds. Microbiological analysis, performed on day 0 and after 3 and 7 days of storage at 4 °C showed opening, confirmed the absence of Escherichia coli and Staphylococcus aureus in all samples and had an influence on the growth of fungi. The acceptability of fish-based products is often limited by odor perception, which is one of the main factors leading to consumer rejection. Sensory evaluation demonstrated that citrus-enriched samples were distinguished by the perception of particular sensory attributes. This formulation presents a practical solution to address this constraint, thereby enhancing the product’s sensory acceptability. The integration of OE and PPO yielded a more harmonized sensory profile, as evidenced by elevated hedonic scores and an intermediate placement in both principal component analysis (PCA) and external preference mapping. This research furnishes a thorough characterization of a fish-based food product, underscoring its potential as a viable option for balanced dietary regimens. Simultaneously, the findings support the product’s adherence to sustainability principles through the utilization of bioactive compounds sourced from plant byproducts, thus satisfying contemporary requirements for foods that possess an optimal nutritional profile and a diminished environmental footprint. Full article

Plant-parasitic nematodes (PPNs) cause substantial yield losses across a wide range of economically important crops worldwide, and the progressive withdrawal of synthetic nematicides due to toxicological and environmental concerns has created an urgent need for safer alternatives. Botanical extracts, owing to their chemically diverse secondary metabolites and multi-target nematicidal activity, represent one of the most thoroughly studied options. The present work synthesizes and critically evaluates the current state of knowledge on botanical extracts as nematicidal agents, encompassing phytochemical diversity, extraction methodology, nematicidal mechanisms, advanced formulation strategies, and the principal constraints limiting field-scale applicability. Research coverage has been markedly uneven: most studies have concentrated on a small set of plant families, particularly Lamiaceae, Asteraceae, Brassicaceae, and Meliaceae, with Meloidogyne spp. as the predominant target, while many other taxa remain underexplored. Proposed nematicidal mechanisms include oxidative stress, cholinergic interference, disrupted intracellular pH regulation, impaired detoxification, and induction of cell death; yet mechanistic integration through multi-omics approaches remains limited. Activity under laboratory conditions often declines markedly in soil, largely due to compound instability or volatility, a limitation that encapsulation and nanoemulsion formulations are beginning to address. Future research should prioritize standardized mechanistic studies and replicated field trials to bridge the gap between laboratory promise and practical nematode management. Full article

Pavement management systems (PMS) are essential for formulating a cost-effective capital improvement plan (CIP) that adheres to budget constraints. Optimization techniques are vital in enhancing the efficiency of these plans. Among the various methods available, genetic algorithms (GA) are particularly effective at identifying optimal solutions in complex scenarios. This study introduces a GA-based priority optimization model designed to select the most beneficial road improvement projects while staying within budgetary limits. The model was applied to the extensive road network of Fort Wayne, Indiana, considering critical factors such as budget allocation, roadway classification, PASERs, treatment options, and associated costs. The results demonstrate the model’s effectiveness in prioritizing projects, ensuring that available funds are utilized to achieve maximum impact on roadway conditions. By leveraging GA, this approach not only enhances decision-making processes but also provides a robust framework for future pavement management efforts. Overall, the integration of genetic algorithms into PMS can lead to more strategic and economically sound infrastructure improvements. Full article