Search Results (4,502)

Inadvertent perioperative hypothermia increases susceptibility to surgical site infection (SSI) through impaired immune function, reduced oxidative killing, and altered collagen deposition. We performed a narrative review of recent clinical and translational studies evaluating active thermal management for SSI prevention, with emphasis on forced-air warming (FAW) and conductive systems, and on high-risk settings (prolonged surgery, elevated BMI, and pediatric patients). Across studies, active warming more reliably maintains intraoperative normothermia than passive insulation; however, evidence for a consistent reduction in SSI rates is strongest in vulnerable cohorts and lengthy procedures and remains heterogeneous across specialties. FAW demonstrates high warming efficiency, yet its use in implant-based operations continues to be debated because of potential airflow disruption and bacterial mobilization concerns. The literature increasingly supports precision approaches, including pre-warming protocols, improved perioperative temperature monitoring, and predictive models to identify patients at greatest risk of hypothermia. In conclusion, effective SSI prevention requires procedure- and patient-specific thermal strategies, selecting devices that balance warming performance with sterility considerations while integrating perioperative risk stratification and real-time monitoring. Full article

In the present work, a novel series of eleven sulfonate derivatives with potent inhibitory activity against butyrylcholinesterase (BChE) is reported. Of these, compounds 2-[(E)-(2-Benzoylhydrazinylidene)methyl]phenyl 5-(dimethylamino)naphthalene-1-sulfonate (5c, IC₅₀ = 1.11 µM) and tert-butyl (2E)-2-[(2-{[5-(dimethylamino)naphthalene-1-sulfonyl]oxy}phenyl)methylidene]hydrazine-1-carboxylate (5b, IC₅₀ = 11.51 µM) exhibit stronger inhibitory activity than rivastigmine, the reference compound, and exhibit high selectivity for BChE over AChE (e.g., selectivity index 57 for 5c). Interestingly, compound 5c also exhibited anti-inflammatory effects, which is important for potential therapeutic applications, especially in Alzheimer’s disease. These new compounds were designed through a structure-based approach using molecular modeling techniques (docking, molecular dynamic (MD) simulations, and QTAIM (quantum theory of atoms in molecules) calculations). The most promising compounds show no detectable toxic effects and satisfy Lipinski’s rule of five, indicating that they represent attractive starting structures for the design of new derivatives acting as specific BChE inhibitors. In addition, our results indicate that relatively simple computational techniques such as docking calculations and toxicity prediction programs can be valuable when properly used in the search of new candidates for this particular target. Docking calculations show that the more active compounds of this series reach the bottom region of the gorge interacting with residues within the active site of BChE. However, our data further suggest that the use of more precise techniques, such as MD simulations and QTAIM analysis, is necessary to obtain detailed insight into ligand–enzyme interactions. Regarding QTAIM calculations, they demonstrate that such computations are very useful to evaluate the molecular interactions of the different molecular complexes. In summary, we report a new series of sulfonate derivatives as promising starting structures for the development of new selective BChE inhibitors. Full article

Climate change poses an urgent challenge to Canada’s sustainable development. The country experiences increasing extreme weather events, rising temperatures, and pressures on energy systems—particularly in remote northern regions. In Newfoundland and Labrador, isolated communities are vulnerable because reliance on diesel-based electricity increases greenhouse gas emissions, energy costs, and environmental risks, highlighting the need for resilient energy solutions. This study uses a systematic methodology combining literature review, local energy demand data, and site-specific wind resources to design and optimize hybrid renewable energy systems (HRESs) for Makkovik. It employs HOMER Pro and the Monte Carlo method to evaluate uncertainties in cost, fuel consumption, and renewable fraction. The objectives are to quantify how renewable integration can reduce emissions, improve energy reliability, and support sustainable development in remote communities. The novelty lies in combining location-specific modeling with probabilistic Monte Carlo analysis and providing robust, system-level insights into environmental and economic outcomes while guiding climate-resilient energy planning. The proposed HRES significantly mitigates climate change impacts, reducing annual CO₂ emissions from 72,500 kg/year to 15,190 kg/year. Monte Carlo analysis indicates economic feasibility with a net present cost of $14.5 million, a levelized cost of electricity of 0.256 $/kWh, and diesel consumption reduced from 29,970 L/year to 5854 L/year. Wind energy provides 99.6% of total annual electricity, ensuring a high renewable fraction and reliable power, enhancing energy resilience and adaptation potential. This study demonstrates that a well-designed hybrid renewable energy system can deliver measurable emission reductions, economic feasibility, and enhanced energy resilience. It supports sustainable development and climate change mitigation in remote Canadian communities. Full article

Plant-mediated CH₄ transport can enhance ecosystem CH₄ emission by transporting soil-produced CH₄. This pathway can exceed diffusion and ebullition as the dominant CH₄ emission route. However, limited studies have investigated the morphological and anatomical factors influencing CH₄ transport in plants. Through a series of manipulative experiments on the shoots and roots, this study examines the role of root and shoot structures in CH₄ transport and release in six widespread wetland species: Carex rostrata Stokes, Carex lasiocarpa Ehrh., Carex aquatilis Wahlenb., Iris pseudacorus L., Juncus effusus L., and Alocasia odora (Lodd.) Spach. CH₄ flux from all investigated species dropped significantly after clipping fine roots, while it did not change significantly after removing coarse roots. Shoot clipping and sealing significantly decreased CH₄ flux from the investigated Carex species, but not from the other species. Our results demonstrate the important role of fine roots in controlling CH₄ flux, whereas coarse roots play a minor role. Leaf blades are the major release site of CH₄ from Carex species, while micropores at the shoot base are the primary release site of CH₄ from the other species. Our study suggests that integrating plant-specific anatomical and morphological characteristics into global methane models is crucial to better predict and mitigate climate change impacts. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Phosphorus availability is extremely limited in Florida’s sandy soils due to intense sorption by aluminum (Al), iron (Fe) oxides, and fertilizer retention. Current fertilization recommendations do not account for P-fixation, a defining characteristic of Florida’s soils. Site-specific and multi-year yield-based thresholds for snap bean under these conditions have not been established. This study is among the first to derive yield-based thresholds from a multi-year linear–plateau model using nonlinear mixed-effects modeling that accounts for stochastic variability across sites and years, thereby defining a threshold range for this crop in this soil system. This work assessed snap bean (Phaseolus vulgaris L.) pod yield responses to phosphorus fertilization from 2022 to 2025. Field experiments employing increasing P₂O₅ rates and fertilizer sources were conducted. Hastings and Citra were selected to represent sandy soil conditions across northeast and north-central Florida’s commercial snap bean production areas, where soil tests consistently indicated elevated extractable Al and Fe in the rhizosphere, key drivers of P fixation and fertilizer demand. At low-to-moderate P₂O₅ rates, yield increased linearly over site-years before plateauing. A breakpoint of 215.6 kg ha⁻¹ P₂O₅ was found in Hastings by the multi-year model. A single-year fit at Citra in 2025 revealed a breakpoint of 265.7 kg ha⁻¹ P₂O₅. Confidence intervals were wide due to year and plot variability, with values of 148.2–283 kg ha⁻¹ P₂O₅. When all site-years were pooled, the population-level breakpoint was estimated at 223.5 kg ha⁻¹ P₂O₅, with 90% and 95% model estimates of maximum yield obtained at about 164 and 194 kg ha⁻¹ P₂O₅, respectively. These findings provide a fertilizer range for snap bean production in Florida’s sandy soils under similar conditions, with implications for regional fertilizer guidelines. Full article

Objective: The objective of this study was to map and critically synthesize empirical evidence on ML/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020–2025) applying ML/AI to surface mining activities, training/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R², RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study’s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software/tooling). Conclusions: ML/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605/OSF.IO/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727). Full article

Chitooligosaccharides (COS) are short-chain chitosan derivatives with a wide range of biomedical, agricultural, and environmental applications, including antimicrobial therapy, wound healing, and pollutant removal. Reliable quantification of COS is essential but currently relies on high-performance liquid chromatography, mass spectrometry, or capillary electrophoresis, which require costly equipment, complex sample preparation, and are unsuitable for routine or on-site applications. This study reports a rapid, solvent-free, colorimetric assay for COS based on the reaction of 5% aqueous ninhydrin with free amino groups in McIlvaine buffer. The assay was optimized using glucosamine as a model analyte, yielding maximal sensitivity at pH 7.0. The chromophore generated (Ruhemann’s purple) remained stable for over 120 min after reaction, allowing measurements to be taken without strict time constraints. Calibration was linear from 0.4 to 2.2 mM (R² = 0.9926), with low limits of detection (0.006 mM) and quantification (0.018 mM). Increasing absorbance with COS polymerization degree (DP1–DP6) demonstrates specificity for free amino groups, while N-acetyl glucosamine showed a negligible response. Furthermore, the assay was successfully adapted for solid-phase detection on ninhydrin-pretreated filter paper and nitrocellulose, with enhanced sensitivity. This simple, efficient, and low-cost method provides an accessible alternative to instrumental techniques, supporting COS monitoring in laboratory workflows and enabling portable applications in biomedicine, agriculture, and environmental diagnostics. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

Trichothecene mycotoxins, especially T-2 toxin, represent a significant threat to food safety and public health. Although the enzymatic degradation of deoxynivalenol has been extensively investigated, there are few reports of enzymes capable of efficiently degrading T-2 toxin. This study identified that the aldo-keto reductase AKR13B3 from Devosia A6-243 exhibits 3-keto-DON-degrading and a little T-2 toxin-degrading activity. To address this limitation, a rational design strategy targeting the substrate-binding pocket was employed to enhance its activity. Utilizing site-directed and combinatorial mutagenesis, a double mutant R134F/D217A was successfully screened. R134F/D217A retains catalytic activity towards 3-keto-DON while significantly enhancing its catalytic capacity for T-2. Specifically, the R134F/D217A variant exhibited a 2.88-fold increase in catalytic activity and a 3.15-fold enhancement in catalytic efficiency (k_cat/K_m) relative to the wild type enzyme. Notably, a substantial improvement in thermal stability was also observed. After incubation at 55 °C, the residual activity of the R134F/D217A mutant was 2.63 times that of the wild type. Molecular dynamics (MD) simulations and three-dimensional structural modeling suggested the mechanistic basis for the enhanced performance of the R134F/D217A double mutant. Catalytic enhancement stems from a shortened nucleophilic attack distance, a positively biased electrostatic environment, combined with an enlarged pocket and reduced binding free energy. Concurrently, the increased thermal stability results from decreased flexibility and a more rigid structural architecture. This work presents the first report of AKR13B3 as an effective enzyme for T-2 toxin transformation, and its catalytic activity was significantly enhanced through rational design. Thus, a novel enzymatic strategy was proposed, and could inform future approaches to study issues related to T-2 toxin contamination. Full article

Ebola virus disease remains one of the most serious viral infections with no approved small-molecule treatments. The Ebola virus glycoprotein (EBOV-GP), which enables the virus’s entry to host cells, is a promising target for drug discovery. In this study, a multistage computer-aided drug discovery approach was used to identify new specific EBOV-GP inhibitors. A reliable QSAR model was built using 55 terpenoid derivatives. This model was able to predict the activity of newly designed compounds with good accuracy and validated statistical metrics ($R_{tr}^2$= 0.70; $R_{ext}^2$ = 0.73). It was subsequently applied to screen over 15,500 newly generated compounds from three lead molecules by fragment-based design tools. Predicted activity, binding affinity toward EBOV-GP, and good ADMET drug-like properties prioritized the eleven most promising hits. Through 150 ns molecular dynamics simulations, these compounds remained stable in the EBOV-GP binding site. Further binding free energy analysis (MM/PBSA) showed strong binding affinities, especially for the compounds L-60, L-832, M-1618, and L-1366. This study showed how combining QSAR, fragment-based design, docking, ADMET, and molecular dynamics could help in identifying potent and safe small molecules against the EBOV-GP. The top compounds are ready for further experimental and in vitro biological testing. Full article

Weed identification and quantification are processes that are usually manual, subjective, and error-prone. Weeds compete with crops for nutrients, minerals, physical space, sunlight, and water. Thus, weed identification is a crucial component of precision agriculture for autonomous removal and site-specific treatments, efficient weed control, and sustainability. Convolutional Neural Networks (CNNs) are very common in weed identification. This work implemented CNN models for semantic segmentation based on the U-Net architecture for automatically segmenting and quantifying weeds in potato crops using RGB images acquired by a drone at 9–10 m height, flying at 1 m/s. Remote sensing images are affected by factors that degrade image quality and the model’s accuracy. Five U-Net variants were evaluated: the original U-Net, Residual U-Net, Double U-Net, Modified U-Net, and AU-Net. The models were trained using the TensorFlow/Keras frameworks on Google Colab Pro+, following the Knowledge Discovery in Databases (KDD) methodology for image analysis. Each model was trained using a diverse custom dataset in uncontrolled environments, considering six classes: background, Broadleaf dock (Rumex obtusifolius), Dandelion (Taraxacum officinale), Kikuyu grass (Cenchrus clandestinum), other weed species, and the crop potato (Solanum tuberosum L.). The models’ segmentation was widely assessed using Mean Dice Coefficient, Mean IoU, and Dice Loss metrics. The results showed that the Residual U-Net model performed the best in multi-class segmentation, achieving a Mean IoU of 0.8021, a performance comparable to or superior to that reported by other authors. Additionally, a Student’s t-test was applied to complement the data analysis, suggesting that the model is reliable for weed quantification. Full article

Adsorption is a method widely used to remove low-molecular-weight organics from wastewaters, and phenolic compounds from olive mill wastewater are a persistent class of bioactive pollutants of environmental concern. We screened eleven microalgal candidates at 0.10 g·L⁻¹ using batch kinetics fitted with the Lagergren pseudo-first-order model to obtain rate constants (k) and fitted equilibrium capacities (q_e). Cyanobacteria, particularly Anabaena spp. and Limnospira platensis, exhibited the highest adsorptive potential in the screening; wall-less species (e.g., Dunaliella salina, Isochrysis galbana) showed negligible surface adsorption, indicating that the presence and type of cell wall highly influence biosorption. L. platensis was selected for detailed study because of its established industrial cultivation and valorisation potential. Equilibrium experiments with HCl-functionalized L. platensis at four biomass loadings (0.10–1.00 g·L⁻¹; initial phenolic mix 30 mg·L⁻¹) showed that increasing dose reduced equilibrium concentration (C_e) but decreased specific uptake from ≈77 mg·g⁻¹ to ≈18 mg·g⁻¹ while removal rose from ~26% to ~61%. Nonlinear isotherm fitting favoured the Freundlich model (1/n < 1), consistent with heterogeneous, multi-site adsorption. Targeted macromolecular extractions abolished phenol uptake, demonstrating that the intact protein–polysaccharide matrix is essential for binding. L. platensis route delivered higher single-cycle removal (≈61%) compared to the maize-derived activated carbon reference (≈49%) while also incurring a 1.3-fold lower GWP (approximately 3 kg CO₂-eq per treatment) than the activated carbon route (approximately 4 kg CO₂-eq per treatment) in our model. Overall, L. platensis represents a lower-impact alternative for natural phenols remediation, especially when integrated into valorisation pathways that recover algal co-products. Full article