Search Results (7,143)

Biochar has emerged as one of the most promising nature-based strategies for improving soil quality, enhancing crop productivity and supporting climate-smart agriculture. However, the agronomic performance of biochar remains highly variable because its effects are governed by complex interactions among feedstock characteristics, pyrolysis conditions, soil properties and management practices. This review synthesizes recent advances in biochar research (2019–2026), examining how production variables determine biochar physicochemical properties and how these properties subsequently influence soil functioning, plant performance and long-term agricultural sustainability. The review integrates evidence on feedstock selection, pyrolysis technologies, biochar modification strategies and the relationships between biochar properties and soil physical, chemical and biological processes. Particular attention is given to crop productivity, nutrient use efficiency, stress mitigation, contaminant immobilization, greenhouse gas mitigation and long-term soil resilience. Across the literature, the most consistent agronomic benefits were observed when biochar was applied to degraded or resource-limited soils and integrated with complementary management practices, whereas responses were often limited under fertile soils, low application rates or short experimental periods. Rather than identifying a universally superior biochar, the evidence indicates that agronomic performance depends on matching biochar characteristics to specific production objectives and environmental conditions. Based on these findings, this review proposes a transition from generalized biochar application towards optimized deployment strategies supported by standardized characterization, long-term multi-site validation and integrated environmental and economic assessments. This synthesis provides a comprehensive framework for guiding future research and facilitating the effective implementation of biochar within sustainable and regenerative agricultural systems. Full article

This study evaluates the effects of plant part (leaves vs. stems), harvest season (spring vs. autumn), and irrigation on the chemical composition, in vitro ruminal fermentation kinetics, and digestibility of three Mediterranean leguminous shrubs: Acacia fimbriata, Medicago arborea and Anthyllis barba-jovis. Samples were collected under field conditions, analysed for chemical composition, and assessed for in vitro ruminal fermentation, including incubations with and without polyethylene glycol (PEG) to assess the biological activity of phenolic compounds. Crude protein and fibre fractions did not differ significantly among species; ash was highest (p < 0.05) in Ac. fimbriata. In vitro fermentation kinetics were similar among species; however, the response to PEG was higher (p < 0.05) for Ac. fimbriata than for M. arborea and An. barba-jovis. In vitro digestibility was superior (p < 0.05) in M. arborea and An. barba-jovis. Leaves contained higher ash and protein contents and less fibre than stems, resulting in greater gas production, fermentation rate, and digestibility (p < 0.05). Spring-harvested fodder showed higher digestibility than autumn samples. Irrigation had negligible effects, except for a marginal numerical but nonsignificant (p = 0.05) greater extent of degradation observed in browse prior to irrigation. Overall, M. arborea showed a better nutritional quality, whereas Ac. fimbriata was the species with the lowest nutritive value. Forage quality was strongly influenced by plant part and harvest season, with leaves and fodder in spring showing superior potential as a feedstuff for ruminants. Full article

Background/Objectives: Menopause is a complex physiological transition characterized by progressive estrogen deficiency and systemic biological changes that can affect multiple organs and tissues, including the oral cavity. Growing evidence suggests that hormonal fluctuations during the menopausal transition may influence periodontal health, salivary function, oral sensory perception, and overall oral health-related quality of life. Objective: This narrative review aims to provide a comprehensive overview of the biological mechanisms and clinical manifestations associated with the relationship between menopause and oral health, with particular attention to periodontal outcomes, salivary changes, oral discomfort, dental status, and the potential role of hormone replacement therapy (HRT). Methods: This narrative review was based on a structured literature search conducted in PubMed/MEDLINE and Scopus to identify studies published between January 2005 and May 2026. Predefined eligibility criteria were applied to identify relevant human studies. The retrieved evidence was synthesized narratively according to major oral health domains and menopausal phenotypes. Results: Fifty studies met the inclusion criteria. Overall, menopause was associated with poorer periodontal parameters, including increased probing depth, clinical attachment loss, and periodontal inflammation. Reduced salivary flow, dry mouth, altered salivary composition, burning symptoms, and taste disturbances were frequently reported in peri- and postmenopausal women. A higher prevalence of caries and tooth loss was also reported, although the contribution of age and other confounding factors varied across studies. New evidence suggests that estrogen deficiency may influence oral health through interconnected pathways involving immune regulation, bone metabolism, salivary gland function, and host–microbiome interactions. Evidence regarding the effects of HRT has been mixed, although several studies have reported improvements in salivary function and periodontal outcomes among treated women. Conclusions: Menopause appears to act as an important systemic modifier of oral health through multifactorial biological mechanisms. Menopause-associated oral manifestations go beyond local tissue changes and reflect broader interactions between hormonal status, inflammation, bone metabolism, and microbial ecology. Increased awareness among dental and medical professionals and a multidisciplinary approach could improve the prevention, diagnosis, and management of oral diseases in postmenopausal women. Further, well-designed longitudinal studies are needed to clarify causal relationships and identify effective therapeutic strategies. Full article

Background/Objectives: Major depressive disorder (MDD) has been increasingly associated with alterations of the gut microbiome through the microbiota–gut–brain axis. However, published findings remain highly heterogeneous, limiting identification of reproducible microbial signatures associated with depression. This systematic review aimed to evaluate reproducible taxonomic and functional gut microbiome alterations in patients with MDD compared with healthy controls. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science Core Collection, and the Cochrane Library for studies published between January 2016 and December 2025. Observational human studies evaluating gut microbiome composition in adults with clinically diagnosed MDD and healthy control groups were included. Methodological quality was assessed using the Newcastle-Ottawa Scale. Due to substantial methodological heterogeneity, findings were synthesized using structured qualitative narrative analysis. Results: Sixteen observational studies were included in the qualitative synthesis. Findings related to alpha diversity were inconsistent across studies, whereas beta diversity alterations demonstrated greater reproducibility across independent cohorts. The most recurrent microbiome pattern involved depletion of short-chain fatty acid (SCFA)-producing bacteria, particularly Faecalibacterium and Roseburia, together with recurrent alterations affecting members of the Ruminococcaceae, Lachnospiraceae, and Clostridia groups. Functional microbiome alterations demonstrated greater consistency than higher-level taxonomic findings and included reduced butyrate synthesis pathways, dysregulated amino acid and tryptophan metabolism, increased lipopolysaccharide biosynthesis, and enrichment of pro-inflammatory microbial signatures. Antidepressant-naïve cohorts generally demonstrated more homogeneous dysbiosis patterns than mixed-treated populations. Conclusions: Current evidence suggests that functional gut microbiome dysregulation may represent a more reproducible biological feature of MDD than isolated taxonomic alterations alone. However, substantial heterogeneity in study design, participant characteristics, sequencing methodologies, and analytical approaches continues to limit clinical translation. Large-scale longitudinal multi-omics studies using standardized methodologies are required to clarify the role of the gut microbiome in depressive disorders and to evaluate the potential utility of microbiome-based biomarkers and interventions in mental health and public health practice. Full article

To provide a clinically oriented overview of ultrasound elastography in chronic liver disease, focusing on currently available techniques, their clinical applications, quality assurance, technical pitfalls, and emerging developments that may shape future liver imaging practice. A narrative review of the current literature was conducted using international guidelines, systematic reviews, and original studies retrieved from major scientific databases. Particular emphasis was placed on the clinical applications of ultrasound elastography, quality assurance procedures, interpretation of liver stiffness measurements, and future technological developments. Vibration-controlled transient elastography (VCTE) and shear wave elastography (SWE) have become established non-invasive techniques for liver fibrosis assessment, demonstrating excellent diagnostic performance, particularly for advanced fibrosis and cirrhosis. Beyond fibrosis staging, ultrasound elastography contributes to prognostic stratification, treatment planning, and longitudinal disease monitoring across a broad spectrum of chronic liver diseases. However, liver stiffness measurements may be influenced by technical and biological confounding factors, including inflammation, cholestasis, hepatic congestion, obesity, and postprandial status, highlighting the importance of standardised acquisition protocols and careful clinical interpretation. Emerging developments, including spleen stiffness assessment, multiparametric ultrasound, and artificial intelligence-assisted image analysis, are expected to further improve diagnostic accuracy and support precision hepatology. Ultrasound elastography has become an essential component of the non-invasive evaluation of chronic liver disease, substantially reducing the need for liver biopsy while improving fibrosis staging, prognostic stratification, and longitudinal patient monitoring. Its optimal clinical implementation requires appropriate patient selection, adherence to quality assurance procedures, and careful interpretation within the broader clinical context. Future advances in multiparametric ultrasound, artificial intelligence, and international standardisation are expected to further strengthen the role of ultrasound elastography within precision hepatology and personalised liver disease management. Ultrasound elastography should be integrated into routine liver imaging as part of a multimodal diagnostic approach. Standardised acquisition protocols, continuous quality assurance, and appropriate operator training are essential to ensure reliable and reproducible liver stiffness measurements. Radiographers play a key role in examination quality, protocol adherence, and multidisciplinary patient care, contributing to accurate diagnosis and improved clinical decision-making. Full article

Citrus fruits are among the most widely cultivated and consumed crops worldwide, exhibiting a complex evolutionary history driven by extensive hybridization and genomic admixture. They are widely appreciated for their sensory quality, rich nutritional composition, and health-promoting benefits, as well as for their significant contribution to the global economy. Considering the growing interest in citrus crops and particularly in citrus by-products as valuable natural resources, as evidenced in the comprehensive literature search, this study provides an overview of recent advances in our understanding of citrus origin, genetic diversity and global production trends. It also examines the phytochemical composition of citrus fruits and by-products and discusses the biological activities associated with these constituents, including antioxidant, antimicrobial and anti-inflammatory effects, as well as their potential health benefits. Additionally, conventional and environmentally friendly strategies for the utilization of citrus residues are presented within a biorefinery framework, with particular emphasis on the recovery and valorization of bioactive compounds. These circular economy approaches highlight the potential use of citrus by-products as valuable raw materials for applications in food, cosmetic and pharmaceutical fields. Overall, this review aims to provide a better understanding of citrus history, composition and potential applications that could support the sustainable and resource-efficient use of citrus substrates in industrial practice for the development of high-value products. Full article

Background: This study applies Buysse’s sleep health framework to examine sleep in children and adolescents with neurofibromatosis type 1 (NF1). By examining sleep timing, daytime sleepiness, sleep quality, sleep behavior, sleep duration, and sleep efficiency together, this framework captures the multidimensional nature of sleep and its relationship with biopsychosocial factors and health-related quality of life (HR-QoL) in NF1. Methods: This multi-site, prospective, cross-sectional study included 131 children and adolescents with NF1 and 71 typically developing (TD) controls aged 6 to 16 years. A sleep health composite was derived from carer rating scales and 7 days of actigraphy. A biopsychosocial framework was used to examine factors associated with sleep health in NF1, including sociodemographic, cognitive, psychopathology, and biological variables. Independent predictors of QoL were examined to assess the unique contributions of sleep quality, sleep duration, and previously established predictors of HR-QoL in NF1. Results: Poorer sleep health was evident in children with NF1. Compared with TD controls, children with NF1 were five times as likely to have poor sleep quality, with almost 78% demonstrating impaired sleep efficiency and nearly half not obtaining sufficient sleep at night. The strongest risk factors were being male, elevated pain, and having greater levels of ADHD and autism spectrum disorder traits. Conclusions: Findings suggest sleep health in NF1 is interconnected with multiple biopsychosocial factors. A better understanding of these relationships will help identify early risk markers, improve prediction of clinical trajectories, and guide the development of targeted multimodal interventions for sleep disruption in NF1. Full article

Recovery in elite athletes represents a critical determinant of performance and health outcomes. The gut microbiota has been proposed as a modulating factor for recovery through anti-inflammatory mechanisms, oxidative stress management, sleep regulation, and biosynthetic potential for essential micronutrients. This review examines the mechanisms linking gut microbiota composition and function to athletic recovery and critically evaluates the evidence supporting its application in sports medicine. Athletes appear to harbor a more enriched microbial biosynthetic potential, with substantially greater numbers of high-biological-impact synthases involved in the production of vitamins, amino acids, and bioactive metabolites. Short-chain fatty acids, particularly butyrate and propionate, have demonstrated anti-inflammatory effects in preclinical studies, with emerging evidence in humans. The gut–brain axis has been proposed to modulate recovery by regulating neurotransmitter production and controlling circadian rhythms. Sport-associated microbial signatures seem to reflect metabolic demands, with endurance athletes showing enrichment for Prevotella and Veillonella, while strength athletes tend to harbor higher levels of proteolytic bacteria. Probiotic interventions with multi-strain Lactobacillus and Bifidobacterium formulations have reported reductions in inflammatory markers, improvements in oxidative stress biomarkers, and enhanced sleep quality in small-scale randomized controlled trials involving athletic populations, and improvements in self-reported sleep quality in a controlled, non-randomized study in elite athletes. Optimizing gut microbiota composition and function offers a promising complementary strategy for enhancing recovery in elite athletes. Potential applications that require prospective validation include sport-specific probiotic interventions, nutritional strategies to enhance short-chain fatty acid production, and the integration of microbiota assessment with traditional recovery monitoring. Further research is needed to establish standardized protocols and identify predictive biomarkers of individual response to microbiota-targeted interventions. Full article

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate. Full article

Microplastics (MPs) are emerging environmental contaminants increasingly detected in foods, beverages, and food-contact materials, making dietary intake a relevant route of human exposure. In sports nutrition, this issue may be particularly important because athletes often have high food and fluid consumption, frequent use of packaged sports nutrition products, dietary supplements, bottled beverages, and sport-specific hydration strategies. This narrative review, supported by a structured literature search, examines dietary MP exposure and its potential relevance to gastrointestinal function, gut microbiota, oxidative stress, inflammation, mitochondrial activity, endocrine regulation, metabolism, recovery, adaptation, and performance-related outcomes in athletes. Current evidence suggests that MPs and nanoplastics may interact with biological systems through mechanisms involving intestinal barrier disruption, microbiota alterations, inflammatory activation, oxidative damage, mitochondrial perturbation, endocrine-disrupting chemicals, and metabolic dysregulation. However, most available data derive from in vitro studies, animal models, food contamination analyses, exposure-estimation studies, and indirect human biomonitoring evidence. Direct studies in athletic populations are currently lacking. Therefore, the possible implications of MP exposure on recovery, adaptation, and exercise performance should be interpreted as biologically plausible but unproven. From a practical perspective, evidence-informed strategies may include reducing avoidable plastic-related exposure while maintaining adequate hydration, energy availability, nutrient timing, supplement quality, and dietary patterns that support antioxidant defenses, inflammatory balance, gut health, and physiological resilience. Future research should prioritize standardized exposure assessment, validated biomarkers, human biomonitoring, and sport-specific studies evaluating MP exposure in relation to physiological and performance-related outcomes. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate for up to two decades before cognitive symptoms emerge, placing the preclinical and mild cognitive impairment (MCI) stages at the centre of the early detection problem. Despite this, current diagnostic practice in routine clinical settings remains unreliable, with post-mortem studies placing the specificity of clinical AD diagnosis between 44.3 and 70.8% even in specialist memory clinics. Machine learning (ML) and deep learning (DL) applied to neuroimaging and electrophysiological data have emerged as candidate tools for closing this diagnostic gap, yet whether the accuracy figures reported in published studies translate into clinically useful performance on independent data remains unresolved. This study presents a structured comparative review of machine learning and deep learning methods reported across four publicly available Alzheimer’s disease datasets, namely the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS), the OpenNeuro ds004504 electroencephalography (EEG) dataset, and the Kaggle Alzheimer’s magnetic resonance imaging (MRI) dataset. Thirteen model families are examined through the published literature rather than through new experiments, and for each model and dataset combination, the best accuracy reported in the source study is recorded alongside the model’s mathematical formulation. All performance figures reported in this abstract and throughout the paper are taken from the published studies reviewed, not from new experiments conducted by the authors. Across the reviewed studies, deep learning architectures pre-trained on ImageNet and fine-tuned on neuroimaging data are reported to produce the highest accuracy on MRI classification tasks. Residual Network (ResNet)-101 is reported at 98.21 percent on ADNI and 97.45 percent on OASIS, while the IncepRes fusion architecture reaches 98.35% on OASIS by combining multi-scale feature extraction from InceptionV3 with residual connectivity from ResNet152V2. Traditional machine learning classifiers remain competitive on tabular clinical and biomarker data, with Extreme Gradient Boosting (XGBoost) reaching 91% on ADNI multiclass features. Logistic Regression achieves 82 to 85% on binary ADNI classification and is the only classifier in this review that provides explicit per-feature prediction contributions without post hoc tooling. Gaussian Naïve Bayes achieves 80 to 83% on the same task. On the OpenNeuro EEG dataset, K-nearest neighbours (KNN) with singular value decomposition (SVD) entropy features achieves 91% binary accuracy, with feature engineering quality determining performance more reliably than classifier architecture. Eight principal findings emerge from the cross-dataset analysis. Binary classification consistently outperforms multiclass by 10 to 30% across all datasets, reflecting the genuine biological ambiguity of the mild cognitive impairment category. Dataset size and augmentation predict reported accuracy more reliably than model architecture. Ensemble methods outperform individual classifiers by 5 to 8% in nearly every imaging study. Deeper architectures can overfit small clinical cohorts. EEG models trail MRI models by approximately 10 to 15% on comparable binary tasks. Cross-dataset generalisation has not been systematically evaluated in most studies, and the few that have tested it report accuracy drops of 5 to 10% or more when models encounter data from different scanners or cohorts. Eight recurring limitations constrain the clinical utility of these findings. Small sample sizes and limited demographic diversity, severe class imbalance inflating raw accuracy metrics, poor cross-dataset generalisation driven by scanner heterogeneity, limited deep learning interpretability, the dominance of binary over multiclass tasks, the absence of longitudinal modelling despite available datasets, inadequate standardisation of preprocessing and evaluation protocols, and the signal-to-noise ratio constraints specific to EEG recordings of elderly patients collectively define the gap between benchmark performance and clinical readiness. Future work must prioritise multi-centre training cohorts, multimodal fusion architectures, longitudinal progression modelling, and standardised interpretability evaluation as non-optional requirements for any system intended for clinical deployment. Full article

Background and Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease of the nasal and paranasal sinus mucosa, associated with significant impairment in quality of life, frequent postoperative recurrence, and repeated need for systemic glucocorticosteroid therapy. Despite the availability of biologics targeting IL-4/IL-13, IL-5, and IgE, a subset of patients shows incomplete or insufficient clinical response. In this context, upstream targeting of epithelial alarmins, particularly thymic stromal lymphopoietin (TSLP), has emerged as a potential therapeutic strategy. To critically review current evidence on the role of TSLP in CRSwNP and to evaluate available data on the mechanism of action, clinical efficacy, and therapeutic potential of tezepelumab in severe and recurrent disease. Materials and Methods: A narrative review was conducted using PubMed, Scopus, and Web of Science. Studies published between 2016 and 2026 were included, comprising experimental research, phase II–III clinical trials, systematic reviews, and international guidelines. Results: TSLP functions as an epithelial alarmin that initiates and amplifies type 2 inflammation via dendritic cell activation, Th2 polarization, and activation of type 2 innate lymphoid cells (ILC2). Data suggests that tezepelumab, a monoclonal antibody targeting TSLP, may reduce inflammation and regulate the immune system. Evidence from asthma populations and relevant CRSwNP subgroups indicates potential improvements in nasal polyp score, congestion, olfactory function and quality of life. It is our understanding that the safety profile appears comparable to placebo, with no new safety concerns having been identified in long-term studies. Conclusions: Tezepelumab is a promising biologic that targets inflammation in CRSwNP. It may benefit severe, recurrent, treatment-resistant disease by modulating immune pathways. However, evidence is indirect and limited, and more trials are needed to define its efficacy, identify biomarkers, and clarify its role in treatment algorithms. Full article

This study systematically evaluated substrate-dependent variations in yield, nutritional composition, mineral profiles, taste-active compounds, electronic tongue profile, and metabolome of A. bisporus cultivated on corn stover-based substrates (CM1, CM2, CM3) and wheat straw-based substrate (WM4). Corn stover-based substrates supported the cultivation of A. bisporus, yielding 20.56–25.63 kg/m², comparable to WM4 (24.23 kg/m²). Biological efficiency of the corn stover treatments ranged from 80.32% to 88.58%; CM1 and CM2 were comparable to WM4 (91.35%), whereas CM3 showed a significantly lower biological efficiency than WM4 (p < 0.05). Crude protein and mineral elements (Cu, Fe, Zn) were substantially higher in mushrooms from corn stover-based substrates, whereas crude polysaccharide content was greater in WM4. Taste profile analysis revealed that corn stover-based substrates elevated umami and sweet amino acids alongside 5′-GMP and 5′-UMP, leading to enhanced equivalent umami concentration (EUC) and umami perception by the electronic tongue, while WM4 supported higher 5′-AMP accumulation. Non-targeted metabolomics analysis (CM3 vs. WM4) identified 3997 metabolic features. KEGG pathway enrichment analysis revealed that these substrate-dependent metabolic alterations were primarily driven by pathways such as the biosynthesis of amino acids, nucleotide metabolism, and 2-oxocarboxylic acid metabolism, underlying the metabolic adaptation of A. bisporus. These findings provide new insights into the compositional characteristics and quality formation of A. bisporus cultivated on corn stover-based substrates. Full article

Respiratory sound monitoring is a promising non-invasive tool for precision pig farming, but practical evidence from calibrated room-level deployment under degraded air-quality conditions remains limited. This study reports a 28-day exploratory room-level case study in which 52 growing pigs were housed in two rooms: one standard-ventilation room and one ventilation-restricted room, and monitored with one microphone per room emphasizing mixed room-level soundscape monitoring rather than individual pig cough counts or replicated treatment inference. Because the design lacked independent room-level replication, all room contrasts and p-values were interpreted as exploratory descriptive screening summaries rather than causal treatment effects. Airflow verification, playback calibration at multiple pen positions, and background-noise spectral analysis were performed to address measurement bias. Signal inspection showed that biologically relevant vocal energy was retained after 16 kHz resampling, while class imbalance was handled by inverse-frequency weighting and macro-F1-based model selection. The Audio Spectrogram Transformer (AST) pipeline was subjected to five-fold group-blocked cross-validation, and temporal validation. The model achieved a test macro-F1 of 0.937, five-fold macro-F1 of 0.928 ± 0.019, and three-day deployment validation macro-F1 of 0.914. In this two-room dataset, the ventilation-restricted room displayed higher room-level cough-like detections, aggressive vocalizations, normal vocalizations, lower silence, reduced growth, and poorer air quality. Cough-like detections showed recurring clock-time clustering, with the most sustained elevation during 19:00–22:00 and a smaller peak around 10:00 with the highest occurrences at 20.00 (2.84 room-level cough-like detections standardized to group size). Audio-only early-warning analysis flagged deteriorated air-quality windows with AUROC = 0.91 and AUPRC = 0.88 and provided a median 34 min lead time before environmental threshold exceedance, highlighting practical utility as an early inspection cue for farmers before air-quality deterioration becomes more pronounced. Cough-like events descriptively co-varied positively with NH₃, temperature, and CO₂. Overall, calibrated AST-based monitoring can summarize group-level acoustic changes associated with degraded room environments, while multi-room and multi-farm replication remains necessary for causal inference and generalization. Full article

Dissolved bio-essential trace metals (Cu, Zn, Co, Ni, and Fe) in seawater and 17 elements in surface sediments were studied to assess the metal contamination and associated ecological risks in the Sulaibikhat Bay Marine Protected Area (MPA) in the northwestern Arabian Gulf. Dissolved metals were mostly at low to moderate levels and within the limits of most international water quality standards. However, dissolved Cu concentrations (0.54 to 4.73 µg L⁻¹) in the MPA stations (MPA-1 and MPA-2) exceeded the Oslo–Paris Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) ecotoxicological assessment thresholds, suggesting possible adverse ecological effects. Sediment showed higher levels of Cd, Cr, Ni, V, Cu, and Zn at stations within the MPA, with Principal Component Analysis (PCA) linking these enrichments to nearby industrial and desalination discharge sources. The calculated enrichment factor (EF) and geoaccumulation index (I_geo) values confirmed moderate to substantial anthropogenic contributions for Cd, Cr, and Ni, while the major lithogenic elements were derived from natural mineralogical inputs. A pollution load index (PLI) > 1 at stations MPA-1, MPA-3 and MPA-4 in Sulaibikhat Bay indicated cumulative metal contamination. Ecological risk assessment criteria classified sediments within the MPA and Sulaibikhat Bay as having a moderate level of ecological risk, with Cd identified as the principal contributor. The toxicological indices, namely mean Effect Range Median Quotient (m-ERM-Q) and mean Probable Effect Level Quotient (m-PEL-Q), indicated a low-to-moderate probability of adverse biological effects due to Cu and Ni exposure in the MPA. In contrast, the reference station in the Kuwait Bay showed very low contamination and negligible ecological risk. These findings stress the need for continuous monitoring and effective source control to protect the ecosystem of this newly established MPA in Kuwait. Full article