Search Results (3,685)

The cultivation of blueberry (Vaccinium spp.) has undergone an unprecedented global expansion, driven by its nutraceutical value and the diversification of production zones across the Americas, Europe, and Asia. Its consolidation as a strategic crop has prompted intensive scientific activity aimed at optimizing every stage of management from propagation and physiology to harvest, postharvest, and environmental sustainability. However, the available evidence remains fragmented, limiting the integration of results and the formulation of knowledge-based, comparative production strategies. The objective of this systematic review was to synthesize scientific and technological advances related to the integrated management of blueberry cultivation, incorporating physiological, agronomic, technological, and environmental dimensions. The PRISMA 2020 methodology (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was applied to ensure transparency and reproducibility in the search, selection, and analysis of scientific literature indexed in the Scopus database. After screening, 367 articles met the inclusion criteria and were analyzed comparatively and thematically. The results reveal significant progress in propagation using hydrogel and micropropagation techniques, efficient fertigation practices, and the integration of climate control operations within greenhouses, leading to improved yield and fruit quality. Likewise, non-thermal technologies, edible coatings, and harvest automation enhance postharvest quality and reduce losses. In terms of sustainability, the incorporation of water reuse and waste biorefinery has emerged as key strategies to reduce the environmental footprint and promote circular systems. Among the main limitations are the lack of methodological standardization, the scarce economic evaluation of innovations, and the weak linkage between experimental and commercial scales. It is concluded that integrating physiology, technology, and sustainability within a unified management framework is essential to consolidate a resilient, low-carbon, and technologically advanced fruit-growing system. Full article

Personalized anesthesia has emerged as a key direction in modern perioperative medicine, driven by advances in molecular biology, analytical technologies, and digital monitoring. Traditional physiological parameters often fail to detect early stages of organ dysfunction, whereas molecular biomarkers provide earlier and more sensitive insight into inflammation, oxidative stress, neurotoxicity, and renal or hepatic injury. Inflammatory markers such as IL-6, CRP, and PCT indicate early immune activation, while oxidative stress biomarkers, including 8-isoprostanes and malondialdehyde, quantify metabolic imbalance and ischemia–reperfusion injury. Neurotoxicity biomarkers such as S100β, NSE, and GFAP allow early detection of subclinical cerebral injury, whereas kynurenine-pathway metabolites reflect neuroinflammation and the risk of postoperative cognitive dysfunction. Renal biomarkers such as NGAL, KIM-1, and cystatin C detect acute kidney injury significantly earlier than creatinine, and miR-122 holds strong potential as an early marker of hepatocellular injury. Genetic and epigenetic biomarkers—including polymorphisms in CYP2D6, CYP3A4/5, RYR1, OPRM1, and COMT, as well as microRNA-based signatures—enable individualized drug dosing and optimization of anesthetic strategies. Meanwhile, digital biomarkers such as EEG-derived indices, HRV, and NIRS provide continuous real-time physiological monitoring and can integrate with AI-based algorithms for predictive, adaptive anesthesia management. Although no single biomarker meets all criteria for an ideal clinical indicator, combining molecular, genetic, and digital biomarkers represents the most promising pathway toward fully personalized, safe, and outcome-optimized perioperative care. Full article

Climate change reshapes crop–weed interactions and challenges the cultivation of oilseed rape (Brassica napus L.). Common ragweed (Ambrosia artemisiifolia L.) strongly suppresses early crop development, increases stress sensitivity and leads to yield loss. The stress–physiological responses of oilseed rape to ragweed competition were investigated using a combination of conventional and non-invasive methods. A pot experiment was conducted with increasing ragweed densities (0, 1, 3, 5 and 10 plants). Plant height and biomass were evaluated via non-destructive indicators (SPAD, NDVI) and different stages (1–15 and 16–30 min) of delayed fluorescence (DF) alongside ferric reducing antioxidant power (FRAP). Increasing ragweed density caused changes in growth, altered DF magnitude and decay kinetics, indicating photosynthetic imbalance. Moderate weed competition (1–5) induced an adaptive, eustress-like response characterised by enhanced non-enzymatic antioxidant capacity, whereas higher ragweed densities overwhelmed this compensatory mechanism, resulting in oxidative stress-like responses. Among all measured traits, DF₁–₁₅ proved to be the earliest and most sensitive indicator of the transition from adaptive to disruptive stress: T1: 0 ragweed: 213.07 ± 10.36 cps/mm² and 92.66 ± 6.67 cps/mm². These results demonstrate that delayed fluorescence, combined with conventional physiological and antioxidant-based parameters, enables the early detection of competitive stress in oilseed rape well before visible symptoms appear. Full article

Unmanned aerial vehicle (UAV)-based multispectral monitoring has become an increasingly important tool for assessing crop vigor and stress under commercial agricultural conditions. However, most UAV-based studies using the normalized difference vegetation index (NDVI) in citrus systems have focused on yield estimation, disease detection, or canopy characterization during active growth phases, while the immediate post-harvest recovery period remains poorly documented. In this study, UAV-derived NDVI products were used to evaluate the canopy response in a commercial ‘Washington Navel’ orange orchard located in La Yarada Los Palos district (Tacna, Peru) following harvest. The study specifically assessed the effect of an on-farm, residue-based organic biostimulant produced from local organic wastes within a circular economy framework. The results indicate that treated plots exhibited a faster and more pronounced recovery of canopy vigor compared to untreated controls during the early post-harvest period. By integrating high-resolution UAV-based multispectral monitoring with a residue-derived biostimulant strategy, this work advances current NDVI-based applications in citrus by shifting the analytical focus from productive stages to post-harvest physiological recovery. The proposed approach provides a scalable and non-invasive framework for evaluating post-harvest canopy dynamics under water-limited, hyper-arid conditions and highlights the potential of locally sourced biostimulants as complementary management tools in precision agriculture systems. Full article

Background: Perinatal depression and anxiety are common but often under-detected. Current screening relies on depression-centered instruments and may miss relational drivers including sexual dysfunction, low self-esteem, and psychosocial adversity. Objective: To synthesize evidence on sexual function, self-esteem/body image, and psychosocial context as correlates of perinatal depression and anxiety, and propose a risk-stratified screening framework. Methods: We conducted a narrative evidence synthesis of studies from January 2010 to May 2025 (PubMed/MEDLINE, Scopus, Web of Science) examining associations between perinatal mood/anxiety outcomes and sexual function (Female Sexual Function Index), self-esteem/body image (Rosenberg Self-Esteem Scale), and psychosocial factors (perceived support, intimate partner violence). Results: Sexual dysfunction was highly prevalent and consistently associated with depressive and anxiety symptoms. Longitudinal evidence demonstrated bidirectional pathways: mood symptoms reduced sexual satisfaction, while sexual difficulties intensified relational strain and symptom persistence. Low self-esteem and negative body image mediated links between physiological changes and postpartum depression. Psychosocial adversity, particularly low partner support and intimate partner violence, identified high-risk subgroups with greater severity and slower recovery. Single-instrument approaches (Edinburgh Postnatal Depression Scale alone) may miss pregnancy-specific anxiety and postpartum relational drivers. Conclusions: A staged, risk-stratified model is recommended: assess pregnancy-specific anxiety alongside depression screening in the second/third trimesters; postpartum, selectively add sexual function and self-esteem assessment for women with elevated symptoms or psychosocial risk. Integration within defined referral pathways may improve detection and enable targeted perinatal mental health care. Full article

Scar formation is a common outcome of post-injury repair and can compromise both esthetic appearance and physiological function. Fibroblasts are central mediators of this process; their aberrant activation or differentiation into myofibroblasts drives fibrosis and excessive scar tissue accumulation. Nanodrug delivery systems (NDDSs) offer unique opportunities to modulate fibroblast behavior through cell-/microenvironment-guided targeting, controlled release, and stimuli-adaptive designs. Here, we summarize fibroblast biology across scar repair and delineate the mechanistic underpinnings of scar pathogenesis. We then synthesize recent progress in NDDS-enabled interventions for pathological scarring, with an emphasis on how materials design can be matched to fibroblast states and wound-stage cues. By connecting mechanisms to delivery strategies, this review provides a framework to guide the development of scar-minimizing therapies and functional tissue regeneration. Full article

Barley is a marginalized crop subjected to several types of abiotic stress but need to intensify for future climate smart crop. This study investigated the drought resistance of barley landraces focusing on agro-morphological and physiological traits under controlled drought conditions. The experiment employed a two-factorial completely randomized design (CRD) with 14 barley landraces (of which 8 completed the maturity period examination) subjected to drought stress at three growth stages (CRI, tillering, and grain filling). Key parameters such as SPAD values (chlorophyll content), tiller number, and yield attributes were measured and analyzed using drought tolerance indices. Fourteen genotypes were initially tested, of which six failed to reach maturity; eight genotypes completed the full growth cycle and were used for yield and stress index analysis. Results revealed significant genotypic variation in drought response. Eight landraces exhibited higher SPAD values under drought, indicating better photosynthetic retention. Notably, AFU202501 demonstrated high yield stability (Stress Tolerance Index, STI = 1.782) under both stress and non-stress conditions, while Saptari Local showed exceptional drought avoidance (low Stress Susceptibility Index, SSI = −0.068) through early maturity and minimal yield reduction. In contrast, genotypes like Muktinath and NGRC 6010 were highly sensitive to drought, with significant yield losses (49–87%). Physiological traits such as chlorophyll retention and phenological plasticity (e.g., accelerated maturity under stress) were critical for drought adaptation. The findings highlight the potential of landraces like AFU202501 and Saptari Local as genetic resources for breeding climate-resilient barley varieties. The study underscores the importance of integrating traditional landraces into modern breeding programs to enhance food security in drought-prone regions. Full article

Cauliflower (Brassica oleracea var. botrytis) curd formation is a highly complex developmental process governed by tightly coordinated genetic and physiological regulation. Here, we performed transcriptome sequencing of curd and peduncle tissues across multiple developmental stages, generating 171.52 Gb of high-quality data. Genes associated with photosynthesis and glucosinolate biosynthesis were strongly upregulated in the shoot apical meristem (SAM), highlighting substantial metabolic investment during the pre-initiation phase of curd morphogenesis. Key floral transition regulators, particularly AP2 and MADS-box transcription factors, were activated to drive the vegetative-to-reproductive switch and initiate curd primordia, ultimately giving rise to the arrested inflorescence architecture characteristic of cauliflower. Furthermore, hormone signaling pathways—including auxin (AUX), jasmonic acid (JA), and brassinosteroid (BR)—showed marked activation during SAM proliferation and peduncle elongation, underscoring their crucial roles in structural patterning. Collectively, our findings delineate an integrated regulatory network that links metabolic activity, hormone signaling, and developmental programs, providing novel molecular insights into curd formation and identifying potential breeding targets for the genetic improvement of Brassicaceae crops. Full article

Background/Objectives: Soil salinization and alkalization critically limit global agricultural production. This study aimed to investigate the differential response mechanisms of rapeseed (Brassica napus L.) varieties to saline and alkaline stresses at the seedling stage. Methods: Seedlings of a salt-tolerant variety, Huayouza 62 (H62), and a non-salt-tolerant variety, Xiangyou 15 (X15), were exposed to saline (NaCl:Na₂SO₄ = 1:1) and alkaline (Na₂CO₃:NaHCO₃ = 1:1) stresses. An integrated analysis combining physiology, biochemistry, transcriptomics, and metabolomics was conducted to systematically elucidate their differential stress responses. Results: (1) H62 maintained favorable photosynthetic and carbon–nitrogen homeostasis. Notably, under saline and alkaline stresses, the activity of glutamate dehydrogenase (GDH) in H62 showed a significant increasing trend, whereas it was inhibited in X15. (2) Alkaline stress triggered more differential genes than saline stress, with H62 exhibiting broader transcriptional up-regulation in carbon–nitrogen metabolism. (3) Metabolomic profiling showed that H62 accumulated more beneficial metabolites than X15 under both stresses, such as phenolic acids, amino acids, and their derivatives. (4) In multi-omics analysis, key genes in starch–sucrose and amino acid metabolism in H62 were up-regulated to accumulate osmolytes, enabling an efficient defense network. However, X15’s responses were disordered. Conclusions: H62 leverages robust transcriptional reprogramming to coordinate carbon–nitrogen metabolism, constituting a multidimensional defense network. This study provides potential physiological indicators, candidate genes, and metabolite markers associated with short-term saline–alkaline stress responses, laying a foundation for further exploration of stress response mechanisms. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments. Full article

Non-invasive electrocardiogram (ECG) and respiratory signals are easy to acquire via low-cost sensors, making them promising alternatives for sleep staging. However, existing methods using these signals often yield insufficient accuracy. To address this challenge, we incrementally optimized the sleep staging model by designing a structured experimental workflow: we first preprocessed respiratory and ECG signals, then extracted fused features using an enhanced feature selection technique, which not only reduces redundant features, but also significantly improves the class discriminability of features. The resulting fused features serve as a reliable feature subset for the classifier. In the meantime, we proposed a hybrid optimization algorithm that integrates the snake optimization algorithm (SO) and ant colony optimization algorithm (ACO) for automated hyperparameter optimization of support vector machines (SVMs). Experiments were conducted using two PSG-derived public datasets, the Sleep Heart Health Study (SHHS) and MIT-BIH Polysomnography Database (MIT-BPD), to evaluate the classification performance of multimodal features compared with single-modal features. Results demonstrate that the bimodal staging using SHHS multimodal signals significantly outperformed single-modal ECG-based methods, and the overall accuracy of the SHHS dataset was improved by 12%. The SVM model optimized using the hybrid Snake–ACO algorithm achieved an average accuracy of 89.6% for wake versus sleep classification on the SHHS dataset, representing a 5.1% improvement over traditional grid search methods. Under the subject-independent partitioning experiment, the wake versus sleep classification task maintained good stability with only a 1.8% reduction in accuracy. This study provides novel insights for non-invasive sleep monitoring and clinical decision support. Full article

Ex vivo functional testing for multiple myeloma is rapidly evolving, yet no single assay has reached the level of reliability and clinical utility needed for routine decision-making. Existing approaches generally fall into three categories: 2D cultures, 3D models, and dynamic systems. Each contributes valuable but incomplete insight into therapeutic response. Among these, 2D assays remain the most mature, with the most extensive clinical correlations to date, though their simplified architecture limits their ability to reflect the full complexity of the bone marrow microenvironment. However, 3D systems, including spheroids and matrix-based organoids, offer improved preservation of tumor heterogeneity and microenvironmental cues. These platforms show emerging clinical relevance and may hold advantages over traditional 2D formats, and validation efforts are developing. Dynamic systems, including microfluidic models and perfused bone-marrow mimetics, represent the most physiologically ambitious category, yet their technical intricacy and early stage of development have so far limited broad clinical correlation. Altogether, the current landscape highlights substantial progress but lacks an optimal assay. In this review, we take the unique approach of examining published ex vivo tests that have demonstrated a level of clinical correlation. We evaluate their respective formats, strengths and limitations, and discuss considerations for what an ideal future assay may encompass. Full article

Molting is an important biological and physiological stage in penguins, influenced by environmental and nutritional factors. Feather composition analysis before and after molting can consequently place boundaries on element bioaccumulation and excretion. We quantified and compared elemental concentrations in African penguin (Spheniscus demersus) feathers collected pre- and post-molt across three zoos to evaluate how molt stage and zoo-specific conditions influence feather elemental composition. Feathers were retrieved from individual penguins at Zoom Torino (Italy), Overloon ZooParc (Netherlands), and Zoo Magdeburg (Germany). Quantification of elemental concentrations were performed by analytical methods, with both ICP-OES and HR-ICP-MS techniques. A statistical approach involving MANOVA and factorial analysis helped identify important trends. Pre-molt features had more variability than post-molt, with both showing significant differences in elemental concentrations. Factorial analysis showed geogenic trends in Mg, Sr, and Ni trends as well as anthropogenic trends in Pb. While Na and K differed among all treatment groups, this likely points to physiological adaptations in response to increased demand during feather regrowth. Additionally, inter-zoo comparisons highlighted distinct elemental profiles linked to local environmental and dietary conditions, particularly in Zoo Magdeburg, where Na levels were markedly elevated. This study highlights the influence of environmental and dietary conditions on feather composition during molt, offering insights for improving captive penguin welfare and broader ecological implications related to climate change and pollution. Full article

Humans have an inherent connection to nature, and exposure to natural elements has been shown to reduce stress, improve mood, and support cognitive performance, forming the basis of biophilic design in the built environment. However, existing biophilic design guidance remains largely conceptual and offers limited evidence-based direction on how design properties should be applied. This scoping review addresses this gap by systematically mapping and synthesizing empirical evidence on indoor biophilic design interventions and their properties. Following PRISMA-ScR guidelines, 136 studies published between 2000 and 2025 were reviewed across seven intervention types, including green walls, indoor plants, window views, natural light, natural materials, water features, and nature-inspired visual references. Cross-category analyses identified design properties most consistently associated with restorative outcomes and human cognitive and physiological responses. The findings highlight the importance of moderate greenery levels, high-visibility placement, multi-sensory integration, and the enhanced restorative effects of combining multiple interventions. Contextual factors such as exposure duration and user characteristics were found to influence effectiveness. Based on these findings, the study introduces the Biophilic Intensity Matrix (BIMx), a matrix-based decision-support framework that supports early-stage design by helping designers select biophilic intervention types and compare their relative scale and intensity ranges according to exposure duration. Full article