Search Results (9,875)

Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data types utilized to train models. This narrative review aims to describe predictive model performance in greater detail across various disease outcomes, input data types, and algorithms with a specific focus on accuracy, sensitivity, specificity, and positive and negative predictive values. A secondary goal is to address important areas for consideration for future work in the beef cattle sector. In total, 19 articles were included. Broad categories of disease were covered, including respiratory disease, bovine tuberculosis, and others. Various input data types were reported, including demographic data, images, and laboratory test results, among others. Several algorithms were utilized, including neural networks, linear models, and others. Accuracy, sensitivity, and specificity values ranged widely across disease outcome and algorithm categories. Negative predictive values were greater than positive predictive values for most disease outcomes. This review highlights the importance of utilizing several performance metrics and concludes that future work should address prevalence of outcomes and class-imbalanced data. Full article

Background: Pulmonary arterial pressure is a key parameter for diagnosing cardiovascular and pulmonary diseases. Its measurement through right heart catheterization is considered the gold standard, and it is an invasive procedure that entails significant risks for patients. This has motivated the development of non-invasive techniques based on patient-specific imaging, such as Physics-Informed Neural Networks (PINNs), which integrate clinical measurements with physical models, such as the 1D reduced Navier–Stokes model, enabling biologically plausible predictions with limited data. Methods: This work implements a PINN model that uses velocity and area measurements in the main bifurcation of the pulmonary artery, comprising the main artery and its secondary branches, to predict pressure, velocity, and area variations throughout the bifurcation. The model training includes penalties to satisfy the laws of flow and momentum conservation. Results: The results show that, using 4D Flow MRI images from a healthy patient as clinical data, the pressure estimates provided by the model are consistent with the expected ranges reported in the literature, reaching a mean arterial pressure of 21.5 mmHg. Conclusions: This model presents an innovative approach that avoids invasive methods, being the first study to apply PINNs to estimate pulmonary arterial pressure in bifurcations. In future work, we aim to validate the model in larger populations and confirm pulmonary hypertension cases diagnosed through catheterization. Full article

Objective: Lithospermum erythrorhizon has been extensively used for the clinical treatment of skin diseases, but its material basis and mechanism of action remain unclear. This study integrates network pharmacology, untargeted metabolomics, and in vitro experimental validation to elucidate the anti-inflammatory effects and underlying mechanisms of β-acetoxyisovalerylalkannin, a bioactive naphthoquinone compound isolated from Arnebiae Radix, using inflammatory skin disease models. Methods: Core targets for β-Acetoxyisovalerylalkannin and skin inflammation were identified via network pharmacology and validated through molecular docking. In vitro assays assessed β-Acetoxyisovalerylalkannin’s impact on keratinocyte proliferation, migration, apoptosis, and inflammatory factors (CXCL1, CXCL2, CXCL8, CCL20, IFN-γ, MCP-1, TNF-α, NF-κB). Non-targeted metabolomics identified differential metabolites and pathways. Results: Network pharmacology revealed 66 common targets significantly enriched in the MAPK/STAT3 signaling pathway. In vitro, β-Acetoxyisovalerylalkannin suppressed proliferative viability and hypermigration and induced apoptosis in HaCaTs. Moreover, it downregulated the mRNA levels of inflammatory markers (CXCL1, CXCL2, CXCL8, CCL20, IFN-γ, MCP-1, TNF-α, and NF-κB) by inhibiting the activation of the MAPK/STAT3 signaling pathway. Metabolomics identified 177 modified metabolites, associating them with the arginine/proline, glycine/serine/threonine, glutathione, and nitrogen metabolic pathways. Conclusions: β-Acetoxyisovalerylalkannin exerts protective effects against skin inflammation by reducing abnormal cell proliferation and inflammatory responses, promoting apoptosis, and effectively improving the metabolic abnormalities of HaCaTs. β-Acetoxyisovalerylalkannin is, therefore, a potential therapeutic option for mitigating skin inflammation-related damage. Full article

Introduction: Osteoarthritis (OA) is a chronic degenerative joint disease with limited treatment options focused primarily on symptom management. Emerging evidence suggests that dietary interventions may influence inflammation and pain through modulation of the gut microbiome and metabolome. Methods: We conducted a 4-week open-label pilot trial evaluating the effects of an anti-inflammatory dietary intervention (ITIS diet) in 20 patients with knee OA (ClinicalTrials.gov ID: NCT05559463, registered prior to enrollment; sponsor: University of California, San Diego; responsible party: Monica Guma; study start date: 1 October 2021). The following were assessed before and after the intervention: (1) clinical outcomes; (2) gut and salivary microbiomes; and (3) salivary, stool, and plasma metabolomes. Responders were defined as patients achieving ≥30% reduction in Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain scores. Results: The ITIS diet was well-tolerated, with good adherence (66.2%) and a significant improvement in clinical outcomes, including reduced pain and improved overall health measured with the visual analog scale (VAS). Responders (n = 8) showed distinct gut microbiome and metabolome profiles compared to non-responders (n = 12). Notably, taxa within the Lachnospiraceae family exhibited dynamic, bidirectional shifts post-intervention: Anaerostipes and Limivivens were enriched among responders and negatively correlated with pain scores, while Oliverpabstia and Fusicatenibacter were depleted following dietary intervention. These taxa also showed strong correlations with anti-inflammatory metabolites, including hydroxydecanoic acid derivatives and pyridoxine. Furthermore, subsequent network analysis revealed more structured and selective microbiome–metabolome interactions in responders, specifically post-intervention. Conclusions: This pilot study shows that a short-term anti-inflammatory dietary intervention was associated with meaningful changes in the gut microbiome and metabolome. Members of the Lachnospiraceae family emerged as key taxa associated with pain reduction and anti-inflammatory metabolite production. Our findings suggest that specific microbial responses—rather than global diversity changes—may underlie dietary responsiveness in OA. Although exploratory and limited by sample size, our results support further investigation into personalized, microbiome-informed nutritional strategies for OA management. Full article

Background: Public beliefs about dietary risks, such as excessive salt intake, are often not isolated misconceptions but part of structured cognitive systems. This study aimed to explore how individuals organize their beliefs and misperceptions regarding salt-related health consequences. Material and Methods: Using data from an international online survey, we applied a system of multivariate proportional odds logistic regression (POLR) models to estimate conditional associations among beliefs about salt’s links to various diseases—including cardiovascular, metabolic, renal, neuropsychiatric, and mortality outcomes. In addition, exploratory and confirmatory factor analyses (EFA and CFA) were conducted to identify and validate latent constructs underlying the belief items. Beliefs were modeled as interdependent, controlling for latent constructs, sociodemographics, and self-reported health awareness. Statistically significant associations (p < 0.05) were visualized via a heatmap of beta coefficients. Results: Physicians showed almost universal agreement that salt contributes to hypertension (µ = 0.97), compared to non-medical respondents (µ = 0.85; p < 0.0001). Beliefs about mortality (µ = 1.55 for MDs vs. 0.99 for non-medical; p < 0.0001) emerged as central hubs in the belief network. Strong inter-item associations were observed, such as between hypertension and heart failure (β = −0.39), and between obesity and type 2 diabetes (β = −0.94). Notably, cognitive gaps were found, including a lack of association between atrial fibrillation and stroke, and non-reciprocal links between hypertension and heart failure. Conclusions: Beliefs about the health effects of salt are structured and sometimes asymmetrical, reflecting underlying reasoning patterns rather than isolated ignorance. Understanding these structures provides a systems-level view of health literacy and may inform more effective public health communication and education strategies. Full article

Medical image segmentation is crucial for disease diagnosis, as precise results aid clinicians in locating lesion regions. However, lesions often have blurred boundaries and complex shapes, challenging traditional methods in capturing clear edges and impacting accurate localization and complete excision. Small lesions are also critical but prone to detail loss during downsampling, reducing segmentation accuracy. To address these issues, we propose a novel adaptive scale thresholding network (AdSTNet) that acts as a post-processing lightweight network for enhancing sensitivity to lesion edges and cores through a dual-threshold adaptive mechanism. The dual-threshold adaptive mechanism is a key architectural component that includes a main threshold map for core localization and an edge threshold map for more precise boundary detection. AdSTNet is compatible with any segmentation network and introduces only a small computational and parameter cost. Additionally, Spatial Attention and Channel Attention (SACA), the Laplacian operator, and the Fusion Enhancement module are introduced to improve feature processing. SACA enhances spatial and channel attention for core localization; the Laplacian operator retains edge details without added complexity; and the Fusion Enhancement module adapts concatenation operation and Convolutional Gated Linear Unit (ConvGLU) to improve feature intensities to improve edge and small lesion segmentation. Experiments show that AdSTNet achieves notable performance gains on ISIC 2018, BUSI, and Kvasir-SEG datasets. Compared with the original U-Net, our method attains mIoU/mDice of 83.40%/90.24% on ISIC, 71.66%/80.32% on BUSI, and 73.08%/81.91% on Kvasir-SEG. Moreover, similar improvements are observed in the rest of the networks. Full article

Verticillium wilt, a prevalent soil-borne disease, poses a significant challenge to cotton production in Xinjiang, China. Continuous cotton monoculture has increased disease incidence and affected soil microbial diversity in Xinjiang, while crop rotation is recognized as an effective strategy for soil pathogen control. This study investigates how a one-year maize rotation affects Verticillium wilt incidence and soil microbiome composition in cotton fields across northern and southern Xinjiang. The results demonstrated that short-term rotation significantly reduced Verticillium wilt occurrence in both northern and southern Xinjiang. Using high-throughput sequencing of fungal ITS and bacterial 16S rRNA regions, microbial community analysis revealed minimal changes in alpha-diversity but significant structural reorganization between continuous cropping (CC) and rotation (CR) systems, particularly in fungal and bacterial genera composition, with distinct spatial patterns between northern and southern fields. Crop rotation promoted beneficial taxa such as Sphingomonas and Pseudogymnoascus, while reducing the abundance of pathogens such as Verticillium dahliae. LEfSe study suggested Tepidisphaerales and Lasiosphaeriaceae as biomarkers for CR systems, whereas Hypocreales and Blastocatellia dominated in CC soils. Co-occurrence network analysis revealed more bacterial connectivity and modularity under CR, suggesting better microbial interactions and ecological resilience. The increased structural complexity of bacterial networks under CR indicates their greater contribution to soil health maintenance and ecosystem resilience. Our findings demonstrate that short-term crop rotation not only effectively reduces Verticillium wilt incidence but also restructures soil microbial communities, providing an actionable strategy for sustainable cotton cultivation in Xinjiang. Full article

In recent years, the bidirectional regulatory mechanism of the bone-brain axis has become a hotspot for interdisciplinary research. In this paper, we systematically review the anatomical and functional links between bone and the central nervous system, focusing on the regulation of brain function by bone-derived signals and their clinical translational potential. At the anatomical level, the blood–brain barrier permeability mechanism and the unique structure of the periventricular organs establish the anatomical basis for bone-brain information transmission. Innovative discoveries indicate that the bone cell network (bone marrow mesenchymal stem cells, osteoblasts, osteoclasts, and bone marrow monocytes) directly regulates neuroplasticity and the inflammatory microenvironment through the secretion of factors such as osteocalcin, lipid transporter protein 2, nuclear factor κB receptor-activating factor ligand, and fibroblast growth factor 23, as well as exosome-mediated remote signaling. Clinical studies have revealed a bidirectional vicious cycle between osteoporosis and Alzheimer’s disease: reduced bone density exacerbates Alzheimer’s disease pathology through pathways such as PDGF-BB, while AD-related neurodegeneration further accelerates bone loss. The breakthrough lies in the discovery that anti-osteoporotic drugs, such as bisphosphonates, improve cognitive function. In contrast, neuroactive drugs modulate bone metabolism, providing new strategies for the treatment of comorbid conditions. Additionally, whole-body vibration therapy shows potential for non-pharmacological interventions by modulating bone-brain interactions through the mechano-osteoclast signaling axis. In the future, it will be essential to integrate multiple groups of biomarkers to develop early diagnostic tools that promote precise prevention and treatment of bone-brain comorbidities. This article provides a new perspective on the mechanisms and therapeutic strategies of neuroskeletal comorbidities. Full article

Intercellular communication in the central nervous system (CNS) is essential for maintaining neural function and coordinating responses to injury or disease. With recent advances in single-cell and spatial transcriptomics, a growing body of research has revealed that this communication is highly dynamic, shifting across states of health, aging, demyelination, and neurodegeneration. In this review, we synthesize the current findings on intercellular communication networks involving neurons, astrocytes, microglia, oligodendrocytes, and other glial populations in the CNS across four major states: healthy homeostasis, aging, demyelinating diseases, and Alzheimer’s disease (AD). We focus on how changes in intercellular communication contribute to the maintenance or disruption of CNS integrity and function. Mechanistic insights into these signaling networks have revealed new molecular targets and pathways that may be exploited for therapeutic intervention. By comparing the intercellular signaling mechanisms across different disease contexts, we underscore the importance of CNS crosstalk not only as a hallmark of disease progression, but also as a potential gateway for precision therapy. Full article

Background/Objectives: Pacific peoples in Aotearoa, New Zealand experience the highest burden of gout globally, yet there is still a lack of awareness and understanding of the disease. A Pacific community group and Pacific health professional network co-designed Pacific gout resources to improve understanding. The aim of this study is to identify and discuss the current state and perceptions of Pacific gout education, and explore health professionals’ views on Pacific co-designed resources and their usefulness in clinical settings. Methods: The Fa’afaletui model was utilised to conduct semi-structured Talanga interviews with 14 health professionals in Auckland, New Zealand who work in primary care clinics. The interview explored their views on providing gout education and on the feasibility of the Pacific co-designed gout resources. Talanga interviews were audio recorded and thematically analysed. Results: Overall, health professionals responded positively to the co-designed resources, identifying the benefits of supporting primary care consultations and improving Pacific patients’ understanding of gout. The key findings were summarised in five main themes: (1) health system barriers to gout education, (2) misleading information, (3) health professionals’ experiences of providing health education, (4) general impressions of Pacific co-designed resources, and (5) the feasibility of Pacific co-designed educational resources in a clinical setting. Conclusions: This study presents the views of health professionals in providing health education related to gout and on the feasibility of Pacific co-designed educational resources. It reinforces the significance of involving communities in the design and implementation of interventions to ensure they are culturally safe, relevant, and have long-term impacts on gout management. Full article

Psoriasis is a chronic inflammatory skin disease driven by genetic, environmental, and immune factors. While biologics like adalimumab (anti-TNFα) and risankizumab (anti-IL-23) have improved outcomes, patient response variability remains unclear. This study examined immune-related transcriptomic differences between lesional (L) and non-lesional (NL) psoriatic skin, focusing on immune-related hub genes, their plasma levels, and their correlations with severity and treatment response. Patients with moderate-to-severe psoriasis were enrolled before treatment with anti-TNFα (n = 16) or anti-IL-23 (n = 18). Plasma and paired L and NL skin biopsies were collected for RNA sequencing. Gene ontology enrichment analysis found four immune-related terms enriched in L skin: T-helper 17, granulocyte and lymphocyte chemotaxis, and antimicrobial humoral response. A protein–protein interaction network identified ten immune-related hub genes upregulated in L skin that correlated with clinical severity. Patients with prior treatments expressed distinctive gene profiles. Plasma levels of CCL20 strongly correlated with disease severity. Decision tree models identified CCL20 expression in skin and plasma levels of IL-6 and CXCL8 as candidate predictors for anti-TNFα response. Similarly, skin expression of CXCL8, IL-6, and CXCL10, alongside plasma levels of CCL20, IL-6, and CXCL8, may predict anti-IL-23 response. Ten immune-related hubs may serve as possible biomarkers for disease severity and therapeutic response in psoriasis. Full article

Postharvest losses due to fungal decay pose a significant challenge to global fruit and vegetable production, especially in regions where rot pathogens are prevalent. Traditional control methods rely heavily on synthetic fungicides, which are increasingly criticized for their environmental risks, human health concerns, and their role in fostering pathogen resistance. These issues underscore the urgent need for sustainable, residue-free alternatives that not only manage postharvest diseases but also enhance produce quality. Light-emitting diode [LED] technology has emerged as a promising, eco-friendly solution capable of modulating plant physiological responses through specific light wavelengths. However, the exact defense mechanisms activated by LED exposure in postharvest decay control and nutritional enhancement remain underexplored. This review provides a comprehensive synthesis of recent findings on LED-induced control of fungal decay, focusing on how LED treatments modulate pathogen–fruit interactions, activate innate defense pathways, regulate gene networks linked to defense and nutritional traits, and contribute to improved fruit and vegetable quality and health benefits. Full article

The renin-angiotensin system (RAS) has evolved from being considered solely a peripheral endocrine system for cardiovascular control to being recognized as a complex molecular network with important functions in the central nervous system (CNS) and peripheral nervous system (PNS). Here we examine the organization, mechanisms of action, and clinical implications of cerebral RAS in physiological conditions and in various neurological pathologies. The cerebral RAS operates autonomously, synthesizing its main components locally due to restrictions imposed by the blood–brain barrier. The key elements of the system are (pro)renin; (pro)renin receptor (PRR); angiotensinogen; angiotensin-converting enzyme types 1 and 2 (ACE1 and ACE2); angiotensin I (AngI), angiotensin II (AngII), angiotensin III (AngIII), angiotensin IV (AngIV), angiotensin A (AngA), and angiotensin 1-7 (Ang(1-7)) peptides; RAS-regulating aminopeptidases; and AT1 (AT1R), AT2 (AT2R), AT4 (AT4R/IRAP), and Mas (MasR) receptors. More recently, alamandine and its MrgD receptor have been included. They are distributed in specific brain regions such as the hypothalamus, hippocampus, cerebral cortex, and brainstem. The system is organized into two opposing axes: the classical axis (renin/ACE1/AngII/AT1R) with vasoconstrictive, proinflammatory, and prooxidative effects, and the alternative axes AngII/AT2R, AngIV/AT4R/IRAP, ACE2/Ang(1-7)/MasR and alamandine/MrgD receptor, with vasodilatory, anti-inflammatory, and neuroprotective properties. This functional duality allows us to understand its role in neurological physiopathology. RAS dysregulation is implicated in multiple neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and neuropsychiatric disorders such as depression and anxiety. In brain aging, an imbalance toward hyperactivation of the renin/ACE1/AngII/AT1R axis is observed, contributing to cognitive impairment and neuroinflammation. Epidemiological studies and clinical trials have shown that pharmacological modulation of the RAS using ACE inhibitors (ACEIs) and AT1R antagonists (ARA-II) not only controls blood pressure but also offers neuroprotective benefits, reducing the incidence of cognitive decline and dementia. These effects are attributed to direct mechanisms on the CNS, including reduction of oxidative stress, decreased neuroinflammation, and improved cerebral blood flow. Full article

Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 111 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice. Full article

Rice, wheat, and maize are important food grains consumed by most of the population in Asian countries (like India, Japan, Singapore, Malaysia, China, and Thailand). These crops’ production is affected by biotic and abiotic factors that cause diseases in several parts of the crops (including leaves, stems, roots, nodes, and panicles). A severe infection affects the growth of the plant, thereby undermining the economy of a country, if not detected at an early stage. This may cause extensive damage to crops, resulting in decreased yield and productivity. Early safeguarding methods are overlooked because of farmers’ lack of awareness and the variety of crop diseases. This causes significant crop damage and can consequently lower productivity. In this manuscript, a lightweight vision transformer (MaxViT) with 814.7 K learnable parameters and 85 layers is designed for classifying crop diseases in paddy and wheat. The MaxViT DNN architecture consists of a convolutional block attention module (CBAM), squeeze and excitation (SE), and depth-wise (DW) convolution, followed by a ConvNeXt module. This network architecture enhances feature representation by eliminating redundant information (using CBAM) and aggregating spatial information (using SE), and spatial filtering by the DW layer cumulatively enhances the overall classification performance. The proposed model was tested using a paddy dataset (with 7857 images and eight classes, obtained from local paddy farms in Lalgudi district, Tiruchirappalli) and a wheat dataset (with 5000 images and five classes, downloaded from the Kaggle platform). The model’s classification performance for various diseases has been evaluated based on accuracy, sensitivity, specificity, mean accuracy, precision, F1-score, and MCC. During training and testing, the model’s overall accuracy on the paddy dataset was 99.43% and 98.47%, respectively. Training and testing accuracies were 94% and 92.8%, respectively, for the wheat dataset. Ablation analysis was carried out to study the significant contribution of each module to improving the performance. It was found that the model’s performance was immune to the presence of noise. Additionally, there are a minimal number of parameters involved in the proposed model as compared to pre-trained networks, which ensures that the model trains faster. Full article