Search Results (1,910)

This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced deep canonical correlation analysis (DCCA) network with correlation alignment (CORAL) loss for superior domain-invariant representation learning. This combination enables more effective alignment of source and target feature distributions without requiring any labelled data from the target domain. Comprehensive validation on both experimental and numerically simulated rotor datasets across three health conditions—i.e., normal, unbalanced, and misaligned—demonstrates that WaveCORAL-DCCA achieves an average diagnostic accuracy of 95%. Notably, it outperforms established UDA benchmarks by at least 5–17% in cross-domain scenarios. These results confirm that WaveCORAL-DCCA provides robust generalisation across machines, fault severities, and operational conditions, even with scarce target domain samples, offering a scalable and practical solution for industrial rotor fault diagnosis. Full article

Biothiols, including cysteine (Cys), homocysteine (Hcy), and glutathione (GSH), are crucial for physiological regulation and their imbalance poses severe health risks. Herein, we developed a pH-responsive liquid crystal (LC)-based sensing platform for detection of biothiols by doping 4-n-pentylbiphenyl-4-carboxylic acid (PBA) into 4-n-pentyl-4-cyanobiphenyl (5CB). Urease catalyzed urea hydrolysis to produce OH⁻, triggering the deprotonation of PBA, thereby inducing a vertical alignment of LC molecules at the interface corresponding to dark optical appearances. Heavy metal ions (e.g., Hg²⁺) could inhibit urease activity, under which condition LC presents bright optical images and LC molecules maintain a state of tilted arrangement. However, biothiols competitively bind to Hg²⁺, the activity of urease is maintained which enables the occurrence of urea hydrolysis. This case triggers LC molecules to align in a vertical orientation, resulting in bright optical images. This pH-driven reorientation of LCs provides a visual readout (bright-to-dark transition) correlated with biothiol concentration. The detection limits of Cys/Hcy and GSH for the PBA-doped LC platform are 0.1 μM and 0.5 μM, respectively. Overall, this study provides a simple, label-free and low-cost strategy that has a broad application prospect for the detection of biothiols. Full article

Down syndrome (DS), stemming from the triplication of human chromosome 21, results in intellectual disability, with early mid-life onset of Alzheimer’s disease (AD) pathology. Early interventions to reduce cognitive impairments and neuropathology are lacking. One modality, maternal choline supplementation (MCS), has shown beneficial effects on behavior and gene expression in neurodevelopmental and neurodegenerative disorders, including trisomic mice. Loss of basal forebrain cholinergic neurons (BFCNs) and other DS/AD relevant hallmarks were observed in a well-established trisomic model (Ts65Dn, Ts). MCS attenuates these endophenotypes with beneficial behavioral effects in trisomic offspring. We postulate MCS ameliorates dysregulated cellular mechanisms within vulnerable BFCNs, with attenuation driven by novel gene expression. Here, choline acetyltransferase immunohistochemical labeling identified BFCNs in the medial septal/ventral diagonal band nuclei of the basal forebrain in Ts and normal disomic (2N) offspring at ~11 months of age from dams exposed to MCS or normal choline during the perinatal period. BFCNs (~500 per mouse) were microisolated and processed for RNA-sequencing. Bioinformatic assessment elucidated differentially expressed genes (DEGs) and pathway alterations in the context of genotype (Ts, 2N) and maternal diet (MCS, normal choline). MCS attenuated select dysregulated DEGs and relevant pathways in aged BFCNs. Trisomic MCS-responsive improvements included pathways such as cognitive impairment and nicotinamide adenine dinucleotide signaling, among others, indicative of increased behavioral and bioenergetic fitness. Although MCS does not eliminate the DS/AD phenotype, early choline delivery provides long-lasting benefits to aged trisomic BFCNs, indicating that MCS prolongs neuronal health in the context of DS/AD. Full article

Boron-containing compounds (BCCs) have emerged as potential drugs. Their drug-like effects are mainly explained by their mechanisms of action in enzymes. Nowadays, some experimental data support the effects of specific BCCs on GPCRs, provided there are crystal structures that show them bound to G protein-coupled receptors (GPCRs). Some BCCs are recognized as potential ligands of GPCRs—the drug targets of many diseases. Objective: The aim of this study was to collecte up-to-date data on the interactions of BCCs with GPCRs. Methods: Data were collected from the National Center of Biotechnology Information, PubMed, Global Health, Embase, the Web of Science, and Google Scholar databases and reviewed. Results: Some experimental reports support the interactions of BCCs with several GPCRs, acting as their labels, agonists, or antagonists. These interactions can be inferred based on in silico and in vitro results if there are no available crystal structures for validating them. Conclusions: The actions of BCCs on GPCRs are no longer hypothetical, as the existing evidence supports BCCs’ interactions with and actions on GPCRs. Full article

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention. Full article

The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

The lateral flow immunoassay (LFIA) is a widely utilized, rapid diagnostic technique characterized by its short analysis duration, cost efficiency, visual result interpretation, portability and suitability for point-of-care applications. However, conventional LFIAs have limited sensitivity, a challenge that can be overcome by the introduction of gold nanoparticles, which provide enhanced sensitivity and selectivity (compared, for example, to latex beads or carbon nanoparticles) for the detection of target analytes, due to their optical properties, chemical stability and ease of functionalization. In this work, gold nanoparticle-based LFIAs are developed for the detection of aflatoxin B1, and the relative performance of different morphology particles is evaluated. LFIA using gold nano-labels allowed for aflatoxin B1 detection over a range of 0.01 ng/mL–100 ng/mL. Compared to spherical gold nanoparticles and gold nano-flowers, star-shaped gold nanoparticles show increased antibody binding efficiency of 86% due to their greater surface area. Gold nano-stars demonstrated the highest sensitivity, achieving a limit of detection of 0.01ng/mL, surpassing the performance of both spherical gold nanoparticles and gold nano-flowers. The use of star-shaped particles as nano-labels has demonstrated a five-fold improvement in sensitivity, underscoring the potential of integrating diverse nanostructures into LFIA for significantly improving analyte detection. Moreover, the robustness and feasibility of gold nano-stars employed as labels in LFIA was assessed in detecting aflatoxin B1 in a wheat matrix. Improved sensitivity with gold nano-stars holds promise for applications in food safety monitoring, public health diagnostics and rapid point-of-care diagnostics. This work opens the pathway for further development of LFIA utilizing novel nanostructures to achieve unparallel precision in diagnostics and sensing. Full article

Functional foods like flax (Linum usitatissimum L.) are rich sources of specialized metabolites that contribute to their nutritional and health-promoting properties. Understanding the biosynthesis of these compounds is essential for improving their quality and potential applications. However, dissecting complex metabolic networks in plants remains challenging due to the dynamic nature and interconnectedness of biosynthetic pathways. In this study, we present a synergistic approach combining stable isotopic labeling (SIL), Candidate Substrate–Product Pair (CSPP) networks, and a time-course study with high temporal resolution to reveal the biosynthetic fluxes shaping phenylpropanoid metabolism in young flax seedlings. By feeding the seedlings with ¹³C₃-p-coumaric acid and isolating isotopically labeled metabolization products prior to the construction of CSPP networks, the biochemical validity of the connections in the network was supported by SIL, independent of spectral similarity or abundance correlation. This method, in combination with multistage mass spectrometry (MSⁿ), allowed confident structural proposals of lignans, neolignans, and hydroxycinnamic acid conjugates, including the presence of newly identified chicoric acid and related tartaric acid esters in flax. High-resolution time-course analyses revealed successive waves of metabolite formation, providing insights into distinct biosynthetic fluxes toward lignans and early lignification intermediates. No evidence was found here for the involvement of chlorogenic or caftaric acid intermediates in chicoric acid biosynthesis in flax, as has been described in other species. Instead, our findings suggest that in flax seedlings, chicoric acid is synthesized through successive hydroxylation steps of p-coumaroyl tartaric acid esters. This work demonstrates the power of combining SIL and CSPP strategies to uncover novel metabolic routes and highlights the nutritional potential of flax sprouts rich in chicoric acid. Full article

More than 50 years ago, children were viewed as naturally resilient and often labeled invulnerable or invincible. Resilience is now understood to be the result of dynamic interactions between individual, familial, social, and environmental systems, decentralizing the focus from the individual to the global society. Experiences with adversity may emanate from the youth’s family environment, their community, the school system, and larger structural challenges related to poverty, discrimination, health disparities, and educational inequities. Youth experiences with adversity, trauma, and tragedy have the potential to negatively impact youth well-being, with consequences manifesting across the lifespan. Children and adolescents generally hold limited power to change their circumstances and are often ill-equipped to resolve the adverse or traumatic experiences occurring within their ecosystem. The value of social supports in the young person’s ability to be resilient has been affirmed. This understanding is particularly important for children growing up in poverty or in Low- and Middle-Income Countries (LMICs) where significant challenges occur as a result of economic and social disadvantage. Resilience at the individual level is unlikely to eliminate macrolevel issues. Developing and deploying strategies to enhance the ability of youth to rebound from adversity represents a positive step at the micro level, but the larger issues of economic and social disadvantage are unlikely to change without macro-level interventions. Glancing toward the future, traumatized youth may grow into traumatized adults without appropriate interventions and changes in social policies, programs, and protections. Full article

Background: Down syndrome (DS) is a genetic disorder characterized by an extra copy of chromosome 21, often leading to intellectual disabilities, developmental delays, and an increased risk of various comorbidities, including thyroid dysfunction and mental health disorders. The relationship between thyroid dysfunction and mood disorders, particularly depression in DS populations, requires further investigation. Objective: This study aims to investigate the presence of a correlative relationship between hypothyroidism and depression in 178,840 individuals with DS, utilizing data from the National Inpatient Sample (NIS) to determine if those with comorbid hypothyroidism exhibit higher rates of depression compared to their counterparts without hypothyroidism. Methods: A retrospective analysis of the 2016–2019 NIS dataset was conducted, focusing on patients with DS, hypothyroidism, and depression diagnoses. The diagnoses were determined and labeled based on ICD-10 codes associated with NIS datapoints. Survey-weighted linear regression analyses were employed to assess the association between hypothyroidism and depression within the DS cohort, adjusting for demographic factors such as age, gender, and race. Results: This study found that individuals with DS exhibit a significantly higher prevalence of hypothyroidism (29.88%) compared to the general population (10.28%). Additionally, individuals with DS and comorbid hypothyroidism demonstrated a higher prevalence of depression (8.67%) compared to those without hypothyroidism (3.00%). These findings suggest a significant association between hypothyroidism and increased depression risk among individuals with DS. However, the overall prevalence of depression in DS (4.69%) remains substantially lower than in the general population (12.27%). Conclusions: This study highlights the importance of considering hypothyroidism as a potential contributor to depression in individuals with DS. Further research is needed to explore the underlying mechanisms of this association and potential screening and management strategies to address thyroid dysfunction and its potential psychiatric implications in DS. Full article

The demand for sustainable animal production is increasing. Microalgae such as Chlorella and Spirulina show promise as sustainable and functional ingredients in animal (poultry) feed. However, little is known about consumer perceptions regarding the use of algae in broiler diets and potential effects of algae on chicken meat. Residents of Flanders (Belgium) were surveyed to evaluate consumer knowledge, attitudes and willingness to buy chicken meat produced with algae-supplemented feed. Demographic data were collected, and both descriptive and inferential statistics were applied to assess influencing factors (n = 275 respondents who purchase chicken meat). While most respondents (69.6%) had tasted macroalgae (seaweed), only 11.4% and 24.6% indicated having tasted Chlorella and Spirulina before, respectively. Health, taste and safety were the most important drivers for consuming algae. Meat quality was the most important factor when purchasing chicken meat, while organic production was least valued. Regarding algae-fed chicken, 72.5% expressed willingness to purchase meat labeled as such, and 83.7% would buy algae-fed chicken regardless of its color. Sustainability beliefs significantly influenced willingness to accept a yellower meat color (β = 0.42 to 0.66, p < 0.001). Educational level and age also played a role, with higher-educated consumers showing greater acceptance. The influence of age was also related to the price of the meat, with consumers over 30 expressing a greater willingness to pay more than young people (under 30). Despite limited general knowledge about microalgae, the consumers surveyed are open to the idea of algae-fed chicken meat, particularly when it is framed as more sustainable. Clear ingredient labeling and consumer education may further support market acceptance. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

The designs of clinical trials of drugs for rare diseases are challenged by health technology assessment organisations and payers. Phase II pivotal studies, single-arm or open-label designs, the extensive use of non-final endpoints, and the limited use of patient-reported outcomes (PROs) are the main points of contention. The evidence on the actual design of these trials is limited, but corroborates the concerns of the above. Our aim is to scrutinise whether the design of pivotal studies of drugs for rare diseases to be launched into the Italian market by 2026 present similar issues. The drugs and the relevant pivotal studies were retrieved from Biomedtracker and US and European clinical trial databases. We identified 154 new drugs for rare diseases. Single-arm designs account for 36% of trials. Almost 50% of randomised control trials (RCTs) are designed using an active comparator and 61% are double-blinded. Primary endpoints are mostly (82%) surrogate. A total of 59% of studies include PROs. Our findings were partially expected (e.g., extensive use of surrogate endpoints) and partially not (e.g., RCTs and an active comparator), considering previous studies on the same topic. Having more head-to-head studies may reduce uncertainty concerning evidence at market launch, but different issues persist, including the still limited role of PROs. Full article