Search Results (1,077)

Measuring platelet function is important for patient stratification to judge bleeding vs. thrombotic risk and for research into antiplatelet drugs to prevent cardiovascular disease. Variability in platelet function is not fully understood, and large studies of inter-individual variation are making gradual progress using laboratory measurements, but rapid and high-performance hematological tests are also needed. We present here a novel microfluidic technology for platelet function analysis that images light scatter by platelets, using a low-cost, open-source, high-throughput and customizable darkfield imaging system called Dark-Pi. The hardware consists of a camera and a simple LED light source controlled by a Raspberry Pi, with 3D-printed parts. Using the Dark-Pi, platelet aggregation was imaged within adenosine 5′-diphosphate-loaded microcapillaries, revealing clearly visible patterns. This darkfield cellular light scatter approach was previously developed for bacterial cells, and here we adapted and optimized it for directly monitoring platelet aggregation. Capturing high-quality time-resolved images of platelets undergoing activation within microcapillaries allowed us to measure changes in light scattering in platelet-rich plasma that correspond with aggregation measured using conventional laboratory methods. This novel prototype system shows that this approach may have potential for use in large-scale studies of platelet function, combining simplicity with low-cost components and using a disposable dip-and-test microfluidic format. Full article

Reliable detection of small agricultural targets is fundamental to precision crop protection, phenotyping, yield estimation, and robotic intervention. Typical examples include detecting aphids such as Aphis gossypii, whiteflies such as Bemisia tabaci, planthoppers such as Nilaparvata lugens, and other tiny pests on sticky traps or crop canopies for early warning, identifying crop-like weed seedlings for site-specific herbicide spraying, locating early disease lesions for targeted treatment, and detecting young fruits, flowers, or wheat heads for yield estimation and robotic manipulation. Agricultural small-object detection differs from generic small-object detection because target visibility is jointly determined by pixel area, physical size, imaging distance, ground sampling distance, canopy structure, biological similarity, and task-specific intervention requirements. Existing reviews have summarized agricultural object detection or general small-object detection, but they rarely connect agricultural failure modes with detector-level mechanisms and reproducible evaluation practices. This review addresses this gap through a mechanism-oriented synthesis of agricultural small-object detection. First, we revisit the limitations of the COCO-style 32²-pixel threshold and propose an agricultural scale-reporting framework that combines pixel area, physical scale, relative image occupancy, and acquisition geometry. Second, we organize recent methods according to the mechanisms by which they address detail loss, scale shift, occlusion, dense distributions, foreground–background confusion, localization uncertainty, and edge-deployment constraints. Third, we summarize public datasets, quantitative evaluation metrics, reporting checklists, and real-device deployment evidence to support fair and field-oriented comparison. Finally, we identify future directions in multimodal sensing, foundation-model adaptation, label-efficient learning, and hardware-aware optimization. By linking agricultural scene characteristics, detector mechanisms, and evaluation requirements, this review aims to provide a more actionable framework for developing robust small-object detection systems in precision agriculture. Full article

Introduction: Aquatic chemical pollution is among the most worrying threats to ecosystem health. There is an ever-increasing variety of pollutant substances detected across the source-to-sea continuum, causing loss of biodiversity and ecological disequilibrium. Achieving cleaner and healthier systems relies on carrying out sustained, cost-effective, diagnosis and aquatic effects monitoring, within the adaptive management cycle. The available methods are, however, cumbersome, which creates a clear need for innovative expeditious approaches for low-cost surveillance monitoring. In the last decade, Raman Spectroscopy (RS) has gained wide recognition for application to biological questions, for its ability to uncover the complexity of molecules and their interactions. Various fields, from pharmacology to disease diagnosis and prognosis, have suffered an innovation revolution through the application of RS. In this technique inelastic light scattering of a small part of photons of an incident electromagnetic monochromatic light beam (ranging from near-infrared to visible or ultraviolet) is caused by the molecular vibration of chemical bonds. This results in shifts in energy, which indicate discrete vibrational modes of polarisable molecules, providing qualitative and quantitative assessments of the chemical composition and molecular structure of the sample. The technique shows high sensitivity, no need for sample preparation and the possibility of use in non-invasive and label-free analysis. Objective: The aim of this work is to present and discuss evidence about the application of Raman Spectroscopy (RS) to environmental diagnosis and aquatic effect monitoring of pollution. Methodology: The technique was applied to different biological models, i.e., diatoms, zebrafish embryos and larvae and freshwater snails. Quality assessments with diatoms were tested in environmental monitoring, while assessments with other models were done upon exposure to metals and organic contaminants. Results and conclusions: The Raman spectra obtained from the samples analysed comprised bands detected within the 800 to 2000 cm⁻¹ wavenumber range. These were related to bond vibrations of carbohydrates, DNA phosphate groups, proteins or CH, NH and OH stretching in lipids and proteins. Data analysis using chemometric methods clearly distinguished pollutant exposure from control sites or treatments, pointing out the potential for surveyance monitoring. The next steps include the comparison with other sensitive methods (e.g., locomotion and avoidance behaviours, omics methods) to assess efficiency and bring further mechanistic understanding. Full article

Extracellular vesicles (EVs) have become a key area of research, as scientists study their role in various biological processes. These vesicles appear to play a key role in the use of mesenchymal signaling cells (MSCs, formerly known as mesenchymal stem cells) to treat various diseases, such as osteoarthritis (OA), and other degenerative conditions. In our experiments, we examined EVs formed by canine mesenchymal signaling cells (MSCs) to identify them according to current guidelines and define their content, particularly the microRNA (miRNA) they contain, for future research projects. After obtaining the EVs, we demonstrated via Western blotting and transmission electron microscopy that the nanoparticles visible in the nanotracking analysis were positive for CD9 and ALIX and positive for CD9 and CD81, respectively. Markers for nanoparticles that do not represent extracellular particles—tested here as cytochrome C for mitochondrial particles and histones for nuclear particles—were negative. Finally, we detected a total of 85 different miRNAs in the negative controls. To determine the potential influence of various cell stimulations intended to induce osteoarthritis (e.g., interleukin-1β stimulation) or a possible treatment (e.g., shockwave therapy), or the influence of ITS prior to extracellular vesicle extraction, we detected a total of 208 different miRNAs. These results demonstrate how canine EVs from MSCs can be detected in vitro and how the EVs’ miRNA profile changes after stimulation of the producing cells. This information may provide valuable insight into the understanding and treatment of osteoarthritis. Additionally, we demonstrated that using ITS instead of FCS to produce EVs should be reconsidered due to the significant change in miRNA expression levels. Full article

Saudi Arabia is expanding its domestic coffee sector under Vision 2030, yet coffee farming remains vulnerable to leaf diseases and pest damage. Image-based artificial intelligence studies conducted under Saudi field conditions remain limited, particularly in relation to assessing image-based visible disease severity. This study designs a hierarchical deep learning framework for screening coffee leaf diseases using field-collected images of Saudi coffee leaves. Three tasks were addressed: binary health status classification, four-class disease or pest damage identification, and binary visible severity classification. A dataset of 550 RGB images was collected from Al-Dayer Governorate, Jazan, under natural field conditions. ResNet50, DenseNet121, and EfficientNet-B0 were evaluated via transfer learning in two phases: a Saudi-only phase and an integrated phase that combined Saudi data with selected JMuBEN and JMuBEN2 samples. In the Saudi-only phase, ResNet50 achieved 96.47% accuracy for binary classification, while DenseNet121 achieved 68.66% and 78.12% for disease and visible severity classification, respectively. In the integrated phase, performance improved to 99.74%, 97.76%, and 97.37%. These integrated-phase results are interpreted as evidence that dataset expansion and increased visual diversity can improve model performance, rather than as definitive estimates of field deployment performance. The results show that binary classification is feasible under limited local data, whereas fine-grained disease classification is more constrained by dataset size and class imbalance. Grad-CAM visualizations were used to support qualitative interpretability and should not be interpreted as biological validation of disease localization. The framework is positioned as a decision-support screening approach that requires further expert-validated, multi-farm, and multi-season evaluation before deployment. Full article

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security. Full article

Background/Objectives: Dengue prevention in rural Amazonian communities is shaped by knowledge, household feasibility, sociocultural dynamics, institutional continuity, and trusted communication. This study explored behavioral, sociocultural, and institutional barriers to dengue prevention and control in rural communities of the Peruvian Amazon. Methods: An exploratory qualitative study with an ethnographic orientation, informed by the Communication for Behavioural Impact (COMBI) framework, was conducted in three anonymized rural settlements in San Martín, Peru. The qualitative corpus included 120 adults, 84 in-depth interviews, six focus group discussions with 36 participants, 22 household and community observation records, 13 institutional communication materials, and seven local operational documents. Data were analyzed using an inductive thematic approach and triangulated across participant profiles, settlements, and sources. Results: Dengue was widely recognized as a mosquito-borne disease, but the central finding was a gap between general awareness and practical, routine application. Participants’ understanding of breeding sites, warning signs, and feasible source reduction was uneven. Prevention was mainly reactive, increasing after nearby cases, alerts, or fumigation, but weakening when risk was not visible. Irregular water supply, water storage, waste accumulation, gendered domestic labor, competing household priorities, reluctance to confront neighbors, and intermittent institutional action limited sustained prevention. Fumigation was perceived as the most visible institutional response, while communication was more credible when mediated by trusted local actors. Conclusions: Dengue prevention requires locally feasible household practices, safe water-storage guidance, trusted communicators, neighborhood coordination, continuous pre-outbreak engagement, and intersectoral support. Full article

Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications—pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protocols, and virtual wards—now generate measurable clinical and economic value. Yet a decade of evaluations and implementation studies suggests that the surrounding ecosystem has matured unevenly: working applications coexist with persistent cross-cutting fragility. In this Perspective we argue that four structural gaps continue to constrain RPM’s promise at scale: (i) economic models that do not credibly compensate the asynchronous clinical work that RPM generates; (ii) ambiguous frameworks for professional liability and accountability for continuous data streams, intensified by artificial-intelligence (AI)-mediated decision support; (iii) privacy, equity, and benefit-sharing arrangements that do not yet make patients unambiguous net beneficiaries—a gap visible across very different health systems internationally; and (iv) engagement and adherence dynamics that determine whether programmes deliver value at all, but are still treated as secondary outcomes. The COVID-19 emergency briefly suspended much of the friction in this ecosystem and produced a useful natural experiment: what scaled rapidly under emergency conditions, and what subsequently atrophied, illuminates which gaps are technical, which are economic, and which are institutional. We close with a six-point research and policy agenda intended to move RPM from localised successes to a trustworthy, generalisable standard of care. Full article

Neuroblastoma (NB), the most common extracranial solid tumor of childhood, exemplifies one of the most formidable paradigms of tumor immune evasion (TIME) in pediatric oncology. Despite significant advances in multimodal therapy and the clinical integration of immunotherapeutic strategies, high-risk NB (HR-NB) remains largely refractory to durable immune control. This failure reflects not an absence of immune engagement, but the presence of a highly evolved and developmentally wired immune escape architecture. In this review, we synthesize emerging insights from single-cell, multi-omics, and functional studies to define how developmental lineage, cellular plasticity, metabolic rewiring, epigenetic regulation, and therapy-induced adaptation converge to engineer immune blindness in NB. We discuss how NB’s neural crest origin establishes a baseline of low immunogenicity, which is subsequently reinforced through coordinated suppression of antigen presentation, dominance of immune checkpoint signaling, and profound dysfunction of cytotoxic T and natural killer cells within an immunosuppressive tumor microenvironment. Central to this process is tumor-intrinsic plasticity, whereby lineage instability and dedifferentiation, exacerbated by therapeutic pressure, embed immune silence as a stable tumor state. We highlight evidence positioning RD3 as a master upstream regulator linking cellular identity to immune visibility, governing antigen presentation, innate immune sensing, checkpoint expression, and cytotoxic lymphocyte engagement. Beyond tumor-intrinsic mechanisms, we examine the roles of immunosuppressive myeloid populations, tumor-derived exosomes, metabolic stress, hypoxia, and ferroptosis-associated pathways in reinforcing immune paralysis. Finally, we outline emerging therapeutic strategies aimed at dismantling this architecture, including combinatorial checkpoint blockade, metabolic and epigenetic reprogramming, exosome-targeted interventions, and next-generation immune engineering platforms. Together, this review reframes TIME in NB as a programmable, developmentally rooted process and provides a mechanistic roadmap for restoring immune competence and therapeutic susceptibility in HR disease. Full article

Background: Actinic keratoses are common keratinocytic precursor lesions arising within chronically ultraviolet-damaged skin and are associated with an increased risk of progression to cutaneous squamous cell carcinoma. The concept of field cancerization has shifted therapeutic strategies from the treatment of isolated visible lesions toward broader field-directed approaches targeting both clinical and subclinical disease. Methods: This narrative review summarizes the rationale, mechanisms of action, efficacy profile, tolerability, and practical limitations of currently available field-directed therapies for actinic keratosis, including 5-fluorouracil, imiquimod, diclofenac, photodynamic therapy, and tirbanibulin. Based on their distinct biological targets, we propose a mechanism-based framework for sequential treatment strategies. Results: Available therapies act through partially non-overlapping mechanisms, including cytotoxic activity, immune activation, cyclooxygenase-2 inhibition, photodynamic oxidative damage, and tubulin/Src pathway inhibition. These complementary effects provide a biological rationale for sequential regimens aimed at addressing the heterogeneity of field cancerization. However, direct clinical evidence supporting specific treatment sequences remains limited, and proposed regimens should be interpreted as hypothesis-generating rather than as validated therapeutic protocols. Conclusions: Mechanism-based sequential field therapy may represent a rational strategy to optimize long-term control of actinic keratosis and field cancerization. Prospective comparative studies are needed to define optimal sequences, treatment intervals, patient selection criteria, and clinically meaningful endpoints, including sustained field clearance, recurrence reduction, tolerability, adherence, and prevention of progression to invasive cutaneous squamous cell carcinoma. Full article

Lysosomal diseases (LDs, or Lysosomal Storage Disorders) have become increasingly visible in the newborn screening community, with the addition of mucopolysaccharidosis type II (MPS-II) into the Recommended Uniform Screening Panel in August 2022 and Infantile Krabbe disease in June 2024. As more LDs are expected to be considered for screening adoption, the ability to multiplex conditions and expand proficiency testing (PT) using quality control materials is essential. This study examines the use of recombinant enzymes to produce first-tier PT materials for mucopolysaccharidosis type I, MPS-II, Gaucher, Fabry, Krabbe, Pompe, and Niemann–Pick A/B (acid sphingomyelinase deficiency)—adding four disorders to the CDC’s Newborn Screening Quality Assurance Program (NSQAP) LD PT panel. Through an iterative process that included two prototype phases, two pilot phases, and external testing by up to 31 external laboratories, a new manufacturing process was developed for producing high-performing dried blood spot-based LD PT specimens. Materials were evaluated using several methods commonly employed by newborn screening laboratories, including tandem mass spectrometry with flow injection and liquid chromatography, digital microfluidics, and fluorometric assays. This novel process for producing LD PT materials offers several advantages over previous manufacturing methods that relied on immortalized cell lines from affected patients. Improved scalability, for example, has enabled NSQAP to expand LD PT enrollment internationally. Furthermore, the new process makes it easier to support future expansions of the LD screening panel. The updated specimens and expanded program were launched in January 2025. Full article

Background/Objectives: To directly compare the capabilities of hybrid-type iterative reconstruction (IR) with the newly developed deep learning reconstruction (DLR) for the inner ear on high-definition CT (HDCT) obtained using the super-high-resolution (SHR) mode for external, middle and inner ear evaluations and diagnosis in patients with and without otologic diseases. Methods: Included in this study were 140 patients who had undergone HDCT, consisting of 32 otologic disease patients and 108 non-otologic disease patients, and 280 inner and middle ears and temporal bones were evaluated on a per ear analysis. Signal-to-noise ratios (SNRs) of the temporal bone surrounding the aural vestibule of the ear and in the vestibule as well as the cerebellar hemisphere, overall image and detailed evaluation of the visibility of anatomical landmarks in the middle and inner ear and temporal bone obtained with the two methods were assessed and statistically compared using the paired t-test or Wilcoxon’s signed-rank test. Then, receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance between two reconstruction methods. Results: Each SNR of DLR was significantly higher than that of hybrid-type IR (p < 0.05). Overall image quality and detailed visualization of each anatomical structure obtained with DLR were significantly better than those obtained with hybrid-type IR (p < 0.05). The area under the curve of DLR had no significant difference with hybrid-type IR (p = 0.18). Conclusions: DLR has superior potential to hybrid-type IR for better image quality and visualization of anatomical landmarks in middle and inner ears and temporal bones on HDCT, although diagnostic performance was not affected in clinical practice. Full article

A 51-year-old woman was admitted with a malodorous ulceration covering the whole area of both breasts, without visible breast contour or remnants of breast tissue. After excision of a skin nodule an invasive ductal carcinoma was diagnosed; grade-2, hormone receptor (HR)-positive, HER2-negative, Ki-67 at 25%. Computed tomography of the thorax and abdomen showed pulmonary and osseous metastases. Six cycles of systemic chemotherapy with epirubicin and cyclophosphamide at three-week intervals were administered, followed by endocrine therapy with letrozole. Almost four years later, palbociclib became available and it was added to the patient’s treatment. Loco-regional and distant disease control was achieved attaining maximum response at 11 months after initial diagnosis and since then the patient remains progression-free with good quality of life for more than eight years. This is to the best of our knowledge an extreme case of primary metastatic ulcerative breast cancer with complete local tissue destruction and markedly prolonged progression-free survival. As this is a single-case clinical observation, any conclusions have limited generalizability. Given the rarity of primary metastatic ulcerative breast cancer there are no specific evidence-based treatment guidelines available and published studies have high heterogeneity and low level of evidence, necessitating multidisciplinary approach on a case-by-case basis. Full article

Background/Objectives: Scabies is a contagious dermatological infestation that can cause not only physical symptoms but also considerable psychosocial burden. This study aimed to investigate the relationships between fear of scabies, internalized stigma, and dermatology-related quality of life in patients with scabies. Methods: This cross-sectional study included 131 patients diagnosed with scabies in a dermatology outpatient clinic. Data were collected using a structured questionnaire including sociodemographic and clinical characteristics, the Fear of Scabies Scale (FSS), the Internalized Stigma Scale (ISS), and the Dermatology Life Quality Index (DLQI). Correlation and regression analyses were conducted to examine the associations between fear of scabies, internalized stigma, and quality of life. Results: The mean DLQI score was 15.82 ± 5.69, indicating a considerable impairment in dermatology-related quality of life. Fear of scabies showed a weak but significant positive correlation with DLQI scores (r = 0.326, p < 0.001), whereas internalized stigma demonstrated a stronger correlation (r = 0.484, p < 0.001). Among the stigma subdimensions, social withdrawal showed the strongest association with impaired quality of life (r = 0.622, p < 0.001). Regression analyses revealed that internalized stigma explained 23% of the variance in DLQI scores (R² = 0.234), while fear of scabies explained 10% (R² = 0.106). In addition, longer symptom duration (β = 0.708, p < 0.001), nocturnal pruritus (β = 0.408, p = 0.009), and visible skin lesions (β = 0.263, p = 0.002) were associated with higher levels of fear of scabies. Conclusions: Internalized stigma and disease-related fear were associated with reduced quality of life, with stigma-related mechanisms appearing to play a particularly prominent role. These findings suggest that addressing stigma and providing psychosocial support may be important components of comprehensive scabies management. Full article

Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture. Full article