Search Results (1,975)

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Honey bees are essential pollinators for the ecosystem and food crops. However, their health and survival face threats from both biotic and abiotic stresses. Fungi, microsporidia, and bacteria might significantly contribute to colony losses. Therefore, rapid and sensitive diagnostic tools are crucial for effective disease management. In this study, molecular assays were developed to quickly and efficiently detect the main honey bee pathogens: Nosema ceranae, Aspergillus flavus, Paenibacillus larvae, and Black queen cell virus. In this context, new primer pairs were designed for use in quantitative Real-time PCR (qPCR) reactions. Various protocols for extracting total nucleic acids from bee tissues were tested, indicating a CTAB-based protocol as the most efficient and cost-effective. Furthermore, excluding the head of the bee from the extraction, better results were obtained in terms of quantity and purity of extracted nucleic acids. These assays showed high specificity and sensitivity, detecting up to 250 fg of N. ceranae, 25 fg of P. larvae, and 2.5 pg of A. flavus DNA, and 5 pg of BQCV cDNA, without interference from bee DNA. These qPCR assays allowed pathogen detection within 3 h and at early stages of infection, supporting timely and efficient management interventions. Full article

Kidney transplantation is the treatment of choice for patients with end-stage kidney disease. Despite significant advances in graft survival, rejection continues to pose a major clinical challenge. Conventional monitoring tools, such as serum creatinine, donor-specific antibodies, and proteinuria, lack sensitivity and specificity for early detection of graft injury. Moreover, while biopsy remains the current gold standard for diagnosing rejection, it is prone to confounders, invasive, and associated with procedural risks. However, non-invasive novel biomarkers have emerged as promising alternatives for earlier rejection detection and improved immunosuppression management. This review focuses on the leading candidate biomarkers currently under clinical investigation, with an emphasis on their diagnostic performance, prognostic value, and potential to support personalised immunosuppressive strategies in kidney transplantation. Full article

Colorectal cancer (CRC) is not only the third most common cancer worldwide, with 1.1 million new cases per year; it is also the second leading cause of cancer death. However, mortality has decreased since 2012 due to early detection programs and better therapeutic approaches. While many patients are diagnosed at an early stage, there is up to 50% relapse after optimal initial treatment. Therefore, it is crucial to explore the mechanism underlying the development of recurrences and metastasis. It is known that tumors release dormant cells that escape chemotherapy and nest in a target organ without proliferating. Under certain circumstances that are not yet entirely clear, they can be activated and metastasize. Therefore, the objective of this work is to explore the detailed mechanisms of dormancy, including early detection of recurrence and therapeutic approaches for the treatment of CRC. The specific objectives are to determine biomarkers that may be useful in identifying dormant cells to detect minimal residual disease (MRD) after surgery and predicting disease progression, as well as evaluating biomarkers that are susceptible to therapeutic intervention. Full article

Background/Objectives: Chronic kidney disease of unknown etiology (CKDu) disproportionately affects young male agricultural workers who are otherwise healthy. There is a scarcity of biomarkers for early detection of this type of kidney disease. We hypothesized that small extracellular vesicles (sEVs) released into urine may provide novel biomarkers. Methods: We obtained two urine samples at the start and the end of a workday in the fields from a limited set of workers with and without kidney impairment. Isolated sEVs were characterized for size, surface marker expression, and purity and, subsequently, their lipid composition was determined by mass spectrometry. Results: The number of particles per ml of urine normalized to osmolality and the size variance were larger in workers with possible CKDu than in control workers. Surface markers CD9, CD63, and CD81 are characteristic of sEVs and a second set of surface markers suggested the kidney as the origin. Differential expression of CD25 and CD45 suggested early inflammation in CKDu workers. Of the twenty-one lipids differentially expressed, several were bioactive, suggesting that they may have essential functions. Remarkably, fourteen of the lipids showed intermediate expression values in sEVs from healthy individuals with acute creatinine increases after a day of work. Conclusions: We identified twenty-one possible lipid biomarkers in sEVs isolated from urine that may be able to distinguish agricultural workers with early onset of CKDu. Differentially expressed surface proteins in these sEVs suggested early-stage inflammation. This pilot study was limited in the number of workers evaluated, but the approach should be further evaluated in a larger population. Full article

Yellow rust (Puccinia striiformis) is a common wheat disease that significantly reduces yields, particularly in seasons with cooler temperatures and frequent rainfall. Early detection is essential for effective control, especially in key wheat-producing regions such as Southern Dobrudja, Bulgaria. This study presents a UAV-based approach for detecting yellow rust using only RGB imagery and deep learning for pixel-based classification. The methodology involves data acquisition, preprocessing through histogram equalization, model training, and evaluation. Among the tested models, a UnetClassifier with ResNet34 backbone achieved the highest accuracy and reliability, enabling clear differentiation between healthy and infected wheat zones. Field experiments confirmed the approach’s potential for identifying infection patterns suitable for precision fungicide application. The model also showed signs of detecting early-stage infections, although further validation is needed due to limited ground-truth data. The proposed solution offers a low-cost, accessible tool for small and medium-sized farms, reducing pesticide use while improving disease monitoring. Future work will aim to refine detection accuracy in low-infection areas and extend the model’s application to other cereal diseases. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

Objectives: Circulating tumor DNA (ctDNA) has emerged as a reliable prognostic biomarker in both early- and late-stage non-small cell lung cancer (NSCLC) patients. However, its role in NSCLC with pleural dissemination (M1a), a subset of disease with indolent biology, remains to be elucidated. Methods: We collected 41 M1a patients with serial ctDNA and CEA monitoring. Progression-free survival (PFS) was assessed between patients with different levels of ctDNA and CEA. An independent cohort of 61 M1a patients was included for validation. Results: At the diagnostic landmark, the detection rates for ctDNA and CEA were 22% and 55%, respectively. Among patients who experienced disease progression with pleural metastases, only ten had detectable ctDNA in longitudinal timepoints, resulting in a sensitivity of 50%. Moreover, there was no significant difference in PFS between patients with longitudinally detectable and undetectable ctDNA (HR: 0.86, 95% CI 0.33–2.23, p = 0.76). In contrast, patients with a decreasing CEA trend within 3 months after diagnosis were associated with an improved PFS (HR: 0.22; 95% CI, 0.03–1.48, p = 0.004). This finding is confirmed in an independent M1a patient cohort. Conclusions: Together, our findings suggest that M1a NSCLC with isolated pleural dissemination may represent a “non-shedding” tumor type, where ctDNA shows limited diagnostic and prognostic value. Monitoring early changes in CEA could be a more cost-effective predictor of disease progression. Full article

With the growing use of deep learning in medical image analysis, automated classification of fundus images is crucial for the early detection of fundus diseases. However, the complexity of fluorescein fundus angiography (FFA) images poses challenges in the accurate identification of lesions. To address these issues, we propose the Enhanced Feature Fusion ConvNeXt (EFF-ConvNeXt) model, a novel architecture combining VGG16 and an enhanced ConvNeXt for FFA image classification. VGG16 is employed to extract edge features, while an improved ConvNeXt incorporates the Context-Aware Feature Fusion (CAFF) strategy to enhance global contextual understanding. CAFF integrates an Improved Global Context (IGC) module with multi-scale feature fusion to jointly capture local and global features. Furthermore, an SKNet module is used in the final stages to adaptively recalibrate channel-wise features. The model demonstrates improved classification accuracy and robustness, achieving 92.50% accuracy and 92.30% F1 score on the APTOS2023 dataset—surpassing the baseline ConvNeXt-T by 3.12% in accuracy and 4.01% in F1 score. These results highlight the model’s ability to better recognize complex disease features, providing significant support for more accurate diagnosis of fundus diseases. Full article

Background: Aortic stenosis (AS) has long been considered a degenerative disease and is typically diagnosed in older men at an advanced stage. However, accumulating evidence has highlighted the similarities between AS and atherosclerosis, particularly regarding shared risk factors and overlapping pathophysiological mechanisms. This connection has led to a paradigm shift, suggesting that AS may be preventable in its early stages. Methods: This narrative review synthesizes the existing literature exploring the parallels between AS and atherosclerosis, focusing on common risk factors, pathogenic pathways, and evolving therapeutic strategies. Clinical trials and translational studies were examined to assess the effectiveness of atherosclerosis-based treatments for AS. Results: Multiple studies have confirmed the shared inflammatory, lipid-mediated, and calcific mechanisms of AS and atherosclerosis. Despite these similarities, therapeutic strategies effective in atherosclerosis, such as statin therapy, have not consistently shown benefits in AS. New medical approaches aim to delay aortic valve replacement and reduce the associated morbidity. The partially overlapping pathogenesis continues to guide future research. Conclusions: While AS and atherosclerosis share several pathogenic features, their clinical courses and treatment responses diverge. Understanding the limits and potential of their overlap may inform future preventive and therapeutic strategies. Earlier detection and targeted intervention in AS remain key goals, drawing on insights from cardiovascular disease management. Full article

Mammary Paget disease (MPD) is a rare cutaneous malignancy associated with underlying ductal carcinoma in situ (DCIS) or invasive ductal carcinoma (IDC). Clinically, it appears as eczematous changes in the nipple and areola complex (NAC), which may include itching, redness, crusting, and ulceration; these symptoms can sometimes mimic benign dermatologic conditions such as nipple eczema, making early diagnosis challenging. A 56-year-old woman presented with persistent erythema and scaling of the left nipple, which did not respond to conventional dermatologic treatments: a high degree of suspicion prompted further investigation. Reflectance confocal microscopy (RCM) revealed atypical, enlarged epidermal cells with irregular boundaries, while line-field confocal–optical coherence tomography (LC-OCT) demonstrated thickening of the epidermis, hypo-reflective vacuous spaces and abnormally large round cells (Paget cells). These non-invasive imaging findings were consistent with an aggressive case of Paget disease despite the absence of clear mammographic evidence of underlying carcinoma: in fact, several biopsies were needed, and at the end, massive surgery was necessary. Non-invasive imaging techniques, such as dermoscopy, RCM, and LC-OCT, offer a valuable diagnostic tool in detecting Paget disease, especially in early stages and atypical forms. Full article

Background: Cell-free DNA (cfDNA) has become a cornerstone of liquid biopsy applications, offering promise for early disease detection and monitoring. However, its widespread clinical adoption is limited by variability in pre-analytical processing, especially during isolation. Current extraction methods face challenges in yield, purity, and reproducibility. Methods: We developed and optimized SafeCAP 2.0, a novel magnetic bead-based cfDNA extraction kit, focusing on efficient recovery, minimal genomic DNA contamination, and PCR compatibility. Optimization involved systematic evaluation of magnetic bead chemistry, buffer composition, and reagent volumes. Performance was benchmarked against a commercial reference kit (Apostle MiniMax) using spiked oligonucleotides and plasma from patients with stage IV NSCLC. Results: The optimized protocol demonstrated superior recovery with a limit of detection (LoD) as low as 0.3 pg/µL and a limit of quantification (LoQ) of 1 pg/μL with no detectable PCR inhibition. In comparative studies, SafeCAP 2.0 showed equivalent or improved performance over the commercial kit. Clinical validation using 47 patient plasma samples confirmed robust cfDNA recovery and fragment integrity. Conclusions: SafeCAP 2.0 offers a cost-effective, high-performance solution for cfDNA extraction in both research and clinical workflows. Its design and validation address key pre-analytical barriers, supporting integration into routine diagnostics and precision medicine platforms. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

About one billion people worldwide are affected by neurologic disorders. Among the various neurologic disorders, one of the most common is Alzheimer’s disease (AD). AD is a neurodegenerative disorder that progressively affects cognitive functions, disrupting the daily lives of millions of individuals. Mild cognitive impairment (MCI) is often considered a prodromal stage of Alzheimer’s disease. In this article, we retrieved data from the online available dataset GSE63060, which includes transcriptomic data of 329 blood samples, of which there are 104 cognitively normal controls, 80 MCI patients, and 145 AD patients. We used transcriptomic data related to all three groups to perform an over-representation analysis of the gene ontologies followed by a network analysis. The aim of our study is to pinpoint alterations, detectable through a non-invasive method, in biological processes affected in MCI that persist during AD. Our goal is to uncover transcriptomic changes that could support earlier diagnosis and the development of more effective therapeutic strategies, starting from the early stages of the disease, to slow down or mitigate its progression. Our work provides a consistent picture of the transcriptomic unbalance of many genes strongly involved in ribosomal formation and biogenesis and splicing processes both in patients with MCI and with AD. Full article

Background: The early detection of melanoma is crucial for improving patient outcomes, as survival rates decline dramatically with disease progression. Despite significant achievements in deep learning methods for skin lesion analysis, several challenges limit their effectiveness in clinical practice. One of the key issues is the lack of knowledge about the melanoma stage distribution in the training data, raising concerns about the ability of these models to detect early-stage melanoma accurately. Additionally, publicly available datasets that include detailed information on melanoma stage and tumor thickness remain scarce, restricting researchers from developing and benchmarking methods specifically tailored for early diagnosis. Another major limitation is the lack of cross-dataset evaluations. Most deep learning models are tested on the same dataset they were trained on, so they fail to assess their generalization ability when applied to unseen data. This reduces their reliability in real-world clinical settings. Methods: We introduce an early-stage melanoma benchmark dataset to address these issues, featuring images labeled according to T-category based on Breslow thickness. Results: We evaluated several state-of-the-art deep learning models on this dataset and observed a significant drop in performance compared to their results on the ISIC Challenge datasets. Conclusions: This finding highlights the models’ limited capability in detecting early-stage melanoma. This work seeks to advance the development and clinical applicability of automated melanoma diagnostic systems by providing a resource for T-category-specific analysis and supporting cross-dataset evaluation. Full article