The mature plasmacytoid dendritic cell proliferation associated with myeloid neoplasm represents a clonal proliferation of plasmacytoid dendritic cells within myeloproliferative and myelodysplastic disorders. This entity was recently recognized as a distinct condition in the fifth edition of the World Health Organization classification of hematolymphoid tumors. It occurs in approximately 4.9% of acute myeloid leukemia cases. The pathogenic mechanisms underlying this proliferation and the role of these cells in disease progression remain poorly understood. Nevertheless, the plasmacytoid dendritic cell proliferation associated with acute myeloid leukemia is related to distinct genetic abnormalities, worse prognosis, reduced overall survival, lower sensitivity to conventional acute myeloid leukemia therapies, and an increased risk of relapse. It also displays distinct immunophenotypic features compared to other types of mature plasmacytoid dendritic cell proliferation, raising questions about its classification and diagnostic criteria. This review provides a comprehensive overview of current knowledge regarding the plasmacytoid dendritic cell proliferation associated with acute myeloid leukemia, including terminology inconsistencies; the role of plasmacytoid dendritic cells in this entity; associated genetic alterations; immunophenotypic and morphological characteristics of blasts and plasmacytoid dendritic cells; clinical outcomes and prognostic impact; and therapeutic approaches and perspectives. Synthesizing current evidence may help improve disease recognition and highlight gaps in knowledge to guide future research.

MSMF-DBNet: Dual-branch model for pulmonary disease recognition with multi-scale feature enhancement and multichannel fusion

PAG-MSBNet:Prototype-aware gated multi-scale branch network for open-set crop disease recognition

Kiwifruit gray mold (caused by Botrytis cinerea) causes severe post-harvest losses. Traditional manual disease identification is inefficient, and post-harvest control measures lack systematic comparison. The aim of this study is to combine the residual network ResNet 18 model (ResNet 18-A) using Adam optimizer (which can maintain the adaptive learning rate of each parameter separately) with meta-analysis methods for preharvest diagnosis of kiwifruit gray mold and screening of effective post-harvest control measures, providing reference for scientific management of kiwifruit orchards. In terms of disease diagnosis, the disease recognition model based on ResNet 18 achieved an accuracy of 98.70% on a dataset of 9702 live images, meets the routine diagnostic needs. In terms of screening for efficient post-harvest prevention and control measures, the meta-analysis results showed that the treatment of soaked in MT solution for 3 min and stored at 22-24 °C with a relative humidity of 85%-95% (E1/E2), and the treatment of sprayed with Bacillus tequilensis KXF 6501 fertilization broth (1 × 107 CFU mL-1) has a good comprehensive effect on various kiwifruit quality indicators. The combination of ResNet 18 and meta-analysis can effectively improve the management efficiency of kiwifruit gray mold. Both models have great potential in achieving efficient disease diagnosis and targeted post-harvest prevention and control measures selection, which can help optimize disease management strategies for horticultural crops. © 2026 Society of Chemical Industry.

The rapid growth of the global population and the intensification of agricultural practices have significantly increased the vulnerability of crops to diseases and pest infestations, leading to substantial yield losses and economic instability in the agri-food sector. Traditional crop disease identification and pesticide application methods are largely manual, time-consuming, and prone to human error, often resulting in delayed intervention and excessive or inefficient pesticide usage. In recent years, deep learning has emerged as a powerful paradigm for automated crop disease detection due to its superior capability in learning complex visual patterns from largescale image data. This research presents a deep learning–based approach for enhancing crop disease detection and pesticide management by leveraging advanced convolutional neural networks and intelligent decision-support mechanisms. The proposed approach aims to achieve accurate and early-stage disease identification while facilitating targeted and optimized pesticide recommendations, thereby minimizing chemical overuse and environmental impact. By integrating image-based disease recognition with intelligent inference models, the system supports precision agriculture objectives, including improved crop health monitoring, sustainable pest control, and increased agricultural productivity. The study synthesizes recent advances in deep learning architectures, dataset augmentation strategies, and evaluation metrics relevant to real-world agricultural deployment. The findings underscore the potential of deep learning–driven systems to transform crop protection practices by enabling scalable, real-time, and cost-effective disease detection and pesticide optimization.

Plant disease diagnosis in real-world agricultural environments is challenged by data scarcity, domain shift, privacy constraints, and limited edge-device resources. This paper proposes FMEL-FSDA, a Federated Multimodal Edge Learning framework with Few-Shot Domain Adaptation for robust field-based plant disease recognition. The framework integrates attention-based RGB–text feature fusion, privacy-preserving federated learning, rapid few-shot personalization, and uncertainty-aware inference within an edge-efficient architecture. Federated training enables collaborative learning across distributed farms without sharing raw data, while few-shot adaptation allows fast deployment to new regions using only 1–10 labeled samples per class. Experiments on the PlantWild in-the-wild dataset show that FMEL-FSDA outperforms centralized, federated, and few-shot baselines, achieving 93.78% accuracy, 93.33% F1-score, and 0.97 AUC. The model maintains strong performance under privacy mechanisms such as gradient perturbation and secure aggregation, reduces communication overhead by up to 4×, and supports low-latency edge inference. Uncertainty estimation and Grad-CAM-based explainability further enhance reliability by identifying low-confidence cases and highlighting disease-relevant regions. Overall, FMEL-FSDA offers a scalable, privacy-aware, and field-ready solution for intelligent plant disease diagnosis in precision agriculture.

The automatic recognition of plant diseases has become an increasingly relevant area within precision agriculture, as it enables rapid diagnosis, reduction of losses, and improved productivity. This study presents the development and evaluation of a convolutional neural network (CNN) model implemented in MATLAB for the identification of diseases in apple tree leaves. The apple tree leaf dataset, organized into multiple classes, was used and standardized through a balancing technique based on the smallest class size. The dataset was divided into 60% for training and 40% for testing. The proposed CNN architecture consists of three convolutional blocks followed by a fully connected layer and softmax classification. Training was performed using the Stochastic Gradient Descent with Momentum (SGDM) optimizer. The results demonstrated satisfactory accuracy on the test set, indicating that even compact architectures are capable of efficiently classifying leaf images. These findings reinforce the applicability of CNN-based models in computational systems designed to support phytosanitary diagnosis.

Abstract Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare and disabling disease. Preventing unnecessary and/or harmful medical procedures to patients requires early and accurate diagnosis, multidisciplinary care, and access to specialists with FOP expertise. This study aims to assess the needs of healthcare providers (HCPs) when providing care to patients with FOP. Knowledge, confidence and management practices were measured through a short awareness survey, completed by 400 HCPs from different specialties who may encounter patients with FOP. Complementary qualitative data were obtained from a dialogue session about FOP. Quantitative data was analyzed using descriptive and inferential analysis, while qualitative data underwent thematic coding. Results indicate a need for enhanced knowledge and confidence regarding identifying clinical manifestations of FOP, referral to FOP specialists and subsequent patient management. Only 8.8% of HCPs correctly identified FOP from an open-ended question showing malformed great toes, a classical clinical manifestation of FOP, and only 31.8% correctly identified FOP from a multiple-choice question showing heterotopic ossification on a child’s back, another classical clinical manifestation of FOP. Structural and system-level issues were also identified as barriers to optimal patient care pathways, with limited access to FOP specialists reported as a prominent concern. These results document a need for improvements in HCP knowledge in addition to local care collaboration and healthcare system optimization to meet the needs of patients with rare diseases. Given that FOP is an ultra-rare disease, educational interventions are recommended to focus on rare disease recognition, with FOP included as one topic. In addition, greater team collaboration and system-level approaches to assist HCPs in overcoming barriers that impede patient access to care and ongoing management, as well as interventions to enhance patients’ capacity to have informed communication with their HCPs, are needed.

Chronic granulomatous disease (CGD) is a rare inborn error of immunity with both high risk of invasive bacterial and fungal infections as well as inflammatory complications. Though diagnostic testing via the dihydrorhodamine assay is widely available, disease recognition can be challenging due to the broad range of initial clinical presentations. Preventative antimicrobial therapy is the backbone of management, while treatment of inflammatory disease remains a challenge. Definitive therapy via hematopoietic stem cell transplantation is increasingly favored for resolution of long-term disease risks, while gene therapy remains a promising but investigational treatment. Here, we present our consensus approach to diagnosis and management of CGD.

This paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles. With the development of precision agriculture, traditional crop disease monitoring methods have become difficult to meet the demands of large-scale, high-efficiency and early warning. The article first constructs a multispectral image dataset including visible light, near-infrared and red-edge bands, covering common types of crop diseases. Subsequently, an improved deep learning network architecture was proposed. The attention mechanism was adopted to enhance the model's ability to extract disease features, and a multi-scale feature fusion strategy was introduced to handle disease spots of different sizes. The research designed a data augmentation method based on spectral-spatial joint optimization, which effectively solved the problem of unbalanced samples of crop diseases. To improve positioning accuracy, this paper proposes a disease area positioning algorithm combined with geographic information system, achieving centimeter-level positioning accuracy. The experimental results show that the proposed method improves the accuracy of disease identification by 15.3% compared with the traditional methods, reduces the positioning error to an average of 3.2 centimeters, and can maintain high stability in complex field environments. In addition, this paper has established a complete technical system covering data collection, disease identification and information visualization, and has conducted application verification on crops such as wheat and rice. It has been confirmed that this method can effectively support precise pesticide application decisions in agricultural production and has significant economic and ecological benefits

Tomato cultivation is a vital agricultural commodity in Indonesia, yet leaf diseases continue to pose a serious threat to crop quality and yield. While deep learning–based classifiers have achieved high accuracy in laboratory settings, most existing tomato leaf disease detection models rely on computationally intensive architectures that limit their practical deployment on resource-constrained devices commonly used in agricultural environments. To address this gap, this study proposes a lightweight Convolutional Neural Network (CNN) based on the MobileNetV2 architecture, explicitly combined with systematic hyperparameter optimization, for tomato leaf disease classification. Using 14,529 images from the PlantVillage dataset, the research involves image preprocessing, data augmentation, and structured tuning to improve performance while maintaining computational efficiency. The optimized model achieves an accuracy of 81% using a learning rate of 0.001, 128 units, a dropout rate of 0.3, and an alpha value of 0.35. Although this accuracy is slightly lower than that reported by heavyweight CNN models, it is competitive for lightweight architectures and represents a favorable trade-off between classification performance and computational efficiency. Despite its compact design, the model demonstrates reliable disease recognition and suitability for deployment on devices with limited resources. Furthermore, the trained model was implemented in a desktop-based application as a proof-of-concept system, demonstrating scalability and potential adaptation to mobile or edge-based agricultural decision-support platforms. This study highlights the novelty of integrating lightweight CNN design with systematic hyperparameter optimization and demonstrates that optimized lightweight deep learning models can provide effective, efficient, and deployable solutions for real-world precision agriculture applications.

Neurofibromatosis type 1 (NF1) is associated with various pancreatic tumors. Pancreatic tumors in NF1 are most frequently neuroendocrine tumors (NETs) and less commonly. To date, no cases of NF1 combined with solid pseudopapillary neoplasm of the pancreas (SPN) have been reported. This case report provides the first detailed description of the diagnosis and treatment process of a patient with NF1 combined with pancreatic SPN. By presenting this case, we aim to offer clinicians practical experience and valuable insights to enhance the medical community's recognition and understanding of this rare disease. This paper reports a 13-year-old female patient who was diagnosed with NF1 by genetic testing. The pancreatic head mass was found via whole-body MRI screening and was diagnosed as SPN via endoscopic ultrasound-guided fine needle aspiration biopsy. For this patient, our center 's plan is : the patient 's unresectable facial plexiform neurofibroma is treated with the targeted drug Smetitinib, and the patient 's pancreatic head tumor is treated with laparoscopic pancreatic tumor resection. The postoperative recovery was smooth, and there was no disease progression during the 6-month follow-up period. Combined with this case report, we also reviewed the literature concerning NF1 combined with pancreatic tumors. To our knowledge, this represents the first reported case of neurofibromatosis type 1 (NF1) associated with a solid pseudopapillary neoplasm of the pancreas (SPN). We also reviewed the characteristics of pancreatic tumors in NF1 patients reported in the literature. Combined with this case, we propose that pancreatic endocrine tumors may be one of the particularly rare clinical manifestations in patients with NF1, although further data analysis and molecular mechanism verification are needed. Nevertheless, this case provides valuable inspiration for the diagnosis and treatment of similar cases.

Rare diseases affect over 300 million people worldwide, yet patients often endure years-long diagnostic delays that limit timely intervention and trial opportunities. Computational rare disease recognition (RDR) remains constrained by knowledge resources that are often incomplete, heterogeneous, and dependent on extensive multi-disciplinary expert curation that cannot scale. Large language models (LLMs) applied directly for end-to-end diagnosis or disease discrimination face similar knowledge bottlenecks while also raising concerns around cost, reproducibility, and data governance. Here, we introduce GEN-KnowRD, a knowledge-layer-first framework that leverages LLMs to generate schema-guided rare disease profiles, systematically assesses their quality, and constructs a computable knowledge base (PheMAP-RD) for local deployment. GEN-KnowRD integrates this knowledge into lightweight inference pipelines for both general-purpose disease screening and specialized early discrimination from longitudinal electronic health records. In tests using six public benchmarks for general-purpose screening (9,290 patients spanning 798 rare diseases), GEN-KnowRD substantially improved disease ranking versus 1) a state-of-the-art, HPO-centered diagnostic framework (up to 345.8% improvement in top-1 success), 2) advanced end-to-end LLM reasoning (up to 129.1% improvement), and 3) a variant of GEN-KnowRD instantiated with expert-curated knowledge rather than LLM-generated profiles. In two real-world cohorts for early diagnosis of idiopathic pulmonary fibrosis (511 patients) as a use case, GEN-KnowRD also demonstrated robust discrimination performance gains, supporting effective RDR during the pre-diagnostic window. These findings demonstrate that repositioning LLMs from diagnostic reasoning to the knowledge layer-decoupling knowledge construction from patient-level inference-yields stronger RDR, while providing scalable, continuously updatable, and reusable infrastructure for diagnosis, screening, and clinical research across the rare disease landscape.

Diagnostics of respiratory disorders greatly benefit from medical imaging, especially X-ray imaging, which offers important information about the anatomical anomalies of the lungs. As we delve deeper into the field of lung illness recognition, it becomes clear that utilizing multiscale Deep Convolutional Neural Network (DCNN) techniques has the potential to transform the detection of pneumonia and tuberculosis from X-ray images. In this paper, we will classify images through a process that requires only chest X-ray images. We have proposed a deep learning (DL)-based algorithm for lung disease detection, which we term the Convolutional Recurrent Network (C-RNet). In our research, we classify CXR images into four categories according to the publicly available dataset. Our proposed model can calculate the dependency and continuity properties of the intermediate layer output very precisely. At the same time, the features of these intermediate layers can be combined with the final fully-connected network for classification prediction, resulting in better classification accuracy. We have explored the potential of combining CNN and RNN with XAI to identify lung diseases from chest radiographs to improve diagnostic accuracy compared to traditional single-scale methods. Upon comparing our suggested model with the current models, we discovered that, with an accuracy of 93.73%, F1-score of 94.6%, total floating point operation per second (FLOPS) count of 637,222,592, total parameter count of 1,901,764 and model size of 7.25MB on the full dataset, our suggested model achieved the best accuracy of all the architectures we compared. Moreover, our suggested model, C-RNet, was observed to accurately categorize and detect the regions of disease through approaches such as Grad-CAM.

Peptide diagnostics serve an important role for initial disease recognition, pharmaceutical evaluation, and environmental monitoring. Conventional methods for diagnosis typically involve labelling concepts that reduce sensitivity, increase test complexities, and limitations in real-time analysis. In the proposed work, we have introduced Corner-Triangle, Floral Geometry Refractive Index Biosensor (CTFGRIB) for monitoring peptide concentrations by names, Glycylleucine (Gly-Leu), Triglycine (Tri), Glycine (Gly), Glycytyrosine (Gly-Tyr), Diglycine (Dig), and Glycylaspartate (Gly-Asp) with a combination of machine learning evaluation. A periodical arrangement of corner-triangle patterns surrounded by a floral layout, as a distinctive geometry, provides a number of synergistic benefits that directly boost biosensing capabilities. The parametric assessments involve outstanding performance parameters with the favourable values of sensitivity being 1023.25 nm/RIU, and favourable values of detection limit are 0.0733 RIU for the Gly-Leu peptide cell. The favourable quality factor value of 24.0368, and the figure of merit value of 10.9508 RIU-1 have been achieved for the Gly-Leu peptide cell. The favourable transmittance rate of 33.6%, 33.3%, 33.0%, 33.0%, 32.9%, and 32.9% have been observed for Gly-Leu, Tri, Gly, Gly Tyr, Dig, and Gly-Asp, respectively. The optimised R squared value of 0.997604 and the MSE value of 9.607930 × 10-05 have been achieved from the machine learning method.

Multi-Society Expert Panel Consensus Guidance Regarding Clinical Assessment and Clinical Trial Endpoints in Adults With Alpha-1 Antitrypsin Deficiency-Associated Liver Disease.

Abstract In cotton leaf disease detection, low-light conditions can cause the loss of fine-grained lesion details, while lesions are often small and irregular in shape; these factors jointly aggravate missed detections and false detections. To address these issues, we propose a novel cotton leaf disease detection model, ADD-YOLO. A cotton leaf disease dataset was constructed, and YOLO11n was adopted as the baseline network. First, an Adaptive Lighten Cross-Attention (ALCA) lightweight cross-attention module was designed to enhance lesion recognition under low-light conditions. Second, a Deformable Attention Graph Enhancement Module (DAGEM) was introduced to strengthen feature representation for small lesions. Finally, Depthwise Separable Convolution (DWConv) was incorporated into the backbone to reduce computational overhead. Experimental results show that, compared with the baseline model, ADD-YOLO improves Precision, Recall, mAP@0.5, and mAP@[0.5:0.95] by 6.0%, 3.1%, 4.4%, and 5.2%, respectively, while reducing FLOPs and parameter counts by 9.5% and 15.9%, respectively. The model achieves 198.6 FPS, meeting real-time requirements for field deployment. Moreover, generalization experiments on public datasets demonstrate the robustness and transferability of the proposed method, indicating its effectiveness for cotton leaf disease detection tasks.

Epidemiology of eosinophilic granulomatosis with polyangiitis in northern Spain: A population-based study (2000-2024) and literature review.

Plant diseases remain a major threat to global food production, causing significant yield losses and economic impact worldwide. Early and precise disease detection is crucial for effective crop management, yet conventional diagnostic approaches are often slow, labor-intensive, and rely on specialized expertise that may not be widely accessible. Recent advances in artificial intelligence (AI), particularly deep learning-based image analysis, offer scalable and automated solutions for plant disease recognition. This review critically examines forty-one peer-reviewed studies published between 2008 and 2025, selected following PRISMA guidelines from major scientific databases. We summarize key methodological developments, including convolutional neural networks, vision transformers, transfer and few-shot learning, and multimodal sensing approaches, highlighting their reported performance and limitations. Although many models achieve high accuracy in controlled datasets, their effectiveness often decreases under real-field conditions due to environmental variability, limited training data, and practical deployment constraints. We discuss existing challenges and propose future research directions, emphasizing improved robustness in field environments, development of lightweight and explainable models suitable for edge deployment, and integration with precision agriculture systems. This review aims to guide the design of reliable, practical, and scalable AI-driven plant disease detection strategies.

<p>Vascular streak dieback (VSD) is one of the most destructive diseases affecting cocoa production in Southeast Asia, including Indonesia, where early visual symptoms are often subtle and spatially distributed across the leaf sur face. Conventional image-based disease recognition approaches, particularly those relying solely on convolutional neural networks (CNNs), are effective in extracting local visual features but remain limited in modeling long-range structural relationships such as venation disruption and lesion spread. To ad dress this limitation, this study investigates a hybrid CNN-graph neural network (CNN-GNN) framework for automated VSD recognition from cocoa leaf im ages. A primary dataset consisting of 1,000 RGB images collected directly from cocoa plantations in Jember Regency was used to reflect realistic field condi tions. In the proposed approach, CNNs are employedfor local feature extraction, while graph-based representations enable GNNs to capture global relational pat terns through message passing. Experimental results demonstrate stable learning behavior and strong classification performance, achieving a maximum validation accuracy of 95.2% and an area under the curve (AUC) of approximately 0.94. Further analysis shows balanced precision and recall across classes, indicating reliable discrimination between Sehat and VSD-infected leaves. These findings suggest that hybrid CNN-GNN modeling provides an effective strategy for cap turing both local and distributed structural characteristics of VSD symptoms and highlights the potential of graph-based reasoning to complement convolutional feature learning in plant disease diagnostics.</p>