Search Results (1,442)

Richter transformation (RT) represents a rare but highly lethal evolution of chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL), most frequently manifesting as diffuse large B-cell lymphoma (DLBCL). Despite therapeutic advances in CLL, DLBCL-RT remains characterized by rapid progression, profound treatment refractoriness, and short survival with conventional chemoimmunotherapy, underscoring the need for a refined biological and therapeutic framework. A defining feature of RT is clonal relatedness: most cases arise through linear or branched evolution of the antecedent CLL clone and carry an inferior prognosis compared with clonally unrelated cases that resemble de novo DLBCL. Recent multi-omic data further indicate that clonally related RT commonly originates from minute, transformation-primed subclones detectable years before clinical emergence, shifting RT from a late stochastic event to an early-established evolutionary trajectory. At transformation, recurrent genetic lesions of TP53, CDKN2A/B, NOTCH1, and MYC cooperate with B-cell receptor-associated programs, epigenetic reconfiguration, and metabolic rewiring toward OXPHOS- and mTOR-driven states, collectively promoting genomic instability and aggressive growth. In parallel, RT develops within a profoundly immunosuppressive microenvironment marked by PD-1-expressing malignant B cells, PD-L1-rich myeloid niches, exhausted T cells, expanded regulatory T cells, and M2-skewed macrophages interconnected by redundant checkpoint and cytokine networks. Therapeutic strategies are rapidly evolving, including pathway inhibitors, immune checkpoint blockade, T-cell-engaging bispecific antibodies, CAR-T therapies, and antibody–drug conjugates. This review integrates current insights into RT pathogenesis, immune escape, and emerging therapies, highlighting opportunities for biomarker-driven patient stratification, rational combinations, and earlier interception of transformation-prone disease. Full article

AI-driven imaging is becoming central to crop monitoring, with proximal and unmanned aerial vehicle (UAV) platforms now routinely used for disease and stress detection, yield estimation, canopy structure, and fruit counting. Yet, as these models move from plots to farms, the main bottleneck is no longer raw accuracy but robustness under distribution shift. Systems trained in one field, season, cultivar, or sensor often fail when the scene, sensor, protocol, or timing changes in realistic ways. This review synthesizes recent advances on robustness and transferability in proximal and UAV imaging, drawing on a corpus of 42 core studies across field crops, orchards, greenhouse environments, and multi-platform phenotyping. Shift types are organized into four axes, namely scene, sensor, protocol, and time. The article also maps the empirical evidence on when RGB imaging alone is sufficient and when multispectral, hyperspectral, or thermal modalities can potentially improve robustness. This serves as a basis to synthesize acquisition and evaluation practices that often matter more than architectural tweaks, which include phenology-aware flight planning, radiometric standardization, metadata logging, and leave-one-field/season-out splits. Adaptation options are consolidated into a practical symptom/remedy roadmap, ranging from lightweight normalization and small target-set fine-tuning to feature alignment, unsupervised domain adaptation, style translation, and test-time updates. Finally, a benchmark and dataset agenda are outlined with emphasis on object-oriented splits, cross-sensor and cross-scale collections, and longitudinal datasets where the same fields are followed across seasons under different management regimes. The goal is to outline practices and evaluation protocols that support progress toward deployable and auditable systems, noting that such claims require standardized out-of-distribution testing and transparent reporting as emphasized in the benchmark specification and experiment suite proposed here. Full article

Objective: To develop a superoxide dismutase (SOD) fluorescent detection probe based on Phycoerythrin (PE) from Porphyridium cruentum for real-time monitoring of SOD activity, a core biomarker of oxidative stress, in a nonalcoholic fatty liver disease (NAFLD) model, and to explore the regulatory effect of astaxanthin. Methods: Phycoerythrin and SOD were covalently coupled using the heterobifunctional cross-linker N-Succinimidyl 3-(2-pyridyldithio) propionate (SPDP), and the probe concentration and incubation time were optimized. A NAFLD model was established in HepG2 cells induced by free fatty acids (FFAs). The fluorescence intensity of the probe was detected by flow cytometry, and the intervention effect of astaxanthin was evaluated by measuring triglyceride (TG)/total cholesterol (TC) contents and SOD activity. Results: The optimal conditions for the Phycoerythrin-SOD probe were determined. Astaxanthin at 20 μM significantly reduced FFA-induced TG (56.8%) and TC (63.6%) contents and restored SOD activity to 60% of that in the control group. Conclusion: The Phycoerythrin-SOD probe serves as an efficient tool for dynamic monitoring of SOD activity in NAFLD. Astaxanthin alleviates liver injury by multi-target regulation of lipid metabolism and antioxidant pathways. Full article

Clear-cell renal cell carcinoma (ccRCC) accounts for the majority of kidney cancer diagnoses and exhibits widely variable clinical behaviour. The dataset described here was generated to support the discovery of robust biomarkers of tumour cell-cycle arrest and to inform the risk-stratified management of ccRCC. We assembled four independent cohorts including 480 patients from the UK arm of the SORCE adjuvant trial, 300 patients from a surgically treated series in Korea, 120 patients from a retrospective Scottish cohort, and a paired primary–metastatic cohort comprising 62 patients. Formalin-fixed paraffin-embedded nephrectomy specimens were processed for routine hematoxylin and eosin (H&E) histology, and for multiplex immunofluorescence (mIF). The mIF panels detect the cyclin-dependent kinase inhibitor p21^CDKN1a, the DNA replication licencing factor MCM2, endoglin/CD105, Lamin B1 and nuclear DNA (Hoechst). Whole-slide images (WSIs) were acquired at high resolution, and artificial-intelligence pipelines were used to segment nuclei, classify individual cells into arrested phenotypes, and calculate the fraction of cells. Accompanying metadata include demographics, tumour stage, grade, Leibovich score, treatment arm (sorafenib/placebo), relapse events, and disease-free survival. All images and derived tables are released under a CC0 licence via the BioImage Archive, ensuring unrestricted reuse. This multi-cohort dataset provides a rich resource for studying cell-cycle arrest and proliferation markers, training image-analysis algorithms, and developing prognostic signatures in RCC. Full article

Pancreatic cancer (PC) is one of the most lethal malignancies worldwide, characterized by late diagnosis, aggressive progression, and limited responsiveness to current therapeutic strategies. Although extensive genomic analyses have identified key driver protein-coding genes (PCGs), therapeutic approaches targeting individual genes have shown limited clinical benefit. This limitation highlights the molecular complexity of PC, where tumor progression is governed by regulatory networks that extend beyond genetic alterations. Non-coding RNAs (ncRNAs), which constitute nearly 98% of the human genome, have emerged as regulators of gene expression in cancer. Among them, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) regulate oncogenic processes, including aberrant signaling activation, tumor microenvironment remodeling, epithelial–mesenchymal transition, immune evasion, and resistance. Beyond their independent functions, lncRNAs, miRNAs, and mRNAs form an integrated regulatory network known as the lncRNA–miRNA–mRNA TRIAD, enabling control of gene expression. Such network-based regulation provides a framework for multi-target therapeutic strategies. Moreover, the rapid responsiveness and disease-specific expression patterns of ncRNAs suggest strong potential as diagnostic and prognostic biomarkers in PC, where early detection remains challenging. This review summarizes the regulatory roles of PCGs, miRNAs, and lncRNAs in PC and highlights the lncRNA–miRNA–mRNA TRIAD as a framework for understanding gene regulatory networks. Full article

Mouse Mammary Tumor Virus (MMTV) is an established mammary carcinogen in mice, yet the relevance of MMTV-like agents to human breast cancer remains debated. Across cohorts worldwide, PCR-based detection of MMTV-like DNA, in situ RNA localization, and immunohistochemical detection of viral proteins have been reported in a subset of tumors and, in some studies, in pre-invasive lesions; however, results are heterogeneous and vulnerable to methodological confounding, including murine DNA contamination and variable assay design. Here, we synthesize the evidence through a causality-oriented framework that integrates (i) standardized multi-target detection with mandatory contamination controls, (ii) epidemiologic designs that explicitly stratify sporadic versus hereditary/BRCA-driven disease, and (iii) mechanistic endpoints that are demonstrably human-relevant (e.g., in situ viral RNA/protein in tumor cells, integration-site mapping, and functional consequences of viral gene products in human models). Given current evidence, the overall causal plausibility is best considered “possible,” rising to “probable” only for a restricted subset of sporadic tumors, provided that future studies verify bona fide infection in situ using standardized multi-target assays, rigorous murine exclusion controls, and mechanistic evidence linking viral expression and/or integration to tumor cell biology. Without these endpoints, association studies alone are unlikely to resolve causality or enable meaningful clinical translation. Full article

Background: Accurate staging of bladder cancer is critical for guiding clinical management, particularly the distinction between non–muscle-invasive (T1) and muscle-invasive (T2–T4) disease. Although MRI offers superior soft-tissue contrast, image interpretation remains opera-tor-dependent and subject to inter-observer variability. This study proposes an automated deep learning framework for MRI-based bladder cancer staging to support standardized radio-logical interpretation. Methods: A sequential AI-based pipeline was developed, integrating hybrid tumor segmentation using YOLOv11 for lesion detection and DeepLabV3 for boundary refinement, followed by three deep learning classifiers (VGG19, ResNet50, and Vision Transformer) for MRI-based stage prediction. A total of 416 T2-weighted MRI images with radiology-derived stage labels (T1–T4) were included, with data augmentation applied during training. Model performance was evaluated using accuracy, precision, recall, F1-score, and multi-class AUC. Performance un-certainty was characterized using patient-level bootstrap confidence intervals under a fixed training and evaluation pipeline. Results: All evaluated models demonstrated high and broadly comparable discriminative performance for MRI-based bladder cancer staging within the present dataset, with high point estimates of accuracy and AUC, particularly for differentiating non–muscle-invasive from muscle-invasive disease. Calibration analysis characterized the probabilistic behavior of predicted stage probabilities under the current experimental setting. Conclusions: The proposed framework demonstrates the feasibility of automated MRI-based bladder cancer staging derived from radiological reference labels and supports the potential of deep learning for stand-ardizing and reproducing MRI-based staging procedures. Rather than serving as an independent clinical decision-support system, the framework is intended as a methodological and work-flow-oriented tool for automated staging consistency. Further validation using multi-center datasets, patient-level data splitting prior to augmentation, pathology-confirmed reference stand-ards, and explainable AI techniques is required to establish generalizability and clinical relevance. Full article

Background: Accurate detection of cervical lymph node metastases is a critical determinant of prognosis and treatment planning in head and neck squamous cell carcinoma (HNSCC). Although ultrasound-guided fine-needle aspiration biopsy (USgFNAB) is widely used as a minimally invasive diagnostic tool, its sensitivity for detecting occult metastases remains limited. Current preoperative staging modalities are further constrained by operator dependency and suboptimal specificity in early-stage disease. Integration of molecular diagnostics, particularly the analysis of long non-coding RNAs (lncRNAs), represents a promising strategy to enhance diagnostic accuracy. Objective: This review synthesizes the current evidence on lncRNA expression profiles in HNSCC, with an emphasis on their association with lymph node metastasis and potential application in FNAB-derived material for pre-treatment staging. Methods: A structured literature search was conducted, focusing on studies evaluating lncRNA expression profiles in HNSCC and their relevance to lymph node metastasis, with a particular focus on the feasibility of analysis of USgFNAB samples. Results: Multiple lncRNAs, including HOTAIR, MALAT1, UCA1, TUG1, AFAP1-AS1, H19, MEG3, and ADAMTS9-AS2, have been implicated in metastatic progression through their involvement in diverse mechanisms such as epithelial-to-mesenchymal transition, chromatin remodeling, angiogenesis, and pre-metastatic niche formation. Elevated expression of several of these transcripts correlates with adverse clinicopathological features, including advanced tumor stage, extranodal extension, and reduced survival. However, no studies have profiled lncRNA expression in matched primary tumors and metastatic lymph nodes, and transcriptomic analysis of FNAB samples remains largely unexplored in HNSCC. Conclusions: lncRNAs represent promising molecular biomarkers for enhancing the sensitivity and specificity of USgFNAB in detecting occult cervical metastases. Future research should prioritize paired tumor–node lncRNA profiling, validation of FNAB-based molecular assays, and integration of multi-omics data for predictive modeling. Overall, integrating lncRNA analysis into ultrasound-guided fine-needle aspiration biopsy may enhance the detection of occult nodal metastases in head and neck squamous cell carcinoma and support more accurate nodal staging in clinically node-negative patients. Full article

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments. Full article

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

Early and accurate diagnosis is critical for disease surveillance, therapeutic guidance, and relapse monitoring. Sensor arrays have emerged as a multi-analyte detection tool via non-specific interactions to generate unique fingerprint patterns with high levels of selectivity and discrimination. Conjugated polymers (CPs), with their tunable π-conjugated backbones, exceptional light-harvesting capability, and efficient “molecular wire effect,” provide an ideal and versatile material platform for such arrays, enabling significant optical signal amplification and high sensitivity. This review systematically outlines the rational design and functionalization strategies of CPs for constructing high-performance sensor arrays. It delves into the structure–property relationships that govern their sensing performance, covering main-chain engineering, side-chain functionalization, and microenvironmental regulation. Representative applications are discussed, including non-small cell lung cancer, breast cancer, bacterial and viral infections, Alzheimer’s disease, and diabetic nephropathy, highlighting the remarkable diagnostic capabilities achieved through tailored CP materials. Finally, future perspectives are focused on novel material designs and device integration to advance this vibrant field. Full article

The concept of individual cellular intelligence reframes cells as dynamic entities endowed with sensory, reactive, adaptive, and memory-like capabilities, enabling them to navigate lifelong metabolic and extrinsic stressors. A likely vital component of this intelligence system is stress-responsive G protein-coupled receptor (GPCR) networks, interconnected by common signaling adaptors. These stress-regulating networks orchestrate the detection, processing, and experience retention of environmental cues, events, and stressors. These networks, along with other sensory mechanisms such as receptor-mediated signaling and DNA damage detection, allow cells to acknowledge and interpret stressors such as oxidative stress or nutrient scarcity. Reactive responses, including autophagy and apoptosis, mitigate immediate damage, while adaptive strategies, such as metabolic rewiring, receptor expression alteration and epigenetic modifications, enhance long-term survival. Cellular experiences that are effectively translated into ‘memories’, both transient and heritable, likely rely on GPCR-induced epigenetic and mitochondrial adaptations, enabling anticipation of future insults. Dysregulation of these processes and networks can drive pathological states, shaping resilience or susceptibility to chronic diseases like cancer, neurodegeneration, and metabolic disorders. Employing molecular evidence, here, we underscore the presence of an effective cellular intelligence, supported by multi-level sensory GPCR networks. The quality of this intelligence acts as a critical determinant of somatic health and a promising frontier for therapeutic innovation. Future research leveraging single-cell omics and systems biology may unravel the molecular underpinnings of these capabilities, offering new strategies to prevent or reverse stress-induced pathologies. Full article

Background/Objectives: Vestibular schwannoma (VS) is the most common benign tumor in the cerebellopontine angle. In preliminary studies, macrophage infiltration has been suggested to influence disease progression. However, the infiltration of other immune cells in VS remains largely unexplored. The aim of this study was to comprehensively characterize the immune cells in sporadic VS. Methods: Cryosections of five tumor samples from VS patients with different tumor volumes were examined. The abundance of fourteen immune-cell markers, one vascular marker, and two tumor markers were detected using multi-epitope ligand cartography (MELC). This enabled the spatial distribution and colocalization of immune- and tumor cell markers to be examined. Furthermore, using qPCR and bulk RNAseq, the mRNA levels of the immune-cell markers were examined in 204 VS samples of different tumor sizes. Results: VSs with greater tumor volumes showed an increased number of immune cells, more precisely T-helper cells (T_H cells), cytotoxic T cells (T_c cells), CD68⁺, and CD163⁺ macrophages, as well as CD279⁺ (PD-1) and CTLA4⁺ cells (p < 0.05). In addition, an increased number of CD274⁺ (PD-L1) tumor cells were detected in VSs with higher tumor volume (p < 0.05). Conclusions: These results indicate that an increased diversity of immune-cell subtypes influences VS tumor size. Thus, novel diagnostic and therapeutic options could be developed by targeting the tumor-associated immune-cell populations in VSs. Full article

Fungal diseases continue to limit global crop production and drive major economic losses. Conventional diagnostic and control approaches depend on time-consuming culture-based methods and broad-spectrum chemicals, which offer limited precision. Advances in molecular identification have changed this landscape. PCR, qPCR, LAMP, sequencing and portable platforms enable rapid and species-level detection directly from plant tissue. These tools feed into RNA-based control strategies, where knowledge of pathogen genomes and sRNA exchange enables targeted suppression of essential fungal genes. Host-induced and spray-induced gene silencing provide selective control without the long-term environmental costs associated with chemical use. CRISPR/Cas9 based tools now refine both diagnostics and resistance development, and bioinformatics improves target gene selection. Rising integration of artificial intelligence indicates a future in which disease detection, prediction and management connect in near real time. The major challenge lies in limited field validation and the narrow range of fungal species with complete molecular datasets, yet coordinated multi-site trials and expansion of annotated genomic resources can enable wider implementation. The combined use of molecular diagnostics and RNA-based strategies marks a shift from disease reaction to disease prevention and moves crop protection towards a precise, sustainable and responsive management system. This review synthesizes the information related to current molecular identification tools and RNA-based management strategies, and evaluates how their integration supports precise and sustainable approaches for fungal disease control under diverse environmental settings. Full article

Legionella pneumophila (L. pneumophila), the primary causative agent of Legionnaires’ disease, is a waterborne bacterial pathogen that poses significant public health concern. This opportunistic pathogen commonly inhabits both natural and man-made water systems, particularly drinking water distribution systems (DWDSs), where it can proliferate and pose a risk to human health. In this study, we evaluated the potential of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) for rapid and accurate subtyping of L. pneumophila. Our analysis included 70 L. pneumophila strains collected from the Middle East, representing one of the largest and most comprehensive MALDI-TOF MS-based subtyping of strains from this geographically underrepresented region. These strains, representing three Multi-Locus Variable Number Tandem Repeat Analysis (MLVA-8) genotypic groups (GT4, GT6, and GT15), have been extensively characterized in previous studies for their virulence traits, cytotoxicity patterns, and antimicrobial susceptibility profiles. Our findings revealed distinct genotype-associated spectral signatures with 30 discriminatory m/z peaks (p ≤ 0.005). These markers enabled accurate genotype-level classification, achieving over 85% classification accuracy with a Random Forest model and over 71% accuracy using a Decision Tree algorithm. Importantly, the m/z peak at 5358 was uniquely present in the GT15 strains, whereas m/z 5353 was consistently detected in both GT4 and GT6 isolates, demonstrating the potential of specific mass peaks to serve as reliable genotype markers. Furthermore, GT15 strains consistently formed a separate cluster in both Principal Component Analysis (PCA) and hierarchical analyses, whereas GT4 and GT6 exhibited partial overlap, reflecting their exceptionally high genomic similarity. Full article