Search Results (65)

Therapeutic options in renal cell carcinoma (RCC) have expanded rapidly, including adjuvant pembrolizumab, HIF-2α-directed therapy, and multiple effective first-line combinations for metastatic clear-cell RCC (ccRCC), yet treatment selection remains largely clinicopathologic. This review evaluates biomarkers at three clinical decision points: characterization of an indeterminate renal mass; recurrence-risk assessment and adjuvant treatment selection after nephrectomy; and first-line regimen selection in metastatic ccRCC. DNA-methylation classifiers and carbonic anhydrase IX-targeted [⁸⁹Zr]Zr-girentuximab PET/CT can improve characterization of selected renal tumors, but neither replaces histopathology in routine practice. After nephrectomy, elevated plasma kidney injury molecule-1 (KIM-1) and detectable circulating tumor DNA (ctDNA) identify patients at higher risk of recurrence; however, low tumor shedding limits ctDNA sensitivity, so a negative result does not exclude molecular residual disease or justify adjuvant de-escalation; neither biomarker is validated to direct surveillance, adjuvant therapy, or treatment escalation. In metastatic ccRCC, PD-L1 expression, tumor mutational burden, and individual genomic alterations do not reliably distinguish patients who should receive dual immune-checkpoint blockade from those who should receive an immune-checkpoint inhibitor plus a VEGFR tyrosine kinase inhibitor. Transcriptomic states, myeloid composition, and spatial immune organization provide more detailed treatment-relevant biology, but no prospective comparative trial has shown that biomarker-guided regimen selection improves outcomes. Clinical implementation will require standardized assays, independent multicenter validation, and prospective trials powered to test biomarker-by-treatment interactions. Full article

Deepfake videos currently facilitate extensive financial deception, political misinformation, and unauthorized imagery, with anticipated U.S. losses from deepfake-related fraud surpassing $40 billion by 2027; human evaluators accurately recognize high-quality forgeries merely 25% of the time, highlighting the pressing necessity for automated, widely applicable detection mechanisms. Adaptive-depth architectures offer an intriguing alternative to fixed-depth deepfake detectors when the optimal model capacity is indeterminate in advance. This study presents the Adaptive Entropy-Guided ICA State-Space Model Forgery Detector (AEGIS-FD), a deepfake detection framework at the video level that progressively increases its depth from one to eight layers via an entropy-driven growth mechanism, attaining peak validation performance at a depth of six. The design incorporates a three-dimensional spatiotemporal stem, Sinkhorn-normalized manifold-constrained hyper-coupling (mHC) layers for balanced temporal integration, a selected state-space temporal block for sequence depiction, and FastICA-based initialization for newly introduced layers. Evaluated using Celeb-DF v2, AEGIS-FD achieves a test AUC of 0.9600 and a validation AUC of 0.9607, above the performance of a single-layer Mamba SSM baseline (AUC = 0.8355). In comparison to a fixed-depth-6 baseline, the model demonstrates consistent improvements over five random seeds (96.12 ± 0.28 vs. 94.84 ± 0.44 AUC; p = 0.0022), suggesting that adaptive development provides advantages that exceed mere depth. In a zero-shot cross-dataset evaluation—trained on Celeb-DF v2 and assessed without fine-tuning on a FaceForensics++ (FF++) C23 subset comprising 1000 original and 1000 FaceSwap videos—AEGIS-FD achieves an AUC of 89.2 compared to 88.4 for the corresponding baseline (+0.8 AUC), providing initial proof of cross-dataset transferability. These findings suggest that adaptive-depth growth presents a viable approach for detecting deepfakes at the video level, while further validation across various datasets and modification techniques is essential. Full article

Advanced analysis has been shown to improve material efficiency in statically indeterminate steel-framed structures compared with member-based linear-elastic design methods. However, limited research has investigated its applicability to geometrically nonlinear steel structures where residual stresses are induced by the bending process. In this study, the material optimization potential of advanced analysis has been quantified for two arch-based structures by comparing the volume of steel required to satisfy the criteria of both system and member-based analysis methods in accordance with AS 4100:2020. The two structures were assessed using the finite element analysis software Strand7 (R3.1.6) and subjected to combined gravity and wind loading in alignment with the serviceability and ultimate limit states specified in AS 1170.0:2002. System behaviour was analysed through the arc-length plastic zone method. The results indicate that in one of the arch-based structures, advanced analysis improves material utilization by 8.1%. Provided that future research both validates the use of the reduced stiffness method for the treatment of initial geometric imperfections and verifies system reliability factors for structures with curved geometries, advanced analysis presents a practical design method for this structure. A comparison of the two case studies found that advanced analysis can improve material efficiency when linear-elastic failure is governed by ultimate limit state criteria. It is therefore evident that the material optimization findings of this research cannot be generalized to all arch-based structures, as they are contingent upon the geometry of the model analysed, the loading scenarios considered, and the deflection limits adopted. Full article

Thyroid cancer comprises a biologically diverse group of tumors initiated by a limited number of recurrent driver alterations, with additional molecular events promoting dedifferentiation, therapeutic resistance, and aggressive clinical behavior. Advances in tumor sequencing have clarified the molecular architecture of papillary, follicular, oncocytic, poorly differentiated, anaplastic, and medullary thyroid carcinomas, and have already changed management in selected settings. Molecular testing improves diagnostic refinement and risk assessment in cytologically indeterminate thyroid nodules, while alterations involving BRAF, RET, and NTRK can guide targeted therapy in advanced disease. Beyond DNA, transcriptomic, proteomic, epigenetic, metabolomic, immune, spatial, and liquid-biopsy approaches offer functional insight into differentiation state, treatment sensitivity, and resistance, although most remain investigational. Their clinical value depends not only on biological plausibility, but on reproducibility, incremental value beyond established clinicopathological variables, and the ability to alter patient management. Computational tools may further support integration of molecular and clinical data, but their usefulness likewise depends on calibration, external validation, interpretability, and demonstration of decision impact. This review synthesizes the genomic and multi-omics determinants of thyroid cancer management across diagnosis, risk stratification, treatment selection, resistance monitoring, and follow-up, and discusses the practical barriers that continue to limit routine implementation, including assay standardization, cost, access, and real-world feasibility. Progress in precision thyroid oncology will depend on robust validation of emerging biomarkers and clear evidence that they improve patient outcomes. Full article

Clonal hematopoiesis of indeterminate potential (CHIP) is an age-associated premalignant state defined by somatic mutations in hematopoietic cells at a variant allele frequency (VAF) ≥2% in the absence of overt hematologic malignancy. Among CHIP-associated mutations, JAK2 V617F is of particular interest because it occupies a dual biological and clinical role: it is both the principal driver of BCR::ABL1-negative myeloproliferative neoplasms (MPNs) and a clonal hematopoiesis variant conferring approximately 12-fold cardiovascular risk in selected cohorts, exceeding that reported for common DTA CHIP variants. Quantitative assessment of JAK2 V617F allele burden is therefore clinically relevant across the full disease continuum—from subclinical clonal expansion to MPN diagnosis, prognostic stratification, and therapeutic monitoring—as VAF thresholds correlate with disease phenotype, thrombotic risk, molecular response, and fibrotic progression. Digital PCR platforms, including droplet digital PCR (ddPCR) and chip-based digital PCR, have emerged as highly sensitive and reproducible methods for absolute JAK2 V617F quantification without the need for standard curves, with reported limits of detection as low as 0.01%. In this review, we synthesize current evidence on the molecular biology of JAK2-driven clonal hematopoiesis, the clinical significance of allele burden quantification, and the analytical performance of digital PCR compared with quantitative PCR and next-generation sequencing. We interpret these findings through a systems biology lens that draws together JAK-STAT signaling networks and thrombo-inflammatory pathways including inflammasome-dependent IL-1 signaling, clonal architecture, and bone marrow microenvironmental remodeling. We also discuss published quantitative models in which JAK2 V617F allele burden is treated as a dynamic state variable, while emphasizing that the present review offers a conceptual synthesis rather than a new computational model. We provide a structured comparative synthesis of published digital PCR analytical performance data, a stage-adapted proposal for clinical monitoring, and schematic models to guide future implementation. Overall, the evidence supports digital PCR as a precision tool for monitoring JAK2 V617F clonal dynamics across the CHIP–MPN spectrum, and points to several priorities: assay standardization, harmonized reporting, external quality assessment, and prospective clinical validation. Full article

The novel complex [Fe^III(HL)(L)]·2dmf (1) was successfully synthesized by reacting iron chloride with dimethylformamide solution of the Schiff base ligand (H₂L). The ligand was prepared in situ through the condensation of 5-bromo-salicylaldehyde and benzhydrazide. The compound 1 was investigated by single crystal X-ray diffraction, IR spectroscopy, CASSCF/NEVPT2 and DFT theoretical calculations, revealing the high-spin (S = 5/2) state of Fe(III). The Hirshfeld Atom Refinement (HAR) of the crystal structure confirmed the sextet ground spin state of 1 through the comparison of R-factors and Hirshfeld deformation maps of the structures refined assuming high, indeterminate (S = 3/2) and low (S = 1/2) ground spin states. A series of basis sets and DFT functionals was screened, indicating that in most cases the combinations allow the correct determination of the ground state of 1. The def2-SVP double-zeta basis set combined with the GGA (Generalized Gradient Approximation) functional such as PBE or BP86 was found to be a sufficiently precise low-cost computational level. While further investigation is necessary, the results of the work tentatively suggest that the HAR could be successfully used for determination of the ground spin state of Fe(III) complexes even in the case of regular quality X-ray structures. Full article

Burning Mouth Syndrome (BMS) is a chronic orofacial pain condition characterized by persistent burning sensations in the absence of consistently identifiable pathology. This mismatch between severe symptoms and inconclusive findings challenges conventional diagnostic reasoning, which presumes that subjective complaints reflect underlying biological lesions—leading to repeated investigations, diagnostic delay, and uncertainty about symptom legitimacy. This paper uses BMS as a paradigmatic case to question a core assumption of clinical reasoning: that objective pathology constitutes primary evidence. In this Perspective, drawing on phenomenology and medical anthropology, we argue that when pathology is indeterminate, illness—the lived experience of suffering—must be treated as primary clinical evidence rather than as a secondary expression of disease. We propose a bidirectional conceptual framework in which physiological processes, psychological states, and lived experience interact dynamically, with symptoms actively participating in the maintenance of the condition. This framework explains persistent diagnostic uncertainty, heterogeneous treatment responses, and the clinical significance of validation and meaning-making. Reframing BMS in this way supports earlier recognition, reduces reliance on exclusion-based strategies, and justifies multimodal interventions addressing both biological and experiential dimensions of pain. More broadly, it suggests that clinical reasoning must expand to incorporate lived experience as indispensable evidence wherever pathology is indeterminate. Full article

Background: Preoperative screening for methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) is intended to identify patients at increased risk of surgical site infection and guide decolonization and perioperative antimicrobial prophylaxis. However, many molecular assays reduce this decision to a binary positive/negative result, potentially obscuring clinically important distinctions in organism identity, methicillin resistance attribution, and mupirocin resistance risk. Methods: This structured narrative review organized direct perioperative evidence and indirect mechanistic, implementation, and economic evidence around one question: how MRSA/MSSA screening can move from organism detection to actionable molecular triage. Results: Useful preoperative reporting depends on assigning resistance markers to the correct organism. The proposed multi-target NAAT framework organizes concordant and discordant molecular patterns into provisional reportable categories, including MSSA, MRSA, methicillin-resistant non-aureus Staphylococcus/CoNS, mixed populations, SCCmec dropout patterns, mupirocin resistance marker states, and invalid or indeterminate results. No externally validated composite score, universal molecular cutoff, or prospectively validated target-to-action decision rule currently links all of these categories to specific perioperative actions. Conclusions: Preoperative MRSA/MSSA screening may benefit from moving beyond binary reporting, but the framework presented here is a development-stage rule set rather than a validated clinical decision instrument. Assay-specific analytical thresholds, locked target combination rules, and prospective clinical and implementation validation are required before the framework can be used to assign patients reproducibly to management pathways. Until such validation is completed, the proposed categories should be interpreted as a testable reporting and validation architecture rather than as universal prophylaxis or decolonization instructions. Full article

Coffee is a perennial plant that exhibits asynchronous flowering while maintaining concomitant vegetative growth. This growth dichotomy affects fruit development and maturation time. To better understand flowering in coffee, we characterized a phosphatidylethanolamine binding protein (PEBP) homolog with high similarity to Arabidopsis thaliana TERMINAL FLOWER1 (TFL1). The interaction of TFL1 with floral regulator bZIP transcription factor, FD, forms a floral repressor complex that maintains inflorescence meristems in an indeterminate state. Arabidopsis TFL1 is expressed only in shoot apical meristems, yet CaTFL1a transcripts were detected exclusively in coffee leaves. Moreover, leaf-derived CaTFL1a transcript retains an intron, which has not been reported for TFL1 orthologs in other species. The ectopic expression of CaTFL1a in Arabidopsis causes extreme late flowering or prevents flowering altogether. Notably, the most severe floral repressive activity occurred in transgenic plants that spliced out the extra intron from CaTFL1. Yeast Two-Hybrid assays show that full-length CaTFL1a protein (fl-CaTFL1a) encoded by the fully spliced mRNA interacts with FD and Arabidopsis 14-3-3 protein AtGRF3, whereas truncated protein (tr-CaTFL1a) encoded by transcript that retains an intron does not interact. This evidence suggests that CaTFL1a may affect flowering in coffee by acting as a leaf-derived, long-distance floral repressor whose activity is controlled by alternative splicing. Full article

In electronic training scenarios, the safety risk of a soldering iron cannot be determined by object detection alone, as its state must be further distinguished among hand-held, stand-supported, desk-exposed, and uncertain interactions. To address this problem, this paper proposes RISNet, the Relation-aware Interaction State Network, which establishes a two-stage instance-level interaction understanding framework for soldering iron safety monitoring. In the first stage, YOLO is used to generate candidate instances of soldering irons and related environmental objects, and dual-layer feature fusion is adopted to jointly exploit shallow details and deep semantics. In the second stage, the soldering iron is treated as the interaction subject. The Pointer-Head models associations between the subject and contextual objects, and the State-Head predicts the safety state conditioned on subject-object relational constraints. To reduce false alarms from false detections and weak interactions, RISNet introduces a Quality-Head that estimates the reliability of each interaction conclusion and filters low-quality predictions during inference. The unknown label is used during training as conservative supervision for weak, unreliable, or indeterminate interaction evidence, with semantics close to the no-interaction label in HOI. This paper also constructs the Soldering Iron Safety Interaction Dataset (SISID) to support detection, interaction modeling, and state evaluation of slender metallic tools in training scenarios. On the SISID validation split, RISNet achieves an Overall F1 of 95.38%, an Overall Precision of 96.73%, and an inference speed of 57.1 FPS, satisfying the centralized single-frame polling requirement considered in this work. Full article

This study developed two Causal Graphical Models (CGMs) to analyze the transitions associated with Bacterial Vaginosis (BV) and to identify key bacterial species at each stage. BV results from an imbalance in the vaginal microbiota, whose composition varies among women and across developmental stages. A previous CGM identified influential bacteria but did not address changes between microbiota states. Here, we extend that framework to capture these associations. Path Analysis, a structural equation modeling method based on observed variables that estimates effects through correlations and covariances, was applied to a dataset of 132 pregnant women (4–24 weeks of gestation) from Tabasco, Mexico, previously collected by third parties during healthy pregnancy campaigns and associated with BV diagnosis. Models were validated using statistical metrics and evaluation by a clinical microbiologist. The first model, representing the transition from normal microbiota (BV−) to an indeterminate state (I), identified Megasphaera Type 1 as significant. The second model, from I to bacterial vaginosis-positive (BV+), identified Atopobium vaginae and Bacterial Vaginosis-Associated Bacterium Type 2 as significant contributors. These findings highlight the importance of the intermediate state in dysbiosis progression and support the use of CGMs for studying microbiome dynamics. Full article

Clonal hematopoiesis refers to the clonal expansion of hematopoietic stem and progenitor cells, driven by somatic mutations. Major mutated genes in clonal hematopoiesis include genes involved in epigenetic regulation including DNA methylation and/or chromatin modification (e.g., DNMT3A, TET2, and ASXL1), tumor suppressors (e.g., TP53), signal transduction (e.g., JAK2), and RNA splicing (e.g., SF3B1 and SRSF2). Clonal hematopoiesis includes clonal hematopoiesis of indeterminate potential (CHIP), clonal cytopenia of unknown significance (CCUS), and myelodysplastic syndromes/neoplasms (MDS). CHIP occurs when the frequency of the variant allele equals or exceeds 2% (4% for X-linked genes in males) in the absence of cytopenias. CHIP is common among older persons and is associated with an increased risk of hematologic cancer. CHIP is also associated with an increased risk of atherosclerotic disease including acute myocardial infarction, stroke, cardiac failure, and abdominal aneurysm. Increasing evidence suggests that CHIP is associated with venous thromboembolic disease. Somatic mutations lead to proliferation of hematopoietic progenitor cells and their progeny, resulting in excessive activation of granulocytes and monocytes. It could be postulated that chronic inflammation caused by clonal expansion of myeloid cells carrying mutations in DNMT3A, TET2, and ASXL1 (“DTA”) genes may constitute an independent risk factor in clot formation and endothelial-cell damage. DTA mutations correlate with elevated proinflammatory cytokines such as IL-1β and IL-6 and enhanced activation of inflammasomes. Moreover, JAK2 mutations may have a direct role in the activation of platelets and coagulation. In vivo murine studies have demonstrated that activation of the JAK-STAT signaling pathway promotes neutrophil extracellular trap (NET) formation, contributing to a prothrombotic state. Insights from related clonal disorders such as paroxysmal nocturnal hemoglobinuria and the VEXAS syndrome support the concept that mutation-driven innate immune activation can directly perturb hemostatic balance. This review aims to summarize the association between clonal expansion of hematopoietic cells and thrombotic disease, and highlight how somatic mutations in hematopoietic cells may contribute to vascular disease and thrombogenesis. Full article

Determining the optimal harvest time is among the most critical economic decisions for peanut (Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide. Full article

Circulating tumour DNA (ctDNA) assays are increasingly applied in haematological malignancies for non-invasive genotyping, quantitative response assessment, measurable residual disease (MRD) detection, and relapse surveillance, often complementing bone marrow-based testing and, in selected scenarios, potentially reducing its frequency. Yet, translating ctDNA results into comparable clinical decisions across laboratories, platforms, and time remains challenging because ctDNA measurements are influenced by the definition of the measurand (for example, variant allele fraction versus mutant molecules per mL), pre-analytical variables, end-to-end workflow losses, and lineage-specific confounders such as clonal haematopoiesis of indeterminate potential (CHIP), therapy-related clonal haematopoiesis, and compartmental disease (marrow, plasma, cerebrospinal fluid, extramedullary sites). This review proposes a harmonisation framework for haematological ctDNA based on three linked concepts—metrological traceability, which connects reported values to reference systems with stated uncertainty, commutability, which ensures that reference materials behave like patient specimens across diverse workflows and fit-for-purpose reference materials that support calibration, and quality control, external quality assessment, and cut-off setting for intended uses such as early molecular response in large B-cell lymphoma, molecular MRD in acute myeloid leukaemia, and deep response monitoring in multiple myeloma. This framework is accompanied by harmonised CHIP-aware reporting rules for settings without matched cellular DNA and practical change-control/bridging strategies to preserve clinical decision thresholds when platforms or bioinformatic pipelines evolve. Full article

Background and Aim: The management of patients with chronic Hepatitis B Virus (HBV) HBeAg-negative infection in the indeterminate-grey zone (GZ) remains debatable. We conducted a systematic review and meta-analysis to compare these patients with those with chronic HBV HBeAg-negative infection (inactive carriers; IC/HBeAg-negative), regarding the severity of liver inflammation and fibrosis and the risk of developing hepatocellular carcinoma (HCC). Methods: A literature search was conducted to identify all published studies comparing GZ/HBeAg-negative patients with IC/HBeAg-negative patients. Data on the severity of liver inflammation and fibrosis were extracted, and pooled relative risks (RR) and 95% confidence intervals (CI) were calculated. The risk of HCC was estimated by pooled hazard ratios (HR). A random-effects meta-analysis model was performed using R v4.1.2. Results: Eleven studies were finally included. GZ/HBeAg-negative patients had significantly higher mean HBV-DNA and alanine transferase (ALT) levels, compared to their IC/HBeAg-negative counterparts (4089.9 ± 4840.5 vs. 215.9 ± 318.1 IU/mL; p = 0.0004, and 39.6 ± 26.9 IU/L and 20.1 ± 7.6 IU/L/; p < 0.0001, respectively). GZ/HBeAg-negative patients showed a trend towards a higher risk of significant liver inflammation (RR: 5.11; 95%CI: 0.68–38.33; p = 0.1), F2/F3 fibrosis (RR: 2.13; 95%CI: 0.89–5.1; p = 0.09), and cirrhosis (RR: 14.39; 95%CI: 0.5–417.08; p = 0.12), respectively, compared to IC/HBeAg-negative patients. After a median follow-up of 6.2 years, the former group demonstrated a significantly higher risk of developing HCC (HR: 4.7; 95% CI: 1.4–15.6; p < 0.0001). Conclusions: GZ/HBeAg-negative patients have a higher risk of developing HCC compared to IC/HBeAg-negative patients, which raises concerns about the potential need to initiate treatment in this patient group. Full article