Search Results (1,635)

(1) Background/Objectives: Blood-based biomarkers have emerged as practical tools for identifying Alzheimer’s disease (AD) pathology in patients with mild cognitive impairment (MCI). Among them, plasma phosphorylated Tau217 (p-Tau217) demonstrates strong associations with cerebral amyloid deposition. In parallel, the easy Z-score Imaging System (eZIS), a quantitative brain perfusion SPECT analysis tool, has been widely used to detect characteristic AD-related hypoperfusion patterns. Although both measures reflect distinct AD processes, the relationship between plasma p-Tau217 and eZIS in MCI remains unclear. (2) Methods: This retrospective study included 62 patients with MCI who underwent plasma p-Tau217 testing and brain perfusion SPECT with eZIS analysis. Associations between plasma p-Tau217 and the three eZIS indices (severity, extent, and ratio) were evaluated. Exploratory subgroup analyses were performed using a previously reported plasma p-Tau217 threshold of 0.63 pg/mL. In addition, a validation sub-cohort of 21 participants who underwent plasma p-Tau217 testing, eZIS, and amyloid PET was analyzed to assess concordance with cerebral amyloid pathology. (3) Results: Among the three eZIS indices, severity demonstrated the highest sensitivity relative to elevated plasma p-Tau217 levels. However, all eZIS indices showed limited discriminative performance. Optimal eZIS cutoff values derived from the present cohort were higher than previously reported thresholds. In the amyloid PET-validated sub-cohort, plasma p-Tau217 demonstrated closer concordance with amyloid positivity than any individual eZIS parameter. The reduced performance of eZIS appeared to be associated with advanced age, substantial vascular burden, white matter lesions, and cerebral atrophy. (4) Conclusions: Plasma p-Tau217 showed a stronger association with cerebral amyloid pathology than eZIS indices in this elderly MCI cohort. Nevertheless, eZIS may provide complementary information regarding downstream neurodegenerative and cerebrovascular processes that are not directly captured by plasma biomarkers. This integrated approach highlights plasma p-Tau217 as a primary screening tool for amyloid pathology to guide disease-modifying therapies (DMTs), alongside eZIS for tracking follow-up mixed co-pathologies. Full article

Background: Sarcopenia, systemic inflammation, and malnutrition are established poor prognostic factors in renal cell carcinoma (RCC). This study investigated the utility of a novel preoperative score combining psoas muscle density (PMD)—an imaging-based indicator of muscle quality—and the albumin–bilirubin (ALBI) grade—a blood-based biomarker of liver reserve and systemic nutritional status—for predicting disease-free survival (DFS) and overall survival (OS) in patients undergoing curative surgery for RCC. Methods: This retrospective observational study included 274 patients with non-metastatic RCC treated with radical or partial nephrectomy. Preoperative computed tomography was utilized to measure PMD, defining “low PMD” as a value below the sex-specific median (males: 47.75 Hounsfield Units [HU]; females: 46.25 HU). “Worsened ALBI” was defined as an ALBI grade ≥ 2. Patients were stratified into three risk categories: Score 0 (both normal, n = 122), Score 1 (either abnormal, n = 114), and Score 2 (both abnormal, n = 38). Results: Kaplan–Meier analysis revealed a highly significant, stepwise decline in both DFS and OS as the risk score increased (log–rank p < 0.001 and p = 0.004, respectively). Multivariate Cox regression identified the combined risk score as a robust, independent prognostic factor for DFS (Score 1: HR 1.93, 95% CI 1.11–3.37, p = 0.020; Score 2: HR 3.58, 95% CI 1.82–7.02, p < 0.001). Furthermore, after adjusting for age and comorbidities, the score remained an independent predictor of poor OS (Score 1: HR 2.35, 95% CI 1.08–5.13, p = 0.032; Score 2: HR 3.01, 95% CI 1.16–7.82, p = 0.024). The combined model synergistically enhanced risk stratification accuracy compared to evaluating either factor independently. Conclusions: The concurrent presence of preoperative low PMD and a worsened ALBI grade is a powerful, independent predictor of poor prognosis in localized RCC. Derived solely from routine preoperative imaging and laboratory tests, this straightforward scoring system effectively captures the structural and immunometabolic dimensions of cancer cachexia and host vulnerability. This tool can significantly aid in personalizing postoperative surveillance strategies and identifying high-risk patients who may warrant closer postoperative surveillance or who might be considered as high-risk candidates for adjuvant therapy discussions. Full article

Metabolic dysfunction-associated steatohepatitis (MASH) is the progressive inflammatory and fibrotic subtype of metabolic dysfunction-associated steatotic liver disease (MASLD), and its treatment landscape is rapidly moving from nonspecific liver fat reduction towards mechanism-based drug positioning. Since 2024, resmetirom and semaglutide have received U.S. Food and Drug Administration accelerated approval for non-cirrhotic MASH with moderate-to-advanced fibrosis, while tirzepatide, survodutide, and fibroblast growth factor 21 analogues have shown biopsy-based phase 2 or 2b efficacy signals. This narrative review organises approved and emerging pharmacotherapies within a pathogenesis-informed three-axis framework: the weight-insulin resistance-substrate load axis, the intrahepatic lipid reprogramming axis, and the inflammation-fibrosis transition and multi-axis integration axis. We further grade evidence maturity from regulatory or phase 3 histological evidence to phase 2 biopsy-based evidence and earlier imaging- or biomarker-based signals. This framework is intended to support phenotype-sensitive treatment positioning rather than a fixed therapeutic sequence. In particular, weight-centred treatment should not be assumed to apply to all patients, including normal-weight or lean MASH. Overall, future MASH therapy will likely depend on matching drug mechanisms, fibrosis stage, cardiometabolic phenotype, and treatment goals. Full article

Endothelial dysfunction is a key mechanism linking obesity, metabolic disturbances, and cardiovascular disease, contributing to the development and progression of atherosclerosis and other vascular complications. This review provides a comprehensive overview of the molecular mechanisms underlying endothelial dysfunction in obesity and discusses current diagnostic approaches and therapeutic strategies aimed at restoring vascular homeostasis. The available evidence indicates that chronic inflammation, oxidative stress, insulin resistance, reduced nitric oxide bioavailability, increased reactive oxygen species production, and dysregulated adipokine signaling play central roles in endothelial impairment. Recent advances in functional vascular assessment, circulating biomarkers, and imaging techniques have improved the early identification of endothelial dysfunction and cardiovascular risk. Current therapeutic strategies include pharmacological agents, such as glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter 2 inhibitors, metformin, and dipeptidyl peptidase-4 inhibitors, together with lifestyle interventions based on healthy dietary patterns and regular aerobic and resistance exercise. These approaches improve glycemic control, reduce inflammation and oxidative stress, enhance endothelial function, and contribute to cardiovascular protection. Overall, the evidence supports an integrated and personalized management strategy targeting both metabolic and vascular abnormalities to reduce cardiovascular risk and improve long-term clinical outcomes in individuals with obesity. Full article

Microvascular invasion (MVI) is a critical prognostic indicator for recurrence and survival in hepatocellular carcinoma (HCC); however, its accurate preoperative assessment remains clinically challenging. Postoperative histopathology is subject to sampling bias and time delays, while traditional imaging techniques lack the molecular specificity required to predict MVI. Liquid biopsy, through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), and extracellular vesicles (EVs), provides a minimally invasive approach for capturing tumor-derived molecular and cellular signals associated with vascular invasion. This narrative review comprehensively summarizes the current evidence linking these four liquid biopsy analyte categories to MVI in HCC, evaluates their integration into multi-omics predictive models, including multi-marker, clinicopathological-integrated, and imaging-integrated strategies, and proposes an evidence-level framework that categorizes blood biomarkers according to the strength of their support for MVI prediction, distinguishing direct histopathological validation from indirect associations with aggressive tumor biology. Key challenges are critically examined, including the variable specificity of individual biomarkers for MVI, the lack of head-to-head comparative studies, the absence of standardized pre-analytical and analytical protocols, and the methodological limitations of current prediction models. As a narrative review, this work does not employ systematic review methodology, and the evidence synthesis should be interpreted accordingly. The review provides a framework for understanding how liquid biopsy-based MVI risk stratification may inform surgical and perioperative decision-making following prospective validation. Full article

Neurodegenerative diseases represent a major cause of disability and death, but early diagnosis, prognosis, and therapeutic monitoring are challenging due to biological heterogeneity and the absence of disease-specific biomarkers. Neurofilament light chain (NfL) is a highly sensitive fluid biomarker of neuroaxonal injury with well-established clinical utility in selected neurological disorders, especially in disease monitoring and prognostic evaluation. However, since NfL is not disease-specific, the interpretation has to be integrated with complementary molecular, imaging, and clinical biomarkers. Recent advances in genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and neuroimaging provide complementary information about the molecular and biological processes underlying neurodegeneration. Artificial intelligence (AI) and machine-learning approaches also allow the integration of these heterogeneous datasets for multimodal biomarker modeling. This review examines the biological and clinical relevance of NfL across major neurodegenerative diseases and critically discusses its combination with multi-omics, neuroimaging, and AI-based approaches. Special emphasis is placed on disease monitoring, prognosis, patient stratification, and therapeutic-response modeling, distinguishing established clinical applications from emerging research directions. The present review also addresses ongoing methodological challenges, including assay standardization, data harmonization, model interpretability, multicenter validation, and clinical translation. Finally, future potential is discussed for NfL-based multimodal biomarker frameworks in precision neurology. Full article

The rapid growth of multimodal neuroimaging research has produced fragmented literature that limits systematic characterization of cross-modal relationships and disease-specific knowledge structures. To address this, we constructed a multimodal neuroimaging knowledge graph from 1838 peer-reviewed studies (2016–2026) spanning fMRI, EEG, fNIRS, and PET, using an LLM-based extraction and retrieval-augmented semantic merging pipeline. The resulting graph comprised 4190 nodes and 7007 edges, exhibiting a scale-free topology with a dominant connected component covering 76.6% of nodes. Alzheimer’s disease, the hippocampus, and fMRI/PET emerged as the most central hubs linking disease, anatomical, and methodological dimensions. Louvain community detection identified 25 functional modules, with seven major communities—centered on Alzheimer’s biomarker integration, molecular/fluid imaging, and psychiatric functional connectivity—forming the field’s core structure. Cross-modal analysis revealed the strongest coupling between fMRI and PET, indicating high methodological convergence. At the disease level, Alzheimer’s disease displayed a mature, hierarchically organized biomarker system, whereas major depressive disorder and chronic pain showed diffuse, less consolidated knowledge structures. These results reveal pronounced disparities across neuroimaging research domains and demonstrate that LLM-augmented knowledge graphs can systematically uncover latent structural organization relevant to multimodal integration and biomarker discovery. Full article

Background: Distinguishing organizing pneumonia from recurrent malignancy in patients undergoing cancer surveillance remains a major clinical challenge. Although inflammatory biomarkers have emerged as potential diagnostic tools, their incremental value beyond imaging-based assessment remains uncertain. Methods: In this retrospective single-center study, we evaluated 170 patients with prior malignancy who underwent surgical assessment for suspicious lung-only pulmonary lesions between 2013 and 2023. Histopathology confirmed organizing pneumonia in 61 patients and malignancy in 109 patients. Preoperative clinical, radiologic, Positron emission tomography/computed tomography (PET/CT), and inflammatory biomarker variables were incorporated into predefined clinical, radiology-PET/CT, biomarker, and integrated models using Least absolute shrinkage and selection operator (LASSO)-penalized logistic regression with internal cross-validation. Results: The radiology/PET model demonstrated excellent discrimination (AUC 0.927), closely approximating the performance of the integrated model (AUC 0.935). The clinical and biomarker-only models showed moderate discrimination (AUC 0.764 and 0.778, respectively). Adding inflammatory biomarkers to imaging-derived features resulted in only a minimal improvement in diagnostic performance (ΔAUC = 0.009, 95% CI −0.019 to 0.037), indicating no statistically significant incremental benefit. Calibration, decision curve, and predictor stability analyses consistently identified radiologic and PET/CT-derived variables as the principal sources of predictive information. Conclusions: In patients with suspicious pulmonary lesions during cancer surveillance, diagnostic discrimination was driven predominantly by imaging-derived features, whereas conventional inflammatory biomarkers provided limited incremental value. These findings support an imaging-centered approach to preoperative risk stratification and multidisciplinary decision-making. Full article

Background: Sleep-dependent glymphatic clearance has emerged as a potential mechanism linking sleep disruption with Alzheimer’s Disease (AD) pathology. However, the relationship between objectively measured sleep and glymphatic function across the AD continuum remains unclear. Methods: Four databases (PubMed, Embase, Cochrane Library, and PsycINFO) were systematically searched for studies assessing objective sleep metrics and glymphatic-related biomarkers or clearance measures in humans across the AD continuum. Following peer review of the search strategy, supplementary searches of PubMed and Embase using expanded glymphatic and sleep electrophysiology terminology were undertaken to maximize sensitivity. The final database searches identified 416 records. After removal of 72 duplicates, 344 records were screened, 64 reports underwent full-text assessment, and four studies met the inclusion criteria. Results: Four studies involving participants across the AD continuum were included. Objective sleep assessment was performed using polysomnography or electroencephalography, while brain clearance was evaluated using direct or surrogate imaging measures including diffusion tensor image analysis along the perivascular space (DTI-ALPS), perivascular space burden, blood oxygen level-dependent–cerebrospinal fluid (BOLD-CSF) coupling, or direct tracer-based clearance imaging. Across studies, better preserved slow-wave sleep, slow-wave activity, and sleep oscillatory coupling were generally associated with more favorable glymphatic function or glymphatic-related biomarkers. Conversely, disrupted sleep architecture, reduced sleep efficiency, and altered sleep oscillatory coupling were associated with impaired glymphatic clearance or glymphatic dysfunction. Conclusions: Current evidence suggests that objectively measured sleep architecture, particularly slow-wave sleep and sleep oscillatory dynamics, may be associated with biomarkers of brain clearance across the AD continuum. However, the available evidence remains preliminary, is predominantly cross-sectional, and relies largely on indirect measures of brain clearance. Larger longitudinal studies incorporating standardized sleep assessment and validated measures of cerebral clearance are required to clarify temporal relationships, establish causality, and determine whether sleep-targeted interventions influence brain clearance or disease progression. Summary of findings: Preliminary evidence suggests that preserved slow-wave sleep and sleep oscillatory activity are associated with more favorable biomarkers of brain clearance, whereas disrupted sleep architecture is associated with less favorable clearance-related measures. Full article

Background: Optical coherence tomography angiography (OCTA) provides quantitative metrics of the retinal microvasculature, most prominently vessel density (VD), that are increasingly used as biomarkers in ocular, systemic, and cerebrovascular disease. Because OCTA relies on the detection of flow-related motion contrast, image quality has emerged as a pervasive determinant of these metrics, yet its effect has been reported in fragmentary and non-comparable ways across the literature. Methods: We identified OCTA studies indexed in PubMed that examined the relationship between image quality and quantitative OCTA parameters, and we summarised their methods and findings across the macular, foveal avascular zone, and peripapillary regions and across vascular layers. We integrated directly comparable per-unit scan-quality effects using random-effects meta-analysis and separately synthesized direct cross-sectional Pearson correlations between manufacturer-reported image quality and macular vessel-density outcomes from independent healthy cohorts. Results: Higher image quality was associated with higher measured vessel density in every contributing dataset. On the Optovue scan-quality (SQ, 0–10) scale, superficial macular VD increased by 3.46% (95% CI 1.85–5.07) per SQ unit in controlled signal-attenuation experiments and by covariate-adjusted observational estimates ranging from 0.90% to 2.16% per SQ unit; a combined order-of-magnitude estimate across both estimator types was 1.64% (95% CI 1.15–2.12). Peripapillary estimates were heterogeneous and derived from only two independent cohorts; they were therefore summarised descriptively rather than pooled. Across three independent externally authored healthy cohorts reporting direct cross-sectional Pearson correlations, higher image quality was strongly associated with higher macular vessel-density outcomes (random-effects pooled r = 0.64, 95% CI 0.50–0.75; I² = 49%). Conclusions: Image quality is an important, directional confounder of VD-based OCTA metrics whose magnitude can rival the biological or physiological signal of interest. Platform-appropriate standardisation, transparent reporting, and consideration of image quality in acquisition and analysis are therefore important for the valid interpretation of vessel-density measurements, particularly in functional and longitudinal studies. Full article

Platelet-rich plasma (PRP) has emerged as one of the most extensively investigated orthobiologic therapies for tendon disorders because of its potential to modulate inflammation, enhance extracellular matrix remodeling, and promote tissue regeneration through the delivery of concentrated platelets and bioactive molecules. However, despite compelling biological rationale and encouraging preclinical evidence, clinical outcomes remain inconsistent across different tendon pathologies. This narrative review critically examines the principal biological and methodological factors underlying this variability, including differences in cellular composition, growth factor and cytokine profiles, activation strategies, and current PRP classification systems. We further synthesize the available clinical evidence across major tendon disorders, highlighting the influence of disease-specific biology, product heterogeneity, and procedural variability on treatment response. In addition, the emerging role of quantitative imaging biomarkers in objectively evaluating tendon regeneration is discussed as a complementary tool for biological outcome assessment. Based on the evidence reviewed, we propose the quantifiable platelet-rich plasma (Q-PRP) framework, a practical reporting model that integrates cellular, molecular, procedural, and clinical variables into a standardized approach for biologically meaningful PRP characterization. Rather than replacing existing classification systems, the proposed framework aims to improve reproducibility, facilitate cross-study comparison, and support the transition toward precision regenerative medicine. Standardized biological characterization, combined with objective outcome assessment, may represent a critical step toward optimizing PRP research and clinical application in tendon healing. Full article

Background/Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) is the preferred imaging modality for evaluating pulmonary nodules because of its high spatial resolution; however, it primarily provides morphological information, including lesion size, shape, and density. Dual-energy CT (DECT), a more recent imaging technique, uses two different energy levels to enable material decomposition and quantitative parameter assessment. These parameters may provide additional information regarding tumor perfusion, vascularization, and tissue composition. This rapid review aimed to evaluate the current evidence regarding the clinical utility of DECT in the detection, characterization, and staging of lung tumors. Methods: This rapid review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed in the PubMed and Cochrane Library databases for studies published between 2005 and 2026. Studies were included if they evaluated the detection, characterization, or staging of lung tumors using quantitative DECT parameters. Case reports, editorials, duplicate studies, and studies without quantitative DECT data were excluded. Descriptive data analysis was performed using Microsoft Excel. Results: A total of 24 studies were included, comprising 18 retrospective (75%) and 6 prospective studies (25%). Only one study evaluated the role of DECT in lung tumor detection, demonstrating improved detection of mixed ground-glass nodules and invasive adenocarcinoma. Significant correlations were found between iodine uptake and tumor perfusion, highlighting the potential of DECT to improve differentiation between benign and malignant lesions. Several studies also demonstrated associations between DECT parameters and tumor biomarkers, including Ki-67 Proliferation Index (Ki-67) expression, Epidermal Growth Factor Receptor (EGFR) mutation status, Programmed Death-Ligand 1 (PD-L1) expression, and treatment response in non-small cell lung cancer. In addition, DECT provided complementary metabolic information regarding tumor malignancy and showed correlations between iodine uptake and fluorodeoxyglucose (FDG) parameters. Associations between iodine volume and tumor differentiation grade were also reported. One study demonstrated the potential role of DECT in tumor staging by predicting mediastinal lymph node metastasis. Across all included studies, iodine-based parameters (50%), radiomics and material decomposition parameters (16.67% each), and spectral attenuation parameters (12.50%) were the most frequently investigated DECT metrics. Conclusions: DECT appears to be a promising complementary imaging technique that provides quantitative perfusion-related and compositional surrogate information beyond the morphological assessment offered by conventional CT. However, the current evidence remains heterogeneous and is largely based on retrospective studies with relatively small patient cohorts. Larger prospective studies with standardized imaging protocols are necessary to further establish the clinical utility of DECT in lung tumors. Full article

Endometrial cancer is one of the most rapidly increasing gynaecological malignancies worldwide. The clinically adapted molecular classification of endometrial carcinoma, derived from The Cancer Genome Atlas, comprises four major subtypes: POLE-mutated, mismatch repair-deficient, p53-abnormal expression, and no specific molecular profile. Its clinical implementation has improved prognostic stratification, risk assessment, and treatment decision-making in patients with endometrial carcinoma. However, current workflows rely on immunohistochemistry and targeted sequencing, which increase costs, turnaround times, and infrastructure requirements, thereby limiting their universal adoption in routine clinical practice. Recent advances in artificial intelligence (AI), particularly deep learning models capable of predicting molecular features directly from H&E-stained whole-slide images, have emerged as promising tools for precision oncology. In addition to reproducing established molecular classification, these approaches may reveal previously unrecognised biomarker-defined histologic patterns that are difficult to detect using conventional methods. This article synthesises the current evidence on AI-based molecular classification in endometrial carcinoma from a pathologist-centred perspective, emphasising the biological rationale, methodological limitations, and future directions for clinical translation. Full article

The diagnosis and clinical management of myelodysplastic neoplasms are increasingly challenged by the disease’s inherent heterogeneity, particularly with respect to the diagnosis of low-grade variants. While standardized flow cytometric protocols traditionally rely on static biomarkers for lineage assignment, these often fail to capture the dynamic biological behavior of the malignant clone. This review synthesizes studies on the integration of functional biomarkers, specifically the nuclear proliferation marker Ki-67 and the anti-apoptotic protein Bcl-2, into the diagnostic and prognostic workflow. By utilizing high-dimensional multiparameter flow cytometry (MFC) and software-based maturation continuum analysis, the survival and growth kinetics of the myeloid, erythroid, and monocytic lineages can be quantified. These findings redefine myelodysplastic syndromes (MDS) as characterized by a significant decrease in cell-cycle progression and an increase in anti-apoptotic activity during early stages of maturation. Recent studies demonstrate that integrating the erythroid Ki-67 proliferation index as a fifth parameter into the conventional Ogata score dramatically improves diagnostic sensitivity for detecting MDS from 66% to 90% while maintaining 100% specificity. In particular, the sensitivity for detecting low-grade MDS improved from 56% to 91%. Additionally, a reduced erythroid Ki-67 index (≤28%) is a powerful independent predictor of transfusion dependence within 1 year. Beyond diagnostics, the introduction of the Bcl-2:Ki-67 ratio provides a superior metric for biological aggressiveness and a potential predictive tool for precision medicine. A high ratio identifies a quiescent, apoptosis-resistant cell population that is likely refractory to standard chemotherapy but is an ideal candidate for targeted Bcl-2 inhibition with Venetoclax. The integration of functional biomarkers bridges the gap between complex mutational landscapes and clinical manifestations. While digital imaging and artificial intelligence (AI) are beginning to automate blast enumeration and maturation analysis, functional kinetics may provide a necessary biological readout for personalized therapy. Full article

Uterine fibroids, the most common tumors in reproductive-age women, remain without a defined precursor tissue state. Unlike cervical dysplasia preceding cervical cancer, or colonic polyps preceding colorectal malignancy, no equivalent “at-risk” tissue marker exists for fibroids, and diagnosis relies on radiological imaging only after tumors are already well-established and often symptomatic including excessive menstrual bleeding, pelvic pain, infertility and obstetric complications. In this narrative review, we propose that a subset of women with unexplained AUB may harbor a chronic, non-infectious inflammatory condition of the myometrium, which we term Chronic Aseptic Myometritis (CAM). We synthesize mechanistic and human tissue evidence suggesting that sterile inflammation driven by damage-associated molecular patterns, NLRP3 inflammasome activation, oxidative DNA damage, and TGF-β–mediated extracellular-matrix remodeling may underlie the transition from normal myometrium (MyoN) to a pre-fibroid, inflamed and stiffened state (MyoF), and may contribute both to abnormal uterine bleeding (AUB) and to fibroid initiation. We propose a preliminary framework for future CAM research, including the identification of candidate biomarker categories and imaging correlates. We also discuss whether early mechanism-based interventions, such as vitamin D and epigallocatechin gallate (EGCG), may offer a potential pathway toward primary prevention. Because the components of this model derive largely from experimental and cross-sectional human studies, CAM is presented as a hypothesis-generating, myometrium-centered framework rather than a validated clinical entity, and prospective validation is required. Full article