Search Results (580)

The tip-of-the-tongue (TOT) phenomenon is a transient state in which speakers momentarily fail to retrieve a known word despite preserved semantic knowledge and a strong sense of imminent recall. This review integrates cognitive and neural evidence with emerging neurochemical perspectives to develop a comprehensive biomedical framework for word-finding failures. Cognitive models of semantic–phonological transmission and interloper interference have been refined through structural, functional, and metabolic imaging to elucidate the mechanisms underlying TOT states across the lifespan. Functional neuroimaging implicates a left-lateralized fronto-temporal network, particularly the inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), and temporal pole, in retrieval monitoring and conflict resolution. Structural MRI and diffusion imaging link increased TOT frequency to reduced integrity of the arcuate and uncinate fasciculi and diminished network efficiency. Proton magnetic resonance spectroscopy (¹H-MRS) introduces a neurochemical dimension, with studies of related language tasks implicating lower γ-aminobutyric acid (GABA) and altered glutamate concentrations in frontal and temporal cortices as potential contributors to slower naming and heightened retrieval interference. Together, these findings converge on a model in which transient lexical blocks arise from local disruptions in excitation–inhibition (E/I) balance that impair signal propagation within language circuits. By uniting behavioral, neuroimaging, and neurochemical perspectives, TOT research reveals how subtle perturbations in cortical homeostasis manifest as everyday cognitive lapses and highlights potential biomedical strategies to maintain communicative efficiency across the lifespan. Full article

Background: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease and affects male patients more often than women. Prior studies, however, suggested that women are diagnosed later and at advanced stages of the disease, present with more pronounced symptoms, and experience worse outcomes. Objectives: To investigate sex-specific differences in clinical, laboratory, and comprehensive imaging characteristics in a contemporary cohort of HCM patients from a tertiary referral center in Austria. Methods: We retrospectively analyzed 321 HCM patients enrolled in a prospective registry (2018–2024). All patients underwent a comprehensive baseline evaluation, including medical history, laboratory assessment, transthoracic echocardiography, and cardiac magnetic resonance imaging. Results: At diagnosis, women were significantly older (62 vs. 53 years, p < 0.001) and presented with more advanced functional class (NYHA ≥ II: 80% vs. 49%, p < 0.001). Six-minute walking distance was lower and obstructive HCM was more prevalent in women (425 vs. 505 m, p < 0.001, and 55% vs. 32%, p < 0.001, respectively). Echocardiographic assessment revealed higher diastolic filling pressures (E/E′ 18 vs. 10, p < 0.001), larger indexed atrial volumes (29.5 vs. 26.6 mL/m², p < 0.001), a higher left ventricular ejection fraction (70% vs. 62%, p < 0.001), and a larger indexed interventricular septal thickness in women (10.2 vs. 9.3 mm/m², p = 0.004). Moreover, serum levels of NT-proBNP were significantly higher in women (760 vs. 338 pg/L, p < 0.001). Conclusions: Female patients with HCM were diagnosed at an older age, presented with more advanced symptoms, had higher rates of obstructive physiology, and a phenotype characterized by diastolic dysfunction and elevated biomarkers, closely resembling heart failure with preserved ejection fraction. Recognizing these sex-specific disparities is crucial in improving diagnostic awareness and individualized therapeutic management. Full article

Background/Objectives: The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by an enriched stroma, hampering the effectiveness of therapy. This co-clinical study aimed to (1) provide insight into early post-treatment changes in the TME using multiparametric magnetic resonance imaging (mpMRI)-derived quantitative imaging biomarkers (QIBs) in a preclinical PDAC model treated with radiotherapy and correlate these QIBs with histology; (2) evaluate the feasibility of obtaining these QIBs in patients with PDAC using clinically approved mpMRI data acquisitions. Methods: Athymic mice (n = 12) at pre- and post-treatment as well as patients with PDAC (n = 11) at pre-treatment underwent mpMRI including diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) data acquisition sequences. DW and DCE data were analyzed using monoexponential and extended Tofts models, respectively. DeepLIIF quantified the total percentage (%) of tumor cells in hematoxylin and eosin (H&E)-stained tissues from athymic mice. Spearman correlation and Wilcoxon signed rank tests were performed for statistical analysis. Results: In the preclinical PDAC model, mean pre- and post-treatment ADC and K^trans values differed significantly (p < 0.01), changing by 20.50% and 20.41%, respectively, and the median total tumor cells quantified by DeepLIIF was 24% (range: 15–53%). Post-treatment ADC values and relative change in v_e (rΔv_e) showed a significant negative correlation with total tumor cells (ρ = −0.77, p < 0.014 for ADC and ρ = −0.77, p = 0.009 for rΔv_e). In patients with PDAC, pre-treatment mean ADC and K^trans values were 1.76 × 10⁻³ (mm²/s) and 0.24 (min⁻¹), respectively. Conclusions: QIBs in both preclinical and clinical settings underscore their potential for future co-clinical research to evaluate emerging drug combinations targeting both tumor and stroma. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a high-burden disease worldwide with increasing incidence, poor prognosis, and high mortality. Complete surgical resection is the only potentially curative treatment; however, due to a lack of symptoms in the early stages, most patients have advanced disease when diagnosed. Type 2 diabetes mellitus (T2DM) is a significant health concern characterized by hyperglycemia, insulin resistance, and impairment in insulin secretion. T2DM is linked with PDAC, sharing a complex bidirectional relationship. Therefore, dual causality between the two diseases represents significant challenges in practice, distinguishing existing T2DM as a PDAC risk factor from newly onset, potentially pancreatic cancer-related diabetes (PCRD). Evidence showed that new-onset diabetes (NOD) may serve as a biomarker for early diagnosis of PDAC, and several risk prediction models were developed to identify high-risk patients for further intervention. Although early PDAC detection is important, widespread screening is not currently recommended for T2DM patients due to a lack of cost-effective, efficient screening modalities. However, further risk stratification in diabetic patients is warranted to support a targeted screening strategy with economic viability. Diabetes confers ≈2-fold PDAC risk overall, with the highest relative risk in the first 2–3 years after diagnosis. Strategies using clinical signs (age ≥50–60 years, unintentional weight loss, rapid HbA1c escalation/insulin initiation) and predictive risk scores (e.g., ENDPAC) can triage NOD patients for magnetic resonance imaging/computed tomography (MRI/CT) and endoscopic ultrasound (EUS). A targeted screening approach may allow early diagnosis that could improve the prognosis of PDAC patients. This narrative review aims to synthesize current evidence linking T2DM and PDAC; delineate risk factors within diabetes populations; appraise predictive models and biomarkers for differentiating PCRD from typical T2DM; outline pragmatic, risk-adapted screening strategies, especially for NOD, and identify additional areas where further research is needed. Full article

Background: The aim of the present systematic review (SR) is to compile evidence regarding the use of radiomic-based machine learning (ML) models for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC) patients and to assess their reliability, methodological frameworks, and clinical applicability. The SR was conducted following PRISMA 2020 guidelines and registered in PROSPERO (CRD42025640065). Methods: Using the PICOS framework, the review question was defined as follows: “Can radiomic-based ML models accurately predict HPV status in OPSCC?” Electronic databases (Cochrane, Embase, IEEE Xplore, BVS, PubMed, Scopus, Web of Science) and gray literature (arXiv, Google Scholar and ProQuest) were searched. Retrospective cohort studies assessing radiomics for HPV prediction were included. Risk of bias (RoB) was evaluated using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and data were synthesized based on imaging modality, architecture type/learning modalities, and the presence of external validation. Meta-analysis was performed for externally validated models using MetaBayesDTA and RStudio. Results: Twenty-four studies including 8627 patients were analyzed. Imaging modalities included computed tomography (CT), magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CE-CT), and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET). Logistic regression, random forest, eXtreme Gradient Boosting (XGBoost), and convolutional neural networks (CNNs) were commonly used. Most datasets were imbalanced with a predominance of HPV+ cases. Only eight studies reported external validation results. AUROC values ranged between 0.59 and 0.87 in the internal validation and between 0.48 and 0.91 in the external validation results. RoB was high in most studies, mainly due to reliance on p16-only HPV testing, insufficient events, or inadequate handling of class imbalance. Deep Learning (DL) models achieved moderate performance with considerable heterogeneity (sensitivity: 0.61; specificity: 0.65). In contrast, traditional models provided higher, more consistent performance (sensitivity: 0.72; specificity: 0.77). Conclusions: Radiomic-based ML models show potential for HPV status prediction in OPSCC, but methodological heterogeneity and a high RoB limit current clinical applicability. Full article

Background/Objectives: The extent of myocardial scar, visualized by late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR), is associated with mortality after acute myocardial infarction (MI). However, data on optimal cardiac biomarker cut-off values (e.g., high-sensitivity cardiac troponin T, hs-cTnT) for predicting LGE remain limited. This study aimed to evaluate the predictive value of cardiac biomarkers for LGE and their influence on clinical outcomes. Methods: We included 597 patients who underwent CMR a median of 3 days [interquartile range (IQR) 2–4 days] after MI (407 STEMI and 190 NSTEMI patients), with a median follow-up period of 3.0 years [IQR 1.3–3.5 years]. Results: After adjusting for key variables, maximum cardiac biomarker levels were found to have the strongest correlation with the presence and extent of LGE (p < 0.001). LGE mass and LVEF were the most robust predictors of all-cause mortality (hazard ratio [CI] 1.464 [1.050–2.040], p = 0.025, Harrell’s C 0.812; 0.697 [0.491–0.990], p = 0.044, Harrell’s C 0.810, respectively). We determined a receiver operating characteristic (ROC) area under the curve (AUC) of 0.73 and an optimal cut-off of 53 g for LGE mass and mortality, with a maximum hs-cTnT cut-off of 7270 ng/L predicting this extent of LGE. Conclusions: In this large cohort of MI patients with three-year follow-up, cardiac biomarker levels showed a strong correlation with the extent of LGE. While absolute LGE mass was associated with mortality, its predictive value was comparable to that of CMR-derived LVEF. These findings should be interpreted cautiously, given the study’s observational design, and should be considered hypothesis-generating, underscoring the need for prospective validation. Full article

Background/Objectives: Specialized pro-resolving lipid mediators (SPMs), such as Resolvin E1 (RvE1) and Resolvin D1 (RvD1), play a critical role in the resolution phase of inflammation. However, their relevance to tendon pathology and tissue-specific degeneration in rotator cuff tears remains unclear. This study aimed to investigate the relation between serum RvE1 and RvD1 levels and the morphological severity of tendon retraction and muscle fatty degeneration in patients with full-thickness rotator cuff tears. Methods: A total of 70 participants were included: 35 patients with full-thickness rotator cuff tears determined by magnetic resonance imaging (MRI) and 35 healthy controls. Tendon retraction and muscle fatty degeneration were graded using Patte and Goutallier classifications, respectively. Serum RvE1 and RvD1 levels were measured using enzyme-linked immunosorbent assay (ELISA). Group comparisons were performed using Welch’s t-test, and correlations were analyzed with Spearman’s coefficient. Results: RvE1 and RvD1 levels were significantly lower in patients compared to controls (p < 0.001). RvE1 showed a moderate positive correlation with Patte score (ρ = 0.37, p = 0.027), while no significant correlation was observed with Goutallier classification (ρ = 0.19, p = 0.27). RvD1 levels demonstrated no significant relationship with either morphological parameter. Conclusions: These findings suggest that decreased serum RvE1 levels are associated with the severity of tendon retraction but not with muscle fatty degeneration. Therefore, RvE1 may serve as a potential biochemical biomarker reflecting tendon damage severity and the impaired resolution of inflammation in rotator cuff tears. Full article

Multimodal imaging plays a central role in optimizing the evaluation and management of myocardial ischemia by leveraging the complementary strengths of echocardiography, cardiac magnetic resonance imaging (CMR), single photon emission computed tomography (SPECT), positron emission tomography (PET), and invasive coronary angiography (ICA). Noninvasive functional imaging is typically recommended for patients with intermediate to high pre-test probability of coronary artery disease, while coronary computed tomography angiography (CCTA) is preferred for low to intermediate risk. Stress echocardiography is valuable for detecting wall motion abnormalities and is particularly effective in multivessel or left main disease, where perfusion techniques may miss balanced ischemia. CMR offers high spatial resolution and quantitative assessment of myocardial blood flow (MBF), while SPECT and PET quantify ischemic burden, with PET providing superior accuracy for MBF and microvascular disease. ICA remains the gold standard for defining the presence, location, and severity of epicardial coronary stenosis. It is indicated when noninvasive imaging reveals high-risk features, when symptoms are refractory to medical therapy, or when noninvasive results are inconclusive. While ICA offers high spatial resolution, it alone cannot assess the hemodynamic significance of intermediate lesions, nor the coronary microvasculature. Adjunctive invasive hemodynamic and provocative coronary testing (e.g., Fractional Flow Reserve—FFR, invasive Coronary Flow Reserve—CFR, Index of Microcirculatory Resistance—IMR, acetylcholine test) provide essential insights, especially in ischemia with nonobstructive coronary arteries. Given its procedural risks, ICA should be reserved for cases where it will impact management. Intravascular imaging may be used to further characterize lesions. In summary, modality selection should be individualized based on patient characteristics, comorbidities, contraindications, and the need for anatomical versus physiological data. Integrating noninvasive and invasive modalities provides a comprehensive, patient-centered approach to ischemia evaluation. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

Magnetosomes are organelle-like structures within magnetotactic bacteria that store iron biominerals in membrane-bound vesicles. In bacteria, formation of these structures is highly regulated by approximately 30 genes, which are conserved throughout different species. To compartmentalize iron in mammalian cells and provide gene-based contrast for magnetic resonance imaging, we introduced key magnetosome proteins. The expression of essential magnetosome genes mamI and mamL as fluorescent fusion proteins in a human melanoma cell line confirmed their co-localization and interaction. Here, we investigate the expression of two more essential magnetosome genes, mamB and mamE, using confocal microscopy to describe fluorescent fusion protein expression patterns and analyze the observed intracellular mobility. Custom software was developed to characterize fluorescent particle trajectories. In mammalian cells, essential magnetosome proteins display different diffusive behaviours. However, all magnetosome proteins travelled at similar velocities when interacting with mammalian mobile elements, suggesting that MamL, MamL + MamI, MamB, and MamE interact with similar molecular motor proteins. These results confirm that localization and interaction of essential magnetosome proteins are feasible within the mammalian intracellular compartment. Full article

Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine. Full article

Background/Objectives: Magnetic resonance imaging (MRI) is a widely used non-invasive imaging modality that provides high-fidelity soft-tissue contrast without ionizing radiation. However, acquiring high-resolution MRI scans is time-consuming, necessitating accelerated acquisition and reconstruction methods. Recently, self-supervised learning approaches have been introduced for reconstructing undersampled MRI data without external fully sampled ground truth. Methods: In this work, we propose a logarithmic scaled scheme for conventional loss functions (e.g., ${\ell }_1$, ${\ell }_2$) to enhance self-supervised MRI reconstruction. Standard self-supervised methods typically compute loss in the k-space domain, which tends to overemphasize low spatial frequencies while under-representing high-frequency information. Our method introduces a logarithmic scaling to adaptively rescale residuals, emphasizing high-frequency contributions and improving perceptual quality. Results: Experiments on public datasets demonstrate consistent quantitative improvements when the proposed log-scaled loss is applied within a self-supervised MRI reconstruction framework. Conclusions: The proposed approach improves reconstruction fidelity and perceptual quality while remaining lightweight, architecture-agnostic, and readily integrable into existing self-supervised MRI reconstruction pipelines. Full article

Primary spinal osseous tumors are relatively rare, comprising ~5–10% of spinal bone neoplasms, whereas metastases account for the vast majority of spinal lesions. Patients commonly present with insidious back pain, sometimes with a focal mass, and constitutional symptoms are uncommon early in the disease course. As clinical features are often nonspecific and may overlap with degenerative, infectious, and metastatic disease, imaging plays an important role in lesion identification, characterization, and treatment planning. Computed tomography helps to define osseous architecture and matrix characteristics. Magnetic resonance imaging can assess marrow involvement, soft tissue extension, neural compression and intra-canal disease, and tumor vascularity. Together, advanced imaging modalities guide further workup, optimize biopsy planning, inform prognostic assessment and therapeutic decision-making, and anticipate mechanical instability or neural compromise. This narrative pictorial review synthesizes radiographic, CT, and MRI appearances of primary spinal tumors across major histologic lineages (e.g., osteogenic, chondrogenic, notochordal, vascular), illustrated with representative cases. We correlate imaging with clinical presentation to distinguish typical from atypical variants and highlight mimics and pitfalls with implications for diagnostic interpretation and management. Full article

Simultaneous Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) provide a powerful framework for investigating brain dynamics; however, ballistocardiogram (BCG) artifacts in EEG compromise signal quality and limit the assessment of brain connectivity. This study evaluated three widely used artifact removal methods—Average Artifact Subtraction (AAS), Optimal Basis Set (OBS), and Independent Component Analysis (ICA)—together with two hybrid approaches (AAS + ICA and OBS + ICA). Unlike previous studies that focused solely on signal-level metrics, we adopted a holistic framework that combined signal quality indicators with graph-theoretical analysis of EEG-fMRI connectivity in static and dynamic contexts. The results show that AAS provides the best signal quality, whereas OBS better preserves structural similarity. ICA, although weaker in terms of signal metrics, demonstrates sensitivity to frequency-specific patterns in dynamic graphs. Hybrid methods yield benefits, with OBS + ICA producing the lowest p-values across frequency band pairs (e.g., theta–beta and delta–gamma), particularly in dynamic graphs. Topological analyses revealed that artifact removal significantly affected network structure, with dynamic analyses showing more pronounced frequency-specific effects than static analyses. High-frequency bands, such as beta and gamma, exhibit stronger differentiation under dynamic conditions. Overall, this study offers new insights into the relationship between artifact removal and brain network integrity, emphasizing the need for multimodal and frequency-sensitive evaluation strategies. The findings guide preprocessing decisions in EEG-fMRI studies and clarify how methodological choices shape the interpretation of brain connectivity. Full article

Background: Mandibular canal (MC) variants are common and clinically relevant for anesthesia, implant placement, third-molar surgery, and osteotomies. Reported prevalences vary widely because they depend on imaging modality, acquisition parameters, and operational definitions. Methods: This was a focused narrative review with structured methods (PubMed/MEDLINE and Scopus, 2000–6 October 2025; last search 6 October 2025), predefined eligibility criteria and dual independent screening; no meta-analysis was conducted. Study-selection counts are reported in the text. Prevalence statements are contextualized by modality, imaging parameters (e.g., cone-beam computed tomography (CBCT) voxel size magnetic resonance imaging (MRI) field strength/sequences), and diagnostic thresholds (e.g., anterior loop (AL) criteria). Results: Compared with panoramic radiography (PR), CBCT consistently reveals more variant pathways. Typical CBCT estimates for bifid MC fall in the single-digit to low double-digit range, contingent on voxel size and definitions, whereas PR detects far fewer. Trifid canals are uncommon (≈1–2% in CBCT series). Reported retromolar canal frequencies vary broadly across populations and protocols, and AL length and prevalence are threshold-dependent. Selective MRI may complement CBCT by depicting soft-tissue branches not accompanied by a bony canal. We synthesize a variant-aware, imaging-led workflow: PR for screening; CBCT when predefined criteria are met and results are reasonably expected to change management; MRI reserved for targeted soft-tissue questions, in line with As Low as Reasonably Achievable (ALARA)/and As Low As Diagnostically Acceptable (ALADA) principles. We apply the Landfald Clinical Framework (LCF) as a hypothesis-generating, clinical synthesis tool linking variant patterns to procedural modifications and risk mitigation. Conclusions: A narrowed, clinically oriented approach—contextualizing prevalence by modality and definitions and applying an imaging-led, variant-aware workflow—can improve planning and safety in the posterior mandible. The LCF is used pragmatically within this workflow and does not constitute a new anatomical taxonomy; formal reliability and validity testing remain necessary. Full article