Search Results (425)

Lung ischemia–reperfusion injury (LIRI) remains a major contributor to perioperative morbidity and mortality in thoracic surgery, especially for lung transplantations, where it is one of the principal drivers of primary graft dysfunction (PGD). Although substantial advances have been made in surgical technique, donor management, and perioperative care, LIRI continues to pose a significant clinical challenge. Mechanistically, LIRI reflects a combined pathology of oxidative stress, endothelial and glycocalyx disruption, innate immune activation, mitochondrial dysfunction, and regulated cell death, resulting in loss of alveolar–capillary barrier integrity and gas exchange failure. Current management is phase-specific and multimodal, spanning donor care and preservation, controlled reperfusion and lung-protective ventilation, and pharmacological treatments. Treatment candidates that target oxidative stress and inflammatory cascades (e.g., antioxidants, complement and adenosine pathways, mesenchymal stromal cell products, and dipeptidyl-peptidase-4 inhibition) show promise, yet translation into a clinical scenario remains difficult. Increasing evidence supports endothelial-preserving and mitochondria-sparing strategies, rigorous perioperative bundles, and biomarker-guided trials to move from pathophysiology to practice. Ultimately, addressing LIRI requires an integrated, multidisciplinary approach that spans surgical, anesthetic, and pharmacologic domains, with the goal of improving both early outcomes and long-term graft survival in lung transplant patients. Full article

Myelin is essential for efficient neural signaling and can be quantitatively evaluated using the T1-weighted/T2-weighted (T1w/T2w) ratio as a proxy for regional myelin content. Myelin covariance networks (MCNs) reflect correlated myelin patterns across brain regions, enabling the investigation of topological organization. However, a vertex-level map of myelin covariance gradients and their cognitive associations remains underexplored. The objective of this study was to construct and characterize vertex-level MCNs, identify their principal gradients, map their higher-order topological landscape, and determine their associations with cognitive functions and other multimodal cortical features. We conducted a cross-sectional, secondary analysis of publicly available data from the Human Connectome Project (HCP). The dataset included T1w/T2w MRI data from 1096 healthy adult participants (age 22–37). All original data collection and sharing procedures were approved by the Washington University institutional review board. Our procedures involved (1) constructing a vertex-wise MCN from T1w/T2w ratio data; (2) applying gradient analysis to identify principal organizational axes; (3) calculating network connectivity strength; (4) performing cognitive meta-analysis using Neurosynth; and (5) using graphlet analysis to assess higher-order topology. Our results show that the primary myelin gradient (Gradient 1) spans from sensory-motor to association cortices, strongly associates with connectivity strength (r = 0.66), and shows a functional dissociation between affective processing and sensorimotor domains. Furthermore, Gradient 2, as well as the positive and full connectivity strength, showed robust correlations with fractional anisotropy (FA), a DTI metric reflecting white matter microstructure. Our higher-order analysis also revealed that negative and positive myelin covariance connections exhibited distinct topologies. Negative connections were dominated by star-like graphlet structures, while positive connections were dominated by path-like and triangular structures. This systematic vertex-level investigation offers novel insights into the organizational principles of cortical myelin, linking gray matter myelin patterns to white matter integrity, and providing a valuable reference for neuropsychological research and the potential identification of biomarkers for neurological disorders. Full article

Background/Objectives: Periprosthetic joint infection (PJI) remains one of the most devastating complications of arthroplasty, with early diagnosis crucial for successful management. The serum neutrophil–lymphocyte ratio (NLR) and monocyte–lymphocyte ratio (MLR) have been proposed as simple, inexpensive inflammatory biomarkers, but their diagnostic performance in PJI remains unclear. This meta-analysis aimed to compare the diagnostic accuracy of serum NLR and MLR in detecting PJI. Materials and Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus up to April 2025. Twenty-nine eligible studies (n = 14,040 patients; 3418 with PJI, 10,622 without PJI) reporting diagnostic metrics for serum NLR or MLR were included. Extracted data comprised mean biomarker values, cut-off thresholds, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Non-parametric statistical tests and subgroup analyses were applied to examine performance across infection types and PJI definitions. Results: Both serum NLR and MLR were significantly elevated in PJI patients compared with aseptic cases (p < 0.001 and p = 0.003, respectively). Pooled diagnostic accuracy was moderate: mean AUC 0.719 for NLR and 0.700 for MLR. For NLR, mean sensitivity was 69.9% and specificity 69.8%, with an average cut-off of 2.88. For MLR, mean sensitivity was 68.2% and specificity 70.4%, with an average cut-off of 0.34. Subgroup analyses indicated superior diagnostic performance of NLR in acute infections and variability depending on the PJI definition employed (p = 0.037). Strong correlations were observed between standardized mean differences in biomarker levels and corresponding diagnostic accuracy, particularly for NLR (ρ = 0.802, p = 0.002). Conclusions: Serum NLR demonstrates slightly superior diagnostic accuracy over serum MLR in identifying PJI, especially in acute settings. Both markers are inexpensive and widely accessible but show only moderate discriminative capacity, supporting their role as adjunctive rather than standalone diagnostic tools. Further large-scale prospective studies with harmonized methodologies are needed to refine biomarker thresholds and integrate them into multimodal diagnostic algorithms. Full article

Ferroptosis, an iron-dependent form of regulated cell death characterized by lipid peroxidation, has emerged as a pivotal vulnerability in oral squamous cell carcinoma (OSCC). This review provides an overview of ferroptosis mechanisms and their implications for OSCC pathobiology and therapy. OSCC cells exhibit heightened reliance on anti-ferroptotic defenses such as GPX4, SLC7A11, FSP1, and Nrf2, and disrupting these pathways suppresses tumor growth and restores sensitivity to chemotherapy, radiotherapy, and immunotherapy. Genetic and epigenetic regulators, including p53, PER1, circ_0000140, and STARD4-AS1, critically modulate ferroptotic sensitivity, while metabolic enzymes such as ACSL4, LPCAT3, and TPI1 link ferroptosis to cellular plasticity and resistance. Preclinical studies highlight the promise of small-molecule inhibitors, repurposed agents (e.g., sorafenib, artesunate, trifluoperazine), natural compounds (e.g., piperlongumine, Evodia lepta, quercetin), and nanomedicine platforms for targeted ferroptosis induction. We further address ferroptosis within the tumor microenvironment, highlighting its immunogenic and context-dependent dual roles, and summarize genomic and transcriptomic evidence linking ferroptosis-related genes to patient prognosis. Beyond cancer, ferroptosis also contributes to non-malignant oral diseases, including pulpitis, periodontitis, and infection-associated inflammation, where inhibitors may protect tissues. Despite these advances, clinical translation is constrained by the lack of safe ferroptosis inducers and validated biomarkers. Future research should focus on developing pharmacologically viable GPX4 inhibitors, refining biomarker-driven patient stratification, and designing multimodal regimens that combine ferroptosis induction with standard therapies while preserving immune and tissue integrity. Ferroptosis therefore represents both a mechanistic framework and a translational opportunity to reshape oral oncology and broader oral disease management. Full article

Background/Objectives: Alzheimer’s disease (AD) exhibits clinical and biological variability. This study aimed to identify MRI-defined subtypes reflecting distinct biological pathways of neurodegeneration and cognitive decline. Methods: We applied principal component analysis followed by k-means clustering (k = 3) on volumetric MRI data from 924 participants and validated clusters using cerebrospinal fluid (CSF) biomarkers (Aβ₄₂, total tau, p-tau, CTRED, MAPres, glucose, CTWHITE). Results: Three major phenotypes emerged: (1) a tau/vascular limbic subtype with pronounced hippocampal and amygdala atrophy and elevated tau and CTRED levels; (2) a volume-preserved, low-amyloid subtype consistent with early-stage or cognitively resilient AD; and (3) a diffuse-atrophy subtype with high amyloid and tau burden and ventriculomegaly. Comparative analysis revealed progressive biological shifts from amyloid accumulation to tau aggregation and vascular compromise across these clusters. Conclusions: MRI-based clustering validated by CSF biomarkers delineates biologically meaningful AD endophenotypes. The results suggest a gradual cognitive decline driven by amyloid–tau–vascular interactions, supporting multimodal phenotyping as a practical approach for precision staging and intervention. Full article

The LRRK2+ SAA− cohort of Parkinson’s disease (PD), characterized by the absence of hallmark α-synuclein pathology, remains under-explored. This limits opportunities for early detection and targeted intervention. This study analyzes data from this under-characterized subgroup and compares it with the LRRK2+ SAA+ cohort using longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI). The PPMI dataset includes 115 LRRK2+ patients (70 SAA+, 45 SAA−) across 52 features encompassing clinical assessments, cognitive scores, DaTScan SPECT imaging, and motor severity. DaTScan binding ratios were selected as imaging-based indicators of early dopaminergic loss, while NP3TOT (MDS-UPDRS Part III total score) was used as a gold-standard clinical measure of motor symptom severity. Linear mixed-effects models were then applied to evaluate longitudinal predictors of DaTScan decline and NP3TOT progression, and statistical analyses of group comparisons revealed distinct drivers of symptoms differentiating SAA− from SAA+ patients. In SAA− patients, a decline in DaTScan was significantly associated with thermoregulatory impairment (p-value = 0.019), while NP3TOT progression was predicted by constipation (p-value = 0.030), sleep disturbances (p-value = 0.046), and longitudinal time effects (p-value = 0.043). In contrast, SAA+ patients showed significantly lower DaTScan values compared to SAA− (p-value = 0.0004) and stronger coupling with classical motor impairments, including freezing of gait (p-value = 0.016), rising from a chair (p-value = 0.007), and turning in bed (p-value = 0.016), along with cognitive decline (MoCA clock-hands test, p-value = 0.037). These findings support the hypothesis that LRRK2+ SAA− patients follow a distinct pathophysiological course, where progression is influenced more by autonomic and non-motor symptoms than by typical motor dysfunction. This study establishes a robust, multimodal modeling framework for examining heterogeneity in genetic PD and highlights the utility of combining DaTScan, NP3TOT, and symptom-specific features for early subtype differentiation. These findings have direct clinical implications, as stratifying LRRK2 carriers by SAA status may enhance patient monitoring, improve prognostic accuracy, and guide the design of targeted clinical trials for disease-modifying therapies. Full article

Genetic background is a critical determinant of disease expression, arrhythmic vulnerability, and therapeutic response in inherited cardiomyopathies. Implantable cardioverter-defibrillators (ICD) remain the cornerstone for primary prevention of sudden cardiac death, yet conventional selection based on left ventricular ejection fraction does not adequately reflect the heterogeneity of genetic substrates. Increasing evidence demonstrates that pathogenic variants differ not only in prevalence across cardiomyopathy subtypes but also in prognostic impact. Truncating variants, particularly in genes encoding structural proteins, are often associated with severe remodeling, progressive dysfunction, and high arrhythmic risk, whereas missense variants may confer variable expressivity, ranging from aggressive arrhythmogenic phenotypes to milder or late-onset disease. This variability underscores the importance of distinguishing variant classes in clinical decision-making. Integrating genetic information with advanced imaging markers, such as late gadolinium enhancement, allows refinement of arrhythmic risk stratification beyond static thresholds and supports more tailored ICD allocation. Nevertheless, translation into routine practice is limited by challenges in variant interpretation, phenotypic overlap between cardiomyopathy subtypes, and the lack of prospective validation of genotype-based models. In the precision medicine era, evolving strategies should move toward dynamic, multimodal approaches that combine genotype, phenotype, and imaging biomarkers, enabling more accurate prediction of arrhythmic risk and more cost-effective use of ICD therapy. Full article

Background/Objectives: Guibi-tang (GBT) and its variant Kami-guibi-tang (KGBT) are traditional East Asian multi-herb formulas prescribed for memory loss, insomnia, and fatigue. Preclinical data suggest multimodal neuroprotective actions, including cholinergic signaling modulation and activation of the cAMP response element-binding protein (CREB)/extracellular signal-regulated kinase (ERK) pathway; however, clinical evidence for cognitive disorders remains scattered. This scoping review aimed to map the breadth, design characteristics, efficacy signals, and safety profile of GBT and KGBT across the full spectrum of cognitive impairment. Methods: Following the Arksey–O’Malley framework and PRISMA-ScR guidelines, seven databases were searched (MEDLINE, Embase, Cochrane Library, China National Knowledge Infrastructure, ScienceON, Scopus, Citation Information by the National Institute of Informatics) from inception to 31 January 2025, for human studies evaluating GBT or KGBT in subjective cognitive decline, mild cognitive impairment (MCI), dementia, or post-stroke cognitive impairment (PSCI). Two reviewers independently screened, extracted, and charted data on study design, participants, interventions, outcomes, and adverse events. Results: Fifteen studies met the inclusion criteria—nine randomized controlled trials, one crossover trial, and five observational reports—enrolling 555 participants (age range, 59–87 years). All were conducted in the Republic of Korea, Japan, or China. GBT or KGBT, given as monotherapy or adjunctive therapy for 4 weeks to 9 months, produced modest but consistent improvements in global cognition (Mini-Mental State Examination/Montreal Cognitive Assessment), memory domains, activities of daily living, and neuropsychiatric symptoms across MCI, Alzheimer’s disease, and PSCI cohorts. Reported adverse event rates were comparable to or lower than those of placebo, usual care, or conventional drugs, and no serious treatment-related toxicity was identified. Conclusions: Current evidence—although limited by small sample sizes, heterogeneous formulations, short follow-up durations, and regional concentration—indicates that GBT and KGBT are well tolerated and confer clinically meaningful cognitive and functional benefits. Standardized, multicenter, placebo-controlled trials with biomarker end points are warranted to confirm long-term efficacy, clarify mechanisms, and guide integrative clinical use. Full article

Background/Objectives: Whole-body cryotherapy (WBC) is increasingly utilized as a physical modality for managing chronic pain, although its mechanism of action remains incompletely understood. This study evaluated whether WBC influences serum levels of substance P, calprotectin, β-nerve growth factor (β-NGF), and calcitonin gene-related peptide (CGRP), which are implicated in pain modulation. Methods: Serum samples from 61 participants—37 undergoing WBC and 24 not receiving WBC—were collected at the start and end of a multimodal inpatient pain treatment program. Pain intensity was assessed using a numerical rating scale (NRS). Biomarker concentrations were measured by enzyme-linked immunosorbent assay (ELISA). Results: Both groups reported an average significant pain reduction of more than 1.39 points on the NRS. Of the biomarkers analyzed, only calprotectin showed a statistically significant reduction in the overall cohort (p = 0.007) and in the WBC subgroup (p = 0.032). Among patients who did not experience significant pain reduction, those in the WBC group exhibited a greater decline in calprotectin compared to controls (p = 0.042), especially among those without medication changes (p = 0.016). No significant differences were detected for the other serum parameters. Conclusions: The analgesic effects of WBC could not be attributed to changes in the neuromodulatory peptides measured. However, the significant reduction in calprotectin suggests a potential anti-inflammatory effect of WBC on the innate immune response. Full article

Background: Epithelial ovarian cancer is an aggressive malignancy with poor prognosis despite advances in multimodal treatment. The systemic immune-inflammation index (SII) has emerged as a prognostic biomarker in various cancers; however, the impact of surgery-induced inflammatory changes remains unclear. Methods: This study evaluated the prognostic significance of postoperative changes in SII among patients with epithelial ovarian cancer undergoing primary surgery. Data from 374 patients treated at Samsung Medical Center and Kangbuk Samsung Hospital between 2016 and 2021 were retrospectively reviewed. SII was calculated from complete blood counts obtained within one month before surgery and on postoperative day 1. The percentage change in SII was analyzed, and the optimal cutoff was determined using receiver operating characteristic curve analysis. Survival outcomes were assessed using Kaplan–Meier and multivariable Cox regression models. Results: Patients with a postoperative SII increase > 98.4% (Group 2) had significantly poorer overall (HR = 1.86, p = 0.009) and progression-free survival (HR = 1.30, p = 0.112) compared with those with smaller changes (Group 1). Discussion: High-grade histology, serous subtype, and greater intraoperative blood loss were associated with higher postoperative SII. A marked postoperative increase in SII independently predicted poor survival, suggesting that dynamic inflammatory responses rather than static baseline levels provide additional prognostic information. Conclusions: Perioperative SII monitoring, easily obtainable from routine blood tests, may help identify high-risk patients who could benefit from intensified surveillance or adjuvant treatment. Prospective multicenter studies are warranted to validate these findings and explore whether perioperative modulation of systemic inflammation can improve outcomes. Full article

Radiomics has recently begun as a transformative approach in medical imaging, shifting radiology from qualitative description to quantitative analysis. By extracting high-throughput features from CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET/CT (Positron Emission Tomography/Computed Tomography), and CBCT (Cone Beam Computed Tomography), radiomics enables the characterization of tissue heterogeneity and the development of imaging biomarkers with diagnostic, prognostic, and predictive values. This narrative review explores the historical evolution of radiomics and its methodological foundations, including acquisition, segmentation, feature extraction and modeling, and platforms supporting these workflows. Clinical applications are highlighted in oncology, cardiology, neurology, and musculoskeletal and dentomaxillofacial imaging. Despite being promising, radiomics faces challenges related to standardization, reproducibility, PACS/RIS (Picture Archiving and Communication System/Radiology Information System) integration and interpretability. Professional initiatives, such as the Image Biomarker Standardization Initiative (IBSI) and guidelines from radiological societies, are addressing these barriers by promoting harmonization and clinical translation. The ultimate vision is a radiomics-augmented radiology report in which validated biomarkers and predictive signatures complement conventional findings, thus enhancing objectivity, reproducibility, and advancing precision medicine. Full article

Background: While prostate-specific membrane antigen (PSMA)-targeted imaging has revolutionized metastatic detection, unspecific bone uptake (UBU)—particularly in the ribs—is a common but diagnostically challenging finding in prostate cancer (PCa) patients. This review aims to synthesize current evidence on PSMA-avid rib lesions in PCa and to propose a structured approach for differentiating true metastases from benign mimics. Methods: A comprehensive literature search across PubMed, EMBASE, Scopus, and Web of Science identified relevant studies on PSMA imaging interpretation, tracer-specific patterns, rib lesion morphology, and clinical correlates. Data on uptake intensity, CT features, lesion number, location, tracer type, patient-specific risk factors, and follow-up behavior were extracted and analyzed. Results: Most solitary rib lesions are benign, particularly in low-risk patients or when located in the anterior/lateral arcs. Metastatic lesions are more likely to present as multiple foci, show cortical destruction on CT, exhibit high uptake intensity, and occur in patients with elevated PSA, high Gleason score, or ongoing androgen deprivation. ¹⁸F-PSMA-1007 is especially prone to UBU in the ribs compared to ⁶⁸Ga-PSMA-11. Based on these variables, we propose a clinical decision tree to guide interpretation of PSMA-avid rib lesions. Conclusions: Accurate interpretation of rib lesions on PSMA PET/CT requires a multimodal, context-sensitive approach. Our diagnostic decision tree guides precise differentiation of benign versus metastatic rib lesions, enhancing staging accuracy and clinical decision-making. Biomarker-guided therapies offer potential for personalized treatment, though rib-specific validation remains a critical need. Full article

This scoping review systematically maps the rapidly evolving application of Artificial Intelligence (AI) in Autism Spectrum Disorder (ASD) diagnostics, specifically focusing on computational behavioral phenotyping. Recognizing that observable traits like speech and movement are critical for early, timely intervention, the study synthesizes AI’s use across eight key behavioral modalities. These include voice biomarkers, conversational dynamics, linguistic analysis, movement analysis, activity recognition, facial gestures, visual attention, and multimodal approaches. The review analyzed 158 studies published between 2015 and 2025, revealing that modern Machine Learning and Deep Learning techniques demonstrate highly promising diagnostic performance in controlled environments, with reported accuracies of up to 99%. Despite this significant capability, the review identifies critical challenges that impede clinical implementation and generalizability. These persistent limitations include pervasive issues with dataset heterogeneity, gender bias in samples, and small overall sample sizes. By detailing the current landscape of observable data types, computational methodologies, and available datasets, this work establishes a comprehensive overview of AI’s current strengths and fundamental weaknesses in ASD diagnosis. The article concludes by providing actionable recommendations aimed at guiding future research toward developing diagnostic solutions that are more inclusive, generalizable, and ultimately applicable in clinical settings. Full article

Modern clinical oncology faces an unprecedented data complexity that exceeds human analytical capacity, making artificial intelligence (AI) integration essential rather than optional. This review examines the dual impact of AI on productivity enhancement and creative discovery in cancer care. We trace the evolution from traditional machine learning to deep learning and transformer-based foundation models, analyzing their clinical applications. AI enhances productivity by automating diagnostic tasks, streamlining documentation, and accelerating research workflows across imaging modalities and clinical data processing. More importantly, AI enables creative discovery by integrating multimodal data to identify computational biomarkers, performing unsupervised phenotyping to reveal hidden patient subgroups, and accelerating drug development. Finally, we introduce the FUTURE-AI framework, outlining the essential requirements for translating AI models into clinical practice. This ensures the responsible deployment of AI, which augments rather than replaces clinical judgment, while maintaining patient-centered care. Full article

Background: Polyuria–polydipsia syndrome (PPS) in children poses a major diagnostic challenge, as central diabetes insipidus (CDI) and primary polydipsia (PP) require distinct treatments. Although copeptin is a robust diagnostic biomarker, using only fixed thresholds may not adequately support decision making in borderline cases. To address this gap, we evaluated a multimodal diagnostic approach that integrates copeptin dynamics with clinical profiling. Methods: In a prospective diagnostic study (2019–2025), 24 children with PPS (CDI = 11, PP = 13) underwent hypertonic saline testing with serial sodium, osmolality, and copeptin sampling. Predictors included stimulated copeptin, peak sodium, peak osmolality, test duration, and tolerability. A Ridge regression model was applied and internally validated with stratified cross-validation. Results: Stimulated copeptin was the strongest discriminator, while sodium/osmolality dynamics and tolerability provided complementary value. The multimodal model achieved cross-validated AUC of 0.937 with 83.3% accuracy, and the procedure was safe and feasible in children. These findings support moving beyond biomarker cut-offs toward integrative diagnostic approaches that better reflect real-world clinical practice. Conclusions: Combining copeptin with clinical profiling in a penalized regression framework yields a robust and interpretable tool for distinguishing CDI from PP. More broadly, such integrative models may enhance diagnostic precision in rare pediatric disorders and provide a foundation for future multicenter validation and clinical decision-support applications. Full article