Search Results (6,039)

Accurate delineation of infarct core and ischemic penumbra in acute ischemic stroke primarily relies on computed tomography perfusion (CTP), where the arterial input function (AIF) is essential for reliable perfusion quantification. However, reliable and fast AIF selection remains challenging in clinical practice due to noise, vascular heterogeneity, and inter-patient variability in bolus dynamics. In this study, we propose TriAIF-RWKV, a three-stage framework for robust and automated AIF extraction. Specifically, ACSANet is first employed for spatial vascular localization using axial and channel-aware attention mechanisms, thereby narrowing the candidate arterial region and reducing the AIF search space. Then, a Dilated-RWKV network is introduced to model temporal intensity dynamics from a global sequence perspective, allowing robust identification of AIF-consistent patterns. Finally, a physiology-informed scoring strategy is used to select the optimal AIF by evaluating baseline stability, peak enhancement, and washout characteristics. Extensive experiments on CTP datasets were conducted from multiple perspectives, including AIF waveform fidelity, perfusion parameter estimation, and lesion-level analysis. The results demonstrate that the proposed method achieved high agreement with expert-selected AIFs, with a global waveform PCC of 0.973, peak correlation of 0.942, and TTP correlation of 0.973 with a mean error of 0.923 s. Furthermore, the proposed method provides more consistent downstream perfusion quantification, achieving higher consistency of CTP-derived parameters and improved lesion-to-normal tissue discrimination compared with existing approaches. These results highlight its potential for reliable clinical perfusion assessment. Full article

Liquid biopsy, the analysis of tumor-derived material in blood, saliva, and other body fluids, is increasingly explored for the diagnosis, surveillance, and molecular characterization of head and neck squamous cell carcinoma (HNSCC). Its performance, however, is not uniform across the disease, and the reason is fundamentally molecular. human papillomavirus (HPV)-positive oropharyngeal cancers carry viral oncogenes that are absent from the host genome and therefore provide a near ideal, tumor specific circulating marker, whereas HPV-negative tumors are driven by a heterogeneous somatic landscape that offers no single universal target. In this narrative review, we adopt a molecular perspective. We first examine the biological origin of circulating tumor DNA and of the other analytes that liquid biopsy can interrogate including circulating tumor HPV DNA, viral transcripts, microRNAs, extracellular vesicles, and methylation signatures. We then consider how analytical platforms, from droplet digital PCR to next generation and ultrasensitive whole-genome sequencing, translate these molecules into measurements. Only afterward do we discuss the clinical questions, organized by clinical objective rather than by individual study: diagnosis and early detection, prognosis and risk stratification, treatment response monitoring, minimal residual disease and surveillance, and biomarker guided de-escalation in HPV-positive disease. Twelve registered clinical trials, involving approximately 1183 patients, are presented as illustrations of these questions. We close on the biological and technical gaps that still separate promising signals from clinical practice, and on the multi analyte and dynamic strategies most likely to bridge them. At present, liquid biopsy should be regarded as a complementary tool rather than as a replacement for established clinicopathological assessment. Full article

Breast cancer (BRCA) possesses prominent molecular heterogeneity, where aberrant expression of genes annotated to asparagine metabolism networks drives malignant progression and therapeutic resistance. However, systematic construction of prognostic signatures from a holistic asparagine metabolic pathway perspective remains scarce, limiting the clinical translation of metabolic insights into prognostic tools. We integrated TCGA and GEO BRCA transcriptomic datasets to screen asparagine metabolism-related differentially expressed genes and build a prognostic model. Six biomarkers, SLC35A2, SRD5A2, NT5E, CEL, IFNG and CNR1, were selected via univariate Cox, LASSO and multivariate Cox regression. SRD5A2 and IFNG were enriched in low-risk patients, while the other four genes were upregulated in high-risk subgroups. This signature reliably stratifies patient prognosis, with risk scores correlating strongly with pathway activity, immune infiltration, immune checkpoints, mutation landscapes and drug responsiveness. Bioinformatic results were validated via TCGA cohort analysis, in vitro cellular assays and Western blot. Two in vivo models were established: 4T1 xenografts in 6-week-old BALB/c mice and DMBA/hormone-induced spontaneous breast tumors in 8-week-old SD rats. Tumors were generated by cell injection or DMBA gavage plus cyclic hormone treatment, and tissue sections were processed for immunohistochemistry. Consistent differential expression of the six core genes was validated across all in vitro and in vivo systems. In conclusion, this asparagine metabolism-associated signature offers candidate biomarkers for personalized prognosis and provides preclinical evidence for metabolism-targeted BRCA therapy. Full article

Osteonecrosis of the femoral head (ONFH) is a progressive and potentially disabling condition caused by compromised blood supply to the femoral head, leading to bone necrosis and collapse. Early diagnosis is essential to enable joint-preserving interventions and improve patient outcomes. Magnetic resonance imaging (MRI) is currently considered the most sensitive modality for early detection, whereas computed tomography (CT) provides superior assessment of subchondral bone integrity and structural collapse. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, has emerged as a promising tool to enhance diagnostic accuracy, automate lesion segmentation, and predict disease progression. This review aims to provide an overview of current AI applications in MRI for ONFH, focusing on early diagnostic, disease staging and classification, volumetric assessment, differential diagnosis and prognostic prediction. A total of 61 articles were initially identified, of which 13 studies (2021–2025) met the inclusion criteria. Results indicate that DL models, particularly convolutional neural networks (CNNs), achieve excellent diagnostic performance, with reported accuracies up to 98.4% and area under the curve (AUC) values reaching 0.98 for early-stage detection. Several models demonstrated performance comparable to or exceeding that of experienced clinicians, particularly in differentiating ONFH from other hip pathologies and in early disease recognition. AI algorithms also showed high accuracy in staging and classification (AUC up to 99.7% in internal validation), as well as in automated segmentation and volumetric assessment (Dice coefficients up to 0.89), enabling objective quantification of necrotic lesions. Furthermore, prognostic models integrating radiomics and ML techniques demonstrated promising results in predicting femoral head collapse (AUC up to 0.85). From a clinical perspective, AI appears to function primarily as a supportive tool, improving diagnostic consistency, efficiency, and reproducibility, and acting as a “second reader” capable of reducing variability among less experienced clinicians. However, significant limitations remain, including dataset heterogeneity, predominance of retrospective and monocentric studies, and limited integration of clinical data. In conclusion, AI-based MRI analysis shows strong potential to enhance the diagnosis, staging, and management of ONFH. Future research should focus on multicenter prospective validation, integration of multimodal clinical data, and development of explainable and generalizable models to facilitate widespread clinical adoption. Full article

Background/Objectives: Surgically treated oral cancer patients often face velopharyngeal insufficiency, which adversely impacts their quality of life (QOL). Our past investigations revealed noticeable MR signal shifts in velopharyngeal-related muscles during water deglutition on CMR among healthy individuals. We hypothesized that analyzing these distinct signal fluctuations could offer a novel visual perspective for detecting deglutition anomalies in surgical oral cancer cases. This feasibility study aimed to assess velopharyngeal-related muscular performance via CMR in post-operative oral cancer patients. Methods: Nineteen patients with oral malignancies were prospectively evaluated pre- and post-operatively. Twelve quantitative CMR metrics alongside subjective dysphagia scores were investigated. We analyzed whether signal intensity (SI) ratio alterations in these muscles correlated with dysphagia severity and clinical parameters. Results: Although three specific CMR parameters significantly worsened post-operatively, muscle MR signal values increased during deglutition without significant variation between pre- and post-surgical states. Subjective swallowing assessments remained entirely normal across all subjects. Conclusions: This preliminary investigation indicates that CMR can visualize stable velopharyngeal-related muscle activity after surgery in oral cancer patients maintaining adequate swallowing status. Nevertheless, confirmation via larger, symptomatic patient cohorts alongside gold-standard techniques remains mandatory. Full article

Background: Adoption of surgical robotic systems is accelerating globally, driven by advances in clinical capability, ergonomics, and workflow efficiency. However, robotic surgery programs introduce complex organizational challenges related to patient safety, workforce capability, infrastructure readiness, governance, vendor dependency, procurement, and long-term sustainability. Hospitals often lack structured, leadership-oriented frameworks to support systematic, context-appropriate evaluation, adoption, and governance of these technologies. Objective: To develop a multidisciplinary, consensus-based framework to support leadership-level evaluation, organizational readiness assessment, adoption, and governance of surgical robotic systems. Methods: A two-round modified Delphi study was conducted at Singapore General Hospital involving eleven stakeholder groups spanning clinical, perioperative, technical, operational, governance, and executive domains. Department-level consensus responses were generated. In Round 1, stakeholders completed role-specific questionnaires comprising Likert-scale items and open-ended questions. Items rated as low importance were excluded, while items of moderate importance were refined through thematic analysis. In Round 2, refined and newly generated items were re-evaluated. Items achieving ≥70% agreement for high importance within each stakeholder group were retained. Results: A total of 417 consensus-derived items were identified and organized into nine thematic domains encompassing patient safety and risk management, technical capability and surgical performance, workflow integration, training and credentialing, governance and sustainability, vendor support, infrastructure, logistics, and operational readiness. The framework was operationalized through category-specific interpretive scoring rubrics and three domain-weighted decision models reflecting clinician, safety, and procurement perspectives. Conclusions: This study presents a leadership-oriented, multi-stakeholder framework to support the risk-aware evaluation, adoption, implementation, and governance of surgical robotic systems. By integrating considerations across institutional readiness, comparative robotic platform assessment, and program governance, the framework provides a structured approach to decision-making throughout the robotic surgery lifecycle. While it may serve as a useful starting point for institutions seeking to establish or expand robotic surgery programs, the framework was developed and evaluated within a single academic tertiary healthcare institution. Further validation across diverse healthcare settings, including community hospitals, non-academic institutions, specialty centers, and resource-constrained environments, is required before broader adoption can be recommended. Full article

Background/Objectives: Chronic orofacial pain disorders are highly heterogeneous and therapeutically challenging. Although current classifications such as the International Classification of Orofacial Pain (ICOP) and the concept of nociplastic pain have improved diagnostic consistency, patients with the same diagnosis may show substantial differences in symptom severity, underlying biology, and treatment response. This suggests that disease classifications may describe clinical phenotypes more effectively than the biological mechanisms sustaining persistent pain. This article proposes the Adaptive Hierarchical Domain Network (AHDN) as a conceptual framework for understanding chronic orofacial pain from a precision medicine perspective. Conceptual Framework: AHDN integrates concepts from systems biology, network neuroscience, predictive processing, biomarker research, and precision medicine. It organizes chronic pain across five hierarchically related but dynamically interacting layers: biological susceptibility, peripheral nociceptive drivers, central adaptive plasticity, behavioral adaptation, and social embedding. The framework is intended to complement, rather than replace, established diagnostic classifications. Implications: The proposed framework suggests that burning mouth syndrome, persistent dentoalveolar pain disorder, persistent idiopathic facial pain, occlusal dysesthesia, and subsets of temporomandibular disorders may represent different mechanistic configurations within a shared adaptive network. AHDN provides a hypothesis-generating structure for mechanistic phenotyping, biomarker development, and individualized management. However, the framework remains conceptual and requires prospective clinical validation, standardized assessment methods, and evaluation of its clinical utility before routine implementation. Full article

Background and Objectives: Dentofacial deformities require a multidisciplinary surgical–orthodontic protocol (SOP) in which orthodontic preparation, orthognathic surgery, postoperative finishing, and retention are closely coordinated to achieve stable functional and aesthetic outcomes. The increasing adoption of clear aligners, digital workflows, and artificial intelligence (AI) has profoundly modified this therapeutic pathway. This communication examines the role of clear-aligner therapy and AI throughout the contemporary surgical–orthodontic protocol, with particular emphasis on their integration into clinical practice. Discussion: From the surgical perspective, clear aligners provide treatment outcomes comparable to those achieved with conventional fixed appliances while offering greater predictability of treatment duration through digital treatment planning. However, their use requires careful management of specific clinical aspects, including surgical timing, intraoperative anchorage, postoperative dentoalveolar stabilisation, transverse dimension management, and retention, all of which demand close collaboration between the orthodontist and the maxillofacial surgeon. The communication also summarises current AI applications in orthodontics and orthognathic surgery, including automated cephalometric analysis, image segmentation, treatment-decision support, virtual patient construction, clear-aligner setup, soft-tissue prediction, refinement-risk assessment, and remote monitoring. Although AI has significantly improved the efficiency, reproducibility, and standardisation of diagnosis and treatment planning, current evidence supports its use as a clinician-supervised decision-support technology rather than as an autonomous system capable of managing the entire orthodontic–surgical pathway. Conclusions: The integration of clear aligners, digital planning, and AI represents an important step toward a more personalised and efficient workflow, while continued clinical validation and multidisciplinary expertise remain essential for achieving predictable long-term outcomes. Full article

Cardiac rehabilitation (CR) is a central component of optimized secondary prevention. Since their inception, CR programmes have progressively evolved. These incorporate several components in a multidisciplinary setting, aimed at addressing the diverse needs of the complex cardiovascular patient. From ischaemic heart disease to heart failure and cardio-oncology, CR has shown its relevance across various moments of the cardiovascular continuum. Notably, data has underscored the importance of an individualized assessment coupled to programme tailoring according to patient characteristics, to harness the full benefits of CR while ensuring safety. Against this background, current guidelines have endorsed CR in different clinical settings. Despite this, several challenges concerning CR implementation remain, including suboptimal referral and compliance rates, as well as underrepresentation of specific patient subgroups. In this article, we provide an overview concerning current paradigms for CR in contemporary clinical practice, while also exploring novel developments and future perspectives on this pivotal field. Full article

Background/Objectives: Empathy is a fundamental component of patient-centered care and plays a critical role in the physician–patient relationship. This study aimed to evaluate empathy levels among undergraduate medical students at a medical university in Romania, examined from a Medical Humanities perspective, and to analyze how these scores are distributed according to gender, academic year, clinical stage, and direct experience with patient contact. Methods: A descriptive, cross-sectional study was conducted among 209 undergraduate medical students across all six academic years. Empathy levels were assessed using the Romanian-language version of the Jefferson Scale of Empathy—Student Version (JSE-S). Differences in empathy scores were analyzed using independent samples t-tests and a one-way analysis of variance (ANOVA) with Tukey Honestly Significant Difference (HSD) post hoc comparisons. Statistical significance was set at an alpha level of less than 0.05. Results: Internal consistency of the JSE-S total scale was good (Cronbach’s α = 0.829). The overall mean total JSE-S score was 107.34 ± 14.34. Female students demonstrated significantly higher empathy scores compared to male students (109.92 ± 13.10 versus 99.37 ± 15.20; t = 4.450; p < 0.001; Cohen’s d = 0.77). No statistically significant differences were found between preclinical and clinical stage students (106.84 ± 14.75 versus 108.98 ± 12.90; p = 0.363; Cohen’s d = −0.15), between students with and without direct patient contact (108.20 ± 14.14 versus 104.48 ± 14.78; p = 0.115; Cohen’s d = 0.26), or across the six academic years (F = 1.614; p = 0.158; partial η² = 0.038). Conclusions: Medical students demonstrated moderate-to-high empathy levels, with gender representing the only statistically significant demographic factor. The absence of major differences across academic-year cohorts and clinical exposure suggests that empathy levels were comparably represented across the different stages of training within this cross-sectional sample. These findings contribute descriptive data on empathy levels among Romanian medical students and highlight gender as the main factor associated with variation in empathy scores in this sample. Full article

Kounis syndrome (KS) is a largely underdiagnosed cause of acute coronary syndromes triggered by allergic or hypersensitivity reactions. The syndrome results from complex immune cell activation with the release of vasoactive and prothrombotic mediators leading to coronary vasospasm (type I KS) or thrombosis of plaque (type II KS), stent (type III KS) or coronary artery bypass graft (type IV KS). Despite increasing recognition over the past few decades, its pathophysiological mechanisms, diagnostic boundaries, and therapeutic implications remain incompletely understood. A comprehensive diagnostic approach, including signs, symptoms, biochemical findings, electrocardiography, echocardiography, coronary angiography, and multimodality imaging, as well as invasive assessment in selected cases, can be recommended, although its implementation in routine practice remains limited. Available data indicate that angiographically documented epicardial coronary spasm is observed in only a minority of patients, while normal or non-obstructive coronary arteries are frequently encountered. Emerging data from provocative testing, invasive coronary functional assessment, cardiac magnetic resonance and nuclear imaging suggests that coronary microvascular dysfunction may contribute substantially to the clinical phenotype, expanding the traditional concept of allergic epicardial vasospasm. Current evidence supports the recognition of type I KS as a distinct allergic vasomotor acute coronary syndrome within the myocardial infarction non-obstructive coronary artery (MINOCA) spectrum, deserving greater recognition in future diagnostic classifications and clinical practice guidelines. This narrative review critically appraises current evidence, integrating historical perspectives with contemporary insights into classification, pathophysiology, triggers, diagnostic strategies and therapeutic approaches, focusing on the most frequent manifestation represented by the vasospastic variant. Full article

Background: Rural and resource-constrained settings face major barriers to timely cancer screening, diagnosis, and treatment due to limited specialist availability and centralised service-delivery models. In the Eastern Cape, a largely rural province with a constrained oncology workforce, a decentralised–centralised hybrid model was introduced to improve access to cancer care. Nelson Mandela Academic Hospital serves as the central referral hub within this model. This study evaluates the implementation process and impact of this decentralised cancer care model on service utilisation, access, and continuity of care. Methods: A quantitative quasi-experimental pre–post implementation and quality improvement evaluation was conducted using retrospectively collected routine service utilisation and programme data from April 2023 to February 2025. The study assessed the impact of a decentralised oncology care model on access, service integration, and utilisation outcomes. Data from facility registers and district health information systems were managed using Microsoft Excel and analysed using Stata and IBM SPSS Statistics. Descriptive statistics, correlation analysis, and linear regression were used to compare pre- and post-implementation changes in patient volumes, screening coverage, referral completion, workforce capacity, gender distribution, and service uptake. The intervention decentralised screening, diagnosis, follow-up, and patient navigation services to district and satellite facilities while centralising specialised oncology care at referral centres to improve accessibility, efficiency, and continuity of care. Results: Cancer patient attendance increased substantially over the study period, from 355 patients in April 2023 to a peak of 1039 in April 2024, with a sustained upward trend (B = 18.03, p = 0.005), reflecting an average monthly increase of 18 patients. Female patients accounted for most visits, while male attendance showed a significant increasing trend (B = 8.03, p < 0.001). Service integration improved, with strong positive correlations between new patient registrations, follow-up care, palliative services, and inpatient admissions, indicating an expanding continuum of care. Breast and cervical cancers contributed the highest service burden, while cervical and lung cancers showed significant upward trends. Seasonal variation in attendance was observed, particularly during festive periods. From an implementation perspective, screening coverage for priority cancers increased by 18%, while 732,349 individuals were reached through community awareness initiatives. Access improved substantially, evidenced by a reduction of 56,400 km in cumulative patient travel distance over one year. Workforce capacity was strengthened through the training of 517 healthcare workers, and 1943 patients received structured navigation support. Referral efficiency and continuity of care improved, although persistent bottlenecks were observed in diagnostic and referral pathways. Conclusions: The decentralised–centralised oncology care model demonstrated improved cancer service utilisation, access, and continuity of care in a rural, resource-limited setting. However, increasing patient volumes and interconnected service demands place additional pressure on health system capacity. Sustained investment in workforce development, screening—particularly for cervical cancer—and system efficiency is required. This model provides a scalable and context-appropriate framework for strengthening oncology services in similar low-resource settings. Full article

Pediatric acute myeloid leukemia (AML) is a highly heterogeneous malignancy, with cytogenetic and molecular abnormalities playing a critical role in determining prognosis and guiding treatment decisions. Despite therapeutic advances, patients with high-risk genetic mutations and translocations continue to experience suboptimal outcomes. As new targeted therapies emerge, the treatment of pediatric AML could undergo a paradigm shift, where “one-size-fits-all” chemotherapy is no longer the only frontline approach. Identifying genetic markers inform risk stratification and have greater impact on shaping the therapeutic approach, including the integration of targeted therapies such as FLT3 and menin inhibitors into frontline therapy. Furthermore, pediatric AML treatment options are being driven by recent discoveries in adult AML, broadening their clinical trials to include pediatric patients, in part due to the RACE for Children Act that went into effect in August 2020. This review identifies the most prevalent high-risk cytogenetic lesions in pediatric AML, emphasizing their incidence, prognostic significance, and implications for clinical management. By synthesizing current research on these key genetic abnormalities and their associated therapies, we aim to provide an updated perspective on the evolving landscape of high-risk pediatric AML management that can then lead to the establishment of an agile framework to rapidly evaluate, approve, and deploy novel agents. Full article

Background: Microbiome-based mechanisms have emerged as a key area of investigation in epilepsy, given the growing recognition that gut microbial communities can modulate central nervous system (CNS) function through the gut–brain axis. Epilepsy is a common chronic neurological disorder affecting more than 65 million people worldwide, and despite the availability of anti-seizure medications (ASMs), approximately 30% of patients develop drug-resistant epilepsy. Current ASMs primarily suppress seizures rather than prevent disease progression, highlighting the need for alternative therapeutic strategies. In this context, increasing evidence supports a role for microbiota-dependent pathways in modulating seizure activity and treatment responsiveness. However, the mechanistic basis of these interactions remains incompletely understood. Methods: This narrative review synthesizes findings from the existing literature to examine the role of microbiota-derived metabolites, including neurotransmitters, vitamins, and the polyphenol metabolite S-equol, in gut–brain communication relevant to epilepsy. Evidence was drawn from both preclinical animal models and clinical studies to provide an integrated, mechanistic perspective on how these pathways may influence central nervous system function and seizure susceptibility. Emphasis was placed on studies describing molecular, metabolic, and signaling mechanisms linking the gut microbiome to epileptogenesis and treatment response. Results: Current evidence indicates that communication between the gut and CNS occurs through neural pathways, such as the vagus nerve, as well as through circulating microbial metabolites. These metabolites can cross the intestinal barrier and, in some cases, the blood–brain barrier (BBB), serving as key mediators of host–microbiota signaling. Emerging studies suggest that while some microbial metabolites may directly influence neuronal hyperexcitability and seizure susceptibility, others likely exert secondary or modulatory effects through broader metabolic and immune pathways. However, the precise mechanisms underlying these interactions remain incompletely understood. Conclusions: Some microbial-derived metabolites may serve as promising biomarkers and mechanistic mediators of epilepsy; however, further investigation is needed to define the molecular and cellular pathways through which these metabolites influence seizure susceptibility and epileptogenesis. Full article

Large language model-enabled virtual patients may expand access to clinical skills practice by supporting explicitly defined practice tasks. This program evaluation examined medical students’ awareness, use, and perceptions of the McMaster Virtual SP Tool, a custom generative artificial intelligence tool developed to supplement clinical skills practice. We conducted an anonymous, single-institution cross-sectional survey of students across three cohorts at McMaster University. Quantitative responses were summarized descriptively, and free-text responses were analyzed using qualitative content analysis informed by task-aligned fidelity, deliberate practice, learner-centred feedback, and simulation instructional design. Thirty-five students responded; 23 (65.7%) were aware of the tool and 16 (45.7%) had used it. Among the 16 users, 15/16 (93.8%) found the tool at least somewhat easy to navigate; 13 (81.3%) would use it again; and 13 (81.3%) would recommend it. Across all respondents, 14/35 (40.0%) reported using AI-enabled virtual patients for OSCE preparation. Users valued its accessibility, independent low-stakes rehearsal, and usefulness for focused history-taking, question wording, and clinical reasoning. Perceived limitations included reduced human connection, nonverbal and emotional realism, physical examination practice, and feedback specificity. AI-enabled virtual patients may therefore be considered as adjuncts for selected cognitive and structural rehearsal tasks. Objective educational outcomes were not assessed. Full article