Search Results (293)

Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, microbiome interactions, and molecular dysregulations involving gangliosides and sphingolipids. Current diagnostic strategies, including imaging, histopathology, immunohistochemistry, and emerging liquid biopsy techniques, are explored for their role in improving early detection and monitoring. Treatment remains challenging, with standard therapies—surgery, radiotherapy, and temozolomide—offering limited survival benefits. Innovative therapies are increasingly being explored and implemented, including immune checkpoint inhibitors, CAR-T cell therapy, dendritic and peptide vaccines, and oncolytic virotherapy. Advances in nanotechnology and personalized medicine, such as individualized multimodal immunotherapy and NanoTherm therapy, are also discussed as strategies to overcome the blood–brain barrier and tumor heterogeneity. Additionally, stem cell-based approaches show promise in targeted drug delivery and immune modulation. Non-conventional strategies such as ketogenic diets and palliative care are also evaluated for their adjunctive potential. While novel therapies hold promise, GBM’s complexity demands continued interdisciplinary research to improve prognosis, treatment response, and patient quality of life. This review underscores the urgent need for personalized, multimodal strategies in combating this devastating malignancy. Full article

Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical knowledge, capturing global multimodal features, and accurately delineating tumor boundaries. To address these challenges, the Hybrid Network for Multimodal Brain Tumor Segmentation (HN-MBTS) is proposed, which incorporates prior medical knowledge to refine feature extraction and boundary precision. Key innovations include the Two-Branch, Two-Model Attention (TB-TMA) module for efficient multimodal feature fusion, the Linear Attention Mamba (LAM) module for robust multi-scale feature modeling, and the Residual Attention (RA) module for enhanced boundary refinement. Experimental results demonstrate that this method significantly outperforms existing approaches. On the BraT2020 and BraT2023 datasets, the method achieved average Dice scores of 87.66% and 88.07%, respectively. These results confirm the superior segmentation accuracy and efficiency of the approach, highlighting its potential to provide valuable assistance in clinical settings. Full article

Locally advanced rectal cancer (LARC) remains a major clinical challenge due to its high risk of local recurrence and distant metastasis. Although total mesorectal excision (TME) has been established as the gold standard surgical approach, high recurrence rates associated with surgery alone have driven the development of multimodal preoperative strategies, such as radiotherapy and chemoradiotherapy. More recently, total neoadjuvant therapy (TNT)—which integrates systemic chemotherapy and radiotherapy prior to surgery—and non-operative management (NOM) for patients who achieve a clinical complete response (cCR) have further expanded treatment options. These advances aim not only to improve oncologic outcomes but also to enhance quality of life (QOL) by reducing long-term morbidity and preserving organ function. However, several unresolved issues persist, including the optimal sequencing of therapies, precise risk stratification, accurate evaluation of treatment response, and effective surveillance protocols for NOM. The advent of molecular biomarkers, next-generation sequencing, and artificial intelligence (AI) presents new opportunities for individualized treatment and more accurate prognostication. This narrative review provides a comprehensive overview of the current status of preoperative treatment for LARC, critically examines emerging strategies and their supporting evidence, and discusses future directions to optimize both oncological and patient-centered outcomes. By integrating clinical, molecular, and technological advances, the management of rectal cancer is moving toward truly personalized medicine. Full article

Sarcopenia is a progressive age-related musculoskeletal disorder characterized by loss of muscle mass, strength, and physical performance, contributing to functional decline and increased risk of disability. Emerging evidence suggests that vitamin D (Vit D) plays a pivotal role in skeletal muscle physiology beyond its classical functions in bone metabolism. This review aims to critically analyze the relationship between serum Vit D levels and sarcopenia in older adults, focusing on pathophysiological mechanisms, diagnostic criteria, clinical evidence, and preventive strategies. An integrative narrative review of observational studies, randomized controlled trials, and meta-analyses published in the last decade was conducted. The analysis incorporated international diagnostic criteria for sarcopenia (EWGSOP2, AWGS, FNIH, IWGS), current guidelines for Vit D sufficiency, and molecular mechanisms related to Vit D receptor (VDR) signaling in muscle tissue. Low serum 25-hydroxyvitamin D levels are consistently associated with decreased muscle strength, reduced physical performance, and increased prevalence of sarcopenia. Although interventional trials using Vit D supplementation report variable results, benefits are more evident in individuals with baseline deficiency and when combined with protein intake and resistance training. Mechanistically, Vit D influences muscle health via genomic and non-genomic pathways, regulating calcium homeostasis, mitochondrial function, oxidative stress, and inflammatory signaling. Vit D deficiency represents a modifiable risk factor for sarcopenia and functional impairment in older adults. While current evidence supports its role in muscular health, future high-quality trials are needed to establish optimal serum thresholds and dosing strategies for prevention and treatment. An individualized, multimodal approach involving supplementation, exercise, and nutritional optimization appears most promising. Full article

Background: Colorectal liver metastasis (CRLM) represents a major clinical challenge in oncology, affecting 25–50% of colorectal cancer patients and significantly impacting survival. While multimodal therapies—including surgical resection, systemic chemotherapy, and local ablative techniques—have improved outcomes, prognosis remains heterogeneous due to variations in tumor biology, patient factors, and institutional practices. Methods: This review synthesizes current evidence on prognostic factors influencing CRLM management, encompassing clinical (e.g., tumor burden, anatomic distribution, timing of metastases), biological (e.g., CEA levels, inflammatory markers), and molecular (e.g., RAS/BRAF mutations, MSI status, HER2 alterations) determinants. Results: Key findings highlight the critical role of molecular profiling in guiding therapeutic decisions, with RAS/BRAF mutations predicting resistance to anti-EGFR therapies and MSI-H status indicating potential responsiveness to immunotherapy. Emerging tools like circulating tumor DNA (ctDNA) and radiomics offer promise for dynamic risk stratification and early recurrence detection, while the gut microbiome is increasingly recognized as a modulator of treatment response. Conclusions: Despite advancements, challenges persist in standardizing resectability criteria and integrating multidisciplinary approaches. Current guidelines (NCCN, ESMO, ASCO) emphasize personalized strategies but lack granularity in terms of incorporating novel biomarkers. This exhaustive review underscores the imperative for the development of a unified, biomarker-integrated framework to refine CRLM management and improve long-term outcomes. Full article

Artificial intelligence (AI) has emerged as a significant driving force in the development of technology and industry. It is also integrated with music as music AI in music generation and analysis. It originated from early algorithmic composition techniques in the mid-20th century. Recent advancements in machine learning and neural networks have enabled innovative music generation and exploration. This article surveys the development history and technical route of music AI, analyzes the current status and limitations of music artificial intelligence across various areas, including music generation and composition, rehabilitation and treatment, as well as education and learning. It reveals that music AI has become a promising creator in the field of music generation. The influence of music AI on the music industry and the challenges it encounters are explored. Additionally, an emotional music generation system driven by multimodal signals is proposed. Although music artificial intelligence technology still needs to be further improved, with the continuous breakthroughs in technology, it will have a more profound impact on all areas of music. Full article

Background: Chemotherapy-induced peripheral neuropathy is a common and debilitating side effect of cancer treatment. While exercise has shown promise in alleviating this burden, it remains underutilised in clinical practice due to the lack of accessible, clinician-friendly guidance. Aim: This review aimed to synthesise current evidence on exercise interventions for managing chemotherapy-induced peripheral neuropathy and provide practical insights to support clinicians in integrating these approaches into patient care. Methods: A search was conducted across MEDLINE, CINAHL, and SPORTDiscus using keywords related to exercise and CIPN. Studies were included if they involved adults receiving neurotoxic chemotherapy and exercise-based interventions. Two authors independently screened studies and resolved conflicts with a third author. Study quality was assessed using the JBI Critical Appraisal Tools, and only studies meeting a minimum quality standard were included. A balanced sampling approach was employed. Data on study design, participant characteristics, interventions, and outcomes were extracted. Results: Eleven studies were included, covering various exercise modalities: multimodal (n = 5), yoga (n = 2), aerobic (n = 1), resistance (n = 1), balance (n = 1), and sensorimotor (n = 1). Exercise interventions, particularly multimodal exercise, significantly improved symptom severity, functionality, and quality of life (p < 0.05). The studies had high methodological quality, with randomised controlled trials scoring between 9/13 and 11/13, and quasi-experimental studies scoring 8/9 on JBI tools. Conclusions: This review highlights the significant benefits of exercise, especially multimodal exercise, for managing CIPN and provides guidance for integrating these strategies into clinical practice. Future research is needed to refine exercise prescriptions and develop standardised guidelines. Full article

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Background/Objectives: Fatigue and cognitive impairment are common issues for People with Multiple Sclerosis (PwMS), affecting over 80% and 40–65%, respectively. The relationship between these two debilitating conditions is complex, with cognitive deficits exacerbating fatigue and vice versa. This study investigates the effects of a multimodal intervention combining cognitive rehabilitation and neuromodulation to alleviate fatigue and enhance cognitive performance in PwMS. Methods: The research employed multiple baselines across the subjects in a Single-Case Experimental Design (mbSCED) with a cohort of three PwMS diagnosed with Relapsing–Remitting MS. The intervention protocol consisted of a baseline phase followed by a four-week treatment involving transcranial direct current stimulation (tDCS) and cognitive training using RehaCom^® software (version 6.9.0). Fatigue levels were measured using the modified Fatigue Impact Scale (mFIS), while cognitive performance was evaluated through standardized neuropsychological assessments. Results: The multimodal protocol exhibited high feasibility and acceptability, with no dropouts. Individual responsiveness outcomes varied, with two PwMS showing significant decreases in fatigue and improvements in cognitive performance, particularly in the trained domains. Their motor performance and quality of life also improved, suggesting that the treatment had indirect beneficial effects. Conclusions: This study provides preliminary evidence for the potential benefits of integrating neuromodulation and cognitive rehabilitation as a personalized therapeutic strategy for managing fatigue and cognitive impairments in MS. Further research is needed to delineate the specific contributions of each intervention component and establish standardized protocols for clinical implementation. The insights gained may lead to more effective, tailored treatment options for PwMS. Full article

Respiratory diseases represent a persistent global health challenge, underscoring the need for intelligent, accurate, and personalized diagnostic and therapeutic systems. Existing methods frequently suffer from limitations in diagnostic precision, lack of individualized treatment, and constrained adaptability to complex clinical scenarios. To address these challenges, our study introduces a modular AI-powered framework that integrates an audio-based disease classification model with simulated molecular biomarker profiles to evaluate the feasibility of future multimodal diagnostic extensions, alongside a synthetic-data-driven prescription recommendation engine. The disease classification model analyzes respiratory sound recordings and accurately distinguishes among eight clinical classes: bronchiectasis, pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma, chronic obstructive pulmonary disease (COPD), bronchiolitis, and healthy respiratory state. The proposed model achieved a classification accuracy of 99.99% on a holdout test set, including 94.2% accuracy on pediatric samples. In parallel, the prescription module provides individualized treatment recommendations comprising drug, dosage, and frequency trained on a carefully constructed synthetic dataset designed to emulate real-world prescribing logic.The model achieved over 99% accuracy in medication prediction tasks, outperforming baseline models such as those discussed in research. Minimal misclassification in the confusion matrix and strong clinician agreement on 200 prescriptions (Cohen’s $\kappa$ = 0.91 [0.87–0.94] for drug selection, 0.78 [0.74–0.81] for dosage, 0.96 [0.93–0.98] for frequency) further affirm the system’s reliability. Adjusted clinician disagreement rates were 2.7% (drug), 6.4% (dosage), and 1.5% (frequency). SHAP analysis identified age and smoking as key predictors, enhancing model explainability. Dosage accuracy was 91.3%, and most disagreements occurred in renal-impaired and pediatric cases. However, our study is presented strictly as a proof-of-concept. The use of synthetic data and the absence of access to real patient records constitute key limitations. A trialed clinical deployment was conducted under a controlled environment with a positive rate of satisfaction from experts and users, but the proposed system must undergo extensive validation with de-identified electronic medical records (EMRs) and regulatory scrutiny before it can be considered for practical application. Nonetheless, the findings offer a promising foundation for the future development of clinically viable AI-assisted respiratory care tools. Full article

Background and Objectives: Uterine cancer is the most common gynaecologic malignancy in developed countries, yet the psychosocial sequelae of treatment are incompletely described. This prospective, single-centre study quantified six-month changes in the quality of life (QoL) and perceived stress in women with newly diagnosed uterine cancer and explored clinical moderators of change. Methods: Participants completed four validated self-report questionnaires: the 36-item Short-Form Health Survey (SF-36), the 26-item World Health Organization Quality-of-Life-BREF (WHOQOL-BREF), the 30-item EORTC QLQ-C30 and the 10-item Perceived Stress Scale (PSS-10) before therapy and again six months after surgery ± adjuvant chemoradiation. Subgroup analyses were performed for stage (FIGO I–II vs. III–IV). Results: Mean SF-36 Physical Functioning improved from 58.7 ± 12.1 to 63.1 ± 12.6 (Δ = +4.4 ± 7.3; p = 0.000, d = 0.36). PSS declined from 24.1 ± 5.6 to 20.8 ± 5.4 (Δ = −3.3 ± 5.0; p < 0.001, d = 0.66). The WHOQOL-BREF Physical and Psychological domains rose by 4.4 ± 6.9 and 3.5 ± 7.3 points, respectively (both p < 0.01). EORTC QLQ-C30 Global Health increased 5.1 ± 7.6 points (p < 0.001) with parallel reductions in fatigue (−5.4 ± 9.0) and pain (−4.8 ± 8.6). Advanced-stage patients showed larger reductions in stress (ΔPSS −3.5 ± 2.5 vs. −2.3 ± 2.3; p = 0.036) but similar QoL gains. ΔPSS correlated inversely with ΔWHOQOL Psychological (r = −0.53) and ΔSF-36 Mental Health (r = −0.49) and positively with ΔEORTC Global Health (r = −0.42) (all p < 0.001). Conclusions: Over six months, multimodal uterine cancer treatment was associated with clinically meaningful QoL improvements and moderate stress reduction. Greater stress relief paralleled superior gains in psychological and global health indices, highlighting the importance of integrative survivorship care. Full article

Background/Objective: Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with growing relevance in psychiatry. Its ability to measure cortical hemodynamics positions it as a potential tool for monitoring neurofunctional changes related to treatment. However, the specific features and level of consistency of its use in clinical psychiatric settings remain unclear. A scoping review was conducted under PRISMA-ScR guidelines to systematically map how fNIRS has been used in monitoring treatment response among individuals with psychiatric disorders. Methods: Forty-seven studies published between 2009 and 2025 were included based on predefined eligibility criteria. Data was extracted on publication trends, research design, sample characteristics, fNIRS paradigms, signal acquisition, preprocessing methods, and integration of clinical outcomes. Reported limitations and conflicts of interest were also analyzed. Results: The number of publications increased sharply after 2020, predominantly from Asia. Most studies used experimental designs, with 31.9% employing randomized controlled trials. Adults were the primary focus (93.6%), with verbal fluency tasks and DLPFC-targeted paradigms most common. Over half of the studies used high-density (>32-channel) systems. However, only 44.7% reported motion correction procedures, and 53.2% did not report activation direction. Clinical outcome linkage was explicitly stated in only 12.8% of studies. Conclusions: Despite growing clinical interest, with fNIRS showing promise as a non-invasive neuroimaging tool for monitoring psychiatric treatment response, the current evidence base is limited by methodological variability and inconsistent outcome integration. There is a rising need for the adoption of standardized protocols for both design and reporting. Future research should also include longitudinal studies and multimodal approaches to enhance validity and clinical relevance. Full article

The assessment of systemic congestion in acute heart failure (AHF) remains clinically challenging, particularly across different left ventricular ejection fraction (LVEF) phenotypes. This study aimed to evaluate whether differences exist in the degree of congestion, assessed through a multimodal approach including physical examination, biomarkers (NT-proBNP, CA125), and point-of-care ultrasound using the Venous Excess Ultrasound (VExUS) protocol, between patients with preserved (HFpEF) and reduced ejection fraction (HFrEF). We conducted a prospective observational study involving 90 hospitalized AHF patients, 80 of whom underwent a complete VExUS assessment. Although patients with HFrEF exhibited higher levels of NT-proBNP and CA125, and more frequent signs of third-space fluid accumulation such as pleural effusion and ascites, no statistically significant differences were found in VExUS grades between the two groups. These findings suggest that the VExUS protocol provides consistent and reproducible information on systemic venous congestion, regardless of LVEF phenotype. Its integration into clinical practice may help refine congestion assessment and optimize diuretic therapy. Further multicenter studies with larger populations are warranted to validate its diagnostic and prognostic utility and to determine its potential role in guiding individualized treatment strategies in AHF. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article

Viral conjunctivitis is a highly contagious ocular condition that significantly impacts patient quality of life and healthcare resources. Despite its self-limiting nature, the condition remains a significant public health concern due to its high transmissibility, prolonged symptoms, and potential complications such as subepithelial infiltrates (SEIs). This review aimed to synthesize and evaluate current management strategies for adenoviral conjunctivitis and provide an evidence-based treatment framework. A systematic literature search of PubMed and the Cochrane Library was conducted, identifying 25 eligible studies published between 2009 and 2024 that focused on clinical interventions including supportive care, antiseptics, corticosteroids, antivirals, and immune modulators. The findings indicate that while supportive therapy and hygiene measures remain central to care, antiseptic agents, specifically povidone–iodine, and topical steroids offer additional benefit in reducing symptom duration and complications. Combination therapies integrating antiseptics, corticosteroids, and immunomodulators show promise for more severe cases, especially those complicated by SEIs. This review proposes an evidence-based comprehensive, multimodal approach management algorithm while highlighting the need for future research in antiviral development and diagnostic innovation to avoid mistreatment and unnecessary antibiotic use. Full article