Search Results (1,768)

Background: Fibromyalgia syndrome (FMS) is a complex condition with poorly understood pathophysiology, characterized by widespread pain and an increasing recognition of its associations with genitourinary symptoms. The objective of this study was to characterize the prevalence, phenotype, and common comorbidities of lower urinary tract symptoms (LUTS) in women with FMS. Methods: A retrospective observational study was conducted using electronic medical records of 440 women diagnosed with FMS at a single institution between 1 January 2018, and 1 January 2024. Study subjects were evaluated for diagnoses associated with LUTS, including interstitial cystitis (IC), overactive bladder (OAB), and stress urinary incontinence (SUI), alongside comorbidities such as irritable bowel syndrome (IBS), generalized anxiety disorder (GAD), and major depressive disorder (MDD). Multivariate analyses were performed to assess predictors of conditions associated with LUTS. Results: LUTS were identified in 37.0% of FM patients. GAD and IBS were significantly associated with conditions associated with LUTS (OR = 4.62; OR = 8.53, p < 0.001). SUI was present in 17.05% of patients, falling between survey-based and confirmed prevalence rates in the general population. IC was diagnosed in 2.95% of FMS patients. OAB was observed in 6.8% of patients and associated with GAD (OR = 5.98, p < 0.001). Conclusions: This study highlights a substantial burden of diagnoses associated with LUTS in patients with FMS. There is relatively high prevalence of SUI and IC in this dataset. IBS and GAD were commonly found to co-occur with one or more LUTS-associated condition. Future prospective studies are needed to investigate a multimodal approach to the treatment of LUTS in these patients. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

Treatment of locally advanced rectal cancer (LARC), clinical stages II–III, typically involves multimodal treatment options. Over the past decade, the role of radiation therapy as a neoadjuvant treatment for LARC has evolved and is currently a part of total neoadjuvant therapy (TNT). Some recently published studies advocate for the omission of radiation therapy entirely, while others report on a non-operative approach that emphasizes the use of higher radiation therapy doses. This review aims to evaluate the latest literature on the current role of radiation therapy in the management of LARC, with a discussion of how to best select the most appropriate treatment protocol based on individual patient and tumor characteristics, comorbidities, and personal needs and preferences. Full article

The epidermal growth factor receptor (EGFR) is a key target for both cancer diagnosis and therapeutic interventions. Assessing EGFR expression before therapy has become routine in clinical practice, yet current methods like biopsy and immunohistochemistry (IHC) have significant limitations, including invasiveness, limited repeatability, and lack of real-time, whole-body data. EGFR-targeted imaging has emerged as a promising alternative. EGFR-targeting peptides, owing to their favorable physicochemical properties and versatility, are increasingly being explored for a variety of applications, including molecular imaging, drug delivery, and targeted therapy. Recent advances have demonstrated the potential of EGFR-targeting peptides conjugated to imaging probes for non-invasive, real-time in vivo tumor detection, precision therapy, and surgical guidance. Here, we provide a comprehensive overview of the latest progress in EGFR-targeting peptides development, with a particular focus on their application in the development of molecular imaging agents, including fluorescence imaging, PET/CT, magnetic resonance imaging, and multimodal imaging. Furthermore, we examine the challenges and future directions concerning the development and clinical application of EGFR-targeting peptide-based imaging probes. Finally, we highlight emerging technologies such as artificial intelligence, mutation-specific peptides, and multimodal imaging platforms, which offer significant potential for advancing the diagnosis and treatment of EGFR-targeted cancers. Full article

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early detection, refine diagnostic precision, and optimize treatment strategies becomes increasingly evident. However, despite significant progress, various challenges remain, particularly in terms of clinical applicability, generalizability, interpretability, and integration into routine practice. Understanding the current state of research is crucial for identifying gaps in the literature and exploring opportunities for future advancements. This literature review aims to provide a comprehensive overview of the existing studies on AI applications in PDAC, with a focus on disease detection, classification, survival prediction, treatment response assessment, and radiogenomics. By analyzing the methodologies, findings, and limitations of these studies, we aim to highlight the strengths of AI-driven approaches while addressing critical gaps that hinder their clinical translation. Furthermore, this review aims to discuss future directions in the field, emphasizing the need for multi-institutional collaborations, explainable AI models, and the integration of multi-modal data to advance the role of AI in personalized medicine for PDAC. Full article

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

Background: Post-traumatic trigeminal neuropathic pain (PTTNP) is a chronic condition often caused by dental procedures such as implant placement or tooth extraction. It involves persistent pain and sensory disturbances, negatively affecting the quality of life of patients. Methods: This retrospective observational study was conducted at Chosun University Dental Hospital and included 120 patients diagnosed with PTTNP involving the orofacial region. Patient data were collected between January 2014 and December 2023. Among them, 79 patients (65.8%) developed PTTNP following dental implant placement, with a total of 121 implants analyzed. The inferior alveolar nerve was most frequently involved. Clinical factors, including the time to treatment, removal of the causative factor, the Sunderland injury grade, and the type of treatment, were evaluated. Pain intensity and sensory changes were assessed using the visual analog scale (VAS). Results: Treatment initiated within the early post-injury period, commonly regarded as within three months, and implant removal tended to improve outcomes. Pharmacological therapy was the most commonly employed modality, particularly gabapentinoids (e.g., gabapentin, pregabalin) and tricyclic antidepressants such as amitriptyline. However, combined therapy, which included pharmacologic, physical, and surgical approaches, was associated with the greatest sensory improvement. Conclusions: Prompt, multidisciplinary intervention may enhance recovery in patients with PTTNP. Implant-related injuries require careful management, and multimodal strategies appear more effective than monotherapies. 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