Search Results (11,563)

Background: Androgenetic alopecia (AGA) and alopecia areata (AA) impose significant psychosocial burdens. While pharmacological and surgical treatments exist, the role of dietary factors remains underexplored due to methodological limitations in observational studies. This Mendelian randomization (MR) study investigates causal relationships between 187 dietary exposures and hair loss, leveraging genetic variants to address confounding biases. Methods: Genome-wide association study (GWAS) data from 161,625 UK Biobank participants were analyzed, focusing on food preferences and intake patterns. Genetic instruments for each of the 187 dietary exposures were selected at a genome-wide significance threshold (p < 5 × 10⁻⁸), with rigorous sensitivity analyses (MR-Egger, MR-PRESSO) to validate causality. Outcomes included AA and AGA datasets from the FinnGen consortium. Results: MR analysis identified 18 specific dietary exposures significantly associated with non-scarring hair loss (FDR < 0.05). Protective effects emerged for antioxidant-rich dietary exposures, represented by higher preferences for melon, onions, and tea. Elevated risks were observed for certain exposures, including croissants, goat cheese, and whole milk. Alcohol consumption exhibited the strongest risk associations. Our extensive analysis of alcohol intake, combining data from multiple studies, consistently identified it as a significant risk factor for both alopecia areata and androgenetic alopecia. Conclusions: These findings imply modifiable dietary patterns in hair loss pathophysiology. A dual strategy is proposed: prioritizing polyphenol-rich plant foods while minimizing pro-inflammatory triggers like processed carbohydrates and alcohol. Clinically, tailored dietary adjustments—reducing ultra-processed foods and alcohol—may complement existing therapies for hair loss management. Full article

Focal chondral defects of the knee are a common cause of pain and functional limitation in active individuals and may predispose to early degenerative joint changes. Given the limited regenerative capacity of hyaline cartilage, biologically based surgical strategies have emerged to promote tissue repair and restore joint function. This narrative review critically examines current treatment approaches that rely on autologous cell sources and scaffold-supported regeneration. Particular emphasis is placed on techniques that stimulate endogenous repair or support chondrocyte-based tissue restoration through the use of autologous biomaterial constructs. The influence of lesion morphology, joint biomechanics, and patient-specific variables on treatment selection is discussed in detail, focusing on the differences between tibiofemoral and patellofemoral involvement. Biologically driven approaches have shown promising mid- to long-term outcomes in selected patients, and are increasingly favoured over traditional methods in specific clinical scenarios. However, the literature remains limited by heterogeneity in study design, follow-up duration, and outcome measures. This review aims to provide an evidence-based, morphology-informed framework to support the clinical decision-making process in the management of knee cartilage defects. Full article

Background and Objectives: Periodontitis is a chronic inflammatory disease that leads to progressive destruction of periodontal tissues and remains a significant global health burden. While conventional therapies such as scaling and root planning offer short-term improvements, they often fall short in maintaining long-term microbial control, underscoring the need for adjunctive strategies. This study evaluated the clinical and microbiological effects of a novel essential oil (EO)-based gel—SmartGel OV—formulated with Origanum vulgare. Materials and Methods: Thirty adults with periodontitis were enrolled in a 4-month observational study, during which SmartGel OV was applied daily via gingival massage. Clinical outcomes and bacterial profiles were assessed through probing measurements and real-time PCR analysis. Additionally, a pilot AI-based tool was explored as a supplemental method to monitor inflammation progression through intraoral images. Results: Significant reductions were observed in Fusobacterium nucleatum and Capnocytophaga spp., accompanied by improvements in clinical markers, including probing depth, bleeding on probing, and plaque index. The AI framework successfully identified visual inflammation changes and supported early detection of non-responsiveness. Conclusions: SmartGel OV demonstrates promise as a natural adjunctive treatment for periodontitis and AI monitoring was included as an exploratory secondary tool to assess feasibility for future remote tracking. Full article

Oral mucositis (OM) is a severe inflammatory condition of the oral mucosa that is commonly associated with cancer therapies. Traditional treatments typically have limited efficacy and significant side effects, necessitating alternative approaches. Nanobased drug delivery systems (DDSs) present promising solutions, enhancing therapeutic outcomes while minimizing side effects. This review aims to evaluate the use of nanobased DDSs to treat OM. To reach these aims, an extensive literature review was conducted using the following databases: BVS, PubMed, Scopus, and Web of Science. The search strategy included the keywords “microparticles,” “nanoparticles,” “drug delivery system,” “oral mucositis,” “therapy,” and “treatment,” combined with the Boolean operators “AND” and “OR.” After applying filters for language, relevance, full-text availability, exclusion of review articles, and removal of duplicates, a total of 32 articles were selected for analysis. Of the 32 studies included in this review, 25 employed polymeric micro- or nanosystems for the treatment of OM. Regarding the stage of investigation, 10 studies were conducted in vitro, 16 were conducted in vivo, and 6 corresponded to clinical trials. Compared with conventional drug delivery approaches, most of these studies reported improved therapeutic outcomes. These findings highlight the potential of nanosystems as innovative strategies for enhancing OM treatment. Nonetheless, challenges in large-scale manufacturing, including reproducibility and safety, and the limited number of clinical trials warrant careful consideration. Future research with larger clinical trials is essential to validate these findings and effectively guide clinical practice. Full article

Polynucleotide (PN) and polydeoxyribonucleotide (PDRN) are DNA-derived biopolymers increasingly recognized for their potential in dermatology. Despite their structural similarities, PN and PDRN exhibit distinct functions due to differences in polymer length and molecular weight. PN, composed of longer DNA fragments, plays a key role in extracellular matrix remodeling. Conversely, PDRN, composed of relatively shorter oligonucleotide sequences than those of PN, enhances skin condition through adenosine receptor activations and supports nucleotide synthesis via both the salvage and de novo pathways. This review provides a critical comparison of the molecular characteristics and functions of PN and PDRN with particular emphasis on their dermatological applications. By delineating their respective roles in esthetic and regenerative medicine, we aim to highlight recent advances that may guide the development of optimized treatment strategies and foster evidence-based clinical practice. 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

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Background: Anti-programmed death 1 receptor (PD-1) therapy is a promising treatment strategy for patients with unresectable advanced or recurrent gastric/gastroesophageal junction (G/GEJ) cancer. However, its response rate and survival benefits are still limited; an immunological analysis of the residual tumor after anti-PD-1 therapy would be important. Methods: We evaluated the clinical efficacy of tumor resection (TR) after chemotherapy or anti-PD-1 therapy in patients with unresectable advanced or recurrent G/GEJ cancer and analyzed the immune status of tumor microenvironment (TME) by immunohistochemistry using their surgically resected specimens. Results: Patients treated with TR after anti-PD-1 therapy had significantly longer survival compared to those treated with chemotherapy and anti-PD-1 therapy alone. Expression of human leukocyte antigen (HLA) class I and major histocompatibility complex (MHC) class II on tumor cells was markedly downregulated after anti-PD-1 therapy compared to chemotherapy. Furthermore, the downregulation of HLA class I may be associated with the activation of transforming growth factor-β signaling pathway in the TME. Conclusions: Immune escape from cytotoxic T lymphocytes may be induced in the TME in patients with unresectable advanced or recurrent G/GEJ cancer after anti-PD-1 therapy due to the downregulation of HLA class I and MHC class II expression on tumor cells. TR may be a promising treatment strategy for these patients when TR is feasible after anti-PD-1 therapy. Full article

Liquid biopsy has emerged as a valuable tool for the detection and monitoring of colorectal cancer (CRC), providing minimally invasive insights into tumor biology through circulating biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Additional biomarkers, including tumor-educated platelets (TEPs) and exosomal RNAs, offer further potential for early detection and prognostic role, although ongoing clinical validation is still needed. This review summarizes the current evidence on the diagnostic, prognostic, and predictive capabilities of liquid biopsy in both metastatic and non-metastatic CRC. In the non-metastatic setting, liquid biopsy is gaining traction in early detection through screening and in identifying minimal residual disease (MRD), potentially guiding adjuvant treatment and reducing overtreatment. In contrast, liquid biopsy is more established in metastatic CRC for monitoring treatment responses, clonal evolution, and mechanisms of resistance. The integration of ctDNA-guided treatment algorithms into clinical practice could optimize therapeutic strategies and minimize unnecessary interventions. Despite promising advances, challenges remain in assay standardization, early-stage sensitivity, and the integration of multi-omic data for comprehensive tumor profiling. Future efforts should focus on enhancing the sensitivity of liquid biopsy platforms, validating emerging biomarkers, and expanding multi-omic approaches to support more targeted and personalized treatment strategies across CRC stages. Full article

Background: Pulmonary fibrosis (PF), the end-stage manifestation of interstitial lung disease, is defined by excessive extracellular matrix deposition and alveolar destruction. Activated fibroblasts, the primary matrix producers, rely heavily on dysregulated glucose metabolism for their activation. While Salt Inducible Kinase 2 (SIK2) regulates glycolytic pathways in oncogenesis, its specific contributions to fibroblast activation and therapeutic potential in PF pathogenesis remain undefined. This study elucidates the functional role of SIK2 in PF and assesses its viability as a therapeutic target. Methods: SIK2 expression/localization in fibrosis was assessed by Western blot and immunofluorescence. Fibroblast-specific Sik2 KO mice evaluated effects on bleomycin-induced fibrosis. SIK2’s role in fibroblast activation and glucose metabolism impact (enzyme expression, metabolism assays, metabolites) were tested. SIK2 inhibitors were screened and evaluated therapeutically in fibrosis models. Results: It demonstrated significant SIK2 upregulation, specifically within activated fibroblasts of fibrotic lungs from both PF patients and murine models. Functional assays demonstrated that SIK2 is crucial for fibroblast activation, proliferation, and migration. Mechanistically, SIK2 enhances fibroblast glucose metabolism by increasing the expression of glycolysis-related enzymes. Additionally, this study demonstrated that the SIK2 inhibitor YKL06-061 effectively inhibited PF in both bleomycin and FITC-induced PF mouse models with the preliminary safety profile. Furthermore, we identified a novel therapeutic application for the clinically approved drug fostamatinib, demonstrating it inhibits fibroblast activation via SIK2 targeting and alleviates PF in mice. Conclusions: Our findings highlight SIK2 as a promising therapeutic target and provide compelling preclinical evidence for two distinct anti-fibrotic strategies with significant potential for future PF treatment. Full article

In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule modulates anti-cancer immune responses, acting through diverse signaling pathways and cell populations. It has been implicated in the pathogenesis of numerous malignancies, including melanoma, gliomas, lung cancer, gynecological cancers, renal cancer, gastrointestinal tumors, and others, fostering the immunosuppressive environment and marking worse prognosis for the patients. B7-H3 targeting therapies, such as monoclonal antibodies, antibody–drug conjugates, and CAR T-cells, present promising results in preclinical studies and are the subject of ongoing clinical trials. CAR-T therapies against B7-H3 have demonstrated utility in malignancies such as melanoma, glioblastoma, prostate cancer, and RCC. Moreover, ADCs targeting B7-H3 exerted cytotoxic effects on glioblastoma, neuroblastoma cells, prostate cancer, and craniopharyngioma models. B7-H3-targeting also delivers promising results in combined therapies, enhancing the response to other immune checkpoint inhibitors and giving hope for the development of approaches with minimized adverse effects. However, the strategies of B7-H3 blocking deliver substantial challenges, such as poorly understood molecular mechanisms behind B7-H3 protumor properties or therapy toxicity. In this review, we discuss B7-H3’s role in modulating immune responses, its significance for various malignancies, and clinical trials evaluating anti-B7-H3 immunotherapeutic strategies, focusing on the clinical potential of the molecule. Full article

Foreign Language Syndrome (FLS) is a rare neuropsychiatric condition characterized by the sudden, involuntary use of a non-native language, with concurrent loss or suppression of the native language. Distinct from Foreign Accent Syndrome (FAS), FLS often arises acutely following anesthesia, brain injury, or psychological stress. Although neuroimaging typically reveals no structural pathology, functional disconnection within bilingual language control systems has been hypothesized. Case reports suggest contributions from both neurological disruptions—such as transient cortical dysfunction—and psychiatric mechanisms, including dissociation and conversion phenomena. This review synthesizes the clinical features, diagnostic strategies, neurocognitive models, and psychiatric interpretations of FLS. It emphasizes the importance of multidisciplinary evaluation and treatment and outlines prognosis patterns. The need for longitudinal follow-up, functional imaging studies, and centralized case databases is highlighted to better understand the pathophysiology and clinical management of this enigmatic syndrome. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated disorder of the central nervous system (CNS) characterized by inflammation, demyelination, axonal degeneration, and gliosis. Its pathophysiology involves a complex interplay of genetic susceptibility, environmental triggers, and immune dysregulation, ultimately leading to progressive neurodegeneration and functional decline. Although significant advances have been made in disease-modifying therapies (DMTs), many patients continue to experience disease progression and unmet therapeutic needs. Drug repurposing—the identification of new indications for existing drugs—has emerged as a promising strategy in MS research, offering a cost-effective and time-efficient alternative to traditional drug development. Several compounds originally developed for other diseases, including immunomodulatory, anti-inflammatory, and neuroprotective agents, are currently under investigation for their efficacy in MS. Repurposed agents, such as selective sphingosine-1-phosphate (S1P) receptor modulators, kinase inhibitors, and metabolic regulators, have demonstrated potential in promoting neuroprotection, modulating immune responses, and supporting remyelination in both preclinical and clinical settings. Simultaneously, artificial intelligence (AI) is transforming drug discovery and precision medicine in MS. Machine learning and deep learning models are being employed to analyze high-dimensional biomedical data, predict drug–target interactions, streamline drug repurposing workflows, and enhance therapeutic candidate selection. By integrating multiomics and neuroimaging data, AI tools facilitate the identification of novel targets and support patient stratification for individualized treatment. This review highlights recent advances in drug repurposing and discovery for MS, with a particular emphasis on the emerging role of AI in accelerating therapeutic innovation and optimizing treatment strategies. Full article

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS. Full article

Colorectal cancer (CRC) constitutes a major global health concern, ranking as the third most common cancer and the second leading cause of cancer-related mortality. The current review explores sex-based differences in CRC epidemiology, risk factors, tumor biology, and clinical outcomes. Males exhibit a higher incidence and mortality rate, with left-sided (distal) CRC predominating, while females are more frequently diagnosed with right-sided (proximal) tumors, which tend to be more aggressive and less responsive to conventional chemotherapy. Genetic disparities, including microsatellite instability and X-chromosome tumor suppressor genes, contribute to sex-specific differences in tumor progression and treatment response. Immune variations also influence disease outcomes, with females exhibiting stronger immune surveillance but higher exhaustion markers. Lifestyle factors such as body mass index (BMI), smoking, and hormonal influences further modulate CRC risk. While males are more vulnerable to obesity-related CRC, central obesity (waist-to-hip ratio) emerges as a stronger predictor in females. Additionally, smoking increases CRC risk differentially by tumor location. These findings underscore the importance of sex-specific approaches in CRC prevention, screening, and treatment, advocating for personalized medicine strategies tailored to gender-based biological and clinical distinctions. Full article