Search Results (24,255)

Purpose: This paper aimed to identify dosimetric, clinical, and CT-based densitometric predictors of radiation-induced pulmonary events in breast cancer patients treated with moderately hypofractionated radiotherapy. Materials and Methods: A single-institution cohort of 1172 consecutive patients treated with 3D conformal whole-breast radiotherapy (40 Gy/15 fractions) before 2017 was analyzed. Ipsilateral lung DVHs and CT densitometry metrics were extracted. Clinical variables and cardiac calcification (CAC) scores (Agatston_score, CAC_volume, Max_HU_Heart) were included. Univariable and multivariable logistic regressions were performed; collinearity was assessed via Spearman correlation and VIF. Optimal thresholds were derived using the Youden index. Internal validation used bootstrap resampling. Results: After a median follow-up of 6.5 years, 18 patients developed moderate/severe pulmonary events. The univariable analysis showed associations with lung densitometric features (median/mean HU, 10th percentile, the lung volume with HU < −850 (V850)), V37 Gy, lung volume, and CAC scores. Lower lung HU values and larger lung volumes were linked to higher risk. The best models combined V850 (or lung volume) with a CAC metric. The model including V850 > 175 cc and continuous Max_HU_Heart achieved an optimism-corrected AUC of 0.68, with good fit and calibration (Hosmer–Lemeshow p = 0.33, R² = 0.847). Conclusions: The baseline cardiopulmonary status, captured by lung and heart densitometry, predicts pulmonary toxicity better than dosimetry. V850 > 175 cc was associated with a 4-fold higher risk, consistent with air trapping, known as a marker of emphysema. Full article

Background: As the most lethal neuroendocrine tumor, small cell lung cancer (SCLC) can drive its progression by hijacking neuronal mechanisms. At the core of this neural integration is the N-methyl-D-aspartate receptor (NMDAR) complex. However, its pan-cancer expression and clinical significance in SCLC remain poorly understood. Methods: We characterized NMDAR transcriptomic profiles across human cancers to develop the NMDAscore, and analyzed three independent European and Asian SCLC cohorts to identify prognostic biomarkers. Furthermore, we investigated the molecular mechanisms of GRIN2A and evaluated the efficacy of GluN2 inhibitors. Results: The developed NMDAscore exhibited significant prognostic correlations in ACC, COAD, KIRC, UVM, KIRP, OV, PCPG, UCS, THCA, THYM, HNSC, KICH, LGG, and PAAD. Focusing on the SCLC cohorts, we identified GRIN2A (encoding the GluN2A subunit) as a statistically relevant prognostic biomarker associated with poor survival. Mechanistically, GRIN2A upregulation correlates with the activation of neuro-synaptic signaling, metabolic reprogramming, genomic instability, and an immune-cold microenvironment characterized by CD8⁺ T cell exclusion. Pharmacological inhibition of GluN2 using dizocilpine and the FDA-approved antagonist memantine suppressed SCLC proliferation and tumorigenicity in vitro, in 3D tumor spheroids and in vivo xenograft models. Conclusions: Collectively, these findings establish GRIN2A as a prognostic biomarker, linking synaptic hijacking, metabolic plasticity, immune evasion, and drug resistance, and identify the therapeutic potentials of the GluN2 inhibitors dizocilpine and memantine for SCLC. Full article

Laryngeal cancer is a relatively uncommon malignancy with predisposing genetic factors that remain unclear. Single-nucleotide polymorphisms (SNPs) in genes involved in innate immune signaling may contribute to the development and progression of laryngeal carcinoma. This study aimed to evaluate the association of TLR4 (rs7037225, rs11536889, rs7037117) and MYD88 (rs7744, rs6853) polymorphisms with the risk of laryngeal squamous cell carcinoma (LSCC), as well as its clinical and pathological characteristics and survival. A retrospective case–control study involving 172 LSCC patients and 220 healthy controls was conducted. Genotyping was performed using real-time PCR from venous blood samples. MYD88 rs7744 was significantly associated with tumor size and lymph node involvement. Survival analysis showed a significant association between rs7744 and recurrence-free survival (RFS), with the AG and GG genotypes linked to poorer outcomes. Conversely, carriers of the TLR4 rs7037225 CT genotype showed significantly improved RFS, with p ranging from 0.024 to 0.037 across models. Considering the significant roles of TLR4 and MYD88 in Toll-like receptor signaling, these findings may reflect the involvement of innate immune pathways in LSCC progression. In summary, MYD88 rs7744 was associated with clinicopathological features and RFS, while TLR4 rs7037225 appeared to have a potential protective effect on survival. Full article

Background: Response to neoadjuvant therapy in locally advanced rectal cancer (LARC) is heterogeneous, and early identification of non-responders may help optimize treatment strategies and reduce unnecessary toxicity. This study aimed to develop and internally validate a machine learning model based on radiomic features extracted from baseline magnetic resonance imaging (MRI) to predict treatment response defined according to MRI tumor regression grade (mrTRG) at restaging MRI. Methods: In this retrospective single-center study, 86 patients with histologically confirmed LARC who underwent baseline and restaging MRI, neoadjuvant therapy, and surgery were included. Primary tumors were manually segmented on oblique axial T2-weighted images. A total of 107 radiomic features were extracted using PyRadiomics (vrs 3.0.1), with and without N4 bias field correction. Feature selection was performed using LASSO, followed by elastic net–regularized logistic regression. Model performance was evaluated using repeated stratified 5-fold cross-validation (20 repetitions). Treatment response was defined according to MRI tumor regression grade (mrTRG) at restaging, dichotomized into responders (mrTRG ≤ 2) and non-responders (mrTRG ≥ 3). Results: The model achieved a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.73, with an accuracy of 72.5%, sensitivity of 79.2%, and specificity of 50%. Conclusions: Baseline MRI-based radiomics shows potential for identifying patients at higher risk of non-response to neoadjuvant therapy in LARC. However, limited specificity and the absence of external validation restrict immediate clinical applicability. Further validation in larger multicenter cohorts and integration with clinical variables are warranted to improve model robustness and generalizability. Full article

Background: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, is becoming increasingly prevalent with the global population aging. Computed tomography (CT) is widely used for muscle assessment; however, concerns regarding radiation exposure have prompted interest in lower-dose imaging protocols. This study investigated the performance of radiomics-based machine learning (ML) models for sarcopenia detection using abdominal CT (APCT) and low-dose CT (LDCT). Methods: Radiomics features were extracted from CT images following skeletal muscle segmentation, and ML models were developed using logistic regression, support vector machine, and random forest. Model performance was evaluated using fivefold cross-validation with out-of-fold predictions. Results: The random forest model demonstrated the best performance among the evaluated models, achieving an area under the receiver operating characteristic curve of 0.720 (95% CI: 0.532–0.881) for APCT and 0.692 (95% CI: 0.573–0.801) for LDCT. Model interpretation using SHapley Additive exPlanations analysis identified several intensity-based radiomics features, including TotalEnergy, as important contributors to sarcopenia prediction. Conclusions: These findings suggest that radiomics features derived from LDCT images may provide useful information for sarcopenia detection. Because LDCT is widely used in clinical settings such as lung cancer screening, radiomics analysis of LDCT images may offer an additional opportunity for opportunistic sarcopenia assessment. Full article

Computer-aided drug discovery increasingly depends on virtual-screening workflows that remain reliable under severe class imbalance, chemical redundancy and early-recognition constraints. In this study, we developed a leakage-aware prioritization workflow for two cancer-relevant targets, pyruvate kinase M2 (PKM2) and mitogen-activated protein kinase 1 (MAPK1/ERK2), using the LIT-PCBA benchmark. The workflow combines canonical-SMILES curation, duplicate and label-conflict auditing, scaffold-aware validation, a non-learning nearest-active Tanimoto baseline, imbalance-aware machine-learning models, repeated-seed robustness analysis, isotonic probability calibration, ensemble-disagreement estimation, absorption, distribution, metabolism, excretion and toxicity (ADMET)-aware triage, molecular docking, and residue-level contact analysis. Benchmark enrichment is interpreted alongside calibration, ADMET filtering, docking and residue-contact evidence, rather than as a standalone discovery claim. PKM2 emerged as the clearer machine-learning case, with scaffold-aware tree models improving early recognition beyond the nearest-active similarity baseline and yielding top-ranked candidates supported by calibrated activity scores, ADMET profiles, docking scores, and residue-contact fingerprints. MAPK1 provided a biologically relevant contrast target, where ligand-neighborhood similarity remained competitive and downstream structural triage became more decisive than ligand-based ranking alone. These results support a conservative drug-discovery workflow in which leakage-aware benchmarking, calibration, uncertainty, and molecular-level triage remain visible throughout candidate prioritization. Full article

Background: Inflammatory bowel disease (IBD) is a chronic inflammatory condition, with therapy-resistant patients undergoing surgery and a high risk of developing colorectal cancer. Novel therapeutic approaches have shown limited efficacy in IBD treatment, highlighting the need for safer and more personalized strategies. The potential of natural compounds to modulate inflammation suggests their use as a potential adjunct therapy for IBD patients. Methods: Intestinal epithelial cells organoids (IECOs) were derived from IBD and non-IBD tissues from IBD patients, and levels of inflammation markers and epithelial barrier permeability were assayed using qRT-PCR, WB, IF and leaking assays in the presence of Ozoile, an extra virgin olive oil enriched in ozonides. The Luciferase-based IBD-like organoid platform was generated for preliminary screening of anti-inflammatory drugs. Results: In this study, we showed that IBD-ECOs recapitulate tissue architecture and pathological state. We showed that Ozoile has anti-inflammatory and epithelial barrier modulatory effects and that the Luciferase IBD-like organoid model is sensitive to anti-inflammatory compounds. Conclusions: Using IECOs, the specific anti-inflammatory and regenerative properties of Ozoile were assessed. Notably, our study highlights the potential of an IBD-like organoid platform to use in high-throughput screenings for rapid selection of anti-inflammatory drugs. Full article

Background and Objectives: The systemic inflammatory response is important in cancer prognosis and progression. The inflammatory burden index (IBI) provides information about both inflammation and the immune response. Urothelial carcinomas are immunogenic; therefore, it has been suggested that inflammatory indices may predict disease prognosis. The aim of this study was to investigate the effects of systemic inflammatory indices, particularly the inflammatory burden index, on disease progression and overall survival in patients with metastatic urothelial cancer (affecting the bladder and upper urinary system) before first-line treatment and to demonstrate their prognostic importance. Materials and Methods: Within the scope of the study, the medical records of 130 patients who received systemic treatment for metastatic urothelial carcinoma at the medical oncology clinic were retrospectively reviewed. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal threshold values for IBI. Survival rates were calculated using the Kaplan–Meier method, and survival differences between groups were compared with the log-rank test. Univariate and multivariate analyses were performed using the Cox proportional hazards regression model to evaluate prognostic factors. Results: A total of 130 patients were included in the study. The median age was 64.9 years (IQR: 57.2–70.5). The primary tumor location was the bladder in 84.6% of patients, while the remaining 15.4% originated from the ureter and renal pelvis. In first-line systemic treatment, patients received a median of 4 cycles (IQR: 3–6). The median number of total treatment lines administered for metastatic disease was 1 (IQR: 1–2). In progression-free survival (PFS) analyses, the median PFS was 9.20 (95% CI 6.55–11.85) months in the IBI-low group (n = 47) and 5.82 (95% CI 4.56–7.07) months in the IBI-high group (n = 83) (p < 0.001). The median OS was calculated to be 18.96 (95% CI 16.61–21.30) months in the IBI-low group (n = 47), while it was found to be 9.50 (95% CI 7.70–11.29) months in the IBI-high group (n = 83) (p < 0.001). In multivariate analysis, high IBI and the presence of brain metastasis were found to be associated with the risk of progression. In terms of overall survival, the presence of brain metastasis, the presence of visceral metastasis, ECOG PS status, receipt of maintenance therapy, LMR, and the IBI score showed statistically significant prognostic effects. Conclusions: In metastatic urothelial carcinoma, the IBI was identified as an independent prognostic factor associated with progression-free and overall survival. These findings suggest that the IBI may have potential utility as a prognostic biomarker; however, larger, multicenter, and prospective studies are required to further validate its clinical applicability. Full article

Background/Objectives: Melanoma is a highly aggressive cancer with a strong metastatic potential, and therapeutic resistance remains a major clinical challenge despite advances in targeted therapies and immunotherapies. Secreted frizzled-related protein 1 (sFRP1) exhibits context-dependent roles in cancer; however, its function in metastatic melanoma remains poorly defined. This study investigated the role of sFRP1 in melanoma progression and evaluated the anti-tumor effects of the pharmacological compound WAY-316606. Methods: sFRP1 expression was quantified in metastatic melanoma cell lines, xenograft models, and TCGA datasets. The anti-tumor effects of WAY-316606 on cell viability, cell cycle progression, cell migration and invasion, and expression of extracellular matrix (ECM)-related genes were assessed using WST assays, flow cytometry, wound healing and transwell invasion assays, and quantitative real-time PCR, respectively. Results: sFRP1 expression was consistently elevated in metastatic melanoma cell lines, xenograft models, and TCGA datasets, and high sFRP1 expression was associated with poor overall survival. WAY-316606 selectively suppressed melanoma cell viability with minimal cytotoxic effects on non-tumorigenic cells, and induced G1 phase cell cycle arrest. Furthermore, WAY-316606 markedly impaired the migratory and invasive capacities of metastatic melanoma cells, accompanied by downregulation of key ECM remodeling and fibrosis-related genes, including VIM, CCN2, FN1, and TGFBI. sFRP1 knockdown partially phenocopied the anti-migratory and gene expression effects of WAY-316606. Conclusions: Collectively, our findings identify sFRP1-asscoaited signaling contribute to aggressive melanoma phenotypes and highlight the therapeutic potential of its pharmacological inhibition using WAY-316606. Full article

Cancer remains a leading cause of mortality worldwide, largely due to dysregulated apoptotic signaling and the persistent activation of oncogenic pathways. However, natural products are a promising source of multi-target anticancer agents. In this study, we investigated the anticancer activity and underlying mechanisms of Clausena harmandiana root extract and its major carbazole alkaloid, 7-methoxyheptaphylline, both in vitro and in vivo. High-Performance Liquid Chromatography (HPLC) chemical fingerprinting confirmed the presence of bioactive coumarins and carbazole alkaloids in the extract. Cytotoxicity assays demonstrated that the extract significantly reduced the viability of human colorectal adenocarcinoma (HT-29), human hepatocellular carcinoma (HepG2), human lung adenocarcinoma (A549–Luc2), and murine Lewis lung carcinoma (3LL–Luc2) cells in a dose- and time-dependent manner. Our mechanistic investigations revealed the activation of JNK signaling, downregulation of anti-apoptotic proteins (Bcl-2, Bcl-xL, and Mcl-1), and increased cleaved caspase-3 expression, indicating that mitochondrial apoptosis was induced. Notably, 7-methoxyheptaphylline markedly suppressed STAT3 phosphorylation in a concentration-dependent manner, comparable to the STAT3 inhibitor JSI-124. In a syngeneic 3LL–Luciferase2 lung cancer mouse model, oral administration of C. harmandiana capsules significantly reduced tumor growth and bioluminescence intensity compared with controls. These in vivo findings were consistent with the inhibition of STAT3 signaling and induction of apoptosis observed in vitro. Collectively, our results demonstrate that C. harmandiana exerts broad-spectrum anticancer activity through coordinated modulation of the JNK–STAT3 axis, leading to caspase-dependent apoptosis. These findings highlight its potential as a promising candidate for the development of STAT3-targeted anticancer therapies. Full article

Background: The blood–brain barrier (BBB) separates the circulation from the central nervous system (CNS) and serves to maintain brain homeostasis. The BBB comprises highly specialized brain endothelial cells (BECs) with unique properties that allow the BBB to maintain strict regulation of molecules entering and exiting the CNS. These characteristics include tight junctions, low endocytosis rates, and efflux and nutrient transporters. Breast cancer resistance protein (BCRP) is an efflux transporter found at the BBB that plays a key role in protecting the CNS. Together with other efflux transporters, BCRP contributes to multidrug-resistant cancers and difficulty delivering drugs and therapeutics to the brain and other organs. Methods: Using the hCMEC/D3 line, we utilized BCRP substrate rosuvastatin to effectively select for cells expressing high amounts of BCRP, thus generating hCMEC/D3-BCRP. To assess protein abundance, we utilized flow cytometry and confirmed expression via qPCR. To investigate BCRP efflux function in evolved hCMEC/D3-BCRP, we performed substrate accumulation assays with BCRP and P-gp substrates. Results: We found hCMEC/D3-BCRP had increased BCRP abundance and expression relative to parent hCMEC/D3. We also observed an increase in BCRP function via substrate accumulation of two BCRP substrates compared to parent hCMEC/D3. Conclusions: BCRP serves a protective role within the BBB and is a major hurdle in drug delivery. We generated a BCRP overexpression BEC cell line (hCMEC/D3-BCRP) under the influence of endogenous promoters. This cell line can be used to further investigate the role of BCRP in BECs and utilized in efflux transport studies. Full article

Breast cancer histopathological image classification remains a challenging task because reliable diagnosis depends on both fine-grained local lesion characteristics and multi-scale global tissue structures. However, current deep learning approaches often face challenges in effectively integrating these complementary cues, particularly in the presence of staining variations, ambiguous lesion boundaries, and limited annotated datasets. To address these challenges, we propose a novel method called UNI-Phase-Dual Network (UPDNet). This approach enhances the detection of stable lesion boundaries and subtle patterns by incorporating phase congruency, while combining it with global tissue information using the UNI foundation model. The method utilizes two branches to process features from different perspectives, one focusing on fine details and the other capturing broader context. Additionally, we apply a fine-tuning strategy that improves generalization and reduces overfitting in scenarios with small datasets. Experiments on three widely used breast cancer datasets, BRACS, BreakHis, and BACH, demonstrate that UPDNet significantly outperforms existing methods. Specifically, on the 7-class BRACS task, UPDNet achieves 68.58% accuracy, which is a 2.21% improvement over previous methods, and an increase of 1.48% in the weighted F1 score. These results demonstrate the strong potential of UPDNet in breast cancer histopathological image classification. Full article

Skin cancer image data often exhibit long-tailed distributions due to the inherent challenges in data collection and annotation. Specifically, a few predominant classes dominate a dataset of interest, while minority classes, referred to as tail classes, are underrepresented with only limited numbers of samples. Such imbalance is highly likely to adversely affect the performance of deep learning models. To address this issue, previous methods employ mixup techniques to synthesize tail-class images, thereby attempting to balance the training data. However, traditional mixup methods typically do not specifically pay attention to specific regions of interest, blending two images with indistinction between objects of interest and background. Such disregard for important semantic features may result in synthetic samples with broken or distorted diagnostic features. In this work, we introduce a novel framework, the Tail-synthetic Learning for Boosting Long-tailed Skin Cancer Image Classification (TailBoost) framework. Our approach generates a new tail-class image by combining a tail-class image with a head-class image under the guidance of their corresponding saliency maps. This strategy, namely SPMix, preserves and enhances the discriminative features of the tail-class image with minimum interference from the head-class image. We further refine the learned representations by incorporating supervised contrastive learning with class-center rebalance. Extensive experiments on the ISIC2018, ISIC2019, and PAD-UFES-20 datasets demonstrate that TailBoost outperforms existing state-of-the-art long-tailed learning methods. Full article

Healthcare analytics is often limited by data silos and strict privacy requirements, which make it difficult to share patient-level records across organisations and to build robust predictive models. Federated learning (FL) provides an alternative by keeping data local and exchanging model updates instead of raw records. However, many existing FL solutions remain difficult to deploy in healthcare settings, as they provide limited support for auditability, governance-oriented evidence, and system-level transparency. This paper presents MediVault, an auditable and security-aware federated learning-based system for privacy-preserving healthcare collaboration. MediVault combines round-based federated training, prototype-level protected update exchange, audit-ready telemetry, and an interactive dashboard that exposes non-sensitive evidence of collaboration, model progress, and protocol execution. In addition, the system supports controlled reporting to improve stakeholder communication during pilot deployments. We evaluate MediVault on two public healthcare classification datasets, Breast Cancer Wisconsin (Diagnostic) and Heart Disease, under IID and label-skewed Non-IID settings. Experiments are conducted using logistic regression, linear SVM, and an additional lightweight MLP under matched settings. The observed results suggest that federated training remains competitive with centralised training under the evaluated settings. A prototype-level overhead analysis further shows that protected update exchange introduces measurable computational and communication costs, especially for larger update vectors. These findings indicate that MediVault can support initial system-level validation of auditable, privacy-preserving healthcare FL workflows, while further work is needed for larger-scale deployment, stronger adversarial evaluation, and real-world clinical validation. Full article

The anti-apoptotic Bcl-2 protein family, frequently upregulated in a wide range of cancers, contributes to tumor persistence and therapeutic resistance, making these proteins attractive targets for structure-based inhibitor development. Betulinic acid-derived hybrids represent promising scaffolds for apoptosis-oriented anticancer drug discovery due to their reported antiproliferative and pro-apoptotic properties. In this study, a virtual library of 152 betulinic acid-derived hybrids was screened against Bcl-2 and Bcl-XL. This molecular docking study using AutoDock Vina identified BA–Celastrol and BA–Proanthocyanidin B2 as top-ranked ligands, with docking scores ranging from −13.00 to −8.7 kcal/mol. Both compounds were further analyzed by 100 ns molecular dynamics simulation runs, which revealed system-dependent ligand behavior rather than uniform preservation of the initial docked pose across all complexes. BA–Celastrol showed a more compact internal ligand conformation in the ligand property and RMSF analyses, whereas BA–Proanthocyanidin B2 showed greater intramolecular flexibility and conformational adaptability. Ligand displacement relative to the protein differed between targets, with BA–Proanthocyanidin B2 showing a more retained profile in the Bcl-XL model and BA–Celastrol showing more moderate positional behavior in the Bcl-2 model. MM-GBSA calculations resulted in free energy values ranging from −4.95 to −31.82 kcal/mol, indicating protein-dependent energetic differences across the investigated systems. Based on docking performance, molecular dynamics stability, and energetic data, both hybrids were ranked as computational candidates for further exploration against Bcl-2 family targets. The present findings, although confined to computational analysis, underscore the need for prioritizing betulinic acid-based hybrids for subsequent experimental evaluation. Full article