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Developing effective cancer treatments requires identifying novel therapeutic agents with high biological activity. Fucoidan, a sulfated polysaccharide from brown algae, is a promising natural scaffold for anticancer drug development. In this study, oversulfated fucoidans (SFU and SFU-1) were derived from natural fucoidans (FU and FU-1), and the effects of this structural modification on anticancer efficacy were investigated comprehensively. Chemical characterizations of FU/FU-1 and SFU/SFU-1 were performed, and a systematically generated experimental dataset across multiple cancer cell lines was compiled. Using these data, several machine learning (ML) algorithms were applied to predict the anticancer efficacy of fucoidan-based molecules. Specifically, k-Nearest Neighbors Regression (kNNR), Least-Squares Boosting (LSBoost), Support Vector Regression (SVR), Decision Tree Regression (DT), Random Forest Regression (RFR), Linear Regression (LR), and Gaussian Process Regression (GPR) were evaluated with five-fold cross-validation. Model performance was assessed utilizing the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). The results show that FT-IR analysis of the oversulfated derivatives, SFU and SFU-1, confirmed successful modification. For SFU, the appearance of a shoulder peak at 820 cm-1 (equatorial C-2), alongside the characteristic 840 cm-1 peak, verified site-specific sulfonation. In SFU-1, new peaks at 1243 cm-1 (SO stretching) and 583 cm-1 (OSO deformations) were identified. These spectral changes demonstrate the effective integration of sulfate groups into the molecular frameworks. Cytotoxicity assays against five human cancer cell lines revealed dose-dependent inhibition, with the SFU derivative exhibiting the most potent activity, particularly reducing HeLa and MDA-MB-231 cell viability to 23.93% and 25.13% at 2 mg/mL. GPR model achieves superior predictive performance compared to other methods, with the lowest MAE and RMSE (8.4627 and 11.5692, respectively) and the highest R2 (0.7039) values. The findings reveal that models that capture the nonlinear relationship between sulfation degree and anticancer efficacy, especially GPR, are powerful tools for the preliminary evaluation of natural product-based drug candidates. This study demonstrates that integrating chemical modification, experimental validation, and classical ML can accelerate the rational assessment of naturally derived therapeutics in oncology.

Liver cancer, particularly hepatocellular carcinoma (HCC), is a significant global health concern due to its asymptomatic early stages, biological diversity, and frequent late diagnoses that hinder effective treatment and survival rates. Traditional diagnostic methods, such as serum biomarker assays and imaging techniques, often lack the necessary sensitivity and specificity and highlight the urgent need for innovative, non-invasive diagnostic alternatives. This review emphasizes the potential of combining nanobiosensor technologies with physics-informed machine learning (PIML) to address these diagnostic challenges. Nanobiosensors utilize advanced materials like gold nanoparticles and graphene to achieve highly sensitive, real-time detection of HCC biomarkers, including alpha-fetoprotein (AFP) and non-coding RNAs, with detection limits reaching sub-nanomolar to femtomolar levels through various mechanisms. However, the clinical application of nanobiosensors is hindered by issues such as signal instability and environmental interference. PIML offers a solution by incorporating fundamental physical principles into machine learning models which is enhancing their predictive accuracy and robustness against data noise. This hybrid approach facilitates effective signal denoising, adaptive calibration, and the integration of multimodal data, thereby improving the overall diagnostic process. Main findings indicate that PIML-enhanced nanobiosensing systems significantly outperform traditional AI models in biomedical applications, demonstrating superior generalization and biologically relevant outputs even in the presence of limited data. The integration of these technologies creates a promising framework for advanced liver cancer diagnostics, enabling precise, non-invasive detection and personalized clinical decision-making. In conclusion, the convergence of nanobiosensors and PIML holds the potential to revolutionize liver cancer diagnostics, offering improved early detection and dynamic monitoring. However, to realize this potential, ongoing challenges related to computational scalability, sensor reproducibility, and regulatory validation must be systematically addressed through collaborative interdisciplinary efforts.

Analyzing gene expression data is essential for predicting and detecting diseases, including cancer. The data is very repetitive and noisy, which makes it hard to find important information about illnesses. In the past decade, several traditional machine learning and feature selection models have been developed for cancer type classification from gene expression data. Rather than introducing new deep learning primitives, this work presents a principled integration framework that combines sparsity-aware representation learning, structure-inducing spatial embedding, and hierarchical multi-scale attention. This paper presents a method for cancer gene classification based on Hierarchical Attention Assisted Feature Pyramid Network (HA-FPN). The work involved two publicly available datasets. The proposed methodology starts with dimensionality reduction through a Variational Sparse Autoencoder (VSAE), followed by an updated DeepInsight algorithm for image conversion of the input. Next, the classification technique is constructed using the proposed HA-FPN model. Moreover, the improved gradient descent optimization (IGDO) is utilized to change the hyperparameter of the classification model. In addition, the results demonstrate that the model combined with an IGDO outperforms the currently existing methods in terms of accuracy, precision, recall, and F1-score. The method that is presented efficiently brings out different aspects of the data through t-SNE computations. Moreover, the proposed approach is very robust, and hence, it can reach high performance levels on two different datasets.

Multi-modal imaging guided photothermal therapy (PTT) combined with photodynamic therapy (PDT) has shown significant advantages in breast cancer treatment. However, the development of superior phototheranostic nanoagents remains highly challenging. Herein, a "one-for-all" phototheranostic nanoagent was developed based on a fused-ring small molecule (L8-4F), which was functionalized with outer butyloctyl branched side chains for poor molecular planarity and loose intermolecular π-π stacking. The water-soluble L8-4F nanoparticles (NPs) prepared by the nano-precipitation method not only exhibited strong near-infrared I (NIR-I) absorption, a large Stokes shift (108 nm), and near-infrared II (NIR-II) emission in the range of 835-1200 nm, but also achieved a photothermal conversion efficiency (PCE) as high as 58 % under 808 nm laser irradiation, while simultaneously generating type-I superoxide radicals (•O₂-). In animal experiments, high-resolution vascular imaging and accurate tumor localization were achieved in 4T1 tumor bearing mice using L8-4F NPs via integrated fluorescence imaging (FLI) and photoacoustic imaging (PAI). Moreover, the synergistic combination of photothermal therapy (PTT) and type-I photodynamic therapy (PDT) provided effective strategy for breast cancer treatment. In general, this study presented valuable guidance for constructing efficient "one-for-all" phototheranostic nanoagent.

Rearranged during transfection (RET) kinase mutations are frequently observed in the context of human thyroid and lung cancer treatment. Moreover, a considerable amount of effort has been dedicated by the scientific community to the identification of highly potent and selective RET inhibitors. In this study, we identified a series of pyrazolo[1,5-a]pyridine derivatives, and compound 9 as a candidate drug that targets both wild-type (wt) RET and RETV804M by structure-activity relationship (SAR) study. In addition, 9 exhibited remarkable antitumor activity at a dose of 10 mg/kg/day, indicating that it completely hindered the growth of tumors induced by BAF3-KIF3B-RET-WT xenografts. In summary, 9 can be demonstrated to act as a potential RET inhibitor, as well as a treatment for RET-related cancers.

Carotid plaque instability is a major determinant of ischemic stroke and is characterized by heightened inflammation and structural remodeling of the vessel wall. Although macrophages and vascular smooth muscle cells (VSMCs) are central to plaque vulnerability, the mechanisms coordinating immune activation with vascular remodeling remain incompletely understood. Bulk transcriptomic data from multiple Gene Expression Omnibus (GEO) datasets were integrated to compare unstable and stable carotid plaques. Differential gene expression analysis, weighted gene co-expression network analysis, immune-gene curation, and machine learning methods (least absolute shrinkage and selection operator [LASSO] and random forest) were used to identify key genes and construct a nomogram. The findings were validated using independent datasets, single-cell RNA sequencing, and human carotid plaque specimens. The mechanistic roles of CXCL16 were examined using macrophage functional assays, NF-κB inhibition, VSMC co-culture with macrophage-conditioned media, and the establishment of an ApoE-/- carotid atherosclerosis model with local CXCL16 suppression. Cell-cell communication and pseudotime analyses were performed to explore macrophage-VSMC interactions. CXCL16, CCL2, and MMP9 were consistently upregulated in unstable plaques and showed robust diagnostic performance across datasets. A three-gene nomogram generated from this study suggested potential clinical utility. Single-cell analyses indicated that CXCL16 was enriched in plaque-associated M1 macrophages and was associated with inflammatory activation states. Human plaque staining confirmed higher CXCL16/CCL2/MMP9 expression in unstable plaques with increased macrophage and leukocyte infiltration. In vitro, CXCL16 knockdown attenuated NF-κB activation and reduced downstream inflammatory mediators (including CCL2), accompanied by decreased macrophage migration; NF-κB inhibition phenocopied these effects. In vivo, CXCL16 suppression reduced carotid plaque formation and inflammatory cell infiltration. Cell-cell communication analysis revealed enhanced SPP1/osteopontin signaling from M1 macrophages toward VSMCs, with higher SPP1 expression in CXCL16-high M1 macrophages. Co-culture experiments showed that macrophage-derived CXCL16 promoted VSMC migration and phenotypic switching, which was reversed by CXCL16 knockdown. CXCL16 acts as a central inflammatory mediator in carotid plaque destabilization by promoting NF-κB-dependent macrophage activation and migration. It also enhances SPP1/osteopontin-associated macrophage-VSMC crosstalk that drives phenotypic remodeling in VSMCs. Our results collectively suggest CXCL16 as a diagnostic biomarker and a potential therapeutic target for carotid atherosclerosis.

Radiomic models from multiparametric MRI can characterize tumor infiltration within the non-enhancing peritumoral region but remain insufficiently compared with diffusion and perfusion. This study assessed concordance between voxelwise radiomic predictions and these physiological modalities and evaluated prognostic value of infiltration metrics in two external cohorts. UPenn-GBM and UCSF-PDGM datasets were analyzed. Voxelwise radiomic classification generated peritumoral infiltration-probability maps from standard MRI. Fractional anisotropy (FA), dynamic susceptibility contrast-derived relative cerebral blood volume (DSC-rCBV; UPenn), and arterial spin labeling-derived relative cerebral blood flow (ASL-CBF; UCSF) were compared between peritumoral regions classified as high- versus low-infiltration. Radiomic metrics included infiltration burden (voxel fraction exceeding predefined probability thresholds) and radial extent (normalized maximum distance from enhancing margin sustaining high infiltration probability) were quantified, and survival assessed using univariable Cox models. Across 872 subjects, high-infiltration regions showed significantly lower FA (median difference: UPenn -0.232; UCSF -0.226; both p < 0.001) and higher perfusion (median difference: UPenn DSC-rCBV + 0.282; UCSF ASL-CBF + 0.565; both p < 0.001) compared with low-infiltration regions. Infiltration burden at the 0.50 threshold demonstrated prognostic value (UPenn hazard ratio (HR) 2.758, 95% confidence interval (CI) 1.189-6.396, p = 0.018; UCSF HR 21.277, 95% CI 6.024-71.429, p < 0.001). Radial extent was also associated with survival (UPenn HR 2.371, 95% CI 1.215-4.625, p = 0.011; UCSF HR 4.405, 95% CI 1.695-11.494, p = 0.002). Voxelwise radiomic infiltration mapping from standard MRI aligns with diffusion and perfusion abnormalities and yields prognostic value. These metrics highlight the role of structural radiomics for characterizing non-enhancing infiltrative spread in glioblastoma.

Computed tomography (CT) is essential for finding and diagnosing kidney tumors and cysts because good lesion segmentation enables accurate diagnosis, appropriate therapy planning, and disease monitoring. Renal mass (Tumor and cyst) shapes and sizes are diverse and complex, particularly around diffuse borders, and low-intensity contrast and heterogeneous morphology make effective segmentation difficult. To overcome these challenges, introduced internal dilated guided self-attention model based on dilated residual inception U-Net (IDGSA-DRIU-Net), a deep learning model for segmenting renal tumors and cysts. The architecture features a newly built Internal Dilated Guided Self-Attention (IDGSA) module, which combines Dilated Multiscale Position Attention and Channel Attention introducing an attention mechanism that leverages internal guidance to capture spatial dependencies across multiple dilation scales and emphasizes key feature channels, allowing for more effective utilization of local and global contextual information for better multi-scale feature aggregation. A Dilated Residual Inception (DRI) module improves multiscale contextual feature recovery while preserving structural features. In post-processing, a Conditional Random Field (CRF) is utilized to eliminate false positives and refine bounds. The proposed model was evaluated using the KiTS19, KiTS21, and KiTS23 datasets and obtained a Dice Similarity Coefficient (DSC) of 98.27% for the tumor on KiTS19, 96.31% and 94.82% for the tumor and cyst on KiTS21, and 94.98% and 93.64% for the tumor and cyst on KiTS23. The results suggest that IDGSA-DRIU-Net with CRF outperforms the popular state-of-the-art models, indicating that successful in kidney tumor and cyst segmentation.

Androgen deprivation therapy has a negative effect on bone mineral density and trabecular bone score in prostate cancer patients. The addition of androgen receptor pathway inhibitors can result in worsened skeletal fragility. BonEnza is a prospective phase II trial in which metastatic hormone sensitive prostate cancer patients were randomized to receive androgen deprivation therapy plus enzalutamide with (EZ arm) or without (E arm) the addition of zoledronic acid. Bone quantity and quality parameters were evaluated by dual-energy x-ray absorptiometry (DXA) scan at baseline and after 18 months of therapy. Alkaline phosphatase (ALP) and C-terminal telopeptide of type I collagen (CTX) were assessed at baseline and after 18 months of treatment. Eighty-nine patients had paired DXA evaluation at both timepoints. After 18 months of treatment femoral neck and lumbar spine bone mineral density significantly decreased in E arm (-8.6% and - 9.26% respectively; p < 0.001), while improved in EZ arm (+1.83%, p 0.019; and + 5.47%, p < 0.001). Trabecular bone score significantly worsened in E arm (-3.35%, p < 0.001) and improved in EZ arm (+3.01%, p 0.004). Both ALP and CTX showed marked reduction overtime among patients receiving zoledronic acid (-35.6%, p < 0.0001, and - 58.9%, p < 0.0001, respectively), while remaining stable (-0.6%, p 0.934) or significantly increasing (39.5%, p 0.011) respectively among patients from E arm. The addition of zoledronic acid to enzalutamide and androgen deprivation improved bone mineral density, trabecular bone score, and reduced bone turnover markers. Future studies in mHSPC should consider the use of lower doses of bone protecting agents and regard the reduction in morphometric fractures by DXA as a primary endpoint.

Traditional approaches to brain tumor classification frequently encounter issues such as limited efficiency, class imbalance, and high computational time, which can hinder timely clinical decision-making. To address these challenges, a new hybrid framework named Spinal-EfficientNet is introduced, aiming to enhance both classification accuracy and processing speed. The proposed pipeline starts with Magnetic Resonance Imaging (MRI) brain scans obtained from a curated dataset, followed by a preprocessing stage where noise and artifacts are reduced and image quality is improved using wavelet-domain filtering techniques. Tumor segmentation is then performed using SegNet, followed by image augmentation techniques including random erasing, rotation, and shearing to strengthen model generalization. Next, significant features are extracted, encompassing texture descriptors like Angular Second Moment (ASM), contrast, sum entropy, maximal correlation coefficient, Pyramid Histogram of Oriented Gradients (PHOG), Complete Local Binary Pattern (CLBP) and statistical measures such as mean, variance, kurtosis, skewness. In the final stage, tumor classification is performed using Spinal-EfficientNet, a hybrid architecture that combines EfficientNet with SpinalNet via customized layer modifications, allowing more reliable and accurate identification of brain tumors. Experimental evaluation demonstrates that the proposed model achieves strong performance, with specificity of 92.5%, sensitivity of 92.9%, accuracy of 92.5%, and an F1-score of 91.6% under k-fold cross-validation on the BRATS 2018 dataset. The Spinal-EfficientNet framework demonstrates notable gains in accuracy when compared with existing methods. In performance comparisons, it achieves improvements of 6.05% over Convolutional Neural Network and Support Vector Machine (CNN-SVM), 4.76% over Visual Geometry Group Stacked Classifier Network (VGG-SCNet), 3.46% over Ultra-Light Brain Tumor Detection (UL-BTD), 2.92% over Adaptive Fuzzy Deep Neural Network (AFDNN), 2.70% over ResNet50 with the Enhanced Watershed Segmentation (ResNet50-EWS), 2.59% over EfficientNet-B0, and 1.41% over SpinalNet. These consistent enhancements across a range of architectures indicate its reliability and effectiveness as a strong approach for handling complex classification problems.