Purpose of Review: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but their effects on the skeletal system, independent of the influence of bone metastases, are not fully understood. This critical review specifically summarizes the current evidence on the systemic skeletal effects of ICIs in patients without bone metastases, focusing on changes in bone turnover markers (BTMs), bone mineral density (BMD), and fracture risk, thereby addressing a significant gap in the literature. It also explores potential mechanisms, such as immune-mediated disruption of bone remodeling and alterations in bone quality. Recent Findings: Preclinical in vivo models consistently report that blocking the programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) or cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) pathways leads to bone loss and increased osteoclast activity. However, clinical findings are paradoxical; while one study using opportunistic quantitative computed tomography (QCT) reported preserved or even improved BMD in patients on ICI therapy, large cohort studies and pharmacovigilance analyses have revealed a consistently elevated risk of fragility fractures. Current assessment tools have limitations in capturing the true skeletal burden of ICI therapy. Summary: A significant discrepancy exists between preclinical data suggesting bone loss and clinical data showing both stable BMD and increased fracture risk. Addressing these knowledge gaps through prospective, high-resolution studies is critical for improving survivorship care. A clearer understanding is essential for developing strategies to prevent skeletal complications in the growing population of patients receiving immunotherapy.

The usage of boron in new medical technology is undergoing a sharp advancement, brought about as a result of the interaction between old and new technologies. With the appearance of small hospital-based accelerator neutron sources, Boron Neutron Capture Therapy (BNCT) is no longer limited to the nuclear reactors in the past, thus providing it with an opportunity to be used in a wider variety of clinical settings. Nanotechnology in modern-day biomedical studies is opening the doors to fresh biophysical uses of boron and these include targeted pharmacological delivery, theranostic agents, and radiosensitizers. The review is a synthesis of the basic biophysical biomarker of boron based therapeutics, clinical evidence and future technological advances. The boron neutron capture therapy (BNCT) has received clinical approval in Japan to treat head and neck recurrent malignancies but its overall translational utility is limited due to a lack of optimal selectivity of the delivery agents of boron and lack of in-vivo real-time dosimetry. Such areas of limitation are fertile grounds of innovation particularly using third-generation nanocarriers and advanced imaging modalities. More importantly, the scientific revival of boron therapeutics is discussed in its specific geopolitical context. Known as a commanding figure in the world interms of its boron reserves, Türkiye is a strategic resource, which offers an opportunity of historic proportions to change away as a supplier of raw materials to be among the global pioneers of high-value boron-based medicine. In addition, the review includes a bibliometric evaluation of 26 original research sources (published in Turkish institutions, (2004-2026) which thereby depicts a more advanced shift in a polymer chemistry-based foundation to intricate theranostics and reactor physics. Some strategic perspective of using this resource hegemony is finally looked upon in the review with actionable directions toward translation advancement.

Automated, precise histopathological grading of colorectal cancer (CRC) is vital for prognosis and treatment but is challenged by inter‐observer variability and time demands. This study introduces and evaluates a novel deep learning framework for robust four‐class grading of colorectal adenocarcinoma (Normal, Well, Moderately, and Poorly Differentiated), directly addressing limitations of prior studies focused on survival prediction or limited feature extraction. Our approach integrates Reinhard color normalization for stain variability, data augmentation for robustness, and synergistic feature extraction from fine‐tuned MobileNetV2 and InceptionV3 Convolutional Neural Networks (CNNs). Computationally efficient classification was achieved using Principal Component Analysis (PCA) to reduce features to 100 components, followed by an optimized Support Vector Machine (SVM) with multiple kernel evaluations. The framework, with a polynomial kernel SVM, demonstrated superior performance, achieving 96.41% macro‐averaged accuracy (95% CI, 95.8%–97.0%), with corresponding 0.9641 precision, recall, F 1‐score, and a 0.9915 macro area under the curve (AUC) in fivefold cross‐validation. Polynomial and Radial Basis Function (RBF) kernels significantly outperformed others. The framework's primary contribution is a validated, synergistic feature fusion pipeline that leverages the complementary strengths of diverse CNN architectures and an optimized SVM classifier, representing a notable advancement in automated grading on clinically sourced data. This provides a comprehensive framework for multi‐class CRC grading, showing considerable potential to enhance diagnostic accuracy, consistency, and efficiency.