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Accurate and up-to-date anatomical information is critical for effective treatment planning in breast cancer adaptive radiotherapy. Cone-beam computed tomography facilitates real-time plan optimization but lacks sufficient electron density accuracy for direct clinical application. To address this limitation, we propose a novel unsupervised deep learning framework that integrates the Mamba architecture with an artifact disentanglement network to form the Artifact Disentanglement Network-Mamba model. This study proposes an unsupervised deep learning framework, ADN-Mamba, integrating an Artifact Disentanglement Network (ADN) with the structured state-space model Mamba for high-precision sCT synthesis from breath-hold CBCT (BH-CBCT). The model uses three encoders (CBCT content, CT content, artifact) and two generators to disentangle anatomical features from artifacts in CBCT. Mamba enhances the ability of the model to capture long-range dependencies, improving representation of complex anatomical structures. The Artifact Disentanglement Network-Mamba model achieved a mean absolute error of 54.97 HU within the body. The mean absolute percent errors of synthetic and real CT images in the soft tissue (-150 HU to 150 HU) and bone (200 HU to 1500 HU) regions were 46.26% and 30.98%, respectively. The gamma pass rate of the calculated dose on sCT compared with that on pCT is 97.74% under the 2%/2 mm criterion. The proposed model outperforms six other state-of-the-art methods in terms of image quality, dose accuracy, and radiomic feature consistency. By overcoming challenges such as registration errors and the absence of paired cone-beam computed tomography-computed tomography datasets, the proposed framework demonstrated superior performance in terms of anatomical fidelity and dose calculation accuracy. ADN-Mamba enables precise BH-CBCT-to-CT synthesis via unsupervised artifact disentanglement and Mamba's long-range modeling, demonstrating superior performance in image quality, dose calculation accuracy, and radiomic consistency. This framework provides a reliable tool for online dose calculation and target delineation in breast ART. Future work will focus on extending the model to 3D data and multicenter validation.

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.

Large language models (LLMs) have shown promise in medical imaging, but their utility in cytology remains underexplored. This study evaluates GPT-5 and Gemini 2.5 Pro for cervical Pap test interpretation. Digital cervical Pap test images of 100 cases were obtained from the Hologic Education Site, with Hologic diagnoses considered the gold standard. Representative images were uploaded into GPT-5 and Gemini 2.5 Pro and prompted to provide a diagnosis based on the Third Edition of the Bethesda System for Reporting Cervical Cytopathology. Cases with infectious organisms were assessed using additional images. Concordance was evaluated at exact diagnosis and clinical management groupings, wherein diagnoses with similar management implications were grouped. Sensitivity and specificity for abnormal cytology were also calculated. Concordance of both LLMs for exact diagnosis were comparable (GPT-5: 47%, Gemini: 48%) and increased to 66% for clinical management grouping. GPT-5 performed best for low-grade squamous intraepithelial lesions (75%), whereas Gemini 2.5 Pro showed the highest concordance in the high-grade squamous intraepithelial lesion (HSIL) category (82%), although this was largely attributable to its strong tendency to overcall cases as HSIL. Sensitivity for detecting abnormal cytology was 74% for GPT-5 and 84% for Gemini, with specificity of 74% and 71%, respectively. GPT-5 better identified glandular lesions, while Gemini detected organisms more accurately (71% vs. 20%). Current LLMs demonstrate moderate ability to identify cytologic abnormalities but are not yet reliable for independent cervical Pap test interpretation. Fine-tuning, prompt optimization, and cytology-specific training could enhance their utility as adjunctive tools in cytology workflows.

Glioblastoma (GBM) remains lethal due to high molecular heterogeneity and treatment resistance. While previous studies have proposed various biomarkers, a critical research gap exists: the lack of robust algorithmic validation and systematic linkage to drug discovery. Existing research predominantly relies on single machine learning models or traditional statistics, which often fail to provide stable results across diverse clinical datasets. To address this, we developed a high-dimensional pipeline that compares and ensembles 175 machine learning algorithm combinations. Unlike conventional single-model workflows, this approach ensures superior target identification stability and utilizes SHAP-based explainability and molecular dynamics to bridge the gap between biomarker discovery and precision therapeutics. DEGs from GEO datasets were refined via PPI and functional analyses. The 175-algorithm ensemble identified core genes, with clinical utility validated via survival analysis. A drug discovery pipeline incorporating virtual screening, ADMET, and molecular dynamics (MD) was then implemented to evaluate compounds targeting the identified core genes. From 771 DEGs, 34 key genes were identified, with LOX validated as the core therapeutic target. The optimal predictive model achieved a robust AUC of 0.953, while survival analysis underscored the significant prognostic value of LOX. Following systematic screening, the most outstanding compound was prioritized via MD simulations, exhibiting exceptional binding stability, favorable pharmacokinetics, and minimal toxicity risk. This integrated pipeline provides a robust framework for identifying precision targets and potent candidate compounds, offering a novel strategy for overcoming GBM treatment barriers.

Nanozymes, engineered nanomaterials with enzyme-mimetic activities, have emerged as versatile platforms for ferroptosis-based cancer theranostics. Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a promising strategy to overcome resistance to conventional cancer therapies. By catalyzing redox reactions, nanozymes can generate reactive oxygen species (ROS) and promote ferroptotic lipid peroxidation, thereby triggering cell death in tumor cells that evade apoptosis-based treatments. In parallel, non-redox activities of nanozymes, including hydrolase- and phosphatase-like functions, enable them to remodel the tumor microenvironment (TME), modulate biomolecular signaling, and support targeted therapy. This review provides a systematic and design-oriented overview of nanozymes that interface with ferroptosis. We summarize how redox and non-redox nanozyme activities converge on key ferroptosis-related processes, such as ROS production, glutathione depletion, iron metabolism disruption, and TME regulation. We then highlight rational engineering strategies, including single-atom and multimetallic catalytic centers, biodegradable coordination frameworks, stimuli-responsive architectures, and protein corona engineering, that enhance catalytic specificity, tumor targeting, and biosafety. Theranostic implementations are discussed with emphasis on multimodal imaging-guided platforms and combination regimens that integrate chemotherapy, radiotherapy, phototherapy, and immunotherapy. Finally, we outline major translational challenges and future opportunities, including AI and computation-guided nanozyme design and adaptive, corona-informed systems tailored for personalized cancer therapy. This review aims to serve as a roadmap for developing clinically translatable nanozymes that unify diagnosis and treatment through ferroptosis-oriented precision oncology.

Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment and experience, which limits the accuracy and timeliness of intraoperative subtype diagnosis and margin assessment. In this study, we developed an intelligent diagnostic model by integrating Fourier transform infrared (FTIR) spectroscopy with a Random Forest (RF) classifier. A total of 210 tumor and adjacent tissue samples from 105 patients, including adenocarcinoma, squamous cell carcinoma, and benign lung tumors were analyzed. The constructed RF model achieved an accuracy of 97.95% with an Area Under the Curve (AUC) of 0.99 in binary classification (lung cancer vs. adjacent tissues), and an accuracy of 94.91% in multiclass classification of lung cancer subtypes, significantly outperforming conventional algorithms such as Support Vector Machine, Naive Bayes, and Logistic Regression. In addition, spectral analysis methods, including peak area comparison, peak fitting, and second derivative analysis, revealed distinct differences in nucleic acids, proteins, and lipids, highlighting the characteristic bands responsible for subtype discrimination and providing spectroscopic insights into the pathological features of different lung cancer subtypes. Collectively, our findings demonstrate that the diagnostic model is a powerful approach for distinguishing lung cancer tissues from normal tissues and for subtype classification, offering a promising tool for lung cancer diagnosis.

A series of 2,4-diaylpyridine analogues 8(1-9) was synthesised and evaluated for antiproliferative activity against a panel of human tumour and non-tumour breast cell lines that was characterised for CYP1 A1, A2 and B1 isoforms expression. Diarylpyridine 8 (6) was the most potent analogue with high cytotoxicity towards MDA-MB-468 cells (IC50 = 0.08 μM) and no toxicity towards MCF-10 A cells (IC50 = 100 μM). In vitro enzyme inhibition studies revealed that CYP1 isozymes were responsible for the metabolism and consequent bioactivation of 8 (6). CYP1-catalysed metabolism experiments using 8 (6) revealed the formation of four main metabolites (M1-4) that were characterised by LC-MS analysis. It was found that the primary metabolisation route for 8 (6) consisted in the dealkylation of its 3,4-methylenedioxy A-ring functionality to generate the toxic catechol metabolite M2. The latter was synthesised (9), co-eluted with samples spiked with original CYP1-generated metabolites and evaluated for antiproliferative activity. Our studies confirmed that 9 was the CYP1-generated metabolite (M2) exhibiting cytotoxic activities at low micromolar level against all cell lines in the panel regardless of their expression of CYP1 enzymes. In summary, we demonstrated the pro-drug mode of action of the tumour-selective 8 (6), which upon CYP1-mediated conversion to toxic metabolites, was capable of exerting antiproliferative activity in breast cancer cells.

Chemodynamic therapy (CDT) based on overproduced reactive oxygen species (ROS), activates cytoprotective autophagy, an inexorable phenomenon-that enables tumor cell survival, therefore attenuating ROS-induced therapeutic efficacy. Herein, we develop a tumor microenvironment (TME)-responsive nanoplatform (MnOx-GOx-PM@Ma) composed of manganese oxide nanoflowers (MnOx NFs) co-loaded with glucose oxidase (GOx) and an activatable melittin pro-peptide (PM), and coated with macrophage membranes (Ma) for targeted delivery. Combined MnOx NFs and GOx trigger O2/H2O2 cyclic generation, thereby amplifying CDT in the acidic and glutathione (GSH)-rich TME. Meanwhile, the PM is selectively cleaved by lysosomal legumain to activate melittin, which disrupts lysosomal membranes and converts cytoprotective autophagy into a pro-death process. Additionally, the releasing Mn2+ exhibits excellent magnetic resonance imaging (MRI) contrast properties. Both in vitro and in vivo studies demonstrate that MnOx-GOx-PM@Ma effectively suppresses tumor growth through synergistic starvation therapy, enhanced CDT, and autophagy inhibition. Collectively, this work presents a strategy to overcome autophagy-mediated therapeutic resistance and optimize synergistic CDT-based antitumor therapy.

Artificial Intelligence (AI) applications in health care continue to grow exponentially, and AI-tools such as machine learning (ML), natural language processing (NLP), and generative pre-trained transformers (GPT) continue to transform medical education. The adoption of AI in medical education however remains geographically varied, with studies illustrating a relatively lower research output on medical AI literacy in Latin America (LATAM) countries compared to North America. AI-applications offer significant potential to broaden access to medical education and facilitate evidence-based support with clinical decision-making, particularly in low- and middle-income countries (LMICs). Enhancing AI literacy in such settings is of paramount importance - successful integration may alleviate historical disparities, while improper implementation may deepen regional inequities in medical education and health care. In this viewpoint article, we illustrate the current state of AI-based medical education across the Americas, highlight challenges in implementation, and offer collaborative, equitable, and regionally tailored strategies to enhance medical AI literacy among health care professionals and students.

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.