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Anti-PD-(L)1 agents, inhibitors of programmed cell death protein 1 (PD-1) or its ligand (PD-L1), are established therapies that improve cancer management as well as the disease and societal burden of specific metastatic and early-stage cancers. The aim of the study was to determine the impact of adopting anti-PD-(L)1 agents for the treatment of all eligible patients with early-stage cancers versus reserving anti-PD-(L)1 agents for patients with metastatic disease alone in Hungary. This study evaluated two scenarios, one where anti-PD-(L)1 agents were used to treat all eligible early-stage disease cases (ESD scenario) of melanoma (stage IIB-C and III), renal cell carcinoma (RCC), and triple-negative breast cancer (TNBC) versus a reference scenario where anti-PD-(L)1 agents were only used to treat metastatic disease cases in Hungary (2024-2033). A Markov-modeling approach estimated the health outcomes and productivity losses from each scenario from a societal perspective. Outcomes included recurrence-/event-/disease-free life-years, total life-years, quality-adjusted life-years (QALYs), productive years (patients and caregivers), recurrences/events, active treatments for metastatic disease, and deaths. The cumulative health and productivity impact of ESD treatment with anti-PD-(L)1 agents in Hungary was the difference in health and productivity outcomes between the ESD and reference scenarios for the time horizon of the model. ESD treatment with anti-PD-(L)1 agents was estimated to increase recurrence-/event-/disease-free life-years (+13.8%), total life-years (+3.7%), and QALYs (+4.7%), as well as productive work years for patients (+39.6%) and caregivers (+27.6%). Concurrently, there would be fewer recurrences/events (-31.0%), active treatments for metastatic disease (-34.0%), post-recurrence deaths (-30.3%), and total deaths (-23.1%). Investing in anti-PD-(L)1 agents for early-stage disease may not only increase the life expectancy and QALYs for patients in Hungary but also increase productive work years for both patients and caregivers in Hungary. In addition, it may also help to reduce metastatic disease treatments and cancer-related deaths.

Raman spectroscopy is a well-established technique that can differentiate biological tissues by identifying features associated with established pathological conditions. Despite its significant potential, the adoption of Raman technology in routine medical diagnostics remains challenging, primarily because of difficulties in incorporating Raman data acquisition and analysis into existing clinical workflows. In this study, we show that Raman signals in the CH stretching region (2800-3050 cm-1) offer a robust approach for the detection and diagnosis of brain tumors. Our investigation focuses on medulloblastoma disease, which accounts for 20% of pediatric brain tumors. We investigated analytical approaches ranging from a simple single-channel Bayesian statistical model to more advanced machine learning techniques. Our results show that relying solely on CH band intensities within a Bayesian framework limits the predictive power of the spectra. In contrast, machine learning algorithms substantially enhance diagnostic performance, achieving an accuracy of 90% on the full dataset and exceeding 79% in external validation. These findings suggest that such algorithms are suitable for ex vivo analysis in both laboratory and surgical settings.

Artificial intelligence based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar functionality models raises the challenge of evaluating them on datasets that do not contain ground truth annotations. We introduce a practical framework to assist in this task. We harmonize the segmentation results into a standard, interoperable representation, which enables consistent, terminology-based labeling of the structures. We extend 3D Slicer to streamline loading and comparison of these harmonized segmentations and demonstrate how standard representation simplifies review of the results using interactive summary plots and browser-based visualization using the OHIF Viewer. To demonstrate the utility of the approach, we apply it to evaluating segmentation of 31 anatomical structures (lungs, vertebrae, ribs, and heart) by 6 open-source models-TotalSegmentator 1.5 and 2.6, Auto3DSeg, MOOSE, MultiTalent, and CADS-for a sample of computed tomography scans from the publicly available National Lung Screening Trial dataset. We demonstrate the utility of the framework in enabling automating loading, structure-wise inspection, and comparison across models. Preliminary results ascertain the practical utility of the approach in allowing quick detection and review of problematic results. The comparison shows excellent agreement segmenting some (e.g., lung) but not all structures (e.g., some models produce invalid vertebrae or rib segmentations). The open-source resources developed include segmentation harmonization scripts, interactive summary plots, and visualization tools. These resources assist in segmentation model evaluation in the absence of ground truth, ultimately enabling informed model selection.

Public datasets for training artificial intelligence (AI) models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of two-dimensional (2D) mammograms and three-dimensional (3D) tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images using a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoencoder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations generated using DeepSight. We also provide a web-based annotation tool for expert validation. To demonstrate usability, we fine-tuned MedGemma on a balanced subset of OMAMA-DB. We conducted a preliminary user study comparing human and automated classification of real and synthetic mammograms. OMAMA-DB contains 231,080 images, including 7351 2D and 374 3D cancer cases. Fine-tuned MedGemma achieved 0.989 accuracy, 0.997 sensitivity, and an F 1 score of 0.989 on a balanced validation set of 2942 images. In real-versus-synthetic classification, humans achieved 0.485 accuracy, and logistic regression and convolutional neural network achieved 0.972 and 0.997, respectively. OMAMA-DB provides a large mammography dataset with pathology-based labels and automated lesion annotations to support medical imaging research. Fine-tuned foundation models demonstrate strong cancer classification performance, and the gap between human and automated detection of synthetic images highlights the importance of real clinical data. All data, models, and parameters are openly available for research use.

Follicular thyroid neoplasms represent a common subtype of thyroid tumor that has become the most prevalent endocrine neoplasms in recent decades. Accurate diagnosis and grading of these tumors are critical for clinical management of follicular thyroid neoplasms, yet remain challenging due to the limitations of conventional imaging modalities without histopathological molecular information. There is growing interest in analytical techniques that can provide metabolic and molecular insights without the need for exogenous reagents. In such a context, Fourier transform infrared (FTIR) microspectroscopy has emerged as a promising label-free approach for detecting intrinsic disease biomarkers. In this work, we proposed a label-free framework for screening and grading follicular thyroid neoplasm tissues using FTIR microspectroscopy and machine learning. A total of 32 clinical samples in the form of tissue sections were collected from a real-world cohort and measured for the spectral data, from patients diagnosed with follicular thyroid adenoma, follicular tumor with uncertain malignant potential, and follicular thyroid carcinoma. For tumor screening, disease-specific features were extracted from the FTIR mapping data and further imaged through integration, principal component analysis, and clustering, enabling visual and quantitative assessment of tissue abnormalities. For neoplasms grading, a deep neural network trained with an adversarial learning strategy achieved a grading accuracy of 94.0% on an independent test set. These findings collectively demonstrate the potential of FTIR microspectroscopy as a powerful, reagent-free tool for the diagnosis, pathological evaluation, and clinical management of follicular thyroid neoplasms.

An ester group can serve as a covalent warhead to acylate amino acid residues around the active cavity of the target protein, thereby silencing its biological function. In this study, taking 8-(benzoyloxy) cycloberberine (1) as the lead, twenty-seven 8-esterified cycloberberine (CBBR) derivatives were continuously synthesized and evaluated for their anti-tumor activities. Among them, compound 8a exhibited an appealing potency against a variety of tumor cells with IC50 values ranging 1.91-5.09 μM, significantly outperforming the lead 1. Cell cycle analysis showed 8a-induced G2/M phase arrest, which suggested ANTXR1 as a candidate target via thermal proteome profiling (TPP) assay. Further cellular thermal shift assay (CETSA) and surface plasmon resonance (SPR) analysis identified ANTXR1 as a direct target of 8a (KD = 2.88 μM). LC-MS/MS, molecule docking and molecular dynamics simulations (MD) demonstrated that the 8-adamantane acetyl in 8a could undergo an acylation reaction with Ser229 of ANTXR1, thereby inhibiting tumor cell proliferation. Therefore, compound 8a is a covalent inhibitor of ANTXR1, and introducing an ester group as the chemical warhead represents a highly effective structural modification strategy in medicinal chemistry.

Raman spectroscopy has been proved to have the potential to accurately diagnose a variety of diseases, and novel Raman probes or instruments for clinical applications have been constantly developed. However, biological tissues are usually structurally complex. The Raman signals collected in vivo may be a mixture of various chemical components, even different tissues, which poses challenges for disease analysis and diagnosis. This work proposed a Raman spectral unmixing approach to separate the signals of different tissues from their mixed spectra. Specifically, continuous wavelet transform was performed to extract the multi-scale time-frequency domain features of Raman spectra. TransUnet model was introduced to analyze the multi-scale features from high-frequency to low-frequency through the convolution and transformer modules, and predict the Raman signals of target components. Breast cancer tissues were selected as the research subject, the Raman signals of stroma and adipocyte were successfully separated from their mixed tissues, and multiple biochemical changes in breast cancer tissues were revealed through further analysis of the unmixing signals. This work will contribute to biological in vivo detection of Raman probes or instruments, enabling them to separate signals from different tissues, structures, and even biochemical molecular components for more detailed and accurate analysis of diseases.

Early identification of breast cancer is essential for improving survival rates, yet current screening approaches often exhibit inadequate specificity or excessive invasiveness. Serum Raman spectroscopy (RS) provides a quick, non-destructive option, but its clinical use is impeded by the complexity of spectral data interpretation. Herein, we presented an interpretable deep learning framework, a one-dimensional convolutional neural network with a patient-voting strategy(PV-CNN), to evaluate serum RS from 732 individuals (366 patients and 366 healthy controls). Our model achieved a diagnostic accuracy of 95.21%, a sensitivity of 92.38%, and a specificity of 97.00%, significantly surpassing the performance of conventional ML algorithms. Furthermore, we employed Grad-CAM and SHAP analyses to elucidate the decision-making processes of deep learning, representing a significant advancement in addressing the "black-box" issue. This interpretability analysis found that tryptophan (1017 cm-1) and phenylalanine (1002 cm-1) were key Raman spectral indicators for breast cancer diagnosis. This study demonstrates that interpretable deep learning-enhanced RS can serve as a reliable, label-free, and physiologically explainable method for breast cancer detection.

The P2X7 receptor (P2X7R) is critically involved in tumor progression by promoting cancer cell proliferation, invasion, metastasis, and immune evasion. Through structure-based virtual screening of the TOPSCIENCE L1000 library, we identified pixantrone - an FDA-approved agent - as a promising lead compound targeting P2X7R. Leveraging its well-established safety profile and anthraquinone scaffold, we designed and synthesized a novel series of P2X7R antagonists. Among them, the optimized compound 17d displayed potent antagonistic activity (IC50 = 3.57 μM) and specific binding to P2X7R. In addition, 17d exhibited significant antiproliferative activity against MCF-7 cells (IC50 = 0.42 μM) and effectively inhibited their invasive and metastatic capabilities. Compound 17d also demonstrated favorable oral bioavailability and pharmacokinetic properties. Moveover, in a murine breast cancer model, 17d significantly suppressed tumor growth and metastasis while promoting the activation of CD4 and CD8 T cells to enhance antitumor immunity. Notably, 17d acted synergistically with anti-PD-1 monoclonal antibody, overcoming resistance to anti-PD-1 therapy. These findings highlight 17d as a promising candidate for P2X7R-targeted cancer therapy and underscore the value of repurposing FDA-approved drugs using structure-based approaches to accelerate the development of safer and more effective anticancer agents.

The AAA + ATPase p97/VCP is a central regulator of protein homeostasis and has emerged as an attractive anticancer target. However, first-generation ATP-competitive inhibitors have faced clinical setbacks due to off-target toxicity and sensitivity to ATP concentrations. Herein, we report the design, synthesis, and systematic structure-activity relationship (SAR) study of a novel series of diphenylmethyl-based non-competitive p97/VCP inhibitors derived from the allosteric hit MSC1094308. SAR optimization revealed that conversion of the amide linker to a secondary amine, coupled with the introduction of a tetrahydropyrido[3,4-b]indole scaffold and fluorine substitution on the biphenylmethyl group, dramatically enhanced p97/VCP inhibitory activity. The lead compounds, 10a and 10b, exhibited potent non-competitive inhibition (IC50 = 1.04 μM and 17 nM, respectively) and maintained efficacy independent of ATP concentration. Microscale thermophoresis (MST) confirmed strong binding affinity of 10a to p97/VCP (Kd = 14.99 μM), and limited proteolysis-mass spectrometry (LiP-MS) identified p97/VCP as a direct cellular targ et of 10a. Mechanistically, 10a induced mitochondrial membrane depolarization, leading to concurrent regulation of both apoptotic (caspase-3, PARP cleavage) and autophagic (LC3-II, p62) pathways. In vitro, 10a demonstrated broad-spectrum antiproliferative activity across multiple cancer cell lines and completely suppressed the growth of patient-derived colorectal cancer organoids. In an MC38 colorectal cancer xenograft mouse model, 10a achieved 55% tumor growth inhibition with manageable toxicity. Collectively, this study identifies 10a as a promising lead compound for colorectal cancer therapy and establishes allosteric p97/VCP inhibition via mitochondrial stress as a viable therapeutic strategy.