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The tumor microenvironment (TME) plays a pivotal role in determining tumor progression and treatment response. Within the TME, redox processes mediated by reactive oxygen species (ROS) and nitric oxide (NO) are critically involved in regulating intercellular and intracellular signaling. In this study, we hypothesized that conjugating an NO-releasing moiety to paeonol derivatives and introducing a chalcone structure to enhance thioredoxin reductase (TrxR) targeting would yield compounds with potent anticancer activity. Accordingly, a series of mono- and di-substituted nitrate derivatives were synthesized. The inhibitory activities of all synthesized compounds were evaluated against BGC823, HCT116, Hep G2, and MCF-7 cell lines using the CCK-8 assay. Among the paeonol chalcone derivatives, compound 11f exhibited significant antiproliferative activity across the tested cancer cell panel. It was identified as a promising candidate with potent TrxR inhibitory activity (IC50 = 0.26 ± 0.17 μM in vitro; IC50 = 0.33 μM in vivo). Furthermore, compound 11f induced S-phase arrest and promoted apoptosis in MCF-7 cells. These findings underscore the enhanced anticancer potential of paeonol chalcone derivatives, attributable to the synergistic effects of NO and ROS.

Ovarian clear cell carcinoma (CCC) is an aggressive subtype of ovarian cancer that is resistant to conventional chemotherapy, resulting in poor prognosis. CCC develops from endometriosis, which exposes tumor cells to a hypoxic microenvironment, thereby highlighting the critical role of hypoxia in ovarian CCC progression. Thus, identifying novel therapeutic targets, particularly those associated with hypoxia, is important. Hypoxia-inducible factor 2A (HIF2A) is a key regulator of hypoxic responses, but its role in ovarian CCC remains unclear. This study assessed the prognostic and functional significance of HIF2A in ovarian CCC and investigated its potential as a therapeutic target. Inhibiting HIF2A significantly suppressed ovarian CCC tumor growth through a genetic knockdown cell line as well as pharmacological inhibition using a novel HIF2A inhibitor, NKT2152. In vitro experiments showed that HIF2A suppression enhanced mitochondrial respiration and increased mitochondrial reactive oxygen species production alongside the downregulation of HIF2A target genes. Moreover, treatment with NKT2152 significantly reduced tumor growth in both cell line-derived and patient-derived xenograft models. In conclusion, our findings provide novel insights into the prognostic and functional role of HIF2A in ovarian CCC and underscore its potential as a promising therapeutic target.

Matrix metalloproteinase-2 (MMP-2) is a zinc-dependent endopeptidase which plays a key role in the extracellular matrix-remodeling and cancer metastasis. Nevertheless, despite the vast number of attempts, MMP-2 selective and low-toxicity development is a problematic area because of the insufficient selectivity and the off-target effect of the previous candidates. This work demonstrated that an integrated machine learning-driven virtual screening pipeline can be used to discover better selectivity, and binding stability novel MMP-2 inhibitors. Various models of classification were trained with the help of a set of different molecular fingerprints, and random Forest and radial-basis-function Support Vector Model of classification showed the best predictive results (AUC > 0.97, MCC > 0.86). These models have been used to filter the Maybridge compound library resulting in the selection of the top-ranked ones. Molecular docking and subsequent ADMET profiling of the shortlisted seven potential compounds yielded a list of 1. Molecular dynamics simulations (100 ns) showed that GK03418 and RH00707 had stable binding conformations similar to that of the reference inhibitor. Free energy landscape mapping and principal component analysis was another method that proved thermodynamic stability of GK03418. The energetics of binding free-energy calculations with MM/PBSA and MM/GBSA showed positive results and the most promising inhibitor was GK03418. In general, this paper provides a computationally sound and scalable structure of the discovery of selective MMP-2 inhibitors that have future anticancer applicability.

Glioblastoma multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options, largely owing to the poor blood-brain barrier (BBB) permeability of current drugs. Quinazolinone derivatives represent an important class of heterocycles with diverse pharmacological potential; however, their activity against U87 glioblastoma cells has not been previously reported. In this study, a series of 2,3-dihydroquinazolin-4(1 H)-one derivatives (DHQs) were synthesised through a sustainable PEG-400-mediated multicomponent protocol performed in a sealed tube, providing an efficient and environmentally benign route to access this pharmacologically important scaffold. To identify potential glioblastoma inhibitors, a multiscale computational pipeline integrating DFT descriptors, ADME screening, molecular docking, molecular dynamics (MD) simulations, MM/GBSA, principal component analysis (PCA), and free energy landscape (FEL) calculations was employed. Among the synthesised molecules, compound 1h emerged as the most promising candidate, exhibiting the highest binding affinity towards EGFR (-9.11 kcal mol⁻¹) and favourable CNS-relevant physicochemical properties. MD simulations confirmed the structural stability of the 1h-3POZ complex for over 100 ns, as supported by low RMSD values, restricted residue fluctuations, and a stable free-energy profile. Experimental validation using the MTT assay on U87 glioblastoma cells demonstrated that compound 1h exhibited potent cytotoxicity (IC₅₀ = 16.57 ± 0.90μM), significantly outperforming temozolomide. Overall, this study presents the first integrated green-synthetic and computational-experimental evaluation of DHQs against U87 cells, highlighting compound 1h as a promising lead for glioblastoma drug discovery.

Automatic grading of Gastric Intestinal Metaplasia (GIM) is valuable in assisting the diagnosis of early gastric cancer. Recently, prototypical networks are served as a effective method for medical image processing in few-shot scenarios. However, existing prototypical networks suffer from the following two limitations when applied to GIM grading: 1) Variable camera angles of gastric endoscopes result in diverse sampling granularities of GIM lesions, leading to a multitude of multiscale features. Fully supervised encoders struggle to learn robust multiscale features due to limited labeled endoscopic images and privacy concerns. 2) Class prototypes based on sample means ignore the latent class information of exceptional cases, resulting in one-sided inferences of category prototypes and decision boundaries. To address these challenges, we propose a Self-supervised Exceptional Prototypical Network (Swin-EPN) for few-shot grading of GIM. Specifically, three tailored pretext tasks are designed to jointly pretrain a swin transformer, which is integrated as the model's embedding layer to learning robust multiscale features. We propose an exceptional prototype mining module that identifies exceptional prototypes by defining a prototype score for each sample and updating potential exceptional prototypes in an exceptional prototype bank. These exceptional prototypes are served as supplementary information to class prototypes, and are leveraged to guide the delineation of class decision boundaries. We validated Swin-EPN on a private GIM dataset from a local grade-A tertiary hospital in both 1-shot and 5-shot scenarios, achieving accuracy improvements of 6.12% and 5.61% respectively compared to state-of-the-art (SOTA) models.

Chemical investigation of the ethyl acetate extract of the endolichenic fungus Xylaria sp. LCSS1a resulted in the isolation and identification of 24 natural metabolites. Among them, 15 were known compounds and 9 of them were characterized as previously unreported dearomatized xanthone derivatives with highly oxygenated substituents, which were designated as xylaricins A-I (1-9). The structures and absolute configurations of the previously unreported compounds were elucidated through extensive spectroscopic analysis, quantum chemical calculations (13C NMR DP4+ analysis, and TDDFT-ECD) and X-ray crystallography. The screening for cytotoxic and antibacterial assays revealed that compound 9 exhibited moderate inhibitory activity against A549 lung cancer cells, with an IC50 value of 7.5 μM, while to Staphylococcus aureus, the MIC value was 16 μg/mL. Structure-activity relationships highlighted the importance of a conjugated π-system and free phenolic groups for bioactivity observed in this series.

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality and disease burden worldwide, and its clinical management continues to face substantial challenges. Sorafenib, a widely used systemic therapy for advanced HCC, frequently develops acquired resistance upon long-term treatment, in part due to the overexpression of anti-apoptotic Bcl-2 family proteins. Herein, guided by the structural features of Sorafenib, the selective Bcl-2 inhibitor Venetoclax, and the selective Mcl-1 inhibitor AZD5991, we designed and synthesized a series of novel Sophocarpine-derived analogues bearing a pyridylethyl moiety via a molecular-hybridization strategy. Molecular docking suggested a favorable binding mode, in which the resulting scaffold could occupy the hydrophobic binding pockets of both Bcl-2 and Mcl-1 and engage key residues through hydrogen-bond interactions. In vitro antiproliferative screening (MTT assay) against three human HCC cell lines (Huh-7, MHCC-97H, and HepG2) showed that most compounds exhibited moderate to good activity. Notably, compound S6 emerged as the most potent analogue, with IC₅₀ values of 9.13 ± 0.29 μM (Huh-7), 6.76 ± 0.06 μM (MHCC-97H), and 15.9 ± 0.98 μM (HepG2). Mechanistic studies demonstrated that S6 markedly suppressed proliferation and migration of MHCC-97H cells, induced G1-phase arrest, and promoted apoptosis. Western blot analysis revealed that S6 downregulated anti-apoptotic proteins Bcl-2 and Mcl-1, induced mitochondrial membrane potential (ΔΨm) depolarization, and activated the caspase-dependent apoptotic cascade, as evidenced by caspase-3 activation and PARP1 cleavage. In parallel, a 3D-QSAR (CoMFA) model was constructed to rationalize the structure-activity relationship and to inform further lead optimization. Collectively, these findings identify S6 as a promising Sophocarpine derivative with a putative dual Bcl-2/Mcl-1 targeting profile, with significant anti-HCC activity and potential for preclinical development.

Breast cancer susceptibility gene 1 (BRCA1), a tumor suppressor protein, is closely associated with ovarian and breast cancers. Previously, cathepsin S (CTSS) was reported to prevent BRCA1-mediated apoptosis, contributing to chemoresistance in TNBC. In this study, we screened the CTSS inhibitory activity of 107 pure compounds derived from plant materials and identified 4-hydroxyderricin (12) from Angelica keiskei as a potent CTSS inhibitor. Compound 12 increased BRCA1 stability in TNBC cells in a CTSS-dependent manner. In an in vivo TNBC xenograft mouse model, combination treatment with paclitaxel and compound 12 significantly increased BRCA1 stability, reduced the final tumor weight, and decreased the number of Ki-67-positive proliferative cells. Our findings suggest that combination therapy with compound 12 can enhance BRCA1 function and improve chemotherapeutic efficacy. This study highlights CTSS inhibition as a promising therapeutic strategy for TNBC patients with wild-type BRCA1.

Parthenolide is a natural IκB kinase β (IKKβ) inhibitor. Converting it into a PROTAC (proteolysis-targeting chimeras) may lead to improved pharmacological efficacy. Herein, we report the design, synthesis, and biological evaluation of a novel series of parthenolide-based PROTACs. Among them, compound 8 exhibited potent anti-proliferative activity, especially against triple-negative breast cancer MDA-MB-231 cells. Mechanistic studies revealed that 8 acts as an effective IKKβ degrader, inducing degradation via the ubiquitin-proteasome system (DC50 = 7.15 μM, 91.24% degradation at 10 μM). Furthermore, treatment with 8 was associated with significant apoptosis and G1-phase cell cycle arrest in MDA-MB-231 cells. This work provides initial evidence that the parthenolide scaffold can be leveraged for targeted protein degradation, supporting the future development of IKKβ-directed degraders.

To develop an automated analytical framework that integrates hybrid radiomics and deep learning features from non-contrast CT images for the multi-class classification of bladder pathologies. This retrospective study analyzed 902 CT scans (584 normal, 142 calculi, 66 cancers, 110 cystitis). An integrated pipeline was implemented, comprising: 1) automatic bladder segmentation using a 3D-UNet, 2) hybrid feature extraction combining 100 radiomics features and 256 deep features from a 3D convolutional autoencoder, 3) feature selection via variance thresholding and LASSO regression, and 4) final classification using an XGBoost classifier. The dataset was split into training (80 %) and validation (20 %) sets. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with a one-vs-rest strategy for multi-class classification. Model stability was assessed via stratified five-fold cross-validation, and interpretability was analyzed with SHapley Additive exPlanations (SHAP). The framework achieved one-vs-rest AUROCs of 0.94 (95 % CI: 0.89-0.99) for calculi, 0.92 (0.85-0.99) for cancer, 0.90 (0.84-0.95) for normal bladder, and 0.83 (0.75-0.91) for cystitis. The micro-average AUROC for four-class discrimination was 0.94 (0.92-0.96). Binary normal/abnormal classification demonstrated stable performance across cross-validation folds (AUROC range: 0.89-0.92). SHAP analysis revealed that radiomic features dominated decisions for calculi/normal differentiation, while deep features were critical for distinguishing cancer and cystitis. The proposed hybrid CT analysis framework achieves clinically relevant performance in the automated, multi-class classification of bladder pathologies, excelling particularly in calculi detection. The complementary roles of radiomic and deep features provide an interpretable diagnostic aid, demonstrating potential for integration into clinical workflows to support differential diagnosis.