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Coriandrum sativum L. (coriander) is a medicinal herb with diverse pharmacological properties, but its molecular mechanism in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to systematically investigate the underlying mechanisms of coriander in ccRCC by multi-omics analysis. Active compounds were screened using Traditional Chinese Medicine Systems Pharmacology (TCMSP) and predicted targets identified via SwissTargetPrediction (STP) and Similarity ensemble approach (SEA). Transcriptomic data from GSE53757 were analysed with WGCNA and intersected with coriander targets. Key genes were selected using LASSO, SVM, and random forest models. NEK6 was further analysed for clinical relevance, methylation, immune association, single-cell expression, molecular docking and molecular dynamics simulation. Fourteen coriander compounds were identified, yielding 22 potential ccRCC-related targets. NEK6 and PYGL were consistently selected by all machine learning algorithms. NEK6 was overexpressed in ccRCC and associated with better prognosis, promoter hypomethylation, and lower mutation rates. NEK6 expression correlated with immune infiltration, particularly macrophages, and was enriched in tumour and myeloid cells at the single-cell level. Molecular docking and molecular dynamics simulation revealed strong and stable binding of luteolin, quercetin, and chryseriol to NEK6. NEK6 may function as a prognostic and immune-regulatory biomarker in ccRCC. Coriander flavonoids could target NEK6 to modulate the immune microenvironment, providing new insight into plant-based therapeutic strategies for ccRCC.

This study investigates clinical characteristics influencing post-progression survival (PPS) in locally advanced esophageal squamous cell carcinoma (ESCC) after definitive chemoradiotherapy (dCRT), aiming to develop individualized follow-up strategies using conditional PPS. The correlation between PPS and overall survival (OS) using Spearman correlation analysis. LASSO regression, Cox regression, and machine-learning methods were employed to identify prognostic factors, and a prediction model was constructed. The Shapley additive explanations (SHAP) method was used to interpret the model. Conditional PPS survival rates and recurrence risks were analyzed. This study enrolled 741 patients, with a median follow-up of 27.2 months. PPS was positively correlated with OS. Prognostic factors included: N stage, tumor length, chemotherapy cycles, platelet-to-albumin ratio, lymphocyte-to-monocyte ratio, age, body mass index, radiotherapy dose, and neutrophil to monocyte to lymphocyte ratio. Calibration curves, decision curves, and ROC curves demonstrated the model's stability and predictive performance. Subgroup analyses suggested shorter PPS in high-risk patients. After adjusting for other confounders, multi-model analyses continued to show a positive association between the risk score and unfavorable PPS. Conditional PPS analyses across different risk groups revealed that, with increasing survival time, conditional PPS extended correspondingly, and the relapse risk gradually decreased. Finally, individualized follow-up strategies were proposed, indicating intensified monitoring for high-risk patients. This study fills the research gap in the influencing factors of PPS and personalized follow-up strategies for patients with locally advanced ESCC after dCRT, and provides important clinical evidence for promoting the transformation of post-recurrence management from 'experience-driven' to 'data-driven'.

Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preprocessing and handcrafted feature extraction, limiting scalability and introducing user bias. This study proposes an automated, interpretable, and organelle-focused deep learning framework for classifying breast cancer cell lines from high-resolution fluorescence microscopy images. We developed an end-to-end framework that incorporates patch-based sampling, sparsity filtering, and a channel-wise intermediate fusion strategy to independently extract and integrate organelle-specific features. Model interpretability was assessed using Grad-CAM visualizations and single-organelle classifier analyses. The framework was evaluated on fluorescence microscopy images from six breast cancer cell lines using 5-fold cross-validation. The proposed framework achieved a classification accuracy of 97.1 ± 1.1 %, performing comparably to or exceeding conventional handcrafted feature-based approaches while eliminating the need for manual segmentation and 3D rendering steps. Interpretability and classifier analyses revealed inter-organelle dependencies and mitochondria as the most informative contributors to classification decisions. Organelle morphology and spatial organization provide strong discriminative signals for cancer cell classification. The proposed framework offers a scalable, automated, and interpretable deep learning solution that advances microscopy-based phenotyping and supports broader applications in computational pathology and cellular informatics.

Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But predictive models integrating multimodal data are lacking. This study aimed to develop a novel machine learning-based nomogram integrating clinical, dosimetric, and inflammatory predictors for RP risk assessment in this high-risk population. This retrospective study of 424 ILD patients collected clinical, dosimetric, and inflammatory data. Machine learning algorithms created composite dosimetric (D score) and inflammatory (Inflamm score) scores. A multivariable logistic regression nomogram was built incorporating these scores with clinical risk factors. Model performance was assessed using area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RP occurred in 200 (47%) patients. Independent risk factors included higher performance status, Charlson comorbidity index (CCI), usual interstitial pneumonia (UIP) pattern, immunotherapy, concurrent chemoradiotherapy, more radiation sessions, and lower lung volume. The D score and Inflamm score were both independent predictors. The integrated nomogram (AUC = 0.929) showed excellent discrimination, significantly outperforming the clinical model (AUC = 0.86), D score (AUC = 0.758) (both p < 0.001), and Inflamm score (AUC = 0.910, p = 0.168). Calibration curve and DCA confirmed its strong calibration ability and clinical utility to identify high-risk patients early. The integrated nomogram combining clinical, dosimetric, and inflammatory predictors enables accurate, individualized RP risk assessment in lung cancer patients with ILD. It can guide adjustments to individualized radiotherapy plans or preventive interventions, supporting better patient selection, treatment decisions, and proactive follow-up.

Inadequate adherence to colorectal cancer screening reduces individual and population level health benefits. Blood-based tests offer a new modality that may help patients overcome barriers, but there are concerns about the impact of patients switching from existing guideline-recommended screening modalities. This study estimates the population health outcomes and cost-effectiveness of offering blood-based testing using a validated individual-level simulation model based on patient preference evidence from randomized controlled trials. In this study, a validated discrete-event simulation model was used to evaluate the performance of different combinations of colorectal cancer screening strategy preferences per 10,000 screened individuals beginning at age 45. Preferences for screening options were informed by randomized controlled trials of patients with and without the option of blood-based testing. Adherence to initial patient preferences over a simulated lifetime was modeled as: (1) assumed 100% adherence and (2) longitudinal using a calibrated model. Simulated outcomes included clinical outcomes and cost-effectiveness from a healthcare sector perspective. A strategy was deemed cost-effective at a willingness-to-pay threshold of $100,000 per quality-adjusted life-year gained. The introduction of blood-based testing to an unscreened population with evidence from randomized controlled trials is projected to increase colorectal cancer deaths averted by 35% to 116% and from 68% to 247% relative to no screening, for stated preference and revealed preference scenarios, respectively. These outcomes are cost-effective, with incremental cost-effectiveness ratios ranging from $63,994 to $85,497 and from $30,464 to $54,764 across stated preference and revealed preference scenarios, respectively. Given limited data, natural history and real-world longitudinal adherence to screening are based on evidence-informed assumptions. Using a simulation model to extrapolate data from two recent trials, we demonstrate that the introduction of blood-based tests has the potential to lead to cost-effective increases in the number of CRC deaths averted among the unscreened population.

This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). Differentially expressed NET-RGs (DE-NRGs) between PTCL and normal tissues were screened. Functional enrichment analysis was conducted. Bioinformatics and machine learning were used to identify hub genes and assess their diagnostic value. Univariate and multivariate analyses were used to evaluate prognostic roles. Correlation and immune infiltration analyses were performed to explore relationships with the tumor microenvironment (TME). Clinical data were collected from PTCL patients who received potential agents (lenalidomide) as first-line treatment. A total of 31 DE-NRGs were identified (18 upregulated and 13 downregulated), enriched in inflammatory response, extracellular matrix organization, and infection involvement. Four hub genes (AKT2, MAPK14, IRF1, and TNF) were identified as effective PTCL diagnostic markers. Higher AKT2/MAPK14 expression correlated with poorer overall survival (OS), while elevated TNF expression associated with better OS; AKT2 and TNF independently predicted OS. These genes were implicated in modulating TME remodeling. Potential therapeutic agents (e.g. capivasertib, lenalidomide) were predicted, and lenalidomide may represent a feasible initial treatment option for PTCL, with an objective response rate (ORR) of 40.0% and a maximum survival duration exceeding 50 months. NET-RGs play crucial roles in diagnosis, prognosis, and TME regulation, and lenalidomide, a putative TNF-targeting agent, may represent a feasible initial treatment option in PTCL.

Non-muscle invasive bladder cancer (NMIBC) accounts for over 75% of bladder cancer cases worldwide and is associated with high recurrence rates and significant surveillance costs. Advances in diagnostic modalities, risk stratification, and bladder-preserving therapies have transformed management strategies. This narrative review synthesizes evidence from 70 key publications identified through a comprehensive search of PubMed, MEDLINE, Embase, Scopus, and Google Scholar (2005-2025). Topics include clinical presentation, diagnostic innovations such as enhanced cystoscopy and urinary biomarkers, contemporary risk stratification models, and evolving treatment paradigms including intravesical therapy, immunotherapy, and gene therapy. NMIBC management is shifting toward precision-based, multimodal approaches that integrate molecular biomarkers, immunotherapy, and novel drug delivery systems. While early-phase trials show promise, large-scale studies and real-world data are essential to validate these strategies. Personalized surveillance using circulating and urinary tumor DNA may reduce procedural burden and improve outcomes, marking a paradigm shift toward adaptive, patient-centered care.

To explore the predictive value of machine learning-based multimodal MRI radiomics combined with clinical features in the efficacy of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids. This study included 390 patients with uterine fibroids who underwent HIFU ablation. Patients were stratified into high and low ablation groups based on an 80% non-perfused volume ratio (NPVR) and randomly divided into training (70%) and test (30%) sets. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). The most predictive features were selected via Recursive Feature Elimination (RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO), and combined with clinical characteristics. Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), were constructed to predict ablation efficacy, with performance assessed using the area under the receiver operating characteristic curve (AUC). The results indicated that age, uterine fibroid location, and T2WI signal intensity were independent predictive factors (p < 0.05). The multimodal-clinical fusion XGBoost model exhibited the optimal performance. In the test set, this model achieved an AUC of 0.894, with an accuracy of 82.1%, sensitivity of 88.9%, and specificity of 74.1%.The calibration curve and decision curve analysis (DCA) confirmed that the predicted probabilities of the model were highly consistent with the actual risks, and stable clinical net benefits were achieved. The XGBoost model based on multimodal MRI and clinical features may serve as a reference for predicting HIFU ablation efficacy and optimizing treatment strategies.

Canine mammary tumors (CMTs) are the most common neoplasms in intact female dogs, yet early detection remains challenging due to the lack of clinically validated, noninvasive biomarkers. This study aimed to develop a noninvasive diagnostic model for CMT detection by integrating serum autoantibody biomarkers with machine learning. Serum samples from 154 dogs with mammary tumors (31 benign, 123 malignant) and 39 healthy controls were analyzed using a custom multiplex immunoassay detecting autoantibodies against AGR2, HAPLN1, IGFBP5, and TYMS, normalized to anti-BSA levels. Median fluorescence intensity (MFI), standardized autoantibody ratios, and their combination, together with clinical variables, were used to train random forest classifiers. The model based on standardized autoantibody ratios achieved the best performance, with an AUC of 0.79 (sensitivity 75.3%, specificity 74.4%) for overall CMT detection; 0.78 (92.7%, 61.5%) for malignant CMTs; and 0.77 (82.2%, 71.8%) for early-stagemalignancies. Assuming a CMT prevalence of 0.5 in the hospital-referred population, the positive and negative predictive values ranged from 0.74-0.75 and 0.75-0.91, respectively. This proof-of-concept study demonstrates that a machine learning-assisted multiplex autoantibody assay offers a feasible noninvasive approach for CMT detection. Further validation in larger, independent cohorts is warranted to support clinical translation in veterinary oncology.

PARP1 inhibitors are FDA-approved for BRCA1/2-mutated breast cancer but show limited efficacy in wild-type cancers and face resistance issues. To overcome these, we designed novel 6,7-dihydro-5H-pyrrolo[3,4-d]pyrimidine-based compounds integrating PARP1 inhibitor pharmacophores with the ATR inhibitor AZD6738 scaffold. Substituent modifications influenced PARP1 and ATR selectivity, yielding dual inhibitors or selective PARP1 inhibitors. Compound 38a, the lead candidate, exhibited potent dual inhibition (IC50 < 20 nM) and strong antitumor effects in MDA-MB-231 (IC50 < 0.048 μM) and MDA-MB-468 (IC50: 0.01 μM) cell lines in vitro. Mechanistically, 38a arrested cell cycle progression, induced apoptosis, inhibited colony formation and migration, and suppressed DNA damage repair pathways, outperforming combined Niraparib and AZD6738. These findings underscore the therapeutic potential of PARP1/ATR dual inhibitors for breast cancer and support further investigation.