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Cervical nuclei segmentation is critical for the early detection and accurate diagnosis of cervical cancer. However, this task is challenging due to the presence of clumped nuclei and variations in texture, shape, and contrast. To address these challenges, we proposed a novel synergic conditional generative adversarial network (SCGAN) for cervical nuclei segmentation. The SCGAN integrates densely connected blocks that progressively extract hierarchical features, a Unified Attention Module (UAM) for selective feature refinement and the Scale-Adaptive Feature Integration and upsampling (SAFIU) module for multi-scale feature integration and upsampling, and a synergic discriminator to enhance adversarial learning. The SAFIU module constructs a multi-scale feature pyramid by progressively upsampling across feature levels, effectively retaining fine spatial details critical for segmenting small nuclei. The Scale-Adaptive Fusion (SAF) block further facilitates feature learning by merging high-level features with low-level spatial cues from the encoder, and then forwarding the fused representation to the corresponding decoder stage. On the adversarial side, the synergic discriminator, consisting of ResNet-50 and EfficientNet-B2, is designed for collaborative learning and accelerates convergence with the help of a synergic block. The integration of an Uncertainty-Aware Attention (UAA) mechanism in the synergic block helps the discriminators concentrate on ambiguous or overlapping regions, thereby providing more informative feedback to the generator. Experiments on multiple cervical nuclei datasets demonstrated that the proposed SCGAN outperformed existing methods in terms of sensitivity, specificity, Dice coefficient, and F1-score. By effectively integrating multi-scale features and leveraging adversarial training, our SCGAN achieves more accurate and more consistent cervical nuclei segmentation, paving the way for improved computer-aided diagnosis systems.

Detection of high precision skin lesions, especially melanoma, are still a major challenge in medical imagination due to their close visual equality and lack of reliably labeled datasets. In this study, we introduce a deep learning sketch aimed at balancing clinical accuracy with clinical interpretation. The workflow starts with a series of preprosaresing steps: removing hair from dermoscopic images, correction with the cow and separating the wound area using a U-NET segmentation model. On top of that, a skilled-B4 network was properly set and increased with a competition block meditation module (CBAM) to focus the model on clinically important properties. In order to further strengthen performance, this spine was integrated into a dress with its -201 and Renex -50, where predictions are added through a soft poll. The model output was interpreted by the use of character comb and lime, which provides visual clarification of areas affecting the final decision. The training was held on him10000 datasets and valid against ISIC-2019 and pH, which demonstrated the contour's ability to generalize in different wound categories. The model reached 98.95 % accuracy, 98.7 % balanced accuracy and 99.6 % sensitivity to melanoma, improvement in recent benchmarks. By combining efficiency, interpretation and design of privacy and inconvenience, the framework gives a realistic step towards safe and reliable integration of AI units into dermatology practice.

6PPD-quinone (6PPD-Q), a toxic tire-derived antioxidant transformation product, is a pervasive environmental contaminant linked to reproductive toxicity. However, its role in ovarian cancer (OC) remains elusive. We integrated network toxicology with transcriptomic analysis to elucidate the oncogenic mechanisms of 6PPD-Q. By screening the GEO, SwissTargetPrediction, and SEA databases, we identified 26 intersection targets. Utilizing machine learning and SHAP analysis, five core biomarkers-DDR1, ABL1, PDE2A, FRK, and F10-were prioritized. Molecular docking demonstrated high binding affinities between 6PPD-Q and these core proteins. In vitro validation, including CCK-8, plate colony formation, and qRT-PCR assays, confirmed that 6PPD-Q exposure significantly promotes OC cell proliferation. Mechanistically, 6PPD-Q may regulate the expression of hub targets to affect OC progression. This study establishes a definitive "exposure-target-phenotype" chain, characterizing 6PPD-Q as a potential environmental promoter of OC and suggest potential intervention targets.

This study aims to develop and evaluate a fully automated deep learning-driven postprocessing pipeline for multiparametric renal MRI, enabling accurate kidney alignment, segmentation, and quantitative feature extraction within a single efficient workflow. Our method has three main stages. First, a segmentation network delineates renal structures in high-contrast images. Next, a deep learning-based pairwise image registration algorithm maps the multiparametric image series to a common target and transfers the predicted annotations between the multiparametric images. Finally, clinically relevant quantitative parameters are extracted through region-specific assessment of renal structure and function based on the aligned and segmented multiparametric data. We used five-fold cross-validation to compare the segmentation outcomes and extracted features with manual analyses in 24 patients with prostate cancer or neuroendocrine tumors and 10 healthy subjects, each undergoing repeated scans. Our automated pipeline achieved high agreement with expert kidney segmentation while delivering significant alignment improvements through registration. Volumetric analysis showed a strong correlation (r > 0.9) with manual results, and feature extraction demonstrated high intraclass correlation coefficients with minimal bias. The complete processing pipeline, encompassing coregistration, segmentation, and feature extraction, required approximately 15 s per scan from raw input to final quantitative output. The study establishes a reliable automated pipeline for renal multiparametric MRI postprocessing. The achieved accuracy and efficiency can support improved diagnosis and treatment planning for patients with kidney disease.

There is increasing interest in the use of artificial intelligence (AI) to assist with respiratory diagnosis and risk prediction. Fuzzy logic is a form of AI that has the advantage of being transparent and interpretable, compared to alternatives such as deep neural networks. We systematically reviewed applications of fuzzy logic for outcome prediction in respiratory medicine. We searched PubMed and IEEE Xplore from inception to November 2024 for studies which applied fuzzy logic to respiratory outcome prediction and diagnosis. Three reviewers independently screened titles and abstracts, then all five reviewers assessed full texts for eligibility. Risk of bias was assessed using PROBAST by three reviewers. We performed a narrative synthesis following the SWiM guidelines due to heterogeneity. From 982 records, 29 studies (1998-2024) met the inclusion criteria. Studies addressed asthma (n = 5), obstructive sleep apnoea (n = 8), lung cancer (n = 4) and a variety of other conditions. Mamdani-type systems were the most frequently used (69%). Performance varied dramatically, with sensitivity/specificity ranging from 69 to 100% and 19-100%, respectively. The studies which displayed the highest accuracy (>95%) incorporated well-defined clinical variables, particularly for asthma and tuberculosis. However, 69% of studies displayed high risk of bias, frequently due to inadequate validation. Fuzzy logic systems show potential as a transparent alternative to neural network-based machine learning for outcome prediction and diagnosis in respiratory medicine. However, clinical implementation is limited by frequent methodological limitations. Future research requires prospective validation studies and standardised reporting before fuzzy logic can enhance respiratory medicine.

The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines.

Radiotherapy remains a cornerstone in oncology, yet its efficacy is limited by tumor radioresistance and off-target toxicity. This review elucidates the transformative potential of hafnium (Hf)-based radiosensitizers in overcoming these challenges. Leveraging Hf's high atomic number, these Hf-based biomaterials enhance X-ray energy deposition through photoelectric and Auger effects, generate cytotoxic reactive oxygen species (ROS), and modulate immunosuppressive tumor microenvironments to enhance radiotherapy effect. Their distinctive capability to achieve multimodal synergy by integrating radiotherapy with photodynamic, chemotherapeutic, or immunotherapeutic strategies enables precise targeting and significantly enhances antitumor responses. Subsequently, this review rigorously assessed the current synthetic methodologies for Hf-based radiosensitizers, along with their capacities and limitations in terms of controlling material properties and ensuring scalability. Advanced imaging modalities such as fluorescence, CT, SPECT, MRI, and PA further establish Hf-based systems as theranostic platforms for real-time tumor localization and treatment monitoring. While clinical candidates like NBTXR3 exhibit promising trial outcomes, challenges remain in mechanistic clarification, biocompatibility optimization, and controlled in vivo degradation. Emerging AI-driven design and multidisciplinary integration hold promise to expedite clinical translation, advancing Hf-based radiosensitizers toward intelligent, personalized cancer therapy paradigms. This work highlights Hf's critical role in redefining precision radiotherapy and delineates a roadmap for next-generation oncological intervention.

Determining molecular markers that mediate clinically aggressive phenotypes in prostate cancer is a significant challenge. While traditional linear models offer some interpretability, they often lack the precision needed for complex multi-omics data. Conversely, conventional deep learning methods provide robust predictions but typically remain opaque, hindering the identification of impactful molecular markers and biological mechanisms. To address this, we propose the Cross-omics Interpretable Neural Network (CINN), a biomimetic framework designed to predict prostate cancer states and identify key molecular markers by integrating diverse omics data. CINN innovatively leverages prior biological knowledge from either pathway or protein-protein interaction (PPI) networks, combined with a novel trainable mask layer. This mask dynamically optimizes the strength of pre-defined biological connections, thereby enhancing both knowledge representation and model interpretability. The framework effectively integrates multi-omics data, including gene expression, somatic mutations, and copy number variations, to provide a holistic view of the disease. Extensive experiments on a prostate cancer dataset demonstrate that CINN achieves substantial and statistically significant performance enhancements over a strong baseline (P-NET). Specifically, our best-performing variant, CINN-pw with a trainable mask, improved F1 scores by 13.1% to 0.843, Accuracy by 8.3% to 0.894, and AUC by 2.3% to 0.949. These gains were consistently statistically significant (p<0.0001 for most key metrics), underscoring the robustness of our approach. Crucially, CINN's inherent interpretability facilitated the identification of pivotal molecular candidates, including TBP and TAF2, which are implicated in prostate cancer progression. These findings are supported by existing literature and provide valuable insights into the underlying mechanisms of prostate cancer, offering potential avenues for targeted therapeutic interventions and precision medicine.

Postoperative liver cancer relapse remains a formidable clinical challenge. Photothermal therapy (PTT) holds promise by eliminating residual malignancies and activating antitumor immunity; notably, tumor cells persistently reconstitute proteostasis and survive by integrated stress response (ISR)-mediated heat shock protein 90 (HSP90) activation to constrain PTT efficacy. To address this limitation, we engineered a self-reinforced photothermal-immunomodulation strategy based on electrospun nanofiber scaffolds co-loaded with black phosphorus nanosheets (BPNSs) and the HSP90 inhibitor 17-DMAG. These nanofiber scaffolds exhibited robust hydrophobicity, efficient photothermal conversion, and near-infrared (NIR) responsive controlled drug release. Under NIR irradiation, the nanofiber scaffolds leveraged BPNSs to generate stable PTT while liberating 17-DMAG to amplify proteotoxicity, forcibly redirecting the ISR from pro-survival adaptation toward robust apoptosis and immunogenic cell death (ICD). Consequently, prominently exposed damage-associated molecular patterns potentiated tumor immunogenicity and remodeled immune microenvironment by dendritic cells maturation, cytotoxic T lymphocytes (CTLs) priming, and immunosuppressive populations reprogramming. Crucially, subsequent synergy with anti-PD-L1 reinvigorated CTLs and established durable immune memory. Systematic validation confirmed this localized strategy uniquely integrates precision photothermal energy conversion with potent ISR-ICD cascade, effectively synergizing with anti-PD-L1 to suppress postoperative liver cancer relapse and metastasis, thereby holding substantial translational potential for clinical oncology.

Intrahepatic cholangiocarcinoma (iCCA), a highly lethal subtype of liver cancer with increasing incidence and poor prognosis, displays profound molecular heterogeneity that limits therapeutic efficacy. Combining multi-omics with pathological subtyping in iCCA, we identified a strong association between the most aggressive molecular subtype and the large-duct pathological type, characterized by aberrant mucin overexpression and prominent myeloid cell infiltration. Through integrative analysis, MUC16 was identified as a key molecular marker specifically enriched in large-duct type intrahepatic cholangiocarcinoma (L-iCCA). Functional modulation of MUC16 markedly inhibited L-iCCA cell proliferation and migration. Furthermore, high-throughput drug screening identified the small-molecule compound OSMI-1 as a potent suppressor of MUC16 expression. Using primary iCCA cell lines and patient-derived organoids (PDOs), we confirmed that OSMI-1 inhibits L-iCCA cell proliferation and migration by downregulating MUC16. Mechanistically, OSMI-1 suppresses MUC16 expression by disrupting the transcriptional complex formed between OGT and IRF1. Employing a genetically engineered L-iCCA mouse model, we further validated the crucial role of MUC16 in tumor progression and neutrophil infiltration, demonstrated that OSMI-1 synergized with gemcitabine, effectively inhibiting L-iCCA growth. This study presents a pathological subtype-based precision therapeutic strategy for L-iCCA, thereby providing a foundation for novel translational approaches to the personalized management of this disease.