CX3CL1 (Fractalkine)-CX3CR1 Axis in Inflammation-Induced Angiogenesis and Tumorigenesis

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(9), С. 4679 - 4679

Опубликована: Апрель 25, 2024

The chemotactic cytokine fractalkine (FKN, chemokine CX3CL1) has unique properties resulting from the combination of chemoattractants and adhesion molecules. soluble form (sFKN) strongly attracts T cells monocytes. membrane-bound (mFKN) facilitates diapedesis is responsible for cell-to-cell adhesion, especially by promoting strong leukocytes (monocytes) to activated endothelial with subsequent formation an extracellular matrix angiogenesis. FKN signaling occurs via CX3CR1, which only known member CX3C receptor subfamily. Signaling within FKN-CX3CR1 axis plays important role in many processes related inflammation immune response, often occur simultaneously overlap. upregulated hypoxia and/or inflammation-induced inflammatory release, it may act locally as a key angiogenic factor highly hypoxic tumor microenvironment. importance FKN/CX3CR1 pathway tumorigenesis cancer metastasis results its influence on cell apoptosis, migration. This review presents context angiogenesis cancer. mechanisms determining pro- or anti-tumor effects are presented, cause seemingly contradictory that create confusion regarding therapeutic goals.

Язык: Английский

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Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

Artificial Intelligence Review, Год журнала: 2025, Номер 58(4)

Опубликована: Янв. 25, 2025

Язык: Английский

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Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Фев. 4, 2025

Background Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications chemotherapy development, cancer diagnosis, and predicting response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) deep (DL). Objective This review aims to explore role forecasting outcomes related treatment response, synthesizing current advancements identifying critical gaps field. Methods A comprehensive literature search was conducted across PubMed, Embase, Web Science, Cochrane databases up 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” “Deep (DL)” combined with “chemotherapy development,” “cancer diagnosis,” treatment.” Articles published within last four years written English were included. The Prediction Model Risk Bias Assessment utilized assess risk bias selected studies. Conclusion underscores substantial impact AI, including ML DL, on innovation, response for both solid hematological tumors. Evidence from recent studies highlights AI’s potential reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing plans, improving therapeutic outcomes. Future research should focus addressing challenges clinical implementation, ethical considerations, scalability enhance integration into oncology care.

Язык: Английский

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Advances in covalent organic frameworks for cancer phototherapy

Coordination Chemistry Reviews, Год журнала: 2024, Номер 506, С. 215720 - 215720

Опубликована: Фев. 16, 2024

Язык: Английский

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CDCA8, a mitosis-related gene, as a prospective pan-cancer biomarker: implications for survival prognosis and oncogenic immunology

American Journal of Translational Research, Год журнала: 2024, Номер 16(2), С. 432 - 445

Опубликована: Янв. 1, 2024

Background: Human cell division cycle-associated protein 8 (CDCA8), a critical regulator of mitosis, has been identified as prospective prognostic biomarker in several cancer types, including breast, colon, and lung cancers.This study analyzed the diagnostic/prognostic potential clinical implications CDCA8 across diverse cancers.Methods: Bioinformatics molecular experiments.Results: Analyzing TCGA data via TIMER2 GEPIA2 databases revealed significant up-regulation 23 types compared to normal tissues.Prognostically, elevated expression correlated with poorer overall survival KIRC, LUAD, SKCM, emphasizing its marker.UALCAN analysis demonstrated based on variables, such stage, race, gender, these cancers.Epigenetic exploration indicated reduced promoter methylation levels Kidney Renal Clear Cell Carcinoma (KIRC), Lung Adenocarcinoma (LUAD), Skin Cutaneous Melanoma (SKCM) tissues controls.Promoter mutational analyses showcased hypomethylation low mutation rate for cancers.Correlation positive associations between infiltrating immune cells, particularly CD8+ CD4+ T cells.Protein-protein interaction (PPI) network unveiled key interacting proteins, while gene enrichment highlighted their involvement crucial cellular processes pathways.Additionally, CDCA8associated drugs through DrugBank presented therapeutic options SKCM.In vitro validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) confirmed LUAD lines (A549 H1299) control .Conclusion: This provides concise insights into CDCA8's multifaceted role covering patterns, diagnostic relevance, epigenetic regulation, landscape, infiltration, implications.

Язык: Английский

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Stimulus-Responsive Hydrogels for Targeted Cancer Therapy

Gels, Год журнала: 2024, Номер 10(7), С. 440 - 440

Опубликована: Июль 1, 2024

Cancer is a highly heterogeneous disease and remains global health challenge affecting millions of human lives worldwide. Despite advancements in conventional treatments like surgery, chemotherapy, immunotherapy, the rise multidrug resistance, tumor recurrence, their severe side effects complex nature microenvironment (TME) necessitates innovative therapeutic approaches. Recently, stimulus-responsive nanomedicines designed to target TME characteristics (e.g., pH alterations, redox conditions, enzyme secretion) have gained attention for potential enhance anticancer efficacy while minimizing adverse chemotherapeutics/bioactive compounds. Among various nanocarriers, hydrogels are intriguing due high-water content, adjustable mechanical characteristics, responsiveness external internal stimuli, making them promising candidates cancer therapy. These properties make an ideal nanocarrier controlled drug release within TME. This review comprehensively surveys latest area therapy, exploring stimuli-responsive mechanisms, including biological pH, redox), chemical enzymes, glucose), physical temperature, light), as well dual- or multi-stimuli responsiveness. Furthermore, this addresses current developments challenges treatment. Our aim provide readers with comprehensive understanding treatment, offering novel perspectives on development therapy other medical applications.

Язык: Английский

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Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review

International Journal of Medical Informatics, Год журнала: 2024, Номер 193, С. 105689 - 105689

Опубликована: Ноя. 4, 2024

Язык: Английский

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Towards unbiased skin cancer classification using deep feature fusion

BMC Medical Informatics and Decision Making, Год журнала: 2025, Номер 25(1)

Опубликована: Янв. 31, 2025

Abstract This paper introduces SkinWiseNet (SWNet), a deep convolutional neural network designed for the detection and automatic classification of potentially malignant skin cancer conditions. SWNet optimizes feature extraction through multiple pathways, emphasizing width augmentation to enhance efficiency. The proposed model addresses potential biases associated with conditions, particularly in individuals darker tones or excessive hair, by incorporating fusion assimilate insights from diverse datasets. Extensive experiments were conducted using publicly accessible datasets evaluate SWNet’s effectiveness.This study utilized four datasets-Mnist-HAM10000, ISIC2019, ISIC2020, Melanoma Skin Cancer-comprising images categorized into benign classes. Explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM, employed interpretability model’s decisions. Comparative analysis was performed three pre-existing learning networks-EfficientNet, MobileNet, Darknet. results demonstrate superiority, achieving an accuracy 99.86% F1 score 99.95%, underscoring its efficacy gradient propagation capture across various levels. research highlights significant advancing classification, providing robust tool accurate early diagnosis. integration enhances mitigates hair tones. outcomes this contribute improved patient healthcare practices, showcasing exceptional capabilities classification.

Язык: Английский

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Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection

Electronics, Год журнала: 2025, Номер 14(4), С. 710 - 710

Опубликована: Фев. 12, 2025

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed tumor classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques shown promising results, consistently achieving high accuracy across various types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant EfficientNet multi-grade addressing the gap between performance explainability. Our approach extends capabilities to classify four types: glioma, meningioma, pituitary tumor, non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) improve The input MRI images undergo data augmentation before being passed through feature extraction phase, where underlying patterns are learned. achieves average 98.6%, surpassing other state-of-the-art standard datasets a substantially reduced parameter count. Furthermore, explainable AI (XAI) analysis demonstrates model’s ability focus relevant regions, enhancing its This accurate interpretable classification has potential significantly aid clinical decision-making neuro-oncology.

Язык: Английский

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Fractal feature selection model for enhancing high-dimensional biological problems

BMC Bioinformatics, Год журнала: 2024, Номер 25(1)

Опубликована: Янв. 9, 2024

Abstract The integration of biology, computer science, and statistics has given rise to the interdisciplinary field bioinformatics, which aims decode biological intricacies. It produces extensive diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, intelligent bioinformatics classification system must select most relevant features enhance machine learning performance. This paper proposes a feature selection model based on fractal concept improve performance systems high-dimensional proposed (FFS) divides into blocks, measures similarity between blocks using root mean square error (RMSE), determines importance low RMSE. FFS is tested evaluated over ten datasets. experiment results showed that significantly improved accuracy. average accuracy rate was 79% with full algorithms, while delivered promising 94%.

Язык: Английский

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