Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and HER2 receptors, resulting in limited therapeutic options. This article critically examines the role of Artificial Intelligence (AI) in enhancing the diagnosis and treatment of TNBC treatment. We begin by discussing the incidence of TNBC and the fundamentals of precision medicine, emphasizing the need for innovative diagnostic and therapeutic approaches. Current diagnostic methods, including conventional imaging techniques and histopathological assessments, exhibit limitations such as delayed diagnosis and interpretative discrepancies. This article highlights AI-driven advancements in image analysis, biomarker discovery, and the integration of multi-omics data, leading to enhanced precision and efficiency in diagnosis and treatment. In treatment, AI facilitates personalized therapeutic strategies, accelerates drug discovery, and enables real-time monitoring of patient responses. However, challenges persist, including issues related to data quality, model interpretability, and the societal impact of AI implementation. In the conclusion, we discuss the future prospects of integrating AI into clinical practice and emphasize the importance of multidisciplinary collaboration. This review aims to outline key trends and provide recommendations for utilizing AI to improve TNBC management outcomes, while highlighting the need for further research.

Tailored therapies for triple-negative breast cancer: current landscape and future perceptions

Triple-negative breast cancer (TNBC) has become one of the most challenging cancers to date due to its great variability in biological features, high growth rate, and rare options for treatment. This review examines several innovative strategies for tailored treatment of TNBC, focusing mainly on the most recent developments and potential directions. The molecular landscape of TNBC is covered in the first section, which keeps the focus on transcriptome and genomic profiling while highlighting key molecular targets like mutations in the BRCA1/2, PIK3CA, androgen receptors (AR), epidermal growth factor receptors (EGFR), and immunological checkpoint molecules. This review also covers novel therapies that aim to block well-defined pathways, including immune checkpoint inhibitors (ICI), EGFR inhibitors, drugs that target AR, poly ADP ribose polymerase (PARP) inhibitors, and drugs that disrupt the PI3K/AKT/mTOR pathway. Additionally, it covers novel strategies focusing on combination therapy, targeting the DNA damage response pathway, and epigenetic modulators. Conclusively, it emphasizes perspectives and directions on topics such as personalized medicine, artificial intelligence (AI), predictive biomarkers, and treatment planning with the inclusion of machine learning (ML).

Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer biology, and immunology, with object detection, image feature extraction, classification, and segmentation applications. Advancements in deep learning (DL) research have been a critical factor in advancing computer techniques for biomedical image analysis and data mining. A significant improvement in the accuracy of cell detection and segmentation algorithms has been achieved as a result of the emergence of open-source software and innovative deep neural network architectures. Automated cell segmentation now enables the extraction of quantifiable cellular and spatial features from microscope images of cells and tissues, providing critical insights into cellular organization in various diseases. This review aims to examine the latest AI and DL techniques for cell analysis and data mining in microscopy images, aid the biologists who have less background knowledge in AI and machine learning (ML), and incorporate the ML models into microscopy focus images.

Identifying factors controlling cellular uptake of gold nanoparticles by machine learning

There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.

Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review

Triple negative breast cancer (TNBC) is most aggressive type of breast cancer with multiple invasive sub-types and leading cause of women's death worldwide. Lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) causes it to spread rapidly making its treatment challenging due to unresponsiveness towards anti-HER and endocrine therapy. Hence, needing advanced therapeutic treatments and strategies in order to get better recovery from TNBC. Artificial intelligence (AI) has been emerged by giving its high inputs in the automated diagnosis as well as treatment of several diseases, particularly TNBC. AI based TNBC molecular sub-typing, diagnosis as well as therapeutic treatment has become successful now days. Therefore, present review has reviewed recent advancements in the role and assistance of AI particularly focusing on molecular sub-typing, diagnosis as well as treatment of TNBC. Meanwhile, advantages, certain limitations and future implications of AI assistance in the TNBC diagnosis and treatment are also discussed in order to fully understand readers regarding this issue.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Machine learning assisted-nanomedicine using magnetic nanoparticles for central nervous system diseases

Magnetic nanoparticles possess unique properties distinct from other types of nanoparticles developed for biomedical applications. Their unique magnetic properties and multifunctionalities are especially beneficial for central nervous system (CNS) disease therapy and diagnostics, as well as targeted and personalized applications using image-guided therapy and theranostics. This review discusses the recent development of magnetic nanoparticles for CNS applications, including Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and drug addiction. Machine learning (ML) methods are increasingly applied towards the processing, optimization and development of nanomaterials. By using data-driven approach, ML has the potential to bridge the gap between basic research and clinical research. We review ML approaches used within the various stages of nanomedicine development, from nanoparticle synthesis and characterization to performance prediction and disease diagnosis.

Circular RNAs in gynecologic cancers: mechanisms and implications for chemotherapy resistance

Chemotherapy resistance remains a major challenge in the treatment of gynecologic malignancies. Increasing evidence suggests that circular RNAs (circRNAs) play a significant role in conferring chemoresistance in these cancers. In this review, we summarize the current understanding of the mechanisms by which circRNAs regulate chemotherapy sensitivity and resistance in gynecologic malignancies. We also discuss the potential clinical implications of these findings and highlight areas for future research. CircRNAs are a novel class of RNA molecules that are characterized by their unique circular structure, which confers increased stability and resistance to degradation by exonucleases. Recent studies have shown that circRNAs can act as miRNA sponges, sequestering miRNAs and preventing them from binding to their target mRNAs. This can lead to upregulation of genes involved in drug resistance pathways, ultimately resulting in decreased sensitivity to chemotherapy. We discuss several specific examples of circRNAs that have been implicated in chemoresistance in gynecologic cancers, including cervical cancer, ovarian cancer, and endometrial cancer. We also highlight the potential clinical applications of circRNA-based biomarkers for predicting chemotherapy response and guiding treatment decisions. Overall, this review provides a comprehensive overview of the current state of knowledge regarding the role of circRNAs in chemotherapy resistance in gynecologic malignancies. By elucidating the underlying mechanisms by which circRNAs regulate drug sensitivity, this work has important implications for improving patient outcomes and developing more effective therapeutic strategies for these challenging cancers.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.