RCMNet: A deep learning model assists CAR-T therapy for leukemia

Improving chimeric antigen receptor T-cell therapies by using artificial intelligence and internet of things technologies: A narrative review

Cancer poses a formidable challenge in the field of medical science, prompting the exploration of innovative and efficient treatment strategies. One revolutionary breakthrough in cancer therapy is Chimeric Antigen Receptor (CAR) T-cell therapy, an avant-garde method involving the customization of a patient's immune cells to combat cancer. Particularly successful in addressing blood cancers, CAR T-cell therapy introduces an unprecedented level of effectiveness, offering the prospect of sustained disease management. As ongoing research advances to overcome current challenges, CAR T-cell therapy stands poised to become an essential tool in the fight against cancer. Ongoing enhancements aim to improve its effectiveness and reduce time and cost, with the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. The synergy of AI and IoT could enable more precise tailoring of CAR T-cell therapy to individual patients, streamlining the therapeutic process. This holds the potential to elevate treatment efficacy, mitigate adverse effects, and expedite the overall progress of CAR T-cell therapies.

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/μL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

Harnessing the potential of CAR-T cell therapy: progress, challenges, and future directions in hematological and solid tumor treatments

Traditional cancer treatments use nonspecific drugs and monoclonal antibodies to target tumor cells. Chimeric antigen receptor (CAR)-T cell therapy, however, leverages the immune system's T-cells to recognize and attack tumor cells. T-cells are isolated from patients and modified to target tumor-associated antigens. CAR-T therapy has achieved FDA approval for treating blood cancers like B-cell acute lymphoblastic leukemia, large B-cell lymphoma, and multiple myeloma by targeting CD-19 and B-cell maturation antigens. Bi-specific chimeric antigen receptors may contribute to mitigating tumor antigen escape, but their efficacy could be limited in cases where certain tumor cells do not express the targeted antigens. Despite success in blood cancers, CAR-T technology faces challenges in solid tumors, including lack of reliable tumor-associated antigens, hypoxic cores, immunosuppressive tumor environments, enhanced reactive oxygen species, and decreased T-cell infiltration. To overcome these challenges, current research aims to identify reliable tumor-associated antigens and develop cost-effective, tumor microenvironment-specific CAR-T cells. This review covers the evolution of CAR-T therapy against various tumors, including hematological and solid tumors, highlights challenges faced by CAR-T cell therapy, and suggests strategies to overcome these obstacles, such as utilizing single-cell RNA sequencing and artificial intelligence to optimize clinical-grade CAR-T cells.

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

Background

Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.

Methods

Combining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.

Results

Compared to manual searching, which required 3-4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.

Conclusion

Compared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.