Dataset Information

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

ABSTRACT: Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.

SUBMITTER: Chandler M

PROVIDER: S-EPMC9671856 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

Chandler Morgan M Jain Sankalp S Halman Justin J Hong Enping E Dobrovolskaia Marina A MA Zakharov Alexey V AV Afonin Kirill A KA

Small (Weinheim an der Bergstrasse, Germany) 20221010 46

Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design o ...[more]

PMID: 36216772

Similar Datasets

Project description:PurposeThe application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT-VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT-VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT-VTI and the manual LVOT-VTI acquired by PoCUS trained ICU doctors.MethodsAmong the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2 h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24 h. For ICU doctors, manual LVOT-VTI was obtained for reference and auto LVOT-VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT-VTI, ICU patients was separated into ideal group (n = 31) and average group (n = 15).ResultsLeft ventricular end-diastolic dimension (LVEDd, p = 0.1028), left ventricular ejection fraction (LVEF, p = 0.3251), left atrial dimension (LA-d, p = 0.0962), left ventricular E/A ratio (p = 0.160), left ventricular wall motion (p = 0.317) and pericardial effusion (p = 1) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 ± 1.58 vs. 4.09 ± 0.57, p = 0.022) and lactic acid (3.67 ± 0.86 mmol/L vs. 1.46 ± 0.12 mmol/L, p = 0.0009) with greater value of LVEDd (51.93 ± 1.07 vs. 47.57 ± 0.89, p = 0.0053), LA-d (39.06 ± 1.47 vs. 35.22 ± 0.98, p = 0.0334) and percentage of decreased wall motion (p = 0.0166) than ideal group. There were no significant differences of δLVOT-VTI (|manual LVOT-VTI - auto LVOT-VTI|/manual VTI*100%) between the two groups (8.8% ± 1.3% vs. 10% ± 2%, p = 0.6517). Statistically, significant correlations between manual LVOT-VTI and auto LVOT-VTI were present in the ideal group (R2 = 0.815, p = 0.00) and average group (R2 = 0.741, p = 0.00).ConclusionsICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT-VTI had statistically significant agreement in both ideal and average groups. Auto LVOT-VTI in ideal view was more relevant with the manual LVOT-VTI than the average view. AI might provide real-time guidance among novice operators who lack expertise to acquire the ideal standard view.

Project description:Background and objectiveMedical image segmentation is a vital aspect of medical image processing, allowing healthcare professionals to conduct precise and comprehensive lesion analyses. Traditional segmentation methods are often labor intensive and influenced by the subjectivity of individual physicians. The advent of artificial intelligence (AI) has transformed this field by reducing the workload of physicians, and improving the accuracy and efficiency of disease diagnosis. However, conventional AI techniques are not without challenges. Issues such as inexplicability, uncontrollable decision-making processes, and unpredictability can lead to confusion and uncertainty in clinical decision-making. This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI).MethodsThis review synthesizes existing literature on traditional segmentation methods, AI-based approaches, and the transition from conventional AI to XAI and TAI. The review highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability. It further examines how TAI builds on XAI to improve the reliability, safety, and accountability of AI systems in medical image segmentation.Key content and findingsXAI has emerged as a solution to the limitations of conventional AI by providing greater transparency and interpretability, allowing healthcare professionals to better understand and trust AI-driven decisions. However, XAI itself faces challenges, including those related to safety, robustness, and value alignment. TAI has been developed to overcome these challenges, offering a more reliable framework for AI applications in medical image segmentation. By integrating the principles of XAI with enhanced safety and dependability, TAI addresses the critical need for TAI systems in clinical settings.ConclusionsTAI presents a promising future for medical image segmentation, combining the benefits of AI with improved reliability and safety. Thus, TAI is a more viable and dependable option for healthcare applications, and could ultimately lead to better clinical outcomes for patients, and advance the field of medical image processing.

Dataset Information

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

Publications

Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets