A high-content imaging assay for the quantification of the Burkholderia pseudomallei induced multinucleated giant cell (MNGC) phenotype in murine macrophages

BACKGROUND: Burkholderia pseudomallei (Bp), a Gram-negative, motile, facultative intracellular bacterium is the causative agent of melioidosis in humans and animals. The Bp genome encodes a repertoire of virulence factors, including the cluster 3 type III secretion system (T3SS-3), the cluster 1 type VI secretion system (T6SS-1), and the intracellular motility protein BimA, that enable the pathogen to invade both phagocytic and non-phagocytic cells. A unique hallmark of Bp infection both in vitro and in vivo is its ability to induce cell-to-cell fusion of macrophages to form multinucleated giant cells (MNGCs), which to date are semi-quantitatively reported following visual inspection. RESULTS: In this study we report the development of an automated high-content image acquisition and analysis assay to quantitate the Bp induced MNGC phenotype. Validation of the assay was performed using T6SS-1 (∆hcp1) and T3SS-3 (∆bsaZ) mutants of Bp that have been previously reported to exhibit defects in their ability to induce MNGCs. Finally, screening of a focused small molecule library identified several Histone Deacetylase (HDAC) inhibitors that inhibited Bp-induced MNGC formation of macrophages. CONCLUSIONS: We have successfully developed an automated HCI assay to quantitate MNGCs induced by Bp in macrophages. This assay was then used to characterize the phenotype of the Bp mutants for their ability to induce MNGC formation and identify small molecules that interfere with this process. Successful application of chemical genetics and functional reverse genetics siRNA approaches in the MNGC assay will help gain a better understanding of the molecular targets and cellular mechanisms responsible for the MNGC phenotype induced by Bp, by other bacteria such as Mycobacterium tuberculosis, or by exogenously added cytokines

The 1st International Electronic Conference on Algorithms

This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems

Recurrent neural network and reinforcement learning model for COVID-19 prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate

Computer aided diagnosis of miliary TB in chest X-rays

Includes bibliography.With the improvement in computer technology, Computer Aided Diagnosis (CAD) is becoming an increasingly more powerful tool for radiologists. The focus of this project was on CAD of pulmonary miliary tuberculosis. Several methods for enhancing lung textures were discussed as an aid to the radiologist in diagnosing miliary TB. Some statistical approaches and template matching methods were used to measure characteristics of both healthy and unhealthy (miliary TB) lung textures. These measurements were evaluated to see if a computer can be programmed to differentiate between lung texture from a healthy lung and lung texture from a lung with miliary TB

Summer 2006 Research Symposium Abstract Book

Summer 2006 volume of abstracts for science research projects conducted by Trinity College students

2005 Annual Research Symposium Abstract Book

2005 annual volume of abstracts for science research projects conducted by students at Trinity College

Application of Machine Learning in Healthcare and Medicine: A Review

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration