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General General

Discovery of novel acetylcholinesterase inhibitors through integration of machine learning with genetic algorithm based in silico screening approaches.

In Frontiers in neuroscience ; h5-index 72.0

INTRODUCTION : Alzheimer's disease (AD) is the most studied progressive eurodegenerative disorder, affecting 40-50 million of the global population. This progressive neurodegenerative disease is marked by gradual and irreversible declines in cognitive functions. The unavailability of therapeutic drug candidates restricting/reversing the progression of this dementia has severed the existing challenge. The development of acetylcholinesterase (AChE) inhibitors retains a great research focus for the discovery of an anti-Alzheimer drug.

MATERIALS AND METHODS : This study focused on finding AChE inhibitors by applying the machine learning (ML) predictive modeling approach, which is an integral part of the current drug discovery process. In this study, we have extensively utilized ML and other in silico approaches to search for an effective lead molecule against AChE.

RESULT AND DISCUSSION : The output of this study helped us to identify some promising AChE inhibitors. The selected compounds performed well at different levels of analysis and may provide a possible pathway for the future design of potent AChE inhibitors.

Khan Mohd Imran, Taehwan Park, Cho Yunseong, Scotti Marcus, Priscila Barros de Menezes Renata, Husain Fohad Mabood, Alomar Suliman Yousef, Baig Mohammad Hassan, Dong Jae-June

2022

Alzheimer’s disease, acetylcholinesterase (AChE), machine learning (ML), molecular dynamics (MD), virtual screening

Ophthalmology Ophthalmology

Clinical Applications of Artificial Intelligence in Glaucoma.

In Journal of ophthalmic & vision research

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI-enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.

Yousefi Siamak

2023

** Convolutional Neural Network (CNN), Deep Learning, Glaucoma, Machine Learning, Ophthalmology, Artificial Intelligence**

General General

Daily two-photon neuronal population imaging with targeted single-cell electrophysiology and subcellular imaging in auditory cortex of behaving mice.

In Frontiers in cellular neuroscience ; h5-index 74.0

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca2+ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca2+ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

Huang Junjie, Liang Susu, Li Longhui, Li Xingyi, Liao Xiang, Hu Qianshuo, Zhang Chunqing, Jia Hongbo, Chen Xiaowei, Wang Meng, Li Ruijie

2023

auditory cortex, behaving mouse, daily two-photon Ca2+ imaging, dendritic spines, loose-patch recording

Surgery Surgery

Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning.

In Anesthesiology and pain medicine ; h5-index 21.0

BACKGROUND : Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI.

OBJECTIVES : This study tries to predict postoperative AKI using interpretable machine learning models.

METHODS : For this study, the information of 1435 patients was collected from multiple centers. The gathered data are in six categories: demographic characteristics and type of surgery, past medical history (PMH), drug history (DH), laboratory information, anesthesia and surgery information, and postoperative variables. Machine learning methods, including support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), random forest (RF), logistic regression, XGBoost, and AdaBoost, were used to predict postoperative AKI. Local interpretable model-agnostic explanations (LIME) and the Shapley methods were then leveraged to check the interpretability of models.

RESULTS : Comparing the area under the curves (AUCs) obtained for different machine learning models show that the RF and XGBoost methods with values of 0.81 and 0.80 best predict postoperative AKI. The interpretations obtained for the machine learning models show that creatinine (Cr), cardiopulmonary bypass time (CPB time), blood sugar (BS), and albumin (Alb) have the most significant impact on predictions.

CONCLUSIONS : The treatment team can be informed about the possibility of postoperative AKI before cardiac surgery using machine learning models such as RF and XGBoost and adjust the treatment procedure accordingly. Interpretability of predictions for each patient ensures the validity of obtained predictions.

Ejmalian Azar, Aghaei Atefe, Nabavi Shahabedin, Abedzadeh Darabad Maryam, Tajbakhsh Ardeshir, Abin Ahmad Ali, Ebrahimi Moghaddam Mohsen, Dabbagh Ali, Jahangirifard Alireza, Memary Elham, Sayyadi Shahram

2022-Aug

AKI Prediction, Acute Kidney Injury, Cardiac Surgery, Interpretable Machine Learning

General General

How to prevent malicious use of intelligent unmanned swarms?

In Innovation (Cambridge (Mass.))

With advancements in swarm intelligence, artificial intelligence, and wireless mobile network technology, unmanned swarms such as unmanned aerial vehicles, ground vehicles, ships, and other unmanned systems are becoming increasingly autonomous and intelligent. Benefiting from these technologies, intelligent unmanned swarms are able to efficiently perform complex tasks through collaboration in various fields. However, malicious use of intelligent unmanned swarms raises concerns about the potential for significant damage to national infrastructures such as airports and power facilities. Defending against malicious activities is essential but challenging due to the swarms' abilities to perceive, understand complex environments, and make accurate decisions through multi-system collaboration. This perspective sheds light on recent research in counter-measures and provides new trends and insights on how to prevent malicious actions by intelligent unmanned swarms.

Wang Qi, Li Tingting, Xu Yongjun, Wang Fei, Diao Boyu, Zheng Lei, Huang Jincai

2023-Mar-13

General General

YOUPI: Your powerful and intelligent tool for segmenting cells from imaging mass cytometry data.

In Frontiers in immunology ; h5-index 100.0

The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.

Scuiller Yvonne, Hemon Patrice, Le Rochais Marion, Pers Jacques-Olivier, Jamin Christophe, Foulquier Nathan

2023

U-NET, cell segmentation, images, imaging mass cytometry, new algorithm