From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Netnography of Social Media Addresses on COVID-19

Healthcare professionals' harness social media to encourage responsible behaviour during the COVID-19 pandemic. As internet users often struggle assessing the veracity of the information in these addresses, acoustic characteristics of the presenters' speech may play a significant role in their persuasiveness impact. Using a netnographic approach, we studied YouTubers' reactions to explore the persuasiveness attributes of COVID-19 related speeches included in YouTube videos within a South Africa context. The persuasiveness index was computed from the view count, likes and dislikes of 314 speech segments from YouTube interviews related to COVID-19. Standard acoustic features - Mel frequency cepstral coefficients - of the interviewees' voice were extracted through speech processing. Recurrent neural networks were optimized and evaluated the strength of these acoustic features to classify and predict the persuasiveness index. The cepstral feature set yielded a balanced accuracy of 86.8% and F1 score of 85.0%. These preliminary results exhibit the potential of the vocal cepstrum as predictor of persuasiveness in healthcare addresses on responsible behaviour during the COVID-19 pandemic. The results imply that quantitative acoustic analysis of a presenter's voice, independent from text, can explain the impact of social media addresses. © The Authors, (2022). All Rights Reserved. No reproduction, copy or transmission may be made without written permission from the individual authors.

Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.

Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management.

Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term "AI" indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a "machine learning" process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.

AI (Artificial Intelligence) and Hypertension Research.

PURPOSE OF REVIEW: This review a highlights that to use artificial intelligence (AI) tools effectively for hypertension research, a new foundation to further understand the biology of hypertension needs to occur by leveraging genome and RNA sequencing technology and derived tools on a broad scale in hypertension. RECENT FINDINGS: For the last few years, progress in research and management of essential hypertension has been stagnating while at the same time, the sequencing of the human genome has been generating many new research tools and opportunities to investigate the biology of hypertension. Cancer research has applied modern tools derived from DNA and RNA sequencing on a large scale, enabling the improved understanding of cancer biology and leading to many clinical applications. Compared with cancer, studies in hypertension, using whole genome, exome, or RNA sequencing tools, total less than 2% of the number cancer studies. While true, sequencing the genome of cancer tissue has provided cancer research an advantage, DNA and RNA sequencing derived tools can also be used in hypertension to generate new understanding how complex protein network, in non-cancer tissue, adapts and learns to be effective when for example, somatic mutations or environmental inputs change the gene expression profiles at different network nodes. The amount of data and differences in clinical condition classification at the individual sample level might be of such magnitude to overwhelm and stretch comprehension. Here is the opportunity to use AI tools for the analysis of data streams derived from DNA and RNA sequencing tools combined with clinical data to generate new hypotheses leading to the discovery of mechanisms and potential target molecules from which drugs or treatments can be developed and tested. Basic and clinical research taking advantage of new gene sequencing-based tools, to uncover mechanisms how complex protein networks regulate blood pressure in health and disease, will be critical to lift hypertension research and management from its stagnation. The use of AI analytic tools will help leverage such insights. However, applying AI tools to vast amounts of data that certainly exist in hypertension, without taking advantage of new gene sequencing-based research tools, will generate questionable results and will miss many new potential molecular targets and possibly treatments. Without such approaches, the vision of precision medicine for hypertension will be hard to accomplish and most likely not occur in the near future.