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General General

Various machine learning approaches coupled with molecule simulation in the screening of natural compounds with xanthine oxidase inhibitory activity.

In Food & function

Gout is a common inflammatory arthritis associated with various comorbidities, such as cardiovascular disease and metabolic syndrome. Xanthine oxidase inhibitors (XOIs) have emerged as effective substances to control gout. Much attention has been given to the search for natural XOIs. In this study, a molecular database of natural XOIs was created for modeling purposes. Quantitative structure-activity relationship models were developed by combining various machine learning approaches and three descriptor pools. The models revealed several features of XOIs, including hydrophobicity and steric molecular structures. Experimental results showed the xanthine oxidase (XO) inhibitory activity of predicted compounds. Vanillic acid was identified as a promising new XOI candidate, with an IC50 of 0.593 μg mL-1. The functions of hydrogen bonds and hydrophobic interactions in XO activity inhibition were confirmed by molecular docking. This study fills knowledge gaps pertaining to the discovery of natural XOIs and to the interaction mechanisms between XOIs and XO.

Zhou Qian, Yin Jia-Yi, Liang Wei-Yue, Chen Dong-Mei, Yuan Qing, Feng Bao-Long, Zhang Ying-Hua, Wang Yu-Tang


Cardiology Cardiology

[Advances in the management of heart failure].

In Giornale italiano di cardiologia (2006)

Heart failure (HF) is a syndrome with an uncertain definition for both contents and boundaries, with multiple components and clinical profiles, and treatments to which many HF patients do not respond.This article does not go through the guidelines, but it focuses on some clinically relevant points, in which research is active, to discuss them and, when possible, to make the point under a clinical cardiology vision.Aspects considered include: (i) the concept of "definition"; (ii) HF with preserved or reduced ejection fraction; (iii) sudden death; (iv) briefly: population genetics, polygenic risk scores and the OMICs; (v) atrial fibrillation ablation in HF (underlying inconsistency of international guidelines); (vi) the welcome rampant/crawling penetration of artificial intelligence in daily HF management.

Tavazzi Luigi


Cardiology Cardiology

[Progress in cardiac imaging: from echocardiography to multimodality imaging].

In Giornale italiano di cardiologia (2006)

In the last few decades, echocardiography has represented one of the technological fields with the fastest evolution and progress. As a non-invasive method at relative low cost, it is also suitable for the future to an increasingly integrated use in any situation of clinical approach to the patient's bed, from emergency situations, to interventional environments, surgical rooms, clinical routine, outpatient clinics, diagnostics, prognosis and monitoring of therapies. Miniaturization of the equipment will allow an increasingly profound and complementary integration with the clinical physical examination (clinical echocardiography), and not only by cardiologists, but also by the multiplicity of medical and surgical clinical specialties. This involves great challenges in terms of training (both at university and post-graduate) and organization, aimed at appropriately integrating ultrasound diagnostic methods and multimodality imaging in the different sub-specialties' diagnostic and therapeutic paths. Further advances in miniaturized and handheld technologies are also needed, looking for a reliability at least comparable to that of the most top quality standard equipment. Artificial intelligence could help to improve this multidisciplinary approach to multimodality imaging in cardiology.

Nicolosi Gian Luigi


General General

In silico prediction of mitochondrial toxicity of chemicals using machine learning methods.

In Journal of applied toxicology : JAT

Mitochondria are important organelles in human cells, providing more than 95% of the energy. However, some drugs and environmental chemicals could induce mitochondrial dysfunction, which might cause complex diseases and even worsen the condition of patients with mitochondrial damage. Some drugs have been withdrawn from the market due to their severe mitochondrial toxicity, such as troglitazone. Therefore, there is an urgent need to develop models that could accurately predict the mitochondrial toxicity of chemicals. In this paper, suitable data were obtained from literature and databases first. Then nine types of fingerprints were used to characterize these compounds. Finally, different algorithms were used to build models. Meanwhile, the applicability domain of the prediction models was defined. We have also explored the structural alerts of mitochondrial toxicity, which would be helpful for medicinal chemists to better predict mitochondrial toxicity and further optimize lead compounds.

Zhao Piaopiao, Peng Yayuan, Xu Xuan, Wang Zhiyuan, Wu Zengrui, Li Weihua, Tang Yun, Liu Guixia


applicability domain, computational toxicology, machine learning, mitochondrial toxicity, structural alert

General General

Artificial intelligence against hate: Intervention reducing verbal aggression in the social network environment.

In Aggressive behavior

This article presents a quasi-experimental intervention study designed to reduce the level of verbal aggression on a social networking service (Reddit). The interventions were based on three psychological mechanisms: induction of a descriptive norm, induction of a prescriptive norm, and empathy induction. Each intervention was generated using a communicating bot. Participants exposed to these interventions were compared with a control group that received no intervention. The bot-generated normative communications (both the ones priming descriptive and the ones priming prescriptive norms), as well as the empathizing intervention, reduced the proportion of verbal aggression posted by Reddit accounts. All three interventions proved effective in reducing verbal violence when compared with the control condition.

Bilewicz Michał, Tempska Patrycja, Leliwa Gniewosz, Dowgiałło Maria, Tańska Michalina, Urbaniak Rafał, Wroczyński Michał


artificial intelligence, empathy, hate speech, social media, verbal aggression

General General

From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods.

In Journal of general internal medicine ; h5-index 57.0

Despite increasing interest in how artificial intelligence (AI) can augment and improve healthcare delivery, the development of new AI models continues to outpace adoption in existing healthcare processes. Integration is difficult because current approaches separate the development of AI models from the complex healthcare environments in which they are intended to function, resulting in models developed without a clear and compelling use case and not tested or scalable in a clinical setting. We propose that current approaches and traditional research methods do not support successful AI implementation in healthcare and outline a repeatable mixed-methods approach, along with several examples, that facilitates uptake of AI technologies into human-driven healthcare processes. Unlike traditional research, these methods do not seek to control for variation, but rather understand it to learn how a technology will function in practice coupled with user-centered design techniques. This approach, leveraging design thinking and quality improvement methods, aims to increase the adoption of AI in healthcare and prompt further study to understand which methods are most successful for AI implementations.

Smith Margaret, Sattler Amelia, Hong Grace, Lin Steven


artificial intelligence, design thinking, implementation science, quality improvement