Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.

Two-thirds of differentiated thyroid cancer (DTC) patients with distant metastases would be classified as radioactive iodine-refractory (RAIR-DTC), evolving into a poor outcome. Recent advances underlying DTC molecular mechanisms have shifted the therapy focus from the standard approach to targeting specific genetic dysregulations. Lenvatinib and sorafenib are first-line, multitargeted tyrosine kinase inhibitors (TKIs) approved to treat advanced, progressive RAIR-DTC. However, other anti-angiogenic drugs, including single targeted TKIs, are currently being evaluated as alternative or salvage therapy after the failure of first-line TKIs. Combinatorial therapy of mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signalling cascade inhibitors has become a highly advocated strategy to improve the low efficiency of the single agent treatment. Recent studies pointed out targetable alternative pathways to overcome the resistance to MAPK and PI3K pathways’ inhibitors. Because radioiodine resistance originates in DTC loss of differentiation, redifferentiation therapies are currently being explored for efficacy. The present review will summarize the conventional management of DTC, the first-line and alternative TKIs in RAIR-DTC, and the approaches that seek to overcome the resistance to MAPK and PI3K pathways’ inhibitors. We also aim to emphasize the latest achievements in the research of redifferentiation therapy, immunotherapy, and agents targeting gene rearrangements in advanced DTC.

Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.

Despite recent advances in disease management and prevention, heart failure (HF) prevalence is still high. Hypertension, inflammation and oxidative stress are being investigated as important causative processes in HF. L. barbarum L. polysaccharides (LBPs) are widely used for their anti-inflammatory and antioxidant properties. Thus, the aim of the present study was to evaluate the effects of LBPs on inflammation and oxidative stress markers in a pressure overload-induced HF rat model, surgically induced by abdominal aorta banding in Wistar rats (AAB) (n = 28). Also, control rats (n = 10) were subjected to a sham operation. After echocardiographic confirmation of HF (week 24), AAB rats were divided into three groups: rats treated with LBPs for 12 weeks: 100 mg/kg body weight /day (AAB_100, n = 9), 200 mg/kg body weight /day (AAB_200, n = 7) and no-treatment group (control AAB, n = 12). After 12 weeks of treatment with LBPs, the decline of cardiac function was prevented compared to the control AAB rats. Treatment with 200 mg/kg body weight /day LBPs significantly reduced the inflammation as seen by cytokine levels (IL-6 and TNF-α) and the plasma lipid peroxidation, as seen by malondialdehyde levels. These results suggest that LBPs present anti-inflammatory and antioxidant effects with utility in a HF animal model and encourage further investigation of the cardioprotective effects of these polysaccharides.