Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease

The use of artificial intelligence in the treatment of rare diseases: A scoping review

With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.

Innovative preconditioning strategies for improving the therapeutic efficacy of extracellular vesicles derived from mesenchymal stem cells in gastrointestinal diseases

Gastrointestinal (GI) diseases have become a global health issue and an economic burden due to their wide distribution, late prognosis, and the inefficacy of recent available medications. Therefore, it is crucial to search for new strategies for their management. In the recent decades, mesenchymal stem cells (MSCs) therapy has attracted attention as a viable option for treating a myriad of GI disorders such as hepatic fibrosis (HF), ulcerative colitis (UC), acute liver injury (ALI), and non-alcoholic fatty liver disease (NAFLD) due to their regenerative and paracrine properties. Importantly, recent studies have shown that MSC-derived extracellular vesicles (MSC-EVs) are responsible for most of the therapeutic effects of MSCs. In addition, EVs have revealed several benefits over their parent MSCs, such as being less immunogenic, having a lower risk of tumour formation, being able to cross biological barriers, and being easier to store. MSC-EVs exhibited regenerative, anti-oxidant, anti-inflammatory, anti-apoptotic, and anti-fibrotic effects in different experimental models of GI diseases. However, a key issue with their clinical application is the maintenance of their stability and efficacy following in vivo transplantation. Preconditioning of MSC-EVs or their parent cells is one of the novel methods used to improve their effectiveness and stability. Herein, we discuss the application of MSC-EVs in several GI disorders taking into account their mechanism of action. We also summarise the challenges and restrictions that need to be overcome to promote their clinical application in the treatment of various GI diseases as well as the recent developments to improve their effectiveness. A representation of the innovative preconditioning techniques that have been suggested for improving the therapeutic efficacy of MSC-EVs in GI diseases. The pathological conditions in various GI disorders (ALI, UC, HF and NAFLD) create a harsh environment for EVs and their parents, increasing the risk of apoptosis and senescence of MSCs and thereby diminishing MSC-EVs yield and restricting their large-scale applications. Preconditioning with pharmacological agents or biological mediators can improve the therapeutic efficacy of MSC-EVs through their adaption to the lethal environment to which they are subjected. This can result in establishment of a more conducive environment and activation of numerous vital trajectories that act to improve the immunomodulatory, reparative and regenerative activities of the derived EVs, as a part of MSCs paracrine system. ALI, acute liver injury; GI diseases, gastrointestinal diseases; HF, hepatic fibrosis; HSP, heat shock protein; miRNA, microRNA; mRNA, messenger RNA; MSC-EVs, mesenchymal stem cell-derived extracellular vesicles; NAFLD, non-alcoholic fatty liver disease; UC, ulcerative colitis.

Global Burden of Disease: Digestive Disease Alarm in South Caucasus

Digestive disease-caused death rates are significantly high in the South Caucasus region. The latest Global Burden of Disease (GBD) data are a subject of discussion and should lead to serious steps to be taken. Azerbaijan, Armenia, and Georgia are three countries in the region with similar cultures but different roots. The problem seems to affect every country in the region with slightly different rates. It is crucial to start investigations into the detailed cause and to take serious steps in order to prevent digestive disease-caused deaths in the region. This letter aims to arouse awareness of the problem in the region.

Biological function and clinical application prospect of tsRNAs in digestive system biology and pathology

tsRNAs are small non-coding RNAs originating from tRNA that play important roles in a variety of physiological activities such as RNA silencing, ribosome biogenesis, retrotransposition, and epigenetic inheritance, as well as involvement in cellular differentiation, proliferation, and apoptosis. tsRNA-related abnormalities have a significant influence on the onset, development, and progression of numerous human diseases, including malignant tumors through affecting the cell cycle and specific signaling molecules. This review introduced origins together with tsRNAs classification, providing a summary for regulatory mechanism and physiological function while dysfunctional effect of tsRNAs in digestive system diseases, focusing on the clinical prospects of tsRNAs for diagnostic and prognostic biomarkers. Video Abstract.

Artificial Intelligence in Medicine and Dentistry

INTRODUCTION

Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry.

OBJECTIVE

Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages.

CONCLUSION

The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.

Detection of colorectal adenomas using artificial intelligence models in patients with chronic hepatitis C

BACKGROUND

Hepatitis C virus is known for its oncogenic potential, especially in hepatocellular carcinoma and non-Hodgkin lymphoma. Several studies have shown that chronic hepatitis C (CHC) has an increased risk of the development of colorectal cancer (CRC).

AIM

To analyze this positive relationship and develop an artificial intelligence (AI)-based tool using machine learning (ML) algorithms to stratify these patient populations into risk groups for CRC/adenoma detection.

METHODS

To develop the AI automated calculator, we applied ML to train models to predict the probability and the number of adenomas detected on colonoscopy. Data sets were split into 70:30 ratios for training and internal validation. The Scikit-learn standard scaler was used to scale values of continuous variables. Colonoscopy findings were used as the gold standard and deep learning architecture was used to train six ML models for prediction. A Flask (customizable Python framework) application programming interface (API) was used to deploy the trained ML model with the highest accuracy as a web application. Finally, Heroku was used for the deployment of the web-based API to https://adenomadetection.herokuapp.com.

RESULTS

Of 415 patients, 206 had colonoscopy results. On internal validation, the Bernoulli naive Bayes model predicted the probability of adenoma detection with the highest accuracy of 56%, precision of 55%, recall of 55%, and F1 measure of 54%. Support vector regressor predicted the number of adenomas with the least mean absolute error of 0.905.

CONCLUSION

Our AI-based tool can help providers stratify patients with CHC for early referral for screening colonoscopy. Along with providing a numerical percentage, the calculator can also comment on the number of adenomatous polyps a gastroenterologist can expect, prompting a higher adenoma detection rate.

Artificial neural networks in contemporary toxicology research

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.

The sirtuin family in health and disease

Sirtuins (SIRTs) are nicotine adenine dinucleotide(+)-dependent histone deacetylases regulating critical signaling pathways in prokaryotes and eukaryotes, and are involved in numerous biological processes. Currently, seven mammalian homologs of yeast Sir2 named SIRT1 to SIRT7 have been identified. Increasing evidence has suggested the vital roles of seven members of the SIRT family in health and disease conditions. Notably, this protein family plays a variety of important roles in cellular biology such as inflammation, metabolism, oxidative stress, and apoptosis, etc., thus, it is considered a potential therapeutic target for different kinds of pathologies including cancer, cardiovascular disease, respiratory disease, and other conditions. Moreover, identification of SIRT modulators and exploring the functions of these different modulators have prompted increased efforts to discover new small molecules, which can modify SIRT activity. Furthermore, several randomized controlled trials have indicated that different interventions might affect the expression of SIRT protein in human samples, and supplementation of SIRT modulators might have diverse impact on physiological function in different participants. In this review, we introduce the history and structure of the SIRT protein family, discuss the molecular mechanisms and biological functions of seven members of the SIRT protein family, elaborate on the regulatory roles of SIRTs in human disease, summarize SIRT inhibitors and activators, and review related clinical studies.