Gastric xanthelasma is a rare, benign lesion with uncertain clinical significance. Despite its asymptomatic nature, it may coexist with conditions like chronic gastritis and iron deficiency anemia.

A 71-year-old Turkish male presented with iron deficiency anemia, chronic fatigue, and upper abdominal pain. Endoscopy revealed yellowish-white plaques (2-6 mm) on the antral mucosa, confirmed as gastric xanthelasma on histopathology. Concurrently, a rectal sessile polyp was excised during colonoscopy. The patient recovered following a 3-month course of oral iron supplementation and proton pump inhibitors. Follow-up endoscopy showed resolution of gastric lesions.

This report underscores the diagnostic importance of endoscopy and biopsy in patients with unexplained anemia and highlights the potential association between gastric xanthelasma and ron deficiency anemia, warranting further research.

Unusual symptoms, rapid progression, lack of reliable early diagnostic biomarkers, and lack of efficient treatment choices are the ongoing challenges of pancreatic cancer. Numerous research studies have demonstrated the correlation between exosomes and various aspects of pancreatic cancer. In light of these facts, exosomes possess the potential to play functional roles in the treatment, prognosis, and diagnosis of the pancreatic cancer. In the present study, we reviewed the most recent developments in approaches for exosome separation, modification, monitoring, and communication. Moreover, we discussed the clinical uses of exosomes as less invasive liquid biopsies and drug carriers and their contribution to the control of angiogenic activity of pancreatic cancer. Better investigation of exosome biology would help to effectively engineer therapeutic exosomes with certain nucleic acids, proteins, and even exogenous drugs as their cargo. Circulating exosomes have shown promise as reliable candidates for pancreatic cancer early diagnosis and monitoring in high-risk people without clinical cancer manifestation. Although we have tried to reflect the status of exosome applications in the treatment and detection of pancreatic cancer, it is evident that further studies and clinical trials are required before exosomes may be employed as a routine therapeutic and diagnostic tools for pancreatic cancer.

An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached. This review first defined the concepts underlying XAI, establishing the tools available and how they can benefit digestive healthcare. Examples of the application of XAI in digestive healthcare were provided, and potential future uses were proposed. In addition, aspects of the regulatory frameworks that must be established and the ethical concerns that must be borne in mind during the development of these tools were discussed. Finally, we considered the challenges that this technology faces to ensure that optimal benefits are reaped, highlighting the need for more research into the use of XAI in this field.

In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field.

The large-scale language model, GPT-4-1106-preview, supports text of up to 128 k characters, which has enhanced the capability of processing vast quantities of text. This model can perform efficient and accurate text data mining without the need for retraining, aided by prompt engineering.

The research approach includes prompt engineering and text vectorization processing. In this study, prompt engineering is applied to assist ChatGPT in text mining. Subsequently, the mined results are vectorized and incorporated into a local knowledge base. After cleansing 306 medical papers, data extraction was performed using ChatGPT. Following a validation and filtering process, 241 medical case data entries were obtained, leading to the construction of a local medical knowledge base. Additionally, drawing upon the Langchain framework and utilizing the local knowledge base in conjunction with ChatGPT, we successfully developed a fast and reliable chatbot. This chatbot is capable of providing recommended diagnostic and treatment information for various diseases.

The performance of the designed ChatGPT model, which was enhanced by data from the local knowledge base, exceeded that of the original model by 7.90% on a set of medical questions.

ChatGPT, assisted by prompt engineering, demonstrates effective data mining capabilities for large-scale medical texts. In the future, we plan to incorporate a richer array of medical case data, expand the scale of the knowledge base, and enhance ChatGPT's performance in the medical field.

Artificial intelligence (AI) has become increasingly prevalent and significant across many industries, including the dental field. AI has shown accuracy and precision in detecting, evaluating, and predicting diseases. It can imitate human intelligence to carry out sophisticated predictions and decision-making in the health-care industry, especially in endodontics. AI models have demonstrated a wide range of applications in the field of endodontics. These include examining the anatomy of the root canal system, predicting the survival of dental pulp stem cells, gauging working lengths, identifying per apical lesions and root fractures, and predicting the outcome of retreatment treatments. Future uses of this technology were discussed in terms of robotic endodontic surgery, drug-drug interactions, patient care, scheduling, and prognostic diagnosis.

Evaluating the impact of digestive system diseases is vital for devising effective prevention strategies. However, comprehensive reports on the burden of digestive system diseases in China are lacking. Our study aimed to provide an overview of the burden and trends of digestive system diseases from 1990 to 2019 in China and its provinces.

This cross-sectional study utilized the Global Disease Burden Study 2019 to estimate the incidence, mortality rate, disability-adjusted life years (DALYs), years of life disability, years of life lost, and changes in the burden of digestive diseases across 31 Chinese provinces from 1990 to 2019. The analysis of disease burden primarily examines the characteristics of sub-disease distribution, time trends, age distribution, and sex distribution. Additionally, we compared provincial age-standardized DALYs for digestive diseases with the expected rates based on the socio-demographic index (SDI).

In 2019, there were 499.2 million cases of digestive system diseases in China, resulting in 1,557,310 deaths. Stomach cancer, colon and rectal cancer, and esophageal cancer are the top three diseases associated with mortality and DALY related to digestive system diseases. Meanwhile, cirrhosis and other chronic liver diseases, gastroesophageal reflux disease, and gallbladder and biliary diseases are the top three kinds of diseases with the highest prevalence among digestive system diseases. The risk of gastric cancer sharply increases among men after the age of 40 years, leading to a significant disparity in burden between men and women. As the SDI increased, the DALYs associated with digestive system diseases in China and its provinces showed a downward trend.

Our study highlights the inverse correlation between DALYs associated with digestive system diseases and the SDI, providing valuable insights that can assist public health officials in the estimation of the disease burden in this area.

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.

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.

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.

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 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.

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.

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.

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).

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.

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.

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.

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 (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.

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.