Advancements in biliopancreatic endoscopy: a comprehensive review of artificial intelligence in EUS and ERCP.

The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.

Ligand-based analysis of the antifungal potential of phytosterols and triterpenes isolated from Cryptostegia grandiflora against Candida auris FKBP12.

Candida auris, a pathogenic fungus, has posed significant challenges to conventional medical treatments due to its increasing resistance to antifungal agents. Consequently, due to their promising pharmacological properties, there is a compelling interest in exploring novel bioactive compounds, such as phytosterols and triterpenes. This study aimed to conduct virtual screening utilizing computational methods, including ADMET, molecular docking, and molecular dynamics, to assess the activity and feasibility of phytosterols extracted from Cryptostegia grandiflora as potential therapeutic agents. Computational predictions suggest that compounds bearing structural similarities to Fsp3-rich molecules hold promise for inhibiting enzymes and G protein-coupled receptor (GPCR) modulators, with particular emphasis on ursolic acid, which, in its conjugated form, exhibits high oral bioavailability and metabolic stability rendering it a compelling drug candidate. Molecular docking calculations identified ursolic acid and stigmasterol as promising ligands. While stigmasterol displayed superior affinity during molecular dynamics simulations, it exhibited instability, contrasting with ursolic acid's slightly lower affinity yet sustained stability throughout the dynamic assessments. This suggests that ursolic acid is a robust candidate for inhibiting the FKBP12 isomerase in C. auris. Moreover, further investigations could focus on experimentally validating the molecular docking predictions and evaluating the efficacy of ursolic acid as an FKBP12 isomerase inhibitor in models of C. auris infection.

Gastroenterology healthcare in LGBTQ+ individuals.

Lesbian, gay, bisexual, transgender, queer, or questioning individuals, as well as those with another diverse identity (LGBTQ+), present specific nuances in healthcare that physicians must consider in clinical practice. Particularly, gastroenterologists are nowadays facing different issues in several fields regarding LGBTQ+ healthcare, such as endoscopy, inflammatory bowel disease, hepatology, and proctology. In this study, the authors provide a practice-oriented and up-to-date review reinforcing the importance of some of the most prevalent pathologies associated with sexuality that gastroenterologists may encounter in their clinical practice. In terms of endoscopy, authors describe the endoscopic findings related to human papillomavirus (HPV) infection: the esophageal squamous papilloma and cell carcinoma; also highlight the importance of retroflexion maneuver during a routine colonoscopy that allows detection of anal intraepithelial neoplasia lesions that can be anal cancer precursors. Regarding inflammatory bowel disease, some considerations are made about the differential diagnosis with infectious proctitis, and the topic of the risk of anal cancer due to HPV infection, in this specific population, is also addressed. Considering hepatology, the authors review the most important issues related to hepatotropic sexually transmitted infections. The authors also make some comments regarding the possibility of drug-induced liver injury in gender-affirming hormone therapy and pre-exposure prophylaxis for HIV prevention. Finally, considering the proctology field, an up-to-date review is performed regarding anal cancer screening, HPV infection and related diseases, and infectious proctitis management.

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study.

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Artificial Intelligence and Colposcopy: Automatic Identification of Cervical Squamous Cell Carcinoma Precursors.

Background/Objectives: Proficient colposcopy is crucial for the adequate management of cervical cancer precursor lesions; nonetheless its limitations may impact its cost-effectiveness. The development of artificial intelligence models is experiencing an exponential growth, particularly in image-based specialties. The aim of this study is to develop and validate a Convolutional Neural Network (CNN) for the automatic differentiation of high-grade (HSIL) from low-grade dysplasia (LSIL) in colposcopy. Methods: A unicentric retrospective study was conducted based on 70 colposcopy exams, comprising a total of 22,693 frames. Among these, 8729 were categorized as HSIL based on histopathology. The total dataset was divided into a training (90%, n = 20,423) and a testing set (10%, n = 2270), the latter being used to evaluate the model's performance. The main outcome measures included sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiving operating curve (AUC-ROC). Results: The sensitivity was 99.7% and the specificity was 98.6%. The PPV and NPV were 97.8% and 99.8%, respectively. The overall accuracy was 99.0%. The AUC-ROC was 0.98. The CNN processed 112 frames per second. Conclusions: We developed a CNN capable of differentiating cervical cancer precursors in colposcopy frames. The high levels of accuracy for the differentiation of HSIL from LSIL may improve the diagnostic yield of this exam.

Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation.

Non-pharmacological strategies to reduce stress and anxiety in endovascular procedures: A scoping review.

AIM: This study aimed to identify and map the production of knowledge on non-pharmacological strategies to reduce stress and anxiety in patients undergoing endovascular procedures. DESIGN: Scoping review. METHODS: The review was performed using the PRISMA-ScR guidelines. The searches were conducted in Scopus, PubMed, Web of Science, Wiley Online Library, BVS/BIREME, Lilacs, Gale Academic OneFile, SciELO, Cochrane Library, CAPES Catalog of Dissertations and Theses, Oswaldo Cruz Foundation Portal of Theses and Dissertations, and Theses and Dissertations from Latin America. RESULTS: Twenty-two articles were selected. The articles were published from 2001 to 2022, mostly in Iran, and there was a predominance of randomized clinical trials. The Spielberger State-Trait Anxiety Inventory was the most used instrument. The findings indicated that music therapy, educational guidelines or videos on the procedure, massage, psychological preparation and aromatherapy were the main non-pharmacological therapies used to reduce anxiety and stress in patients undergoing vascular procedures.

Interactions with humans reduce the success of foraging for anthropogenic food by capuchin monkeys (Sapajus libidinosus) in Brasília National Park, Brazil.

The progressive growth of urban environments has increasingly forced populations of nonhuman primates to coexist with humans in many cities, which has resulted in problems such as behavioral alterations, conflicts with humans, and threats to the health of the monkeys, due to their consumption of anthropogenic foodstuffs. These anthropogenic foods, which are rich in calories, are the principal driver of the proximity between humans and primates, even though the acquisition of these foods tends to be risky for the monkeys and involve a variety of challenges derived from specific features of the urban environment. The present study evaluated the success/risk relationship of foraging for anthropogenic food by tufted capuchins (Sapajus libidinosus) in Brasília National Park. The data were analyzed using a binary logistic regression, with the backward-stepwise Wald method, to investigate the factors related to the foraging success of the capuchins, considering variables such as their sex and age, the type of approach and its context, and interactions with humans. The capuchins were influenced by the anthropogenic context, which affected their foraging strategies and diet. Interactions with humans reduced the success of foraging for anthropogenic foods. Conflicts between humans and the capuchins were common, especially in the context of access to food. The capuchins thus preferred to access feeding resources directly, probably due to the reduced human interference, which resulted in greater foraging success for unattended food brought by park visitors and the raiding of trash cans. Based on the observed behavior patterns, a number of measures can be proposed to mitigate these conflicts. These recommendations include not bringing food into areas frequented by the capuchins, not reacting to approaching animals, and removing all trash generated during a visit. A cleaning team dedicated to the maintenance of the visitation area free of anthropogenic waste is also be recommended.

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology.

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors.

INTRODUCTION: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS: A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS: The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION: The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.

Artificial Intelligence and Panendoscopy-Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy.

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

Disputes over provisioned resources are no more intense between groups than within groups in free-ranging Sapajus libidinosus.

Socioecological models predict that disputes between primate groups will be more intense than those within groups, given that the systematic loss of contests over a given resource will restrict the access of all of the members of that group to that resource. Higher levels of aggression are also expected for provisioned resources that have a more lucrative cost:benefit ratio. The levels of aggression in and between two free-ranging tufted capuchin monkey (Sapajus libidinosus) groups in the context of daily provisioning with bananas were evaluated. The aim of a complementary analysis was to identify possible predictors of the frequency of disputes at the site of the provisioned resource. The disputes were recorded using all-events sampling, while the social behaviour of the study groups was recorded by instantaneous scan sampling. The data were analysed using t-test, Mann-Whitney's U, and generalised linear modelling. Between-group disputes were no more intense than within-group events, and did not involve more individuals, or more adult females. The frequency of disputes increased as the number of individuals eating bananas increased. No evidence was found that disputes between groups were any more intense than those within groups. Dominance patterns may have affected these findings, by mediating intergroup disputes. An increase in the number of competitors affected the frequency of disputes at the site of the provisioned resource.

AI-Driven Colon Cleansing Evaluation in Capsule Endoscopy: A Deep Learning Approach.

Gastroenterology is increasingly moving towards minimally invasive diagnostic modalities. The diagnostic exploration of the colon via capsule endoscopy, both in specific protocols for colon capsule endoscopy and during panendoscopic evaluations, is increasingly regarded as an appropriate first-line diagnostic approach. Adequate colonic preparation is essential for conclusive examinations as, contrary to a conventional colonoscopy, the capsule moves passively in the colon and does not have the capacity to clean debris. Several scales have been developed for the classification of bowel preparation for colon capsule endoscopy. Nevertheless, their applications are limited by suboptimal interobserver agreement. Our group developed a deep learning algorithm for the automatic classification of colonic bowel preparation, according to an easily applicable classification. Our neural network achieved high performance levels, with a sensitivity of 91%, a specificity of 97% and an overall accuracy of 95%. The algorithm achieved a good discriminating capacity, with areas under the curve ranging between 0.92 and 0.97. The development of these algorithms is essential for the widespread adoption of capsule endoscopy for the exploration of the colon, as well as for the adoption of minimally invasive panendoscopy.

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care.

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights.

In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

Deep Learning and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy.

INTRODUCTION: Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for detecting gastric lesions is suboptimal. Convolutional neural networks (CNNs) are artificial intelligence models with great performance for image analysis. Nonetheless, their role in gastric evaluation by wireless CE (WCE) has not been explored. METHODS: Our group developed a CNN-based algorithm for the automatic classification of pleomorphic gastric lesions, including vascular lesions (angiectasia, varices, and red spots), protruding lesions, ulcers, and erosions. A total of 12,918 gastric images from 3 different CE devices (PillCam Crohn's; PillCam SB3; OMOM HD CE system) were used from the construction of the CNN: 1,407 from protruding lesions; 994 from ulcers and erosions; 822 from vascular lesions; and 2,851 from hematic residues and the remaining images from normal mucosa. The images were divided into a training (split for three-fold cross-validation) and validation data set. The model's output was compared with a consensus classification by 2 WCE-experienced gastroenterologists. The network's performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value and negative predictive value, and area under the precision-recall curve. RESULTS: The trained CNN had a 97.4% sensitivity; 95.9% specificity; and positive predictive value and negative predictive value of 95.0% and 97.8%, respectively, for gastric lesions, with 96.6% overall accuracy. The CNN had an image processing time of 115 images per second. DISCUSSION: Our group developed, for the first time, a CNN capable of automatically detecting pleomorphic gastric lesions in both small bowel and colon CE devices.

Heterocyclic chalcone (E)-1-(2-hydroxy-3,4,6-trimethoxyphenyl)-3-(thiophen-2-yl) prop-2-en-1-one derived from a natural product with antinociceptive, anti-inflammatory, and hypoglycemic effect in adult zebrafish.

Diabetes is a disease linked to pathologies, such as chronic inflammation, neuropathy, and pain. The synthesis by the Claisen-Schmidt condensation reaction aims to obtain medium to high yield chalconic derivatives. Studies for the synthesis of new chalcone molecules aim at the structural manipulation of aromatic rings, as well as the replacement of rings by heterocycles, and combination through chemical reactions of synthesized structures with other molecules, in order to enhance biological activity. A chalcone was synthesized and evaluated for its antinociceptive, anti-inflammatory and hypoglycemic effect in adult zebrafish. In addition to reducing nociceptive behavior, chalcone (40 mg/kg) reversed post-treatment-induced acute and chronic hyperglycemia and reduced carrageenan-induced abdominal edema in zebrafish. It also showed an inhibitory effect on NO production in J774A.1 cells. When compared with the control groups, the oxidative stress generated after chronic hyperglycemia and after induction of abdominal edema was significantly reduced by chalcone. Molecular docking simulations of chalcone with Cox -1, Cox-2, and TRPA1 channel enzymes were performed and indicated that chalcone has a higher affinity for the COX-1 enzyme and 4 interactions with the TRPA1 channel. Chalcone also showed good pharmacokinetic properties as assessed by ADMET. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03696-8.

Effectiveness of techniques for insertion of nasoenteral probe in critical patients: Randomized clinical trial.

INTRODUCTION: despite being a common procedure, nasally placed small-bowel feeding tube insertion is not risk-free and can compromise patient safety. Due to the fact that nasally placed small-bowel feeding tube is commonly inserted '"blindly," with the patient head in the neutral position, sometimes the process becomes difficult and traumatic, and may present higher level of complexity in physiological or induced coma and intubated patients. Therefore, adverse events (AEs) route errors can occur during this procedure. This study aimed to determine the effectiveness of different nasally placed small-bowel feeding tube insertion techniques in coma and intubated patients, in comparison with conventional method. METHODS: A prospective, randomized and controlled clinical trial will be carried out with coma and intubated patients admitted to the Intensive Care Unit (ICU). Thirty-nine patients will be randomly divided into 3 groups: group who will have the tube inserted in a conventional manner with the head in the neutral position, group with the head positioned laterally to the right, and, finally, with the head in the neutral position, with assistance of a laryngoscope. The primary endpoint will be: first, second and total attempt success rate; and time required for the first successful attempt and the sum of all attempts. Complications during insertion included tube bending, twisting, knotting, mucosal bleeding, and insertion into the trachea. Patient vital signs will be measured.