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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. Plain language summary: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phosphorous is a limiting nutrient in sugarcane cultivated in Argisols in the Northeast of Brazil, and understanding its availability in the soil, as well as determining sufficiency ranges in the plants is essential when recommending levels of phosphorus fertiliser. The aim of this study was to: (i) Define class es for interpreting P availability in Argisols of the Northeast using the Mehlich-1 and Anion Exchange Resin (AER) extractors; (ii) Evaluate P extraction by the same extractors; and (iii) Correlate the P content of the soil with the P content of the plants, and establish sufficiency ranges in the plants. Three field trials were carried out in a sandy (PVAd1), medium (PAdx) and clayey (PVAd2) Argisol, applying the following amounts of P: 0, 40, 80, 120, 160 and 200 kg ha-1 P2O5. The AER extracted more P than did Mehlich-1 from each of the soils. The P content of the classes for interpreting P availability determined by the AER were higher than those determined by Mehlich-1. AER was the best extractor to represent P availability in the Argisol with the clayey texture (PVAd2), and Mehlich-1 in the Argisol with the sandy texture (PVAd1). The ranges of P sufficiency in the leaves of the sugarcane varied between soils, with values of 0.66-0.73 g kg-1 in PVAd1, 0.59-0.69 g kg-1 in PVAd2 and 0.34-0.47 g kg-1 in PAdx. As such, the texture of Argisols cultivated with sugarcane in the Northeast of Brazil should be a criterion for choosing the P extractor used in assessing its availability. [ABSTRACT FROM AUTHOR]

Simple Summary: Device-assisted enteroscopy is the only diagnostic and therapeutic exam capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this procedure is not sufficient enough to assure a cost-effective panendoscopy, and there is significant interobserver variability during the exam. Artificial intelligence tools have been proved to be beneficial in several areas of medicine, namely in Gastroenterology, with a strong image component. However, the development of deep learning models for application in device-assisted enteroscopy is still in an embryonic phase. The authors herein aimed to develop a multidevice convolutional neural network based on 338 exams performed in two renowned centers. The present model was able to accurately identify multiple clinically relevant lesions across the entire gastrointestinal tract, with an image processing time that favors its clinical applicability. The first worldwide panendoscopic model showed the potential of artificial intelligence in augmenting the accuracy and cost-effectiveness of 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. [ABSTRACT FROM AUTHOR]

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AbstractThe plant species C. sativum L. is a staple in cuisine and holds significant ethnopharmacological value. Its essential oil (EO) is of particular interest, yet its toxicity profile remains a subject of inquiry. This study aimed to elucidate the chemical constituents of C. sativum L. EO and evaluate its toxicity through various parameters, including cytotoxicity assays on HaCaT keratinocytes, in vivo toxicity tests on Galleria mellonella larvae, in vivo genotoxicity assessments on mice and cytotoxicity assays on human erythrocytes. Notably, major constituents such as 2-decen-1-ol, dec-(2E)-enal, and 1,6-octadien-3-ol were found to remain predominant. The IC50 value for the essential oil on the keratinocyte cell line was determined to be 60.13 ± 2.02 µg/mL. However, in vivo toxicity tests with G. mellonella larvae demonstrated safety at doses below 4.5 g/kg. Additionally, genotoxicity assessment revealed that a single dose of 20 mg/mL (5 mg/kg) did not induce a significant increase in micronuclei formation. EO concentrations above 250 µg/mL led to significant changes in human erythrocytes cell viability (p < 0.0001), resulting in over 60% hemolysis. These findings collectively suggest that the essential oil of C. sativum L. exhibits a suitable toxicity profile for conducting preclinical studies in vertebrate animal models. [ABSTRACT FROM AUTHOR]

Background: Small bowel capsule endoscopy (SBCE) is an essential tool for evaluation of small bowel (SB) Crohn disease (CD). Fecal calprotectin (FC) represents an important biomarker of intestinal inflammation, widely used in ulcerative colitis and CD. Our aim was to evaluate the role of FC for diagnosing inflammatory activity in patients with isolated SB CD and how it correlates with SBCE findings., Methods: This is a retrospective study conducted in a tertiary inflammatory bowel disease referral center that included patients with SB CD who underwent SBCE between January 2017 and February 2023. FC value was obtained from the closest stool examination to SBCE., Results: One hundred ninety-six patients were included: 123 were women (63%) with a mean age of 44.2 years. In the SBCE, 127 (65%) patients had a Lewis Score ≥135 and, among the 94 patients with FC >200 μg/g, 23 had LS <135, 36 had LS between 135 and 790, and 35 had LS ≥790. FC levels were predictive of endoscopic lesions in SBCE, with significant correlation between FC level and total LS (Pearson correlation coefficient 0.43, P <.001). The sensitivity and specificity were calculated for each cut-off value being respectively 78% and 45% for FC = 100 μg/g, 69% and 59% for FC = 150 μg/g and 67% and 67% for FC = 200 μg/g., Conclusion: FC showed moderate correlation with endoscopic findings in SBCE in SB CD. It is, therefore, a reasonable marker for predicting significant inflammatory lesions in SBCE; however, none of the cut-off had a high sensitivity or specificity., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of PBJ-Associação Porto Biomedical/Porto Biomedical Society.)

Background: A better understanding of the consequences of the Coronavirus Disease 2019 (COVID-19) pandemic on lifestyle of patients with Chagas disease (ChD) is of paramount importance to facilitate the implementation of intervention strategies tailored to this specific population., Objective: The present study aimed to evaluate the level of physical activity (PA) in Chagas disease (ChD) patients during the Coronavirus Disease 2019 (COVID-19) pandemic and its main associated factors., Methods: This is a cross-sectional study with 187 patients of both sexes, aged ≥18 years, followed in a national infectious disease center (Rio de Janeiro, Brazil). The level of PA was determined by the International Physical Activity Questionnaire short version and expressed in terms of total volume of physical activity (PA) (MET-minutes per week). Individuals were classified as physically active following the 2020 World Health Organization PA guideline. The exposure variables were age, sex, race, marital status, schooling, income per capita , number of rooms per domicile, number of residents per domicile, body mass index, clinical form of ChD, COVID-19 antibodies, comorbidities, self-reported anxiety, self-reported depression, self-reported fear, and self-reported sadness. The association between the exposure variables with total PA (as a continuous variable) was determined using univariate and multivariate linear regression models., Results: Mean age was 61.1 ± 11.6 years. Most (62%) were women and self-declared their race as mixed (50.8%). The percentage of physically active individuals according to was 52%. The variables independently associated with total PA levels were non-white race (Exp β = 1.39; 95% CI 1.02 to 1.90), dyslipidemia (Exp β = 0.73; 95% CI 0.56 to 0.95) and self-reported depression during quarantine (Exp β = 0.71; 95% CI 0.52 to 0.96)., Conclusion: Non-white race was positively associated with total levels of PA, while dyslipidemia, and self-reported depression during quarantine were negatively associated with total levels of PA. The identification of associated factors can facilitate the development of tailored strategies to increase PA levels ChD patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xavier, Andrade, Vitor, Barros, Portela, de Holanda, Sangenis, Sperandio da Silva, Mazzoli-Rocha, Mendes, da Costa, Quintana, Hasslocher-Moreno, Araujo, Junqueira, Pinheiro, Georg, Paravidino, Gonçalves, Saraiva and Mediano.)