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.

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.

Synthesis of chalcones and their antimicrobial and drug potentiating activities.

The increased resistance of microorganisms to antimicrobial drugs makes it necessary to search for new active compounds, such as chalcones. Their simple chemical structure makes them molecules easy to synthesize. Therefore, the aim of this study was to evaluate the antimicrobial and potentiating activity of antibiotics and antifungals by synthetic chalcones against strains of Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Candida albicans and Candida tropicalis. The synthesis of chalcones was carried out by Claisen-Schimidt aldol condensation. Nuclear Magnetic Resonance (NMR) and Gas Chromatography Coupled to Mass Spectrometry (GC/MS) were also performed. Microbiological tests were performed by the broth microdilution method, using gentamicin, norfloxacin and penicillin as standard drugs for the antibacterial assay, and fluconazole for the antifungal assay. Three chalcones were obtained (1E,4E)-1,5-diphenylpenta-1,4-dien-3-one (DB-Acetone), (1E,3E,6E,8E)-1,9-diphenylnone-1,3,6,8-tetraen-5-one (DB-CNM), (1E,4E)-1,5-bis (4-methoxyphenyl) penta-1,4-dien-3-one (DB-Anisal). The compound DB-Acetone was able to inhibit P. aeruginosa ATCC 9027 at a concentration of 1.4 × 102 µM (32 µg/mL), while DB-CNM and DB-Anisal inhibited the growth of S. aureus ATCC 25923 at 17.88 × 102 µM and 2.71 × 101 µM (512 µg/mL and 8 µg/mL) respectively. In the combined activity, DB-Anisal was able to potentiate the effect of the three antibacterial drugs tested against E. coli 06, norfloxacin (128 for 4 µg/mL ±1) against P. aeruginosa 24 and penicillin (1,024 for 16 µg/mL ±1) against S. aureus 10. In antifungal assays, chalcones were not able to inhibit the growth of fungal strains tested. However, both showed potentiating activity with fluconazole, ranging from 8.17 x 10-1 µM (0.4909 µg/mL) to 2.35 µM (13.96 µg/mL). It is concluded that synthetic chalcones have antimicrobial potential, demonstrating good intrinsic activity against fungi and bacteria, in addition to potentiating the antibiotics and antifungal tested. Further studies are needed addressing the mechanisms of action responsible for the results found in this work.

Oligogenic Control of Quantitative Resistance Against Powdery Mildew Revealed in Portuguese Common Bean Germplasm.

Common bean (Phaseolus vulgaris L.) is one of the most important food legumes worldwide, and its production is severely affected by fungal diseases such as powdery mildew. Portugal has a diverse germplasm, with accessions of Andean, Mesoamerican, and admixed origin, making it a valuable resource for common bean genetic studies. In this work, we evaluated the response of a Portuguese collection of 146 common bean accessions to Erysiphe diffusa infection, observing a wide range of disease severity and different levels of compatible and incompatible reactions, revealing the presence of different resistance mechanisms. We identified 11 incompletely hypersensitive resistant and 80 partially resistant accessions. We performed a genome-wide association study to clarify its genetic control, resulting in the identification of eight disease severity-associated single-nucleotide polymorphisms, spread across chromosomes Pv03, Pv09, and Pv10. Two of the associations were unique to partial resistance and one to incomplete hypersensitive resistance. The proportion of variance explained by each association varied between 15 and 86%. The absence of a major locus, together with the relatively small number of loci controlling disease severity, suggested an oligogenic inheritance of both types of resistance. Seven candidate genes were proposed, including a disease resistance protein (toll interleukin 1 receptor-nucleotide binding site-leucine-rich repeat class), an NF-Y transcription factor complex component, and an ABC-2 type transporter family protein. This work contributes with new resistance sources and genomic targets valuable to develop selection molecular tools and support powdery mildew resistance precision breeding in common bean.

Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions.

Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision-recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm's accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation.

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy.

Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.

Trichomonas vaginalis as a risk factor for human papillomavirus: a study with women undergoing cervical cancer screening in a northeast region of Brazil.

BACKGROUND: Human papillomavirus (HPV) and Trichomonas vaginalis (TV) infections are the most common sexually transmitted infections (STIs) globally. The latter has contributed to a variety of adverse outcomes for both sexes. Moreover, in Brazil, epidemiological studies on patients with STIs are limited. Therefore, this study aimed to determine the prevalence of TV and its association with HPV in women undergoing cervical cancer screening. METHODS: Women with a normal cervix were recruited from a community-based cervical cancer screening program. Gynecological examinations were conducted, and questionnaires were provided. Vaginal canal and uterine cervix samples were collected for cytological examinations (reported using the 2001 Bethesda System) and tested for the presence of TV and HPV DNA. RESULTS: In total, 562 women who attended public primary healthcare were included in the study. The T. vaginalis was present in 19.0% (107) and HPV DNA was present in 46.8% (263) of women. Among the women of TV 73.8% (79) had a co-infection with HPV (p = 0.001). CONCLUSIONS: We concluded that a TV infection is associated with an HPV infection of the cervix as well as with the cervical cytological abnormalities. Further studies could reveal the mechanisms by which these two organisms interact at the cellular level, with control for shared behavioral risk factors.

The Vocal Repertoire of the Bearded Capuchin (Cebidae: Sapajus libidinosus): Implications for Understanding the Complexity of Neotropical Primate Communication.

Vocal communication is an essential aspect of primate social behaviour. The bearded capuchin Sapajus libidinosus is endemic to Brazil, and some studies have described specific vocalisation types for this species; however, there is still no complete description of its vocal repertoire. Thus, this study aimed to describe the vocal repertoire of a group of S. libidinosus living in theParque Nacional de Brasília, a protected area in the Cerrado area of Central Brazil. We carried out focal samplings and recording of vocalisations of members of an S. libidinosus troop in different behavioural contexts. The call analyses revealed 25 different types of vocalisations, and each call presented significant structural variation. We grouped these vocalisations according to the context of the emission or acoustic structure into the following categories: contact calls (contact note, infant babbling, trill, teeth- and lip-smacking, and sirena); foraging calls (chihui, grgr, and patinado); whistle series (food-associated, long-distance, and intergroup encounter); aggressive calls (aggressive contact note, ascending rapid staccato, cough cough, and pip); calls in response to aggression (scream, squeal, and pulsed scream), sexual display calls (chuck and raspy oestrous call), and stress-related calls (alarm call/bark, hiccup, hip, double hip, and wah wah). S. libidinosus presented a very rich vocal repertoire, revealing a pattern consistent with the repertoire of other capuchin monkey species. This is the first comprehensive description of the S. libidinosus vocal repertoire and highlights the complexity of neotropical primate communication.

Orofacial antinociceptive effect of sulphated polysaccharide from the marine algae Hypnea pseudomusciformis in rodents.

This study aimed to evaluate the antinociceptive effect of sulphated polysaccharide from the marine algae Hypnea pseudomusciformis (PLS) using rodent models of orofacial pain. Acute pain was induced by formalin, capsaicin, cinnamaldehyde, acidified saline or glutamate (cutaneous modes) and hypertonic saline (corneal model). In one experiment, animals were pretreated with ruthenium red, glibenclamide, naloxone, L-NAME, methylene blue or ketamine to investigate the mechanism of antinociception. In another experiment, animals pretreated with PLS or saline were submitted to the temporomandibular joint formalin test. In yet another, animals were submitted to craniofacial pain induced by mustard oil. Motor activity was evaluated with the open-field test. Cytotoxicity and antioxidant activities were also assessed. Pre-treatment with PLS significantly reduced nociceptive behavior associated with acute pain. Antinociception was effectively reduced, but not inhibited, by ruthenium red and ketamine. L-NAME and glibenclamide enhanced the PLS effect. PLS antinociception was resistant to methylene blue, naloxone and heating. PLS presented no cytotoxicity or antioxidant properties. Our results confirm the potential pharmacological relevance of PLS as an inhibitor of orofacial nociception in acute pain probably mediated by glutamatergic, nitrergic, TRPs and K + ATP pathways.

Diversity of nutcracking tool sites used by Sapajus libidinosus in Brazilian Cerrado.

Cracking nuts with tools is a behavior documented in a small number of populations of tufted capuchins, mainly in semi-arid Caatinga and Caatinga-Cerrado transitional environments of northeastern Brazil. Only one of these populations inhabits the less arid Cerrado in Central Brazil, where environments are composed of a heterogeneous mosaic of fields, savannas and forest formations. We conducted surveys in 10 of 20 localities where nutcracking by capuchins was reported by the local inhabitants in the Cerrrado of Northern Goiás and Tocantins. Our purpose was to evaluate nutcracking sites (anvils and associated hammers and nuts) based on indirect evidence of extensive pounding of nuts and seeds. Nutcracking was confirmed at all 10 surveyed localities. A total of 270 sites were identified. Surveyed localities included areas that were ecologically similar to those where capuchins crack nuts in Caatinga, as well as less arid localities with more typical Cerrado habitat. Anvils and hammers were made of materials including quartz, limestone, sandstone and wood, and displayed a wider range of sizes (i.e., 60-3,750 g for hammers' weight) than reported at previously studied localities. Nuts of seven genera were found in association with anvils and hammers. We conclude that nutcracking by capuchins are not restricted to arid environments and argue that the occurrence and diversity of nutcracking tool sites result from complex interactions of environmental variables (e.g., availability of food and mineral resources, density of canopy cover) and social variables (e.g., spatial cohesiveness and tolerance among group members) that need to be examined through long-term research of habituated groups. Localities in the Cerrado of Northern Goiás and Tocantins vary considerable in the ecological conditions faced by wild groups, and therefore offer the opportunity to examine these interactions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.