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.

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.

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.

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.

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.

Use of Lavandula angustifolia essential oil as a complementary therapy in adult health care: A scoping review.

Background: In addition to its low toxicity risk, Lavender (Lavandula angustifolia Mill.) essential oil is recognised worldwide for its sedative, antidepressant, antiseptic, antifungal, relaxing, and antiemetic properties. Thus, the action mechanism of lavender oil has attracted significant attention from researchers interested in improving the physical, emotional, and spiritual well-being of patients. Objective: To investigate the scope of knowledge regarding the use of L. angustifolia essential oil as a complementary therapy in adult health care. Methods: A scoping review was carried out using a PRISMA-ScR checklist followed by a critical assessment being performed using the Joanna Briggs Institute level of evidence. The following databases were used: SCOPUS, MEDLINE/PubMed, Web of Science, Science Direct, SCIELO, Cochrane Library, LILACS, Wiley Online Library, CAPES, and FIOCRUZ Dissertations. Results: Eighty-three articles published between 2002 and 2022 were selected for the analysis; More articles came from Iran than from any other country and most articles reported clinical trials. The applicability of lavender essential oil and its route of administration in different clinical situations were the main topics addressed in the articles. Conclusions: Most studies demonstrate the efficacy of L. angustifolia Mill. essential oil in relieving pain and decreasing anxiety. Few studies evaluated the anti-psoriatic, anti-toxoplasmotic, and wound healing properties and the protective actions against cerebral ischemia. One study reported on its safety, specifically the allergenic potential of linalool, the main chemical component of L. angustifolia essential oil. However, most studies did not involve the extensive investigations on this topic or report the safe quantities of this oil for human treatment, meaning further research into the safety of this treatment is required.

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.

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.

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.

Use of "The Knowledge-to-Action Framework" for the implementation of evidence-based nursing in child and family care: Study protocol.

Nurses are increasingly focused on a practice based on scientific knowledge. However, it is important to distinguish high-quality evidence that can be applied in practice from studies of low or dubious scientific quality. Therefore, nurses must base their practice on structural support that allows for the definition of personalized and context-specific interventions. The objectives of this study are to identify the main barriers and facilitators to the evidence-based nursing approach and to implement an Evidence-Based Practice model (EBP) in clinical practice settings. We seek to contribute to evidence-based nursing by promoting professional skills in nurses, using "The Knowledge-to-Action Framework" (KTA). The research focuses on a participatory action research methodology based on the cyclical process of the KTA framework, contemplating the creation of knowledge and the implementation of existing solutions or new solutions through an action cycle. The participants will be nurses and parents/caregivers) from a pediatric service in Northern Portugal. The study will be conducted in 3 phases: phase 1 will identify the priority issues by exploring the barriers and facilitators of EBP from the nurses' perspective and assessing the parents'/caregivers' satisfaction with nursing care. Phase 2 will be divided into (a) the planning and (b) the implementation of the KTA model, where we aim to build and validate (a) a training plan and (b) the implementation of the KTA model. Phase 3 is for the evaluation of the model implementation and sustaining knowledge. It is recognized that there is a large gap between knowledge production and the subsequent implementation of interventions based on the best available evidence. However, this reality is complex and involves several levels of decision and intervention that oscillate from the individual responsibility of each nurse to the organizational dimension.

Grateful Workers, Satisfied Workers? A Portuguese Study about Organizational Happiness during COVID-19 Quarantine.

Although work satisfaction has been largely studied, gratitude is an emerging field within multiple sciences, including positive psychology, organizational behavior, and human resources marketing. This ex post facto study aims to characterize gratitude and understand its relations to job satisfaction in a non-probabilistic sample of 521 Portuguese workers (62.2% women), 30.90% and 69.10% in the public and private sector, respectively, mean ages of M = 43, SD = 12.6. Data were collected using anonymous questionnaires during the COVID-19 lockdown. Statistical analyses were performed in SPSS 26, and include Student's t-test, one-way ANOVA, Pearson's correlations, and a hierarchical linear regression model. Results confirm that Portuguese workers are grateful and satisfied at work. There were statistically significant differences between groups in sociodemographic (p < 0.001 and p < 0.05), professional (p < 0.01 and p < 0.001), and perceived living conditions variables (p < 0.05) regarding gratitude. Gratitude, alone, explains 8% of job satisfaction. According to the regression model (32.4%), perceptions of satisfaction initiatives and greater job security are also associated with higher levels of job satisfaction (23.6%). Implementation of gratitude-promoting strategies may increase job satisfaction, especially in the post-pandemic period. The investment in workers' organizational happiness, after the impacts of COVID-19 on work dynamics, is a differentiating organizations success dimension.