Novel Benzo[a]phenoxazinium Chlorides Functionalized with Sulfonamide Groups as NIR Fluorescent Probes for Vacuole, Endoplasmic Reticulum, and Plasma Membrane Staining.

The demand for new fluorophores for different biological target imaging is increasing. Benzo[a]phenoxazine derivatives are fluorochromophores that show promising optical properties for bioimaging, namely fluorescent emission at the NIR of the visible region, where biological samples have minimal fluorescence emission. In this study, six new benzo[a]phenoxazinium chlorides possessing sulfonamide groups at 5-amino-positions were synthesized and their optical and biological properties were tested. Compared with previous probes evaluated using fluorescence microscopy, using different S. cerevisiae strains, these probes, with sulfonamide groups, stained the vacuole membrane and/or the perinuclear membrane of the endoplasmic reticulum with great specificity, with some fluorochromophores capable of even staining the plasma membrane. Thus, the addition of a sulfonamide group to the benzo[a]phenoxazinium core increases their specificity and attributes for the fluorescent labeling of cell applications and fractions, highlighting them as quite valid alternatives to commercially available dyes.

Can blood progesterone concentration identify non-pregnant buffaloes to support oestrous resynchronization?

This study compared the plasma progesterone concentrations from pregnant and non-pregnant buffaloes to identify non-pregnant females and submit cows earlier to oestrous resynchronization. Forty-four multiparous mix-breed Murrah buffaloes were selected for the study. The cows were subjected to hormonal oestrous synchronization and separated into 4 groups, P12 (pregnant, n = 8) and P18 (n = 8) at 12 and 18 days post-insemination; NP12 (non-pregnant, n = 7) and NP18 (n = 7) at 23 and 29 days after the onset of synchronization, respectively. The embryos and blood were collected, and the plasma was separated for centrifugation and used to determine progesterone concentration. Progesterone concentration was higher in P18 than P12 (p = .02) and NP18 groups (p < .001). The steroid was also increased in the P12 group compared with NP12 (p = .031). There was no difference between NP12 and NP18 (p = .906). We conclude that the plasma progesterone concentration can be an alternative to identify earlier non-pregnant buffaloes, advancing the oestrous resynchronization or natural service to improve productivity.

Targeting Lysosomes in Colorectal Cancer: Exploring the Anticancer Activity of a New Benzo[a]phenoxazine Derivative.

Colorectal cancer (CRC) has been ranked as one of the cancer types with a higher incidence and one of the most mortal. There are limited therapies available for CRC, which urges the finding of intracellular targets and the discovery of new drugs for innovative therapeutic approaches. In addition to the limited number of effective anticancer agents approved for use in humans, CRC resistance and secondary effects stemming from classical chemotherapy remain a major clinical problem, reinforcing the need for the development of novel drugs. In the recent years, the phenoxazines derivatives, Nile Blue analogues, have been shown to possess anticancer activity, which has created interest in exploring the potential of these compounds as anticancer drugs. In this context, we have synthetized and evaluated the anticancer activity of different benzo[a]phenoxazine derivatives for CRC therapy. Our results revealed that one particular compound, BaP1, displayed promising anticancer activity against CRC cells. We found that BaP1 is selective for CRC cells and reduces cell proliferation, cell survival, and cell migration. We observed that the compound is associated with reactive oxygen species (ROS) generation, accumulates in the lysosomes, and leads to lysosomal membrane permeabilization, cytosolic acidification, and apoptotic cell death. In vivo results using a chicken embryo choriollantoic membrane (CAM) assay showed that BaP1 inhibits tumor growth, angiogenesis, and tumor proliferation. These observations highlight that BaP1 as a very interesting agent to disturb and counteract the important roles of lysosomes in cancer and suggests BaP1 as a promising candidate to be exploited as new anticancer lysosomal-targeted agent, which uses lysosome membrane permeabilization (LMP) as a therapeutic approach in CRC.

BIM in People2People and Things2People Interactive Process.

In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users' perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users' individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals.

Mechanisms for rescue of corpus luteum during pregnancy: gene expression in bovine corpus luteum following intrauterine pulses of prostaglandins E1 and F2α.

In ruminants, uterine pulses of prostaglandin (PG) F2α characterize luteolysis, while increased PGE2/PGE1 distinguish early pregnancy. This study evaluated intrauterine (IU) infusions of PGF2α and PGE1 pulses on corpus luteum (CL) function and gene expression. Cows on day 10 of estrous cycle received 4 IU infusions (every 6 h; n = 5/treatment) of saline, PGE1 (2 mg PGE1), PGF2α (0.25 mg PGF2α), or PGE1 + PGF2α. A luteal biopsy was collected at 30 min after third infusion for determination of gene expression by RNA-Seq. As expected, IU pulses of PGF2α decreased (P < 0.01) P4 luteal volume. However, there were no differences in circulating P4 or luteal volume between saline, PGE1, and PGE1 + PGF2α, indicating inhibition of PGF2α-induced luteolysis by IU pulses of PGE1. After third pulse of PGF2α, luteal expression of 955 genes were altered (false discovery rate [FDR] < 0.01), representing both typical and novel luteolytic transcriptomic changes. Surprisingly, after third pulse of PGE1 or PGE1 + PGF2α, there were no significant changes in luteal gene expression (FDR > 0.10) compared to saline cows. Increased circulating concentrations of the metabolite of PGF2α (PGFM; after PGF2α and PGE1 + PGF2α) and the metabolite PGE (PGEM; after PGE1 and PGE1 + PGF2α) demonstrated that PGF2α and PGE1 are entering bloodstream after IU infusions. Thus, IU pulses of PGF2α and PGE1 allow determination of changes in luteal gene expression that could be relevant to understanding luteolysis and pregnancy. Unexpectedly, by third pulse of PGE1, there is complete blockade of either PGF2α transport to the CL or PGF2α action by PGE1 resulting in complete inhibition of transcriptomic changes following IU PGF2α pulses.

Beacons and BIM Models for Indoor Guidance and Location.

This research work uses a simplified approach to combine location information from a beacon's propagation signal interaction with a mobile device sensor (accelerometer and gyroscope) with local building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user's orientation inside unknown buildings and the data stored from different users can provide useful information about users' movements inside a public building. Beacons installed on the building at specific pre-defined positions emit signals that give a geographic position with an associated imprecision, related with Bluetooth's range. This uncertainty is handled by building layout and users' movement in a developed system that maps users' position, gives guidance, and stores user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user's smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.

Morphology of the Dentin-resin Interface yielded by Two-step Etch-and-rinse Adhesives with Different Solvents.

AIM: The study aimed to analyze the morphology of the dentin-resin interface yielded by two-step etch-and-rinse adhesive systems with different solvents and compositions. MATERIALS AND METHODS: A total of 32 dentine disks were prepared and randomly assigned to four groups of one-bottle etch-and-rinse adhesive systems containing different solvents: group I, Adper Scotchbond-IXT™ (ethanol/water); group II, XP-Bond™ (tertiary butanol); group III, Prime and Bond NT® (acetone); and group IV, One Coat bond® (5% water). Adhesive systems were applied onto dentin disks, which were then thermal cycled, divided into two hemi-disks (n = 16), and prepared for field-emission scanning electron microscopy to examine the dentin-resin interdiffusion zone. Microphotographs were scanned and data were processed. Data were compared with analysis of variance multivariant test after Kolmogorov-Smirnov and Shapiro-Wilk tests using Statistic Package for the Social Sciences. RESULTS: The adhesive layer thickness average found was group I: 45.9 ± 13.41 urn, group II: 20.6 ± 16.32 urn, group III: 17.7 ± 11.75 urn, and group IV: 50.7 ± 27.81 urn. Significant differences were found between groups I and IV and groups II and III (p < 0.000). Groups I (3.23 ± 0.53 µm) and II (3.13 ± 0.73 µm) yielded significantly thicker hybrid layers than groups III (2.53 ± 0.50 µm) and IV (1.84 ± 0.27 µm) (p < 0.003). Group III presented a less homogeneous hybrid layer, with some gaps. Tag length average was greater in groups II (111.0 ± 36.92 µm) and IV (128.9 ± 78.38 µm) than in groups I (61.5 ± 18.10 µm) and III (68.6 ± 15.84 µm) (p < 0.008). CONCLUSION: Adhesives systems with different solvents led to significant differences in the dentin-resin interface morphology. Solvents role in adhesives bond strength should be considered together with the other adhesive system components. CLINICAL SIGNIFICANCE: The adhesive containing tertiary butanol, in addition, seems to originate a good-quality hybrid layer and long, entangled tags and also appears to have greater ability to originate microtags, which may indicate higher bond strength.

MediAlbertina: An European Portuguese medical language model.

BACKGROUND: Patient medical information often exists in unstructured text containing abbreviations and acronyms deemed essential to conserve time and space but posing challenges for automated interpretation. Leveraging the efficacy of Transformers in natural language processing, our objective was to use the knowledge acquired by a language model and continue its pre-training to develop an European Portuguese (PT-PT) healthcare-domain language model. METHODS: After carrying out a filtering process, Albertina PT-PT 900M was selected as our base language model, and we continued its pre-training using more than 2.6 million electronic medical records from Portugal's largest public hospital. MediAlbertina 900M has been created through domain adaptation on this data using masked language modelling. RESULTS: The comparison with our baseline was made through the usage of both perplexity, which decreased from about 20 to 1.6 values, and the fine-tuning and evaluation of information extraction models such as Named Entity Recognition and Assertion Status. MediAlbertina PT-PT outperformed Albertina PT-PT in both tasks by 4-6% on recall and f1-score. CONCLUSIONS: This study contributes with the first publicly available medical language model trained with PT-PT data. It underscores the efficacy of domain adaptation and offers a contribution to the scientific community in overcoming obstacles of non-English languages. With MediAlbertina, further steps can be taken to assist physicians, in creating decision support systems or building medical timelines in order to perform profiling, by fine-tuning MediAlbertina for PT- PT medical tasks.

Health Care Language Models and Their Fine-Tuning for Information Extraction: Scoping Review.

BACKGROUND: In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non-English-language with fewer resources. OBJECTIVE: This study aimed to research health care IE models, regardless of language, to understand the efficacy of transformers and what are the medical entities most commonly extracted. METHODS: This scoping review was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) methodology on Scopus and Web of Science Core Collection databases. Only studies that mentioned the creation of health care LMs or health care IE models were included, while large language models (LLMs) were excluded. The latest were not included since we wanted to research LMs and not LLMs, which are architecturally different and have distinct purposes. RESULTS: Our search query retrieved 137 studies, 60 of which met the inclusion criteria, and none of them were systematic literature reviews. English and Chinese are the languages with the most health care LMs developed. These languages already have disease-specific LMs, while others only have general-health care LMs. European Portuguese does not have any public health care LM and should take examples from other languages to develop, first, general-health care LMs and then, in an advanced phase, disease-specific LMs. Regarding IE models, transformers were the most commonly used method, and named entity recognition was the most popular topic, with only a few studies mentioning Assertion Status or addressing medical lexical problems. The most extracted entities were diagnosis, posology, and symptoms. CONCLUSIONS: The findings indicate that domain adaptation is beneficial, achieving better results in downstream tasks. Our analysis allowed us to understand that the use of transformers is more developed for the English and Chinese languages. European Portuguese lacks relevant studies and should draw examples from other non-English languages to develop these models and drive progress in AI. Health care professionals could benefit from highlighting medically relevant information and optimizing the reading of the textual data, or this information could be used to create patient medical timelines, allowing for profiling.

Deep learning for automatic calcium detection in echocardiography.

Cardiovascular diseases are the main cause of death in the world and cardiovascular imaging techniques are the mainstay of noninvasive diagnosis. Aortic stenosis is a lethal cardiac disease preceded by aortic valve calcification for several years. Data-driven tools developed with Deep Learning (DL) algorithms can process and categorize medical images data, providing fast diagnoses with considered reliability, to improve healthcare effectiveness. A systematic review of DL applications on medical images for pathologic calcium detection concluded that there are established techniques in this field, using primarily CT scans, at the expense of radiation exposure. Echocardiography is an unexplored alternative to detect calcium, but still needs technological developments. In this article, a fully automated method based on Convolutional Neural Networks (CNNs) was developed to detect Aortic Calcification in Echocardiography images, consisting of two essential processes: (1) an object detector to locate aortic valve - achieving 95% of precision and 100% of recall; and (2) a classifier to identify calcium structures in the valve - which achieved 92% of precision and 100% of recall. The outcome of this work is the possibility of automation of the detection with Echocardiography of Aortic Valve Calcification, a lethal and prevalent disease.

Sharing Health Information Using a Blockchain.

Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both retrospective and prospective research at a low cost. Similarly, the Organisation for Economic Co-operation and Development (OECD) encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper proposes adopting a blockchain framework to enable the transparent sharing of medical information among health entities in a secure environment. We develop a laboratory-based prototype using a design science research methodology (DSRM). This approach has its roots in the sciences of engineering and artificial intelligence, and its primary goal is to create relevant artefacts that add value to the fields in which they are used. We adopt a patient-centric approach, according to which a patient is the owner of their data and may allow hospitals and health professionals access to their data.

Intelligent Clinical Decision Support System for Managing COPD Patients.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients' future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work's CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia.

Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

Evaluation of Antifungal Activity and Potential Application as Fluorescent Probes of Indolenine and Benzo[e]Indole-Based Squarylium Dyes.

The antifungal performance and the possible use as fluorescent probes of a series of squarylium dyes derived from indolenine and benzo[e]indole previously synthesized was evaluated. Some photophysical properties were performed in ethanol and phosphate buffer, and the type of aggregates form in phosphate buffer was analyzed. Using the 1,3-diphenylisobenzofuran assay, a qualitative assessment of the capacity of dyes to produce singlet oxygen after irradiation was performed. Regarding the antifungal activity, this was studied through a broth microdilution assay using Saccharomyces cerevisiae PYCC 4072 as a biological model. The effect of irradiation of the dyes, with an appropriate light emitting diode system, on the antifungal activity was also evaluated, and it was verified that some of the dyes improve their activity after irradiation. Using fluorescence microscopy techniques, the colocalization of dyes in S. cerevisae cells was investigated and it was possible to verify that some of the squarylium dyes with a barbituric moiety in the four-membered central ring stained and accumulated preferentially in the mitochondrial web and perinuclear membrane of the cells. The possible use as a fluorescent probe for the detection of HSA was also evaluated for one of the dyes of the series, demonstrating a linear variation in the fluorescence intensity accompanied by the increase in the protein concentration.

Squaraine Dyes Derived from Indolenine and Benzo[e]indole as Potential Fluorescent Probes for HSA Detection and Antifungal Agents.

Four squaraine dyes derived from 2,3,3-trimethylindolenine and 1,1,2-trimethyl-1H-benzo[e]indole with different combinations of barbituric groups attach to the central ring, having ester groups and alkyl chains in the nitrogen atoms of heterocyclic rings were synthesized. These dyes were fully characterized, and their photophysical behavior was studied in ethanol and phosphate-buffered solution. Absorption and emission bands between 631 and 712 nm were detected, with the formation of aggregates in aqueous media, which is typical of this class of dyes. Tests carried out with 1,3-diphenylisobenzofuran allowed us to verify the ability of the dyes to produce singlet oxygen. The interaction of synthesized dyes with human serum albumin (HSA) was also evaluated, being demonstrated a linear correlation between fluorescence intensity and protein concentration. The antifungal potential of the dyes against the yeast Saccharomyces cerevisiae was evaluated using a broth microdilution assay. In order to test the photosensitizing capacity of the synthesized dyes, tests were carried out in the dark and with irradiation, using a custom-built light-emitting diode that emits close to the absorption wavelength of the studied dyes. The results showed that the interaction of dyes with HSA and the antifungal activity depends on the different structural modifications of the dyes.

Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis.

Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images-being always the darker region-blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.

Merging Data Diversity of Clinical Medical Records to Improve Effectiveness.

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.

Changes in biochemical analytes in calves infected by nematode parasites in field conditions.

Parasitic infections caused by nematodes are a major problem in bovines that resulting in losses in animal health and production. Thus, the aim of this study was to evaluate alterations in selected serum biochemical analytes in calves naturally infected with gastrointestinal (GI) and pulmonary nematodes without clinical signs. For this, samples of feces and blood of 86 calves were collected. Fecal egg counts (FEC) were determined using the modified McMaster technique with a sensitivity of 50 eggs per gram of feces (EPG). Positive nematode FEC was processed for coproculture using pooled samples to identify Strongylidae infective larvae (L3). First stage-larvae (L1) of Dictyocaulus viviparous were identified by a modified Baermann method. The biochemical analytes determined were: acute phase proteins such as haptoglobin and paraoxonase type 1; the enzymes acetylcholinesterase; butyrylcholinesterase; the lipid profile (triglycerides and total, HDL, and LDL-cholesterol); serum iron profile (iron and unsaturated iron-binding capacity); total protein and albumin; pancreatic profile (amylase and lipase); and minerals (phosphorus and calcium). The calves were divided into four groups according to the results of EPG and the modified Baermann method. Group 1: healthy control animals (n=16); Group 2: calves with only GI parasites (n=51): This group was sub-divided into sub-groups according to the EPG threshold: 2a-GI parasites with low EPG (n=23), and 2b-GI parasites with high EPG (n=28). Group 3: animals with only lungworms (n=5), and Group 4: calves with lung+GI parasites (n=14). The more prevalent genera in all coprocultures were: Cooperia spp., Haemonchus spp., Oesophagostomum spp., and Trichostrongylus spp. The nonparametric Kruskal-Wallis test was used to compare the groups and Dunn's post-test was used for multiple comparisons as the data was not normally distributed (P<0.05). The haptoglobin concentration increased in calves with GI and pulmonary parasites. A significant increase in acetylcholinesterase was observed in calves infected with lungworms. Cholesterol, triglycerides, HDL, and LDL concentrations decreased but lipase concentration increased in calves with GI parasites. Therefore, this paper provides an overview of the biochemical effects produced by nematode parasites in calves in field conditions. These findings in calves without any evident clinical signs of disease could provide an indication of GI parasites and lungworm infection, especially in an endemic area for these parasites.

Non-Invasive Measurement of Adrenocortical Activity in Blue-Fronted Parrots (Amazona aestiva, Linnaeus, 1758).

Parrots kept in zoos and private households often develop psychological and behavioural disorders. Despite knowing that such disorders have a multifactorial aetiology and that chronic stress is involved, little is known about their development mainly due to a poor understanding of the parrots' physiology and the lack of validated methods to measure stress in these species. In birds, blood corticosterone concentrations provide information about adrenocortical activity. However, blood sampling techniques are difficult, highly invasive and inappropriate to investigate stressful situations and welfare conditions. Thus, a non-invasive method to measure steroid hormones is critically needed. Aiming to perform a physiological validation of a cortisone enzyme immunoassay (EIA) to measure glucocorticoid metabolites (GCM) in droppings of 24 Blue-fronted parrots (Amazona aestiva), two experiments were designed. During the experiments all droppings were collected at 3-h intervals. Initially, birds were sampled for 24 h (experiment 1) and one week later assigned to four different treatments (experiment 2): Control (undisturbed), Saline (0.2 mL of 0.9% NaCl IM), Dexamethasone (1 mg/kg IM) and Adrenocorticotropic hormone (ACTH; 25 IU IM). Treatments (always one week apart) were applied to all animals in a cross-over study design. A daily rhythm pattern in GCM excretion was detected but there were no sex differences (first experiment). Saline and dexamethasone treatments had no effect on GCM (not different from control concentrations). Following ACTH injection, GCM concentration increased about 13.1-fold (median) at the peak (after 3-9 h), and then dropped to pre-treatment concentrations. By a successful physiological validation, we demonstrated the suitability of the cortisone EIA to non-invasively monitor increased adrenocortical activity, and thus, stress in the Blue-fronted parrot. This method opens up new perspectives for investigating the connection between behavioural disorders and stress in this bird species, and could also help in their captive management.

Shear bond strength and SEM morphology evaluation of different dental adhesives to enamel prepared with ER:YAG laser.

CONTEXT: Early observations of enamel surfaces prepared by erbium lasers motivated clinicians to use laser as an alternative to chemical etching. AIMS: Evaluate shear bond strength (SBS) values of different dental adhesives on Erbium:Yttrium Aluminum Garnet (Er:YAG) laser prepared enamel and to evaluate possible etching patterns correlations between dental adhesives and SBS values. SUBJECTS AND METHODS: One hundred bovine incisors were randomly assigned to SBS tests on enamel (n = 15) and to enamel morphology analysis (n = 5) after Er:YAG laser preparation as follows: Group I - 37% phosphoric acid (PA)+ ExciTE(®); Group II - ExciTE(®); Group III - AdheSE(®) self-etching; Group IV - FuturaBond(®) no-rinse. NR; Group V - Xeno(®) V. Teeth were treated with the adhesive systems and subjected to thermal cycling. SBS were performed in a universal testing machine at 5 mm/min. STATISTICAL ANALYSIS USED: One-way ANOVA and post-hoc tests (P < 0.05). For the morphology evaluation, specimens were immersed in Ethylenediamine tetraacetic acid (EDTA) and the etching pattern analyzed under Scanning Electron Microscope (SEM). RESULTS: Mean bond strengths were Group I - 47.17 ± 1.61 MPa (type I etching pattern); Group II - 32.56 ± 1.64 MPa, Group III - 29.10 ± 1.34 MPa, Group IV - 23.32 ± 1.53 MPa (type III etching pattern); Group V - 24.43 MPa ± 1.55 (type II etching pattern). CONCLUSIONS: Different adhesive systems yielded significantly different SBSs. Acid etching significantly increased the adhesion in laser treated enamel. No differences in SBS values were obtained between AdheSE(®) and ExciTE(®) without condition with PA. FuturaBond(®) NR and Xeno(®) V showed similar SBS, which was lower in comparison to the others adhesives. No correlation between enamel surface morphology and SBS values was observed, except when PA was used.