Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts.

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

Simplifying Data Analysis in Biomedical Research: An Automated, User-Friendly Tool.

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool's functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI's GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.

Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint.

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Evaluation of rapid optimized flow cytometry crossmatch (Halifaster) in living donor kidney transplantation.

Kidney transplantation is often the preferred treatment for end-stage renal disease. However, the presence of preformed donor-specific antibodies (DSA), including those against HLA, can lead to antibody-mediated rejection and significantly impact transplant outcomes. The Flow Cytometry Crossmatch (FCXM) is a crucial tool in kidney transplantation, as it also enables the measurement of low levels of anti-HLA DSA antibodies. However, current methodologies for detecting these antibodies, however, are time-consuming and require extensive reagents. In this study, we analyzed the performance of the Halifaster FCXM protocol in 133 consecutive living kidney donor pairs, correlating these results with single antigen-based anti-HLA DSA results. Anti-HLA DSA was identified in 31 patients (23.3%). Both T and B lymphocyte FCXM assays demonstrated high sensitivity and specificity in detecting anti-HLA DSA. Furthermore, a Tree model to determine the levels of anti-HLA DSA to produce a flow crossmatch positivity, was developed offering an accuracy of 93% and 90% for T and B lymphocytes, respectively. Both approaches point to a thresh old of 1000-2000 MFI for T lymphocytes and 3000 MFI for B lymphocytes. Our findings indicate that the Halifaster protocol facilitates fast and efficient FCXM testing without compromising accuracy, marking a significant advancement in the field of kidney transplantation. The inclusion of HLA-specific antibody analysis underscores the protocol's comprehensive approach to improving transplant outcomes.

Exosomes and microvesicles in kidney transplantation: the long road from trash to gold.

Kidney transplantation significantly enhances the survival rate and quality of life of patients with end-stage kidney disease. The ability to predict post-transplantation rejection events in their early phases can reduce subsequent allograft loss. Therefore, it is critical to identify biomarkers of rejection processes that can be acquired on routine analysis of samples collected by non-invasive or minimally invasive procedures. It is also important to develop new therapeutic strategies that facilitate optimisation of the dose of immunotherapeutic drugs and the induction of allograft immunotolerance. This review explores the challenges and opportunities offered by extracellular vesicles (EVs) present in biofluids in the discovery of biomarkers of rejection processes, as drug carriers and in the induction of immunotolerance. Since EVs are highly complex structures and their composition is affected by the parent cell's metabolic status, the importance of defining standardised methods for isolating and characterising EVs is also discussed. Understanding the major bottlenecks associated with all these areas will promote the further investigation of EVs and their translation into a clinical setting.

Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs.

Genotoxicity is an important information that should be included in human biomonitoring programmes. However, the usually applied cytogenetic assays are laborious and time-consuming, reason why it is critical to develop rapid and economic new methods. The aim of this study was to evaluate if the molecular profile of frozen whole blood, acquired by Fourier Transform Infrared (FTIR) spectroscopy, allows to assess genotoxicity in occupational exposure to antineoplastic drugs, as obtained by the cytokinesis-block micronucleus assay. For that purpose, 92 samples of peripheral blood were studied: 46 samples from hospital professionals occupationally exposed to antineoplastic drugs and 46 samples from workers in academia without exposure (controls). It was first evaluated the metabolome from frozen whole blood by methanol precipitation of macromolecules as haemoglobin, followed by centrifugation. The metabolome molecular profile resulted in 3 ratios of spectral bands, significantly different between the exposed and non-exposed group (p < 0.01) and a spectral principal component-linear discriminant analysis (PCA-LDA) model enabling to predict genotoxicity from exposure with 73 % accuracy. After optimization of the dilution degree and solution used, it was possible to obtain a higher number of significant ratios of spectral bands, i.e., 10 ratios significantly different (p < 0.001), highlighting the high sensitivity and specificity of the method. Indeed, the PCA-LDA model, based on the molecular profile of whole blood, enabled to predict genotoxicity from the exposure with an accuracy, sensitivity, and specificity of 92 %, 93 % and 91 %, respectively. All these parameters were achieved based on 1 µL of frozen whole blood, in a high-throughput mode, i.e., based on the simultaneous analysis of 92 samples, in a simple and economic mode. In summary, it can be conclude that this method presents a very promising potential for high-dimension screening of exposure to genotoxic substances.

Proteomics-Driven Biomarkers in Pancreatic Cancer.

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy.

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

The Characteristics and Laboratory Findings of SARS-CoV-2 Infected Patients during the First Three COVID-19 Waves in Portugal-A Retrospective Single-Center Study.

Background and Objectives: Given the wide spectrum of clinical and laboratory manifestations of the coronavirus disease 2019 (COVID-19), it is imperative to identify potential contributing factors to patients' outcomes. However, a limited number of studies have assessed how the different waves affected the progression of the disease, more so in Portugal. Therefore, our main purpose was to study the clinical and laboratory patterns of COVID-19 in an unvaccinated population admitted to the intensive care unit, identifying characteristics associated with death, in each of the first three waves of the pandemic. Materials and Methods: This study included 337 COVID-19 patients admitted to the intensive care unit of a single-center hospital in Lisbon, Portugal, between March 2020 and March 2021. Comparisons were made between three COVID-19 waves, in the second (n = 325) and seventh (n = 216) days after admission, and between discharged and deceased patients. Results: Deceased patients were considerably older (p = 0.021) and needed greater ventilatory assistance (p = 0.023), especially in the first wave. Differences between discharged and deceased patients' biomarkers were minimal in the first wave, on both analyzed days. In the second wave significant differences emerged in troponins, lactate dehydrogenase, procalcitonin, C-reactive protein, and white blood cell subpopulations, as well as platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios (all p < 0.05). Furthermore, in the third wave, platelets and D-dimers were also significantly different between patients' groups (all p < 0.05). From the second to the seventh days, troponins and lactate dehydrogenase showed significant decreases, mainly for discharged patients, while platelet counts increased (all p < 0.01). Lymphocytes significantly increased in discharged patients (all p < 0.05), while white blood cells rose in the second (all p < 0.001) and third (all p < 0.05) waves among deceased patients. Conclusions: This study yields insights into COVID-19 patients' characteristics and mortality-associated biomarkers during Portugal's first three COVID-19 waves, highlighting the importance of considering wave variations in future research due to potential significant outcome differences.

Plasma versus Serum Analysis by FTIR Spectroscopy to Capture the Human Physiological State.

Fourier Transform InfraRed spectroscopy of serum and plasma has been highly explored for medical diagnosis, due to its general simplicity, and high sensitivity and specificity. To evaluate the plasma and serum molecular fingerprint, as obtained by FTIR spectroscopy, to acquire the system metabolic state, serum and plasma spectra were compared to characterize the metabolic state of 30 human volunteers, between 90 days consumption of green tea extract rich in Epigallocatechin-3-gallate (EGCG). Both plasma and serum spectra enabled the high impact of EGCG consumption on the biofluid spectra to be observed, as analyzed by the spectra principal component analysis, hierarchical-cluster analysis, and univariate data analysis. Plasma spectra resulted in the prediction of EGCG consumption with a slightly higher specificity, accuracy, and precision, also pointing to a higher number of significant spectral bands that were different between the 90 days period. Despite this, the lipid regions of the serum spectra were more affected by EGCG consumption than the corresponding plasma spectra. Therefore, in general, if no specific compound analysis is highlighted, plasma is in general the advised biofluid to capture by FTIR spectroscopy the general metabolic state. If the lipid content of the biofluid is relevant, serum spectra could present some advantages over plasma spectra.

Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection.

Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume, proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the 78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Proteomics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclinical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.

Infection Biomarkers Based on Metabolomics.

Current infection biomarkers are highly limited since they have low capability to predict infection in the presence of confounding processes such as in non-infectious inflammatory processes, low capability to predict disease outcomes and have limited applications to guide and evaluate therapeutic regimes. Therefore, it is critical to discover and develop new and effective clinical infection biomarkers, especially applicable in patients at risk of developing severe illness and critically ill patients. Ideal biomarkers would effectively help physicians with better patient management, leading to a decrease of severe outcomes, personalize therapies, minimize antibiotics overuse and hospitalization time, and significantly improve patient survival. Metabolomics, by providing a direct insight into the functional metabolic outcome of an organism, presents a highly appealing strategy to discover these biomarkers. The present work reviews the desired main characteristics of infection biomarkers, the main metabolomics strategies to discover these biomarkers and the next steps for developing the area towards effective clinical biomarkers.

A new method to predict genotoxic effects based on serum molecular profile.

It is critical to develop new methods to assess genotoxic effects in human biomonitoring since the conventional methods are usually laborious, time-consuming, and expensive. It is aimed to evaluate if the analysis of a drop of serum by Fourier Transform Infrared spectroscopy, allow to assess genotoxic effects in occupational exposure to cytostatic drugs in hospital professionals, as obtained by the lymphocyte cytokinesis-block micronucleus assay. It was considered peripheral blood from hospital professionals exposed to cytostatic drugs (n = 22) and from a non-exposed group (n = 36). It was observed that workers occupationally exposed presented a higher number of micronuclei (p < 0.05) in lymphocytes, in relation to the non-exposed group. The serum Fourier Transform Infrared spectra from exposed workers presented diverse different peaks (p < 0.01) in relation to the non-exposed group. The hierarchical cluster analysis of serum spectra separated serum samples of the exposed group from the non-exposed group with 61% sensitivity and 88% specificity. A support vector machine model of serum spectra enables to predict exposure with high accuracy (0.91), precision (0.89), sensitivity (0.86), F1 score (0.87) and AUC (0.96). Therefore, Fourier Transform Infrared spectroscopic analysis of a drop of serum enabled to predict in a rapid and simple mode the genotoxic effects of cytostatic drugs. The method presents therefore potential for high-dimension screening of exposure of genotoxic substances, due to its simplicity and rapid setup mode.

A Simple, Label-Free, and High-Throughput Method to Evaluate the Epigallocatechin-3-Gallate Impact in Plasma Molecular Profile.

Epigallocatechin-3-gallate (EGCG), the major catechin present in green tea, presents diverse appealing biological activities, such as antioxidative, anti-inflammatory, antimicrobial, and antiviral activities, among others. The present work evaluated the impact in the molecular profile of human plasma from daily consumption of 225 mg of EGCG for 90 days. Plasma from peripheral blood was collected from 30 healthy human volunteers and analyzed by high-throughput Fourier transform infrared spectroscopy. To capture the biochemical information while minimizing the interference of physical phenomena, several combinations of spectra pre-processing methods were evaluated by principal component analysis. The pre-processing method that led to the best class separation, that is, between the plasma spectral data collected at the beginning and after the 90 days, was a combination of atmospheric correction with a second derivative spectra. A hierarchical cluster analysis of second derivative spectra also highlighted the fact that plasma acquired before EGCG consumption presented a distinct molecular profile after the 90 days of EGCG consumption. It was also possible by partial least squares regression discriminant analysis to correctly predict all unlabeled plasma samples (not used for model construction) at both timeframes. We observed that the similarity in composition among the plasma samples was higher in samples collected after EGCG consumption when compared with the samples taken prior to EGCG consumption. Diverse negative peaks of the normalized second derivative spectra, associated with lipid and protein regions, were significantly affected (p < 0.001) by EGCG consumption, according to the impact of EGCG consumption on the patients' blood, low density and high density lipoproteins ratio. In conclusion, a single bolus dose of 225 mg of EGCG, ingested throughout a period of 90 days, drastically affected plasma molecular composition in all participants, which raises awareness regarding prolonged human exposure to EGCG. Because the analysis was conducted in a high-throughput, label-free, and economic analysis, it could be applied to high-dimension molecular epidemiological studies to further promote the understanding of the effect of bio-compound consumption mode and frequency.

Proposta de um protótipo de aplicativo Android para diagnósticos de enfermagem utilizando redes neurais artificiais / Propuesta de un protótipo de aplicativo Androide para diagnósticos de enfermería utilizando redes neuronales artificiales / A Proposal of an Android Application Prototype for Nursing Diagnoses Using Artificial Neural Networks

Introdução: A Sistematização da Assistência de Enfermagem deve ser implementada, principalmente nos quais há um nível de cuidado mais avançado com os pacientes, a exemplo das Unidades de Terapia Intensiva que são reconhecidamente locais nos quais se concentram grande especialização e tecnologias. Objetivo: Propor um modelo de um Sistema de Apoio à Decisão utilizando Redes Neurais Artificiais para a elaboração de Diagnósticos de Enfermagem através de um aplicativo para Android. Métodos: O presente estudo se caracteriza como metodológico e tecnológico do tipo prototipagem, no qual onde serão analisados os sinais vitais de pacientes internados em uma Unidade de Terapia Intensiva. Os dados serão obtidos a partir do banco de dados Monitoramento Inteligente Multiparâmetro em Terapia Intensiva que contém sinais fisiológicos e séries de sinais vitais capturados de monitores de pacientes, obtidos de sistemas de informações médicas hospitalares de milhares de pacientes em unidades de terapia intensiva. Resultados: O aplicativo, em fase final de implementação, está projetado com telas ativas trabalhadas junto com corpo de profissionais de enfermagem que opinaram sobre utilidades desejadas e primeiras impressões. Conclusões: No presente momento, os testes para o treinamento da Rede Neural Artificial estão acontecendo, e espera-se o uso de um aplicativo para a promoção dos diagnósticos de enfermagem advindo dos sinais vitais de pacientes, das avaliações sobre o estado geral, e informações do prontuário eletrônico do paciente, juntamente com o julgamento clínico e crítico do profissional enfermeiro(AU)

Introducción: La sistematización de la atención de enfermería debe ser implementada, especialmente en el caso de que haya un nivel más avanzado de atención con pacientes, como en las unidades de cuidados intensivos, que son lugares reconocidos donde se concentran gran experiencia y tecnologías. Objetivo: Proponer un modelo de un Sistema de Apoyo a la Decisión utilizando redes neuronales artificiales para la elaboración de diagnósticos de enfermería a través de una aplicación de Androide. Métodos: Este estudio se caracteriza por ser un tipo de prototipo metodológico y tecnológico en el que se analizarán los signos vitales de los pacientes ingresados en una unidad de cuidados intensivos. Los datos se obtendrán de la base de datos de Monitoreo Inteligente de Parámetros Intensivos de Cuidados Intensivos, que contiene señales fisiológicas y series de signos vitales capturados de monitores de pacientes, obtenidos de los sistemas de información médica hospitalaria de miles de pacientes en unidades de cuidados intensivos. Resultados: La aplicación, en su fase final de implementación, está diseñada con pantallas activas trabajadas junto con un cuerpo de profesionales de enfermería que dieron su opinión sobre las utilidades deseadas y las primeras impresiones. Conclusiones: En este momento, se están realizando pruebas para la capacitación de la Red Neural Artificial, y se espera utilizar una aplicación para promover diagnósticos de enfermería a partir de signos vitales del paciente, evaluaciones generales de salud e información del historial médico electrónico del paciente, junto con el juicio clínico y crítico de la enfermera profesional(AU)

Introduction: Systematization of nursing care must be implemented, especially in the case that there is a more advanced level of patient care, such as in intensive care units, which are recognized places where great experience and technologies are concentrated. Objective: To propose a model of a decision support system using artificial neural networks for the elaboration of nursing diagnoses through an Android application. Methods: This study is characterized by being a type of methodological and technological prototype in which the vital signs of patients admitted to an intensive care unit will be analyzed. The data will be obtained from the database of Smart Monitoring of Intensive Care Parameters, which contains physiological signals and vital sign series captured from patient monitors, and which are obtained from hospital medical information systems of thousands of patients in intensive care units. Results: The application, in its final phase of implementation, is designed with active screens worked together by a body of nursing professionals who gave their opinion on the desired benefits and first impressions. Conclusions: At this time, tests are being carried out to train the artificial neural network, and an application is expected to be used for promoting nursing diagnoses based on the patient's vital signs, general health evaluations, and information on the patient's electronic medical history, together with the clinical and critical judgment of the professional nurse(AU)

Poisoning of sheep by Vernonia plantaginoides (Less.) Hieron in Uruguay.

Vernonia plantaginoides (Less.) Hieron, previously known as Vernonia squarrosa, is a rhizomatous subshrub with purple flowers that is prevalent in the natural grassland of Uruguay, Argentina, and southern Brazil. We report an outbreak of V. plantaginoides (yuyo moro) intoxication in sheep in Treinta y Tres Department, northeastern Uruguay. A total of 54 of 463 (12%) recently weaned lambs died 2-7 days after entering a natural pasture that had been invaded by sprouting V. plantaginoides The first cases were found dead. Affected lambs showed marked jaundice, edema of the face, ears, and eyelids, and severe photodermatitis. At the autopsies of 3 lambs, the carcass was yellow, the liver was enlarged with a marked acinar pattern ("nutmeg liver"), and hemorrhages were observed on serous membranes. Microscopic lesions were characterized by diffuse periacinar hepatocellular necrosis and cholemic nephrosis. Three female lambs were experimentally dosed with the aerial parts of V. plantaginoides collected immediately after the outbreak. The lamb that was dosed once with 40 g/kg body weight died after 36 h with severe hepatic necrosis. The lamb dosed with 20 g/kg daily for 4 days showed clinical signs and microscopic lesions in the liver with multiple apoptotic hepatocytes in the periacinar zone. The third lamb, dosed with 30, 17, and 15 g/kg daily over 3 days, respectively, showed transient clinical signs and a rise in liver enzymes, but recovered, and no lesions were found postmortem. These results demonstrate that V. plantaginoides was responsible for severe field outbreaks of poisoning in sheep in Uruguay.

Musicians' working memory for tones, words, and pseudowords.

Studies investigating factors that influence tone recognition generally use recognition tests, whereas the majority of the studies on verbal material use self-generated responses in the form of serial recall tests. In the present study we intended to investigate whether tonal and verbal materials share the same cognitive mechanisms, by presenting an experimental instrument that evaluates short-term and working memories for tones, using self-generated sung responses that may be compared to verbal tests. This paradigm was designed according to the same structure of the forward and backward digit span tests, but using digits, pseudowords, and tones as stimuli. The profile of amateur singers and professional singers in these tests was compared in forward and backward digit, pseudoword, tone, and contour spans. In addition, an absolute pitch experimental group was included, in order to observe the possible use of verbal labels in tone memorization tasks. In general, we observed that musical schooling has a slight positive influence on the recall of tones, as opposed to verbal material, which is not influenced by musical schooling. Furthermore, the ability to reproduce melodic contours (up and down patterns) is generally higher than the ability to reproduce exact tone sequences. However, backward spans were lower than forward spans for all stimuli (digits, pseudowords, tones, contour). Curiously, backward spans were disproportionately lower for tones than for verbal material-that is, the requirement to recall sequences in backward rather than forward order seems to differentially affect tonal stimuli. This difference does not vary according to musical expertise.

Descentralizaçäo e gestäo dos serviços de saúde: uma avaliaçäo do SUS. 2§ relatório / Descentralization and health services management: an evaluation of the Unified Health System (SUS). 2nd report

Analisa as formas de encaminhamento, pelas Secretarias Estaduais, das questöes relativas à municipalizaçäo dos serviços de saúde sob o Sistema Unico de Saúde (SUS)

Descentralizaçäo e gestäo dos serviços de saúde: uma avaliaçäo do Sistema Unico de Saúde. 1§ relatório / Descentralization and health services management: an evaluation of the Unified Health System (SUS). 1st report

Analisa a descentralizaçäo e gestäo nos Estados da Regiào Sudeste do SUDS ao SUS e complementa com a avaliaçäo do processo de municipalizaçäo e condiçöes para implantaçäo de distritos sanitários em localidades rurais e urbana no Estado do Rio de Janeiro