1,721,025 research outputs found
Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent
Full text linkArtificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists
Urinary metabolomics in newborns and infants
No full textMetabolomics is a new approach based on the systematic study of the full complement of metabolites in a biological sample. This technology consists of two sequential steps: (1) an experimental technique, based on nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry, designed to profile low-molecular-weight compounds, and (2) multivariate data analysis. The metabolomic analysis of biofluids or tissues has been successfully used in the fields of physiology, diagnostics, functional genomics, pharmacology, toxicology, and nutrition. Recent studies have evaluated how physiological variables or pathological conditions can affect metabolomic profiles of different biofluids in pediatric populations. The overall metabolic status of the neonate is little known. If more information on perinatal/neonatal maturational processes and their metabolic background were available, the management of sick or preterm newborns might be improved. Currently, the use of metabolomics in neonatology is still in the pioneering phase. Meaningful diagnostic information and simple, noninvasive collection techniques make urine a particularly suitable biofluid for metabolomic approach in neonatal medicine, although blood has also been investigated. Different fields of neonatology such as postnatal maturation, asphyxia/hypoxia, inborn errors of metabolism, nutrition, nephrouropathies, nephrotoxicity, cardiovascular diseases, and other conditions have been investigated using a metabolomic approach. Together with genomics and proteomics, metabolomics appears to be a promising tool in neonatology for the monitoring of postnatal metabolic maturation, the identification of biomarkers as early predictors of outcome, the diagnosis and monitoring of various diseases, and the "tailored" management of neonatal disorders
Clinical application of metabolomics in neonatology
No full textThe youngest and more rapidly increasing "omic" discipline, called metabolomics, is the process of describing the phenotype of a cell, tissue or organism through the full complement of metabolites present. Metabolomics measure global sets of low molecular weight metabolites (including amino acids, organic acids, sugars, fatty acids, lipids, steroids, small peptides, vitamins, etc.), thus providing a "snapshot" of the metabolic status of a cell, tissue or organism in relation to genetic variations or external stimuli. The use of metabolomics appears to be a promising tool in neonatology. The management of sick newborns might improve if more information on perinatal/neonatal maturational processes and their metabolic background were available. Urine ("a window on the organism") is a biofluid particularly suitable for metabolomic analysis in neonatology because it may be collected by using simple, noninvasive techniques and because it may provide valuable diagnostic information. In this review, the authors report the few literature data on neonatal metabolomics, including their personal experience, in the following fields: intrauterine growth restriction, perinatal transition, asphyxia, brain injury and hypothermia, maternal milk evaluation, postnatal maturation, bronchiolitis, sepsis, patent ductus arteriosus, respiratory distress syndrome, nephrouropathies, metabolic diseases, antibiotic treatment, perinatal programming and long-term outcome in extremely low birth-weight infants
Metabolomics: a new tool for the neonatologist
No full textMetabolomics enables the parallel assessment of the levels of a broad range of metabolites and has been shown to have a great impact in investigation of physiological status, diagnosing diseases, measuring the response to treatment, discovering biomarkers, identifying perturbed pathways due to disease or treatment, functional genomics. Common analytical techniques applied to metabolomics are nuclear magnetic resonance spectroscopy, gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. The most commonly used biological samples for metabolomics studies are urine, blood plasma or serum. Because of its characteristics and simple non-invasive methods of collection, urine is particularly suited for metabolomic analysis even in small babies. The use of non-invasive techniques is an essential requirement in neonatal medicine, especially in very preterm infants. Little is known about the overall metabolic status of the term and preterm neonate, but it can be currently assessed by metabolomic analysis of urine. Other important applications of metabolomic analysis of urine in the newborn could be the monitoring of postnatal metabolic maturation over time, the identification of biomarkers as early predictors of outcome, and the implementation and monitoring of a tailored management of neonatal disorders. The clinical management of neonates could be probably improved if more information about perinatal and neonatal maturation processes and their metabolic background were available. The metabolomics approach, together with transcriptomics and proteomics, will have substantial impact on development of diagnostics, therapeutics and drug development and may be an important new tool in neonatology
On the variability of functional connectivity and network measures in source-reconstructed eeg time-series
Full text linkThe idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable scenario. Even though this idea has developed beyond its initial stages, its practical application is still far away from being widespread. One concurrent cause may be related to the proliferation of different approaches that aim to catch the underlying statistical interdependence among the (interacting) units. This issue has probably con-tributed to hindering comparisons among different studies. Not only do all these approaches go under the same name (functional connectivity), but they have often been tested and validated using different methods, therefore, making it difficult to understand to what extent they are similar or not. In this study, we aim to compare a set of different approaches commonly used to estimate the functional connectivity on a public EEG dataset representing a possible realistic scenario. As expected, our results show that source-level EEG connectivity estimates and the derived network measures, even though pointing to the same direction, may display substantial dependency on the (often ar-bitrary) choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects the ambiguity and concern that should always be discussed when re-porting findings based on any connectivity metric
Commentary: Acute kidney injury after cardiac surgery—Is the “-omics” way the right way?
Full text linkAcid and alkaline phosphatase heterogeneity in liver, heart and intestine of the adult chick
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