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Glioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The grading of these tumors from image information is a far from trivial task for radiologists and one in which they could be assisted by machine-learning-based decision support. However, the machine learning analytical pipeline is also fraught with perils stemming from different sources, such as inadvertent data leakage, adequacy of 2D image sampling, or classifier assessment biases. In this paper, we analyze a glioma database sourced from multiple datasets using a simple classifier, aiming to obtain a reliable tumor grading and, on the way, we provide a few guidelines to ensure such reliability. Our results reveal that by focusing on the tumor region of interest and using data augmentation techniques we significantly enhanced the accuracy and confidence in tumor classifications. Evaluation on an independent test set resulted in an AUC-ROC of 0.932 in the discrimination of low-grade gliomas from high-grade gliomas, and an AUC-ROC of 0.893 in the classification of grades 2, 3, and 4. The study also highlights the importance of providing, beyond generic classification performance, measures of how reliable and trustworthy the model’s output is, thus assessing the model’s certainty and robustness.

In this chapter, we present BECOME (Behavior Change recOMender systEm), a modular Recommender System built to cope with issues like personalization, adaptation, and delivery of contents pertinently designed to solve idiosyncrasies of various topics in the healthcare field. The main objective is to empower citizens or patients to make informed decisions to improve their health condition. It deals with a double-edged personalization process as one of the key aspects to fostering self-empowerment: content dynamically personalized and adapted as new information is gathered and flexibility in the strategies and timings of the delivery. Thus, we take personalization one step further by not only tailoring the content, which is the standard customization strategy, but also adapting its timings and complexity in a dynamic manner while dealing with the feeling of having an entity (the coach) behind, ready to help. To show the modularity of the system and the diverse ways of interaction, different studies representing various use cases are presented.

Decrements in metabolic health elevate disease risk, including type 2 diabetes, heart disease, and certain cancers. Thus, treatment strategies to combat metabolic dysfunction are needed. Reduced ESR1 (estrogen receptor, ERα) expression is observed in muscle from women, men, and animals presenting clinical features of the metabolic syndrome. Human studies of natural expression of ESR1 in metabolic tissues show that muscle expression of ESR1 is positively correlated with markers of metabolic health, including insulin sensitivity. Herein, we highlight the important impact of ERα on mitochondrial form and function and present how these actions of the receptor govern metabolic homeostasis. Studies identifying ERα-regulated pathways for disease prevention will lay the foundation for the design of novel therapeutics to improve the health of women while limiting secondary complications that have plagued traditional hormone replacement interventions.

Objective Social behavior (e.g., loneliness, isolation) has been indicated as an important risk factor for cardiovascular disease. Recent studies show that Type D personality might be an important predictor of social behavior. Hence, the current exploratory study aims to examine, using ecological assessment, whether Type D personality is associated with a lower likelihood to engage in social encounters in patients with cardiovascular disease. Method Cardiac patients who participated in the Do CHANGE (Phase 2) trial were included in current analysis. As part of the Do CHANGE intervention, real-life data were collected in the intervention group using the MOVES app, which was installed on patients' mobile phones. For a period of 6 months, Global Positioning System (GPS) data from the participating patients were collected. From the GPS data, 3 target variables were computed: (a) general activity level, (b) social variety, and (c) social opportunity. Results A total of 70 patients were included in the analysis. Patients with a Type D personality had lower scores on the "social opportunity" variable compared to non-Type D patients (F = 6.72; p = .01). Type D personality was associated with lower social participation after adjusting for depression and anxiety. No association between Type D personality and general activity or behavioral variety was observed. Conclusions This is the first study to use an ecological measure to assess social behavior of cardiac patients with a Type D personality. Results show that Type D personality might be associated with lower social engagement, which could, in turn, partly explain its association with adverse health outcomes. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Endotype; Organ dysfunction; Sepsis Endotipo; Disfunción de órganos; Sepsis Endotip; Disfunció d'òrgans; Sèpsia Sepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support. However, specific subgroups of patients may benefit from a personalized approach with interventions targeted towards specific pathophysiological mechanisms. Herein, we will review the framework for identifying subpopulations of patients with sepsis, septic shock, and multiorgan dysfunction who may benefit from specific therapies. Some of these approaches are still in the early stages of research, while others are already in routine use in clinical practice, but together will help in the effective generation and safe implementation of precision medicine in sepsis.

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction., SCOPUS: ar.j, info:eu-repo/semantics/published

Background & Aims Besides their physiological role in bile formation and fat digestion, bile acids (BAs) synthesised from cholesterol in hepatocytes act as signalling molecules that modulate hepatocellular carcinoma (HCC). Trafficking of cholesterol to mitochondria through steroidogenic acute regulatory protein 1 (STARD1) is the rate-limiting step in the alternative pathway of BA generation, the physiological relevance of which is not well understood. Moreover, the specific contribution of the STARD1-dependent BA synthesis pathway to HCC has not been previously explored. Methods STARD1 expression was analyzed in a cohort of human non-alcoholic steatohepatitis (NASH)-derived HCC specimens. Experimental NASH-driven HCC models included MUP-uPA mice fed a high-fat high-cholesterol (HFHC) diet and diethylnitrosamine (DEN) treatment in wild-type (WT) mice fed a HFHC diet. Molecular species of BAs and oxysterols were analyzed by mass spectrometry. Effects of NASH-derived BA profiles were investigated in tumour-initiated stem-like cells (TICs) and primary mouse hepatocytes (PMHs). Results Patients with NASH-associated HCC exhibited increased hepatic expression of STARD1 and an enhanced BA pool. Using NASH-driven HCC models, STARD1 overexpression in WT mice increased liver tumour multiplicity, whereas hepatocyte-specific STARD1 deletion (Stard1ΔHep) in WT or MUP-uPA mice reduced tumour burden. These findings mirrored the levels of unconjugated primary BAs, β-muricholic acid and cholic acid, and their tauroconjugates in STARD1-overexpressing and Stard1ΔHep mice. Incubation of TICs or PMHs with a mix of BAs mimicking this profile stimulated expression of genes involved in pluripotency, stemness and inflammation. Conclusions The study reveals a previously unrecognised role of STARD1 in HCC pathogenesis, wherein it promotes the synthesis of primary BAs through the mitochondrial pathway, the products of which act in TICs to stimulate self-renewal, stemness and inflammation. Lay summary Effective therapy for hepatocellular carcinoma (HCC) is limited because of our incomplete understanding of its pathogenesis. The contribution of the alternative pathway of bile acid (BA) synthesis to HCC development is unknown. We uncover a key role for steroidogenic acute regulatory protein 1 (STARD1) in non-alcoholic steatohepatitis-driven HCC, wherein it stimulates the generation of BAs in the mitochondrial acidic pathway, the products of which stimulate hepatocyte pluripotency and self-renewal, as well as inflammation., We acknowledge support from grants PID2019-111669RB-100, SAF2017-85877R and SAF2015-73579-JIN from Plan Nacional de I+D funded by the Agencia Estatal de Investigación (AEI), the Fondo Europeo de Desarrollo Regional (FEDER) and CIBEREHD; the center grant P50AA011999 Southern California Research Center for ALPD and Cirrhosis funded by NIAAA / NIH; as well as support from AGAUR of the Generalitat de Catalunya SGR-2017-1112, European Cooperation in Science & Technology (COST) ACTION CA17112 Prospective European Drug-Induced Liver Injury Network, the ‘ER stress-mitochondrial cholesterol axis in obesity-associated insulin resistance and comorbidities’-Ayudas FUNDACION BBVA and the Red Nacional 2018-102799-T de Enfermedades Metabólicas y Cáncer, and Project 201916/31 "Contribution of mitochondrial oxysterol and bile acid metabolism to liver carcinogenesis" 2019 by Fundació Marato TV3. We also acknowledge the support from the Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III, Spain (PI16/00598, co-funded by European Regional Development Fund / European Social Fund, ‘Investing in your future’) and Centro Internacional sobre el Envejecimiento (OLD-HEPAMARKER, 0348_CIE_6_E), Spain. We also acknowledge support from R01 CA2344128 and U01 AA022614 grants to M.K.

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease.

The association of nonalcoholic steatohepatitis (NASH) with obesity and type 2 diabetes is a major determinant factor for the continued rise of NASH-driven HCC. Unfortunately, the mechanisms underlying the progression from NASH to HCC are not well-understood. Steatosis is characterized by the accumulation of different lipid species, and cholesterol has emerged as an important player in NASH development, which has been shown to promote NASH-driven HCC. However, recent findings indicated a tumor suppressor role of cholesterol in liver carcinogenesis and HCC development. Thus, we examined the contribution of hepatic steatosis with or without cholesterol accumulation induced by dietary or genetic approaches in liver tumorigenesis and whether the role of cholesterol in NASH-driven HCC is species-dependent. While diethylnitrosamine (DEN) treatment to rats or mice fed a choline-deficient diet decreased the hepatic steatosis, feeding an atherogenic diet enriched in cholesterol potentiated the liver tumor markers. Similar effects were observed in DEN-treated transgenic SREBP-2 mice but not wild-type (WT) mice fed a regular chow diet. Remarkably, long-term feeding of a high-fat high-cholesterol diet (HFHC) but not a high-fat diet (HFD) to WT mice caused severe NASH with spontaneous progression to HCC. A similar outcome was observed in MUP-uPA transgenic mice fed a HFHC diet, which resulted in increased liver tumors and expression of the genes involved in the immune checkpoints. Ezetimibe treatment ameliorated chronic liver disease and, more importantly, tumor multiplicity in HFHC-fed MUP-uPA mice or DEN-treated WT mice. Thus, these results revealed a differential role of steatosis and cholesterol in NASH-driven HCC and indicated that the tumor-promoter role of cholesterol is species-independent and associated with impaired immunosurveillance, We acknowledge support from grants PID2019-111669RB, SAF2017-85877R, and SAF2015- 73579-JIN from Plan Nacional de I+D funded by the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER) and from the CIBEREHD and the center grant P50AA011999 Southern California Research Center for ALPD and Cirrhosis funded by NIAAA/NIH, as well as support from AGAUR of the Generalitat de Catalunya SGR-2017-1112, European Cooperation in Science & Technology (COST) ACTION CA17112 Prospective European Drug-Induced Liver Injury Network, the “ER stress-mitochondrial cholesterol axis in obesity-associated insulin resistance and comorbidities” Ayudas FUNDACION BBVA, the Red Nacional 2018-102799-T de Enfermedades Metabólicas y Cáncer, and the Project 201916/31 Contribution of mitochondrial oxysterol and bile acid metabolism to liver carcinogenesis 2019 by Fundació Marato TV3.

Obesity is heightened during aging, and although the estrogen receptor α (ERα) has been implicated in the prevention of obesity, its molecular actions in adipocytes remain inadequately understood. Here, we show that adipose tissue ESR1/Esr1 expression inversely associated with adiposity and positively associated with genes involved in mitochondrial metabolism and markers of metabolic health in 700 Finnish men and 100 strains of inbred mice from the UCLA Hybrid Mouse Diversity Panel. To determine the anti-obesity actions of Erα in fat, we selectively deleted Esr1 from white and brown adipocytes in mice. In white adipose tissue, Esr1 controlled oxidative metabolism by restraining the targeted elimination of mitochondria via the E3 ubiquitin ligase parkin. mtDNA content was elevated, and adipose tissue mass was reduced in adipose-selective parkin knockout mice. In brown fat centrally involved in body temperature maintenance, Esr1 was requisite for both mitochondrial remodeling by dynamin-related protein 1 (Drp1) and uncoupled respiration thermogenesis by uncoupled protein 1 (Ucp1). In both white and brown fat of female mice and adipocytes in culture, mitochondrial dysfunction in the context of Esr1 deletion was paralleled by a reduction in the expression of the mtDNA polymerase γ subunit Polg1. We identified Polg1 as an ERα target gene by showing that ERα binds the Polg1 promoter to control its expression in 3T3L1 adipocytes. These findings support strategies leveraging ERα action on mitochondrial function in adipocytes to combat obesity and metabolic dysfunction.

Obesity is heightened during aging, and although the estrogen receptor α (ERα) has been implicated in the prevention of obesity, its molecular actions in adipocytes remain inadequately understood. Here, we show that adipose tissue ESR1/Esr1 expression inversely associated with adiposity and positively associated with genes involved in mitochondrial metabolism and markers of metabolic health in 700 Finnish men and 100 strains of inbred mice from the UCLA Hybrid Mouse Diversity Panel. To determine the anti-obesity actions of ERα in fat, we selectively deleted Esr1 from white and brown adipocytes in mice. In white adipose tissue, Esr1 controlled oxidative metabolism by restraining the targeted elimination of mitochondria via the E3 ubiquitin ligase parkin. mtDNA content was elevated, and adipose tissue mass was reduced in adipose-selective parkin knockout mice. In brown fat centrally involved in body temperature maintenance, Esr1 was requisite for both mitochondrial remodeling by dynamin-related protein 1 (Drp1) and uncoupled respiration thermogenesis by uncoupled protein 1 (Ucp1). In both white and brown fat of female mice and adipocytes in culture, mitochondrial dysfunction in the context of Esr1 deletion was paralleled by a reduction in the expression of the mtDNA polymerase γ subunit Polg1. We identified Polg1 as an ERα target gene by showing that ERα binds the Polg1 promoter to control its expression in 3T3L1 adipocytes. These findings support strategies leveraging ERα action on mitochondrial function in adipocytes to combat obesity and metabolic dysfunction.

Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging's (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged >= 50 years (mean 67, range 50-96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging.

Background and objective:An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tedious and time-consuming procedure and this represents a high obstacle in clinical practice. In this paper, we propose a novel Convolutional Neural Network for nodule segmentation that combines a light and efficient architecture with innovative loss function and segmentation strategy. Methods:In contrast to most of the standard end-to-end architectures for nodule segmentation, our network learns the context of the nodules by producing two masks representing all the background and secondary-important elements in the Computed Tomography scan. The nodule is detected by subtracting the context from the original scan image. Additionally, we introduce an asymmetric loss function that automatically compensates for potential errors in the nodule annotations. We trained and tested our Neural Network on the public LIDC-IDRI database, compared it with the state of the art and run a pseudo-Turing test between four radiologists and the network. Results:The results proved that the behaviour of the algorithm is very near to the human performance and its segmentation masks are almost indistinguishable from the ones made by the radiologists. Our method clearly outperforms the state of the art on CT nodule segmentation in terms of F1 score and IoU of 3.3 % and 4.7 % , respectively. Conclusions: The main structure of the network ensures all the properties of the UNet architecture, while the Multi Convolutional Layers give a more accurate pattern recognition. The newly adopted solutions also increase the details on the border of the nodule, even under the noisiest conditions. This method can be applied now for single CT slice nodule segmentation and it represents a starting point for the future development of a fully automatic 3D segmentation software.

The importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle- and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p < 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p < 0.001) and higher number of steps (F = 8.44, p < 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.

The incidence of chronic disease is elevated in women after menopause. Increased expression of ESR1 (the gene that encodes the estrogen receptor alpha, ERα) in muscle is highly associated with metabolic health and insulin sensitivity. Moreover, reduced muscle expression levels of ESR1 are observed in women, men, and animals presenting clinical features of the metabolic syndrome (MetSyn). Considering that metabolic dysfunction elevates chronic disease risk, including type 2 diabetes, heart disease, and certain cancers, treatment strategies to combat metabolic dysfunction and associated pathologies are desperately needed. This review will provide published work supporting a critical and protective role for skeletal muscle ERα in the regulation of mitochondrial function, metabolic homeostasis, and insulin action. We will provide evidence that muscle-selective targeting of ERα may be effective for the preservation of mitochondrial and metabolic health. Collectively published findings support a compelling role for ERα in the control of muscle metabolism via its regulation of mitochondrial function and quality control. Studies identifying ERα-regulated pathways essential for disease prevention will lay the important foundation for the design of novel therapeutics to improve metabolic health of women while limiting secondary complications that have historically plagued traditional hormone replacement interventions., This work was supported by grants from the National Institutes of Health (DK89109 and DK063491), the NIH Nuclear Receptor Signaling Atlas (NURSA NDSP, parent award U24DK097748), UCLA Department of Medicine, Iris Cantor-UCLA Women’s Health Research Foundation, and the UCLA Jonsson Comprehensive Cancer Center.

Background: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. Results: The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. Conclusion: We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

Background: The incidence of chronic disease is elevated in women after menopause. Natural variation in muscle expression of the estrogen receptor (ER)α is inversely associated with plasma insulin and adiposity. Moreover, reduced muscle ERα expression levels are observed in women and animals presenting clinical features of the metabolic syndrome (MetSyn). Considering that metabolic dysfunction impacts nearly a quarter of the U.S. adult population and elevates chronic disease risk including type 2 diabetes, heart disease, and certain cancers, treatment strategies to combat metabolic dysfunction and associated pathologies are desperately needed. Scope of the review: This review will provide evidence supporting a critical and protective role for skeletal muscle ERα in the regulation of metabolic homeostasis and insulin sensitivity, and propose novel ERα targets involved in the maintenance of metabolic health. Major conclusions: Studies identifying ERα-regulated pathways essential for disease prevention will lay the important foundation for the rational design of novel therapeutics to improve the metabolic health of women while limiting secondary complications that have plagued traditional hormone replacement interventions. Keywords: Estrogen action, Estrogen receptors, Insulin sensitivity, Metabolic homeostasis

Background and objective The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. Methods In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision. Results Lungs’ segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes. Conclusions The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation.

Background: Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made on the basis of signals from monitoring devices. Hemodynamic monitoring is essential in dialysis, surgery, and in critically ill patients. For the most severe patients, blood pressure is normally assessed through a catheter, which is an invasive procedure that may result in adverse effects. Blood pressure can also be monitored noninvasively through different methods and these data can be used for the continuous assessment of pressure using machine learning methods. Previous studies have found pulse transit time to be related to blood pressure. In this short paper, we propose to study the feasibility of implementing a data-driven model based on restricted Boltzmann machine artificial neural networks, delivering a first proof of concept for the validity and viability of a method for blood pressure prediction based on these models. Summary and Key Messages: For the most severe patients (e.g., dialysis, surgery, and the critically ill), blood pressure is normally assessed through invasive catheters. Alternatively, noninvasive methods have also been developed for its monitorization. Data obtained from noninvasive measurements can be used for the continuous assessment of pressure using machine learning methods. In this study, a restricted Boltzmann machine artificial neural network is used to present a first proof of concept for the validity and viability of a method for blood pressure prediction.

Background & Aims Acetaminophen (APAP) overdose is a major cause of acute liver failure (ALF). Mitochondrial SH3BP5 (also called SAB) and phosphorylation of c-Jun N-terminal kinase (JNK) mediate the hepatotoxic effects of APAP. We investigated the involvement of steroidogenic acute regulatory protein (STARD1), a mitochondrial cholesterol transporter, in this process and sensitization by valproic acid (VPA), which depletes glutathione and stimulates steroidogenesis. Methods Nonfasted C57BL/6J mice (control) and mice with liver-specific deletion of STARD1 (Stard1ΔHep), SAB (SabΔHep), or JNK1 and JNK2 (Jnk1+2ΔHep) were given VPA with or without APAP. Liver tissues were collected and analyzed by histology and immunohistochemistry and for APAP metabolism, endoplasmic reticulum (ER) stress, and mitochondrial function. Adult human hepatocytes were transplanted into Fah−/−/Rag2−/−/Il2rg−/−/NOD (FRGN) mice to create mice with humanized livers. Results Administration of VPA before administration of APAP increased the severity of liver damage in control mice. The combination of VPA and APAP increased expression of CYP2E1, formation of NAPQI-protein adducts, and depletion of glutathione from liver tissues of control mice, resulting in ER stress and the upregulation of STARD1. Livers from control mice given VPA and APAP accumulated cholesterol in the mitochondria and had sustained mitochondrial depletion of glutathione and mitochondrial dysfunction. Inhibition of ER stress, by administration of tauroursodeoxycholic acid to control mice, prevented upregulation of STARD1 in liver and protected the mice from hepatoxicity following administration of VPA and APAP. Administration of N-acetylcysteine to control mice prevented VPA- and APAP-induced ER stress and liver injury. Stard1ΔHep mice were resistant to induction of ALF by VPA and APAP, despite increased mitochondrial levels of glutathione and phosphorylated JNK; we made similar observations in fasted Stard1ΔHep mice given APAP alone. SabΔHep mice or Jnk1+2ΔHep mice did not develop ALF following administration of VPA and APAP. The ability of VPA to increase the severity of APAP-induced liver damage was observed in FRGN mice with humanized liver. Conclusions In studies of mice, we found that upregulation of STARD1 following ER stress mediates APAP hepatoxicity via SH3BP5 and phosphorylation of JNK1 and JNK2., We acknowledge the support from grants SAF2014–57674R, SAF-2015–69944R, and SAF2017–85877R from Plan Nacional de I+D, Spain, and by the CIBEREHD; the center grant P50AA011999 Southern California Research Center for ALPD and Cirrhosis funded by National Institute on Alcohol Abuse and Alcoholism/National Institutes of Health (NIH); the NIH R01DK06715 and the USC Research Center for Liver Diseases P30DK485522 Instrumentation (NK), as well as support from AGAUR of the Generalitat de Catalunya SGR-2017–1112, European Cooperation in Science & Technology (COST) ACTION CA17112 Prospective European Drug-Induced Liver Injury Network, and the Fundación BBVA.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient's quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HH

Background Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide lacking universally accepted therapies. Studies suggest that coffee consumption is associated with a reduced risk of NAFLD; however, molecular mechanisms and ingredients involved remain to be fully understood. Here, we determined the effects of regular intake of decaffeinated coffee on the development of NAFLD in mice, and molecular mechanisms involved. Methods Female C57BL/6J mice (n = 6–7/ group) were pair-fed either a liquid control diet (C) or fat-, fructose- and cholesterol-rich diet (FFC) +/- decaffeinated coffee (DeCaf, 6 g/kg BW) for 4 days or 6 weeks. Indices of liver damage, hepatic inflammation and parameters of insulin resistance and intestinal permeability as well as nitric oxide system were determined. Results Early signs of insulin resistance and non-alcoholic steatohepatitis (NASH) found after 6 weeks of FFC feeding were significantly lower in FFC+DeCaf-fed mice when compared to FFC-fed animals. Moreover, elevation of portal endotoxin levels and loss of tight junction proteins in proximal small intestine found in FFC-fed mice were significantly attenuated in FFC+DeCaf-fed animals. These beneficial effects of DeCaf were associated with a protection against the significant induction of inducible NO-synthase protein levels and 3-nitrotyrosine protein adducts found in proximal small intestine of FFC-fed mice. Similar protective effects of DeCaf were also found in mice fed the FFC diet short-term. Conclusion Our results suggest that protective effects of DeCaf on the development of NAFLD are at least in part related to maintaining intestinal barrier function., Graphical abstract fx1, Highlights • decaffeinated coffee protects mice from the development of NAFLD. • decaffeinated coffee attenuated increased translocation of bacterial endotoxins. • decaffeinated coffee prevents diet-induced induction of iNOS in small intestine.

Objective: To analyse skin temperature asymmetries in response to asymmetrical exercise demand in archers. Approach: The skin temperature of the trunk and upper limbs was measured in 30 archers with an infrared camera at three different moments: before (Pre), immediately after (Post), and 10 min after (Post10) a simulated competition (18 warm-up shots and 72 qualifying round shots). Relative and absolute asymmetries were determined. Stepwise multiple linear regressions were performed using the variations of relative and absolute skin temperature asymmetries as predicting variables, and sex, age, experience, body mass index, bow mass, bow power, and rate of perceived exertion of competition as inputs. Main results: Relative symmetry values were lower, i.e. more negative values indicating more asymmetry (higher skin temperature on the bow side, p < 0.05 and ES > 0.5) in the Post than in the Pre moment in the upper back (95% CI [0.1, 0.4 °C]), posterior shoulder (95% CI [0.0, 0.5 °C]), posterior arm (95% CI [0.0, 0.6 °C]) and posterior elbow (95% CI [0.4, 0.9 °C]). Absolute asymmetries were higher (p < 0.01 and ES > 0.8) in the Post than in the Pre moment in the chest (95% CI [0.1, 0.4 °C]), upper back (95% CI [0.1, 0.3 °C]), posterior shoulder (95% CI [0.1, 0.5 °C]) and posterior elbow (95% CI [0.2, 0.7 °C]). The variation of asymmetries after competition could be explained by factors such as experience, effort perception, sex, and bow mass. Significance: Archery exercise results in skin temperature asymmetry related to higher temperature in the bow side, which plays a main role in sustaining muscle activation to keep position. The asymmetry characteristics may result from the archery technique.

Estrogen receptor α (ERα) action plays an important role in pancreatic β-cell function and survival; thus, it is considered a potential therapeutic target for the treatment of type 2 diabetes in women. However, the mechanisms underlying the protective effects of ERα remain unclear. Because ERα regulates mitochondrial metabolism in other cell types, we hypothesized that ERα may act to preserve insulin secretion and promote β-cell survival by regulating mitochondrial-endoplasmic reticulum (EndoRetic) function. We tested this hypothesis using pancreatic islet-specific ERα knockout (PERαKO) mice and Min6 β-cells in culture with Esr1 knockdown (KD). We found that Esr1-KD promoted reactive oxygen species production that associated with reduced fission/fusion dynamics and impaired mitophagy. Electron microscopy showed mitochondrial enlargement and a pro-fusion phenotype. Mitochondrial cristae and endoplasmic reticulum were dilated in Esr1-KD compared with ERα replete Min6 β-cells. Increased expression of Oma1 and Chop was paralleled by increased oxygen consumption and apoptosis susceptibility in ERα-KD cells. In contrast, ERα overexpression and ligand activation reduced both Chop and Oma1 expression, likely by ERα binding to consensus estrogen-response element sites in the Oma1 and Chop promoters. Together, our findings suggest that ERα promotes β-cell survival and insulin secretion through maintenance of mitochondrial fission/fusion-mitophagy dynamics and EndoRetic function, in part by Oma1 and Chop repression.

Detailed description of evaluation metrics used in our experiments. Description of columns in Table 8. (DOCX 6 kb)

Women in the modern era are challenged with facing menopausal symptoms as well as heightened disease risk associated with increasing adiposity and metabolic dysfunction for up to three decades of life. Treatment strategies to combat metabolic dysfunction and associated pathologies have been hampered by our lack of understanding regarding the biological causes of these clinical conditions and our incomplete understanding regarding the effects of estrogens and the tissue-specific functions and molecular actions of its receptors. In this chapter we provide evidence supporting a critical and protective role for skeletal muscle estrogen receptor α in the maintenance of metabolic homeostasis and insulin sensitivity. Studies identifying the critical ER-regulated pathways essential for disease prevention will lay the important foundation for the rational design of novel therapeutic strategies to improve the health of women while limiting secondary complications that have plagued traditional hormone replacement interventions.

Obesity is associated with increased breast cancer (BrCA) incidence. Considering that inactivation of estrogen receptor (ER)α promotes obesity and metabolic dysfunction in women and female mice, understanding the mechanisms and tissue-specific sites of ERα action to combat metabolic-related disease, including BrCA, is of clinical importance. To study the role of ERα in adipose tissue we generated fat-specific ERα knock-out (FERKO) mice. Herein we show that ERα deletion increased adipocyte size, fat pad weight, and tissue expression and circulating levels of the secreted glycoprotein, lipocalin 2 (Lcn2), an adipokine previously associated with BrCA development. Chromatin immunoprecipitation and luciferase reporter studies showed that ERα binds the Lcn2 promoter to repress its expression. Because adipocytes constitute an important cell type of the breast microenvironment, we examined the impact of adipocyte ERα deletion on cancer cell behavior. Conditioned medium from ERα-null adipocytes and medium containing pure Lcn2 increased proliferation and migration of a subset of BrCA cells in culture. The proliferative and promigratory effects of ERα-deficient adipocyte-conditioned medium on BrCA cells was reversed by Lcn2 deletion. BrCA cell responsiveness to exogenous Lcn2 was heightened in cell types where endogenous Lcn2 expression was minimal, but components of the Lcn2 signaling pathway were enriched, i.e. SLC22A17 and 3-hydroxybutyrate dehydrogenase (BDH2). In breast tumor biopsies from women diagnosed with BrCA we found that BDH2 expression was positively associated with adiposity and circulating Lcn2 levels. Collectively these data suggest that reduction of ERα expression in adipose tissue promotes adiposity and is linked with the progression and severity of BrCA via increased adipocyte-specific Lcn2 production and enhanced tumor cell Lcn2 sensitivity.