Both ghrelin deletion and unacylated ghrelin overexpression preserve muscles in aging mice

Sarcopenia, the decline in muscle mass and functionality during aging, might arise from age-associated endocrine dysfunction. Ghrelin is a hormone circulating in both acylated (AG) and unacylated (UnAG) forms with antiatrophic activity on skeletal muscle. Here, we show that not only lifelong overexpression of UnAG (Tg) in mice, but also the deletion of ghrelin gene (Ghrl KO) attenuated the age-associated muscle atrophy and functionality decline, as well as systemic inflammation. Yet, the aging of Tg and Ghrl KO mice occurs with different dynamics: while old Tg mice seem to preserve the characteristics of young animals, Ghrl KO mice features deteriorate with aging. However, young Ghrl KO mice show more favorable traits compared to WT animals that result, on the whole, in better performances in aged Ghrl KO animals. Treatment with pharmacological doses of UnAG improved muscle performance in old mice without modifying the feeding behavior, body weight, and adipose tissue mass. The antiatrophic effect on muscle mass did not correlate with modifications of protein catabolism. However, UnAG treatment induced a strong shift towards oxidative metabolism in muscle. Altogether, these data confirmed and expanded some of the previously reported findings and advocate for the design of UnAG analogs to treat sarcopenia

Frequency of left ventricular hypertrophy in non-valvular atrial fibrillation

Left ventricular hypertrophy (LVH) is significantly related to adverse clinical outcomes in patients at high risk of cardiovascular events. In patients with atrial fibrillation (AF), data on LVH, that is, prevalence and determinants, are inconsistent mainly because of different definitions and heterogeneity of study populations. We determined echocardiographic-based LVH prevalence and clinical factors independently associated with its development in a prospective cohort of patients with non-valvular (NV) AF. From the "Atrial Fibrillation Registry for Ankle-brachial Index Prevalence Assessment: Collaborative Italian Study" (ARAPACIS) population, 1,184 patients with NVAF (mean age 72 \ub1 11 years; 56% men) with complete data to define LVH were selected. ARAPACIS is a multicenter, observational, prospective, longitudinal on-going study designed to estimate prevalence of peripheral artery disease in patients with NVAF. We found a high prevalence of LVH (52%) in patients with NVAF. Compared to those without LVH, patients with AF with LVH were older and had a higher prevalence of hypertension, diabetes, and previous myocardial infarction (MI). A higher prevalence of ankle-brachial index 640.90 was seen in patients with LVH (22 vs 17%, p = 0.0392). Patients with LVH were at significantly higher thromboembolic risk, with CHA2DS2-VASc 652 seen in 93% of LVH and in 73% of patients without LVH (p <0.05). Women with LVH had a higher prevalence of concentric hypertrophy than men (46% vs 29%, p = 0.0003). Logistic regression analysis demonstrated that female gender (odds ratio [OR] 2.80, p <0.0001), age (OR 1.03 per year, p <0.001), hypertension (OR 2.30, p <0.001), diabetes (OR 1.62, p = 0.004), and previous MI (OR 1.96, p = 0.001) were independently associated with LVH. In conclusion, patients with NVAF have a high prevalence of LVH, which is related to female gender, older age, hypertension, and previous MI. These patients are at high thromboembolic risk and deserve a holistic approach to cardiovascular prevention

Adherence to antibiotic treatment guidelines and outcomes in the hospitalized elderly with different types of pneumonia

Background: Few studies evaluated the clinical outcomes of Community Acquired Pneumonia (CAP), Hospital-Acquired Pneumonia (HAP) and Health Care-Associated Pneumonia (HCAP) in relation to the adherence of antibiotic treatment to the guidelines of the Infectious Diseases Society of America (IDSA) and the American Thoracic Society (ATS) in hospitalized elderly people (65 years or older). Methods: Data were obtained from REPOSI, a prospective registry held in 87 Italian internal medicine and geriatric wards. Patients with a diagnosis of pneumonia (ICD-9 480-487) or prescribed with an antibiotic for pneumonia as indication were selected. The empirical antibiotic regimen was defined to be adherent to guidelines if concordant with the treatment regimens recommended by IDSA/ATS for CAP, HAP, and HCAP. Outcomes were assessed by logistic regression models. Results: A diagnosis of pneumonia was made in 317 patients. Only 38.8% of them received an empirical antibiotic regimen that was adherent to guidelines. However, no significant association was found between adherence to guidelines and outcomes. Having HAP, older age, and higher CIRS severity index were the main factors associated with in-hospital mortality. Conclusions: The adherence to antibiotic treatment guidelines was poor, particularly for HAP and HCAP, suggesting the need for more adherence to the optimal management of antibiotics in the elderly with pneumonia

Villa Adriana tra cielo e terra, Tesi di Laurea specialistica/magistrale U.E. in Architettura, Politecnico di Milano

Pubblicata in: S. Vazzana (a cura di), Progettare musei. Nuove esperienze di architettura, Stefanoni editrice, Lecco 2009 R. Neri (a cura di), Architettura Civile Giornale della Facoltà di Architettura Civile del Politecnico di Milano, Araba Fenice, Boves Cuneo 2009 n.2 Esposta in mostra nell'ambito della prima edizione del Premio Rotararch, Salone d'Onore della Triennale al Palazzo dell'Arte di Milano, 2-4 ottobre 2009 Esposta in mostra nell'ambito del Premio Mantero 2009, Scuola di Architettura, Politecnico di Milano 28 ottobre-18 novembre 2009 (menzione speciale

An undercomplete autoencoder to extract muscle synergies for motor intention detection

The growing interest in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Among several human-machine interaction approaches, myoelectric control consists in decoding the motor intention from muscular activity (or EMG signals) with the aim at moving the assistive robotic device accordingly, thus establishing an intimate human-machine connection. In this scenario, bio-inspired approaches, e.g. synergy-based controllers, are reveling to be the most robust.In this work, the authors presented an undercomplete autoencoder (AE) to extract muscles synergies for motion intention detection. The proposed AE topology has been validate with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented AE have shown promising results in muscle synergy extraction comparing its performance with the Non-Negative Matrix Factorization algorithm, i.e. the most used approach in literature. The synergy activations extracted with the AE have been then used for estimating the moment applied at the shoulder and elbow joints. Comparing such estimation with the results of other synergy-based techniques already proposed in literature, it emerged that the proposed method achieves comparable performance

An Innovative Neural Network Framework for Glomerulus Classification Based on Morphological and Texture Features Evaluated in Histological Images of Kidney Biopsy

Medical Imaging Computer Aided Diagnosis (CAD) systems could support physicians in several fields and recently are also applied in histopathology. The goal of this work is to design and test a novel CAD system module for the discrimination between glomeruli with a sclerotic and non-sclerotic condition, through the elaboration of histological images. The dataset was constituted by 26 kidney biopsies coming from 19 donors with Periodic Acid Schiff (PAS) staining. Preparation, digital acquisition and glomeruli annotations have been conducted by experts from the Department of Emergency and Organ Transplantation (DETO) of the University of Bari Aldo Moro (Italy). Starting from the annotated Regions Of Interest (ROIs), several feature extraction techniques were evaluated. Feature reduction and shallow artificial neural network were used for discriminating between the glomeruli classes. The mean and the best performances of the best ANN architecture were evaluated on an independent dataset. Metric comparison and analysis were performed to face the unbalanced dataset problem. Results on the test set asses that the proposed workflow, from the feature extraction to the supervised ANN approach, is consistent and reveals good performance in discriminating sclerotic and non-sclerotic glomeruli

A neural network for glomerulus classification based on histological images of kidney biopsy

Background: Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results: We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions: Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts