244 research outputs found
Analgesia postoperatoria: l' epidurale e le altre tecniche
Background: il dolore è un’ esperienza negativa sia sensoriale che emotiva che
compromette la normale vita quotidiana della persona. Spesso il dolore acuto è causa di
traumi o interventi chirurgici.
Il dolore postoperatorio mette il soggetto in uno stato d’impotenza e di sudditanza fisica
e mentale inoltre può essere temuto più dello stesso intervento e dell’anestesia.
Il dolore acuto, se non trattato, diventa cronico con numerose complicanze a carico
dell’organismo. Dalla letteratura si evince come, nonostante il gran numero di studi
negli ultimi decenni, il problema è ancora sottostimato e dipende da molteplici fattori.
L’obiettivo della ricerca consiste nel cercare in letteratura tra le tecniche analgesiche se
esista un gold standard, mettendone in luce pregi e difetti, e confrontarlo con altre
tecniche equiparabili.
Metodi : lo studio si basa sulla ricerca nelle banche dati, in particolare in PubMed, di
tecniche e metodiche analgesiche, che mettano in comparazione il gold standard, nella
fattispecie l’analgesia epidurale, con altre tecniche altrettanto valide, mettendo in luce
dati su mortalitĂ , morbilitĂ e soddisfazione del paziente.
Risultati: dalla ricerca si evince come la tecnica epidurale nel corso degli ultimi
decenni si sia attestata come gold standard nel trattamento analgesico post operatorio.
Ciò nonostante, altre tecniche regionali si stanno affermando come buoni sostituti come
ad esempio la filtrazione continua della ferita, il blocco paravertebrale toracico e
l’analgesia controllata dal paziente.
Conclusioni: la ricerca ha analizzato prevalentemente i tassi di mortalitĂ e morbilitĂ nel
confronto di due tecniche alla volta. Queste non hanno previsto sempre le stesse
modalitĂ di inserimento cateteri; inoltre diversi farmaci usati e popolazione target non
sempre uguale. Appare comunque evidente la bontĂ analgesica di altre tecniche, diverse
dall’epidurale.ope
Imaging of temporomandibular joint: Approach by direct volume rendering
Materials and Methods: We have studied the temporom-andibular joint anatomy, directly on the living, from 3D images obtained by medical imaging Computed Tomography and Nuclear Magnetic Resonance acquisition, and subsequent re-engineering techniques 3D Surface Rendering and Volume Rendering. Data were analysed with the goal of being able to isolate, identify and distinguish the anatomical structures of the joint, and get the largest possible number of information utilizing software for post-processing work.Results: It was possible to reproduce anatomy of the skeletal structures, as well as through acquisitions of Magnetic Resonance Imaging; it was also possible to visualize the vascular, muscular, ligamentous and tendinous components of the articular complex, and also the capsule and the fibrous cartilaginous disc. We managed the Surface Rendering and Volume Rendering, not only to obtain three-dimensional images for colour and for resolution comparable to the usual anatomical preparations, but also a considerable number of anatomical, minuter details, zooming, rotating and cutting the same images with linking, graduating the colour, transparency and opacity from time to time.Conclusion: These results are encouraging to stimulate further studies in other anatomical districts.Background: The purpose of this study was to conduct a morphological analysis of the temporomandibular joint, a highly specialized synovial joint that permits movement and function of the mandible
Overexpression of melanocortin 2 receptor accessory protein 2 (MRAP2) in adult paraventricular MC4R neurons regulates energy intake and expenditure
The Medical Research Council UK (MRC/Academy of Medical
Sciences Clinician Scientist Fellowship Grant G0802796 (to LFC) and NIH grants R01
DK105571, DK097566 and DK107293 (to SD)
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes
Lately, Nonlinear Model Predictive Control (NMPC)has been successfully
applied to (semi-) autonomous driving problems and has proven to be a very
promising technique. However, accurate control models for real vehicles could
require costly and time-demanding specific measurements. To address this
problem, the exploitation of system data to complement or derive the prediction
model of the NMPC has been explored, employing learning dynamics approaches
within Learning-based NMPC (LbNMPC). Its application to the automotive field
has focused on discrete grey-box modeling, in which a nominal dynamics model is
enhanced by the data-driven component. In this manuscript, we present an LbNMPC
controller for a real go-kart based on a continuous black-box model of the
accelerations obtained by Gaussian Processes. We show the effectiveness of the
proposed approach by testing the controller on a real go-kart vehicle,
highlighting the approximation steps required to get an exploitable GP model on
a real-time application.Comment: Accepted in IEEE Transaction on Control System Technology as Full
Paper for SI: State-of-the-art Applications of Model Predictive Control. 12
pages, 20 figure
Cognitive processess and cognitive reserve in multiple sclerosis
Multiple Sclerosis (MS) is characterized by motor, cognitive, and neuropsychiatric symptoms, which can occur
independently.
While MS is traditionally considered an inflammatory disease of the white matter, degeneration of gray matter is increasingly
recognized as an important contributor to the progressive cognitive decline. A protective factor against the progression
of cognitive dysfunction in MS could be the cognitive reserve, defined as resistance to brain dysfunction.
Aim of the present study is to evaluate the role of cognitive reserve for different aspects of cognitive dysfunction
of patients with MS.
We found that patients with MS and lower cognitive reserve have poorer neuropsychological performance and
slower information speed processing.
These findings support the notion that intellectual reserve may protect some aspects of cognitive function in
patients with MS
An intelligent Medical Cyber-Physical System to support heart valve disease screening and diagnosis
Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary - normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber-Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors
Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks
Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model's development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model's development process, and how to describe model's global behavior based on SHAP's global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI
Embryological considerations on a case of coexistence of persistent left superior vena cava and partially left inferior vena cava
The persistent left superior vena cava (PLSVC) is the most common venous thoracic congenital anomaly. The PLSCV generally drains into the right atrium, which it reaches through a dilated coronary sinus. Its presence is usually unrecognized, until a venous approach is performed. Abnormalities of the inferior vena cava (IVC) are rare (0.2-0.3% of healthy subjects and 0.6-2% of patients with cardiovascular defects). A single left IVC (LIVC) is very rare (11.9% of all the abnormalities) [1]. To the best of our knowledge, the coexistence of PLSCV and LICV has not been previously described. We present a case of a 32-year-old woman on hemodialysis for more than 12 years. An angiography demonstrated both a normal right SVC and a PLSCV and a single IVC with a lower left course, an intermediate circumaortic ring and an upper normal right course. The double SVC can be consequent to the failed development of the anastomosis between the anterior cardinal veins and the patency of the caudal part of the left anterior cardinal vein forming the PLSCV. As to the partially LIVC, its iliac and subrenal parts can be the results of the persistence of the left supracardinal vein. The circumaortic venous ring might indicate that a persistent intersupracardinal anastomosis receiving the left and the right renal veins was maintained around the abdominal aorta [2], while the superior part represents the normal right subcardinal and hepatic derivatives. The existence of anomalies should be considered, as they can have important implications in invasive procedures such as venous catheter placement, and may represent a speculative bridge between clinicians and embryologists
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