Learning-based screening of endothelial dysfunction from photoplethysmographic signals

Endothelial-Dysfunction (ED) screening is of primary importance to early diagnosis cardiovascular diseases. Recently, approaches to ED screening are focusing more and more on photoplethysmography (PPG)-signal analysis, which is performed in a threshold-sensitive way and may not be suitable for tackling the high variability of PPG signals. The goal of this work was to present an innovative machine-learning (ML) approach to ED screening that could tackle such variability. Two research hypotheses guided this work: (H1) ML can support ED screening by classifying PPG features; and (H2) classification performance can be improved when including also anthropometric features. To investigate H1 and H2, a new dataset was built from 59 subject. The dataset is balanced in terms of subjects with and without ED. Support vector machine (SVM), random forest (RF) and k-nearest neighbors (KNN) classifiers were investigated for feature classification. With the leave-one-out evaluation protocol, the best classification results for H1 were obtained with SVM (accuracy = 71%, recall = 59%). When testing H2, the recall was further improved to 67%. Such results are a promising step for developing a novel and intelligent PPG device to assist clinicians in performing large scale and low cost ED screening

Sismómetro para el registro de la actividad volcánica. Diseño Electrónico.

El grupo de investigación de SARTI, en colaboración con el proyecto de investigación de “Peligro Volcánico y Evaluación del Riesgo en Tenerife (PEVERTE)”, precisa realizar el diseño electrónico de un dispositivo de adquisición de datos sísmicos en una zona de riesgo volcánico. Para ello se realizará el estudio de los componentes a utilizar para el diseño del dispositivo, seleccionando los que se adapten mejor a los requisitos de bajo consumo y bajo precio. Los datos adquiridos han de poder enviarse remotamente, a la vez de que han de almacenarse en una memoria externa. Se adquirirán datos provenientes de tres canales diferentes. A raíz de la selección de estos componentes se realizará el diseño del hardware del dispositivo, de las comunicaciones necesarias, y de los componentes extra que sean necesarios para el correcto funcionamiento. Se construirá un prototipo del dispositivo en el que realizar todas las pruebas. Luego se diseñara el software encargado de gestionar el funcionamiento de la aplicación, programada en lenguaje C. Una vez configurados los registros del microprocesador de manera adecuada, deberá gestionar la lectura de los datos con dos velocidades diferentes de adquisición (50 y 100 SPS), según la decisión del usuario, y poder enviar y/o almacenarlos en la memoria externa de tipo SD. También se gestionarán los diferentes tipos de comunicaciones (SPI y RS232). Finalmente, una vez la aplicación sea programada y probada, se testeará su funcionamiento, mediante aplicaciones hechas en LabView y Matlab. Para ello se comprobarán los resultados de diferentes adquisiciones hechas en el laboratorio a través de los instrumentos

MyDi application: Towards automatic activity annotation of young patients with Type 1 diabetes

Type I diabetes mellitus (T1DM) is a widespread metabolic disorder characterized by pancreatic insufficiency. People with T1DM require: a lifelong insulin injection, to constantly monitor glycemia and to take note of their activities. This continuous follow-up, especially at a very young age, may be challenging. Adolescents with T1DM may develop anxiety symptoms and depression which can lead to the loss of glycemic control. An assistive technology that automatizes the activity monitoring process could support these young patient in managing T1DM. The aim of this work is to present the MyDi framework which integrates a smart glycemic diary (for Android users), to automatically record and store patient's activity via pictures and a deep-learning (DL)-based technology able to classify the activity performed by the patients (i.e., meal and sport) via picture analysis. The proposed approach was tested on two different datasets, the Insta-Dataset with 3498 pictures (also used for training and validating the DL model) and the MyDi-Dataset with 126 pictures, achieving very encouraging results in both cases (Preci= 1.0, Reci= 1.0, f1i= 1.0 with i E C:[meal, sport]) prompting the possibility of translating this application in the T1DM monitoring process

A cloud-based healthcare infrastructure for neonatal intensive-care units

Intensive medical attention of preterm babies is crucial to avoid short-term and long- term complications. Within neonatal intensive care units (NICUs), cribs are equipped with electronic devices aimed at: monitoring, administering drugs and supporting clinician in making diagnosis and offer treatments. To manage this huge data flux, a cloud-based healthcare infrastructure that allows data collection from different devices (i.e., patient monitors, bilirubinometers, and transcutaneous bilirubinometers), storage, processing and transferring will be presented. Communication protocols were designed to enable the communication and data transfer between the three different devices and a unique database and an easy to use graphical user interface (GUI) was implemented. The infrastructure is currently used in the “Women’s and Children’s Hospital G.Salesi” in Ancona (Italy), supporting clinicians and health opertators in their daily activities

Reconstructive Options after Oncological Rhinectomy: State of the Art

Background: The nose is a central component of the face, and it is fundamental to an individual's recognition and attractiveness. The aim of this study is to present a review of the last twenty years literature on reconstructive techniques after oncological rhinectomy. Methods: Literature searches were conducted in the databases PubMed, Scopus, Medline and Google Scholar. "Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)" for scoping review was followed. Results: Seventeen articles regarding total rhinectomy reconstruction were finally identified in the English literature, with a total of 447 cases. The prostheses were the reconstructive choice in 213 (47.7%) patients, followed by local flaps in 172 (38.5%) and free flaps in 62 (13.8%). The forehead flap (FF) and the radial forearm free flap (RFFF) are the most frequently used flaps. Conclusions: This study shows that both prosthetic and surgical reconstruction are very suitable solutions in terms of surgical and aesthetic outcomes for the patient

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Objectives: Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. Methods: This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Results: Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). Discussion: This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant

Adjusted Tornado Probabilities

Tornado occurrence rates computed from the available reports are biased low relative to the unknown true rates. To correct for this low bias, the authors demonstrate a method to estimate the annual probability of being struck by a tornado that uses the average report density estimated as a function of distance from nearest city/town center. The method is demonstrated on Kansas and then applied to 15 other tornado-prone states from Nebraska to Tennessee. States are ranked according to their adjusted tornado rate and comparisons are made with raw rates published elsewhere. The adjusted rates, expressed as return periods, arestates, including Alabama, Mississippi, Arkansas, and Oklahoma. The expected annual number of people exposed to tornadoes is highest for Illinois followed by Alabama and Indiana. For the four states with the highest tornado rates, exposure increases since 1980 are largest for Oklahoma (24%) and Alabama (23%)

Prevention of depression and sleep disturbances in elderly with memory-problems by activation of the biological clock with light - a randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Depression frequently occurs in the elderly and in patients suffering from dementia. Its cause is largely unknown, but several studies point to a possible contribution of circadian rhythm disturbances. Post-mortem studies on aging, dementia and depression show impaired functioning of the suprachiasmatic nucleus (SCN) which is thought to be involved in the increased prevalence of day-night rhythm perturbations in these conditions. Bright light enhances neuronal activity in the SCN. Bright light therapy has beneficial effects on rhythms and mood in institutionalized moderate to advanced demented elderly. In spite of the fact that this is a potentially safe and inexpensive treatment option, no previous clinical trial evaluated the use of long-term daily light therapy to prevent worsening of sleep-wake rhythms and depressive symptoms in early to moderately demented home-dwelling elderly.</p> <p>Methods/Design</p> <p>This study investigates whether long-term daily bright light prevents worsening of sleep-wake rhythms and depressive symptoms in elderly people with memory complaints. Patients with early Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Subjective Memory Complaints (SMC), between the ages of 50 and 75, are included in a randomized double-blind placebo-controlled trial. For the duration of two years, patients are exposed to ~10,000 lux in the active condition or ~300 lux in the placebo condition, daily, for two half-hour sessions at fixed times in the morning and evening. Neuropsychological, behavioral, physiological and endocrine measures are assessed at baseline and follow-up every five to six months.</p> <p>Discussion</p> <p>If bright light therapy attenuates the worsening of sleep-wake rhythms and depressive symptoms, it will provide a measure that is easy to implement in the homes of elderly people with memory complaints, to complement treatments with cholinesterase inhibitors, sleep medication or anti-depressants or as a stand-alone treatment.</p> <p>Trial registration</p> <p>ISRCTN29863753</p

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries