Image-processing assisted characterization of spray injection systems

The objective of this work is to investigate the spray characteristics of a fuel injection nozzle. The analysis is performed by means of a framework which exploits different image processing techniques to provide spray-related data to the operator. Innovative metrics are introduced to increase the accuracy and efficiency of the scheme. Experimental results show that it is possible to automatically get useful information about the spray distribution, asymmetries and key properties together with the capability to measure significant angles and other information to detect anomalies in the injection system

A drone-based image processing system for car detection in a smart transport infrastructure

In this paper we present a car detection system prototyped within an experimental project. It analyzes video streams recorded by drones flying over an urban environment. The intended final goal is the automatic provision of helpful information, such as the available parking spaces and the level of congestion of the streets. The system has been tested both in a desktop PC and on an embedded system. The experimental results show a significant accuracy and prove the feasibility of novel on-board services

H.264 sensor aided video encoder for UAV BLOS missions

This paper presents a new low-complexity H.264 encoder, based on x264 implementation, for Unmanned Aerial Vehicles (UAV) applications. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions

Targeting CD20 in the treatment of interstitial lung diseases related to connective tissue diseases: A systematic review

INTRODUCTION: The effectiveness of CD20 targeting in connective tissue diseases (CTD) with lung involvement is controversial. This paper aims to review the current evidence about rituximab (RTX) use in CTD-related interstitial lung disease (ILD). METHODS: We performed a systematic review of papers published between January 2009 and May 2019. We included clinical trials, case/control studies and cohort studies. We excluded letters, case reports, case series, reviews, and full articles when not in English. The selected studies listed as primary or secondary outcome a variation in pulmonary function tests or in the scores used to radiologically stage lung involvement, in CTD-related ILD patients after RTX. RESULTS: Out of 1206 potentially eligible articles, 24 papers were selected: 3 retrospectively described cohorts of patients with different CTD, 14 dealt with systemic sclerosis (SSc)-related ILD, 5 with idiopathic inflammatory myopathies (IIMs)-related ILD, and 2 with Sjögren's Syndrome-related ILD. A direct comparison of the selected studies was hampered by their heterogeneity for outcomes, follow-up duration, the severity of lung involvement, and clinical features of study populations. However, an overall agreement existed concerning the effectiveness of RTX in the stabilization of lung disease, with some studies reporting an improvement of functional parameters from baseline. IIM-related ILD appeared more responsive than other CTD-related ILD to CD20 targeting. CONCLUSION: RTX is a promising therapeutic tool in CTD-related ILD. This systematic review remarks the unmet need of multicenter prospective studies aiming to evaluate the effectiveness of RTX with adequate sample size and study design

Are Baseline Levels of Gas6 and Soluble Mer Predictors of Mortality and Organ Damage in Patients with Sepsis? The Need-Speed Trial Database

Soluble tyrosine kinase receptor Mer (sMer) and its ligand Growth arrest-specific protein 6 (Gas6) are predictors of mortality in patients with sepsis. Our aim is to clarify whether their measurement at emergency department (ED) presentation is useful in risk stratification. We reanalyzed data from the Need-Speed trial, evaluating mortality and the presence of organ damage according to baseline levels of sMer and Gas6. 890 patients were eligible; no association with 7-and 30-day mortality was observed for both biomarkers (p > 0.05). sMer and Gas6 levels were significantly higher in acute kidney injury (AKI) patients compared to non-AKI ones (9.8 [4.1–17.8] vs. 7.9 [3.8–12.9] ng/mL and 34.8 [26.4–47.5] vs. 29.8 [22.1–41.6] ng/mL, respectively, for sMer and Gas6), and Gas6 also emerged as an independent AKI predictor (odds ratio (OR) 1.01 [1.00–1.02]). Both sMer and Gas6 independently predicted thrombocytopenia in sepsis patients not treated with anticoagulants (OR 1.01 [1.00–1.02] and 1.04 [1.02–1.06], respectively). Moreover, sMer was an independent predictor of both prothrombin time-international normalized ratio (PT-INR) > 1.4 (OR 1.03 [1.00–1.05]) and sepsis-induced coagulopathy (SIC) (OR 1.05 [1.02–1.07]). An early measurement of the sMer and Gas6 plasma concentration could not predict mortality. However, the biomarkers were associated with AKI, thrombocytopenia, PT-INR derangement and SIC, suggesting a role in predicting sepsis-related organ damage

Clinical decision modeling system

<p>Abstract</p> <p>Background</p> <p>Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified.</p> <p>Methods</p> <p>We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer.</p> <p>Results</p> <p>Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection.</p> <p>Conclusion</p> <p>The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.</p

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions

Radiation exposure from Chest CT: Issues and Strategies

Concerns have been raised over alleged overuse of CT scanning and inappropriate selection of scanning methods, all of which expose patients to unnecessary radiation. Thus, it is important to identify clinical situations in which techniques with lower radiation dose such as plain radiography or no radiation such as MRI and occasionally ultrasonography can be chosen over CT scanning. This article proposes the arguments for radiation dose reduction in CT scanning of the chest and discusses recommended practices and studies that address means of reducing radiation exposure associated with CT scanning of the chest