Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients. OBJECTIVES: We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR. METHODS: We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini's coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN. RESULTS: Forced LR solutions provided AUC 87.9+/-4.1% (CI: 80.7 to 93.2%) and 85.7+/-5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5+/-3.7% (CI: 83.8 to 95.1%). The Gini's coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini's coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN. CONCLUSIONS: Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly

Effective Rheology of Bubbles Moving in a Capillary Tube

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train from considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.Comment: 4 pages, 1 figur

Sensitivity of the human auditory cortex to acoustic degradation of speech and non-speech sounds

The perception of speech is usually an effortless and reliable process even in highly adverse listening conditions. In addition to external sound sources, the intelligibility of speech can be reduced by degradation of the structure of speech signal itself, for example by digital compression of sound. This kind of distortion may be even more detrimental to speech intelligibility than external distortion, given that the auditory system will not be able to utilize sound source-specific acoustic features, such as spatial location, to separate the distortion from the speech signal. The perceptual consequences of acoustic distortions on speech intelligibility have been extensively studied. However, the cortical mechanisms of speech perception in adverse listening conditions are not well known at present, particularly in situations where the speech signal itself is distorted. The aim of this thesis was to investigate the cortical mechanisms underlying speech perception in conditions where speech is less intelligible due to external distortion or as a result of digital compression. In the studies of this thesis, the intelligibility of speech was varied either by digital compression or addition of stochastic noise. Cortical activity related to the speech stimuli was measured using magnetoencephalography (MEG). The results indicated that degradation of speech sounds by digital compression enhanced the evoked responses originating from the auditory cortex, whereas addition of stochastic noise did not modulate the cortical responses. Furthermore, it was shown that if the distortion was presented continuously in the background, the transient activity of auditory cortex was delayed. On the perceptual level, digital compression reduced the comprehensibility of speech more than additive stochastic noise. In addition, it was also demonstrated that prior knowledge of speech content enhanced the intelligibility of distorted speech substantially, and this perceptual change was associated with an increase in cortical activity within several regions adjacent to auditory cortex. In conclusion, the results of this thesis show that the auditory cortex is very sensitive to the acoustic features of the distortion, while at later processing stages, several cortical areas reflect the intelligibility of speech. These findings suggest that the auditory system rapidly adapts to the variability of the auditory environment, and can efficiently utilize previous knowledge of speech content in deciphering acoustically degraded speech signals.Puheen havaitseminen on useimmiten vaivatonta ja luotettavaa myös erittäin huonoissa kuunteluolosuhteissa. Puheen ymmärrettävyys voi kuitenkin heikentyä ympäristön häiriölähteiden lisäksi myös silloin, kun puhesignaalin rakennetta muutetaan esimerkiksi pakkaamalla digitaalista ääntä. Tällainen häiriö voi heikentää ymmärrettävyyttä jopa ulkoisia häiriöitä voimakkaammin, koska kuulojärjestelmä ei pysty hyödyntämään äänilähteen ominaisuuksia, kuten äänen tulosuuntaa, häiriön erottelemisessa puheesta. Akustisten häiriöiden vaikutuksia puheen havaitsemiseen on tutkttu laajalti, mutta havaitsemiseen liittyvät aivomekanismit tunnetaan edelleen melko puutteelisesti etenkin tilanteissa, joissa itse puhesignaali on laadultaan heikentynyt. Tämän väitöskirjan tavoitteena oli tutkia puheen havaitsemisen aivomekanismeja tilanteissa, joissa puhesignaali on vaikeammin ymmärrettävissä joko ulkoisen äänilähteen tai digitaalisen pakkauksen vuoksi. Väitöskirjan neljässä osatutkimuksessa lyhyiden puheäänien ja jatkuvan puheen ymmärrettävyyttä muokattiin joko digitaalisen pakkauksen kautta tai lisäämällä puhesignaaliin satunnaiskohinaa. Puheärsykkeisiin liittyvää aivotoimintaa tutkittiin magnetoenkefalografia-mittauksilla. Tutkimuksissa havaittiin, että kuuloaivokuorella syntyneet herätevasteet voimistuivat, kun puheääntä pakattiin digitaalisesti. Sen sijaan puheääniin lisätty satunnaiskohina ei vaikuttanut herätevasteisiin. Edelleen, mikäli puheäänien taustalla esitettiin jatkuvaa häiriötä, kuuloaivokuoren aktivoituminen viivästyi häiriön intensiteetin kasvaessa. Kuuntelukokeissa havaittiin, että digitaalinen pakkaus heikentää puheäänien ymmärrettävyyttä voimakkaammin kuin satunnaiskohina. Lisäksi osoitettiin, että aiempi tieto puheen sisällöstä paransi merkittävästi häiriöisen puheen ymmärrettävyyttä, mikä heijastui aivotoimintaan kuuloaivokuoren viereisillä aivoalueilla siten, että ymmärrettävä puhe aiheutti suuremman aktivaation kuin heikosti ymmärrettävä puhe. Väitöskirjan tulokset osoittavat, että kuuloaivokuori on erittäin herkkä puheäänien akustisille häiriöille, ja myöhemmissä prosessoinnin vaiheissa useat kuuloaivokuoren viereiset aivoalueet heijastavat puheen ymmärrettävyyttä. Tulosten mukaan voi olettaa, että kuulojärjestelmä mukautuu nopeasti ääniympäristön vaihteluihin muun muassa hyödyntämällä aiempaa tietoa puheen sisällöstä tulkitessaan häiriöistä puhesignaalia

Impact of pulmonary exposure to gold core silver nanoparticles of different size and capping agents on cardiovascular injury

Background:The uses of engineered nanomaterials have expanded in biomedical technology and consumer manufacturing. Furthermore, pulmonary exposure to various engineered nanomaterials has, likewise, demonstrated the ability to exacerbate cardiac ischemia reperfusion (I/R) injury. However, the influence of particle size or capping agent remains unclear. In an effort to address these influences we explored response to 2 different size gold core nanosilver particles (AgNP) with two different capping agents at 2 different time points. We hypothesized that a pulmonary exposure to AgNP induces cardiovascular toxicity influenced by inflammation and vascular dysfunction resulting in expansion of cardiac I/R Injury that is sensitive to particle size and the capping agent. Methods: Male Sprague–Dawley rats were exposed to 200 μg of 20 or 110 nm polyvinylprryolidone (PVP) or citrate capped AgNP. One and 7 days following intratracheal instillation serum was analyzed for concentrations of selected cytokines; cardiac I/R injury and isolated coronary artery and aorta segment were assessed for constrictor responses and endothelial dependent relaxation and endothelial independent nitric oxide dependent relaxation. Results: AgNP instillation resulted in modest increase in selected serum cytokines with elevations in IL-2, IL-18, and IL-6. Instillation resulted in a derangement of vascular responses to constrictors serotonin or phenylephrine, as well as endothelial dependent relaxations with acetylcholine or endothelial independent relaxations by sodium nitroprusside in a capping and size dependent manner. Exposure to both 20 and 110 nm AgNP resulted in exacerbation cardiac I/R injury 1 day following IT instillation independent of capping agent with 20 nm AgNP inducing marginally greater injury. Seven days following IT instillation the expansion of I/R injury persisted but the greatest injury was associated with exposure to 110 nm PVP capped AgNP resulted in nearly a two-fold larger infarct size compared to naïve. Conclusions: Exposure to AgNP may result in vascular dysfunction, a potentially maladaptive sensitization of the immune system to respond to a secondary insult (e.g., cardiac I/R) which may drive expansion of I/R injury at 1 and 7 days following IT instillation where the extent of injury could be correlated with capping agents and AgNP size.This work was supported by the National Institute of Environmental Health Sciences U19ES019525, U01ES020127, U19ES019544 and East Carolina Universit

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p