Statistical Complexity Analysis of Neurovascular Coupling with Cognitive Stimulation in Healthy Participants.

Neurovascular coupling (NVC) is the tight relationship between changes in cerebral blood flow and neural activation. NVC can be evaluated non-invasively using transcranial Doppler ultrasound (TCD)-measured changes in brain activation (cerebral blood velocity [CBv]) using different cognitive tasks and stimuli. This study used a novel approach to analyzing CBv changes occurring in response to 20 tasks from the Addenbrooke's Cognitive Examination III in 40 healthy individuals.

A multi-objective optimisation approach for the linear modelling of cerebral autoregulation system.

Objective: Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model.

Methods: Data from twenty-nine subjects under normo and hypercapnic (5% CO in air) conditions was used.

Multiscale entropy analysis of retinal signals reveals reduced complexity in a mouse model of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most significant health challenges of our time, affecting a growing number of the elderly population. In recent years, the retina has received increased attention as a candidate for AD biomarkers since it appears to manifest the pathological signatures of the disease. Therefore, its electrical activity may hint at AD-related physiological changes.

Machine Learning Models and Statistical Complexity to Analyze the Effects of Posture on Cerebral Hemodynamics.

The mechanism of cerebral blood flow autoregulation can be of great importance in diagnosing and controlling a diversity of cerebrovascular pathologies such as vascular dementia, brain injury, and neurodegenerative diseases. To assess it, there are several methods that use changing postures, such as sit-stand or squat-stand maneuvers. However, the evaluation of the dynamic cerebral blood flow autoregulation (dCA) in these postures has not been adequately studied using more complex models, such as non-linear ones.

Superior Fitting of Arterial Resistance and Compliance Parameters With Genetic Algorithms in Models of Dynamic Cerebral Autoregulation.

Objective: The capacity of discriminating between normal and impaired dynamic cerebral autoregulation (dCA), based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CBF), has considerable clinical relevance. This study aimed to quantify the separate contributions of vascular resistance and compliance as parameters that could reflect myogenic and metabolic mechanisms to dCA.

Methods: Forty-five subjects were studied under normo and hypercapnic conditions induced by breathing a mixture of 5% carbon dioxide in air.

Using complexity-entropy planes to detect Parkinson's disease from short segments of haemodynamic signals.

. There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available.

Analysis of cerebral blood flow entropy while listening to music with emotional content.

. Music is one of the most sublime stimuli that human beings can experience. Despite being just an acoustic wave that exerts little physical influence on a subject, it triggers profound changes in emotions and physiological states.

Retinal Ganglion Cells Functional Changes in a Mouse Model of Alzheimer's Disease Are Linked with Neurotransmitter Alterations.

Background: Alzheimer's disease (AD) is the most prevalent form of dementia worldwide. This neurodegenerative syndrome affects cognition, memory, behavior, and the visual system, particularly the retina.

Objective: This work aims to determine whether the 5xFAD mouse, a transgenic model of AD, displays changes in the function of retinal ganglion cells (RGCs) and if those alterations are correlated with changes in the expression of glutamate and gamma-aminobutyric acid (GABA) neurotransmitters.

Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods.

Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability.

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis.

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study.

Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies.

Comparing Models of Spontaneous Variations, Maneuvers and Indexes to Assess Dynamic Cerebral Autoregulation.

Objective: We analyzed the performance of linear and nonlinear models to assess dynamic cerebral autoregulation (dCA) from spontaneous variations in healthy subjects and compared it with the use of two known maneuvers to abruptly change arterial blood pressure (BP): thigh cuffs and sit-to-stand.

Materials And Methods: Cerebral blood flow velocity and BP were measured simultaneously at rest and while the maneuvers were performed in 20 healthy subjects. To analyze the spontaneous variations, we implemented two types of models using support vector machine (SVM): linear and nonlinear finite impulse response models.

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation.

A New Strategy to Evaluate Technical Efficiency in Hospitals Using Homogeneous Groups of Casemix : How to Evaluate When There is Not DRGs?

The public health system has restricted economic resources. Because of that, it is necessary to know how the resources are being used and if they are properly distributed. Several works have applied classical approaches based in Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for this purpose.

A new model-free index of dynamic cerebral blood flow autoregulation.

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free.

Laparoscopic treatment of obese patients with gastroesophageal reflux disease and Barrett's esophagus: a prospective study.

Background: Short-segment Barrett's esophagus (SSBE) or long-segment Barrett's esophagus (LSBE) is the consequence of chronic gastroesophageal reflux disease (GERD), which is frequently associated with obesity. Obesity is a significant risk factor for the development of GERD symptoms, erosive esophagitis, Barrett's esophagus, and esophageal adenocarcinoma. Morbidly obese patients who submitted to gastric bypass have an incidence of GERD as high as 50% to 100% and Barrett's esophagus reaches up to 9% of patients.

Non-invasive intracranial pressure estimation using support vector machine.

Intracranial Pressure (ICP) measurements are of great importance for the diagnosis, monitoring and treatment of many vascular brain disturbances. The standard measurement of the ICP is performed invasively by the perforation of the cranial scalp in the presence of traumatic brain injury (TBI). Measuring the ICP in a noninvasive way is relevant for a great number of pathologies where the invasive measurement represents a high risk.

Comparison of artificial neural networks an support vector machines for feature selection in electrogastrography signal processing.

The paper describes a feature selection process applied to electrogastrogram (EGG) processing. The data set is formed by 42 EGG records from functional dyspeptic (FD) patients and 22 from healthy controls. A wrapper configuration classifier was implemented to discriminate between both classes.

Non-linear multivariate modeling of cerebral hemodynamics with autoregressive Support Vector Machines.

Cerebral blood flow (CBF) is normally controlled by myogenic and metabolic mechanisms that can be impaired in different cerebrovascular conditions. Modeling the influences of arterial blood pressure (ABP) and arterial CO(2) (PaCO(2)) on CBF is an essential step to shed light on regulatory mechanisms and extract clinically relevant parameters. Support Vector Machines (SVM) were used to model the influences of ABP and PaCO(2) on CBFV in two different conditions: baseline and during breathing of 5% CO(2) in air, in a group of 16 healthy subjects.

AlterORF: a database of alternate open reading frames.

AlterORF is a searchable database that contains information regarding alternate open reading frames (ORFs) for over 1.5 million genes in 481 prokaryotic genomes. The objective of the database is to provide a platform for improving genome annotation and to serve as an aid for the identification of prokaryotic genes that potentially encode proteins in more than one reading frame.

[Prediction of the length of stay in Intensive Care Units using artificial neural networks].

Background: The prediction of the length of stay at the moment of hospital admission is of utmost importance. Many studies have used lineal models to predict this variable, but there are inherent limitations to these models. The use of non lineal models has been scarce.