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En la medición oscilométrica de la presión arterial, ¿por qué suponemos que las oscilaciones más altas se corresponden con la presión arterial media?

En la medición oscilométrica de la presión arterial, ¿por qué suponemos que las oscilaciones más altas se corresponden con la presión arterial media?



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Por lo que he visto, el punto de la curva oscilométrica donde hay mayores oscilaciones representa la presión arterial media (MAP). Mi pregunta es: ¿por qué? ¿Cuál es la lógica detrás de esta suposición? ¿O es solo una coincidencia observada empíricamente?


Biomarcadores matemáticos para la regulación autónoma del sistema cardiovascular


  • 1 Centro de Innovación, Tecnología y Educación & # x02013 (CITE), Universidad Camilo Castelo Branco (UNICASTELO), Sao José dos Campos, Brasil
  • 2 Departamento de Tecnología de Sistemas y Automatización, Instituto Federal de Ciencia y Tecnología de São Paulo, São José dos Campos, Brasil
  • 3 Departamento de Fisiología, Universidad Sultan Qaboos, Mascate, Omán
  • 4 Instituto de Ingeniería Electrónica y Telemática de Aveiro, Universidad de Aveiro, Aveiro, Portugal

La frecuencia cardíaca y la presión arterial son los signos vitales más importantes para diagnosticar una enfermedad. Tanto la frecuencia cardíaca como la presión arterial se caracterizan por un alto grado de variabilidad a corto plazo de un momento a otro, a medio plazo durante el día y la noche normales, así como a muy largo plazo durante meses a años. El estudio de nuevos algoritmos matemáticos para evaluar la variabilidad de estos parámetros cardiovasculares tiene un alto potencial en el desarrollo de nuevos métodos de detección precoz de enfermedades cardiovasculares, para establecer un diagnóstico diferencial con posibles consecuencias terapéuticas. El sistema nervioso autónomo es un actor importante en la reacción adaptativa general al estrés y la enfermedad. La predicción cuantitativa de las interacciones autónomas en múltiples vías de bucles de control del sistema cardiovascular es directamente aplicable a situaciones clínicas. La exploración de nuevas técnicas analíticas multimodales para la variabilidad del sistema cardiovascular puede detectar nuevos enfoques para la identificación de parámetros deterministas. Se puede estudiar un análisis multimodal de las señales cardiovasculares evaluando sus amplitudes, fases, patrones de dominio del tiempo y sensibilidad a los estímulos impuestos, es decir, fármacos que bloquean el sistema autónomo. Los efectos causales, las ganancias y las relaciones dinámicas se pueden estudiar mediante modelos dinámicos de lógica difusa, como el modelo de tiempo discreto y el modelo de evento discreto. Esperamos un aumento en la precisión del modelado y una mejor estimación de las series de tiempo de frecuencia cardíaca y presión arterial, lo que podría ser beneficioso para la monitorización inteligente del paciente. Prevemos que la identificación de biomarcadores matemáticos cuantitativos para el sistema nervioso autónomo permitirá que los ajustes de la terapia individual apunten al equilibrio simpático-parasimpático más favorable.


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5 Transferencia de oxígeno y dióxido de carbono a través de las branquias de los peces

Este capítulo analiza la transferencia de oxígeno y dióxido de carbono a través de las branquias de los peces. Las branquias de los peces son el sitio principal, aunque no el único, para la transferencia de oxígeno y dióxido de carbono. La piel y las aletas también pueden desempeñar esta función y muchos peces han desarrollado órganos accesorios para respirar aire. Estas pueden ser las modificaciones de la superficie de la piel, la boca, la faringe o las branquias, o pueden ser las regiones del intestino o la vejiga natatoria. En general, las branquias de los peces son la vía principal para la transferencia de oxígeno y dióxido de carbono entre el medio ambiente y los tejidos corporales. Las reservas de oxígeno dentro del cuerpo, con la excepción de la vejiga natatoria, son pequeñas. Las reservas de dióxido de carbono en el cuerpo son grandes en comparación con la tasa de producción. A tasas de CO en reposo2 producción, el animal tardaría varias horas en acumular el equivalente del CO corporal2 historias. Por lo tanto, cambios menores en la magnitud del CO2 las reservas, por ejemplo, relacionadas con la acidificación de los tejidos corporales, pueden tener un efecto marcado sobre el CO2 excreción a través de las branquias.


Materiales y métodos

Sujetos, condiciones experimentales

Utilizamos un subconjunto de grabaciones de 25 minutos de un estudio experimental sobre tolerancia ortostática realizado en nuestro Instituto en el período 2001 & # x20132002 bajo los auspicios de la Agencia Espacial Europea (ESA). El estudio había sido autorizado por las juntas éticas apropiadas y los sujetos habían dado su consentimiento informado por escrito de conformidad con la Declaración de Helsinki. Se utilizaron los datos anonimizados de 10 sujetos (4f / 6m), edad promedio 35 años (rango 27 & # x201347), IMC 21,9 kg / m 2 (19,3 & # x201326,0). Los sujetos descansaban en decúbito supino sobre una mesa basculante durante 10 min, seguido de tres frecuencias de respiración marcada (señal de audio, 10, 6, 15 / min, respectivamente, cada una durante 1,2 min, 1 min de recuperación), la frecuencia y la hiperventilación o hipoventilación fueron comprobado mediante medición continua de CO expirado2 nivel. Luego, los sujetos se inclinaron con la cabeza hacia arriba (HUT) en 1 s hasta una posición relajada de pie de 70 & # x00B0, permanecieron apoyados contra la mesa durante 5 minutos y finalmente se inclinaron hacia atrás y se siguieron 2 minutos de grabación en decúbito supino relajado (Gisolf et al. , 2004). En este estudio utilizamos los datos de IBI y las presiones sistólicas derivadas de los datos de presión sanguínea continua del dedo (Finómetro, BMI-TNO, frecuencia de muestreo de Holanda 200 Hz, el inicio de un IBI lo establece el firmware en un punto que corresponde al mismo inicio de la pendiente sistólica ascendente según lo determinado por un algoritmo patentado).

Los datos se analizaron utilizando el formalismo latido a latido, en el que nortela presión sistólica (SAPnorte) obtiene el mismo índice que el IBI en el que aparece (IBInorte), y para fines de análisis espectral, el tiempo entre elementos de la serie (es decir, la frecuencia de muestreo) se establece para que sea igual al IBI medio (De Boer et al., 1984, 1985). Esta es la forma más inequívoca de estudiar las relaciones de tiempo y fase entre la presión arterial y los datos de IBI (Karemaker y De Boer, 2017).

Datos simulados

Se construyeron datos simulados que son similares a los datos observados de nuestro protocolo experimental. Se preparó una serie de 2000 pares SAP-IBI (& # x201C latidos del corazón & # x201D). Los valores de presión consistieron en un valor medio de 120 mmHg más la suma de dos contribuciones sinusoidales dependientes del tiempo con frecuencias de 0,1 y 0,25 Hz y una amplitud de 5 mmHg cada una. Además, se añadió ruido gaussiano (sigma = 2 mmHg). El control barorreflejo de IBI por las presiones sistólicas está modelado por contribuciones vagales y simpáticas (Figura 1). Para relacionar los datos simulados con el protocolo experimental, el IBI medio y la fuerza vagal se establecieron en valores diferentes durante el período & # x201Csupine & # x201D simulado (los primeros 1500 y los 200 últimos latidos) que durante el & # x201Chead up tilt & # x201D período (supera 1500 & # x20131800). El IBI medio se fijó en 1000 ms (supino) y 700 ms (HUT). Las contribuciones barorreflejas rápidas (& # x201Cvagal & # x201D) y más lentas (& # x201Csimpático & # x201D) transformaron las fluctuaciones en los valores de presión en variaciones de IBI. El BRS vagal, que afecta la duración del mismo intervalo en el que se produce la presión sistólica, se fijó en 9 ms / mmHg en el período supino y en 3 ms / mmHg durante el HUT. La contribución simpática consistió en una contribución variable en el tiempo de las presiones previas, aumentando linealmente de cero a 3 ms / mmHg entre 5,6 y 3,2 s antes del IBI considerado, y luego disminuyendo a cero nuevamente a los 0,8 s. Se añadió ruido gaussiano (sigma = 5 ms). Los parámetros que usamos se toman de nuestro artículo de 1987 (De Boer et al., 1987), donde se da una justificación de sus valores.

Técnica de análisis

Las ondas son señales oscilatorias cortas con una amplitud que va de cero a un máximo y de vuelta a cero las ondas se caracterizan por su forma, frecuencia y duración (Torrence y Compo, 1998). Para aplicar el análisis de ondículas a una señal, por ejemplo, una señal cardiovascular, la ondícula se convoluciona con la señal. Un valor de correlación alto en un momento determinado implica que la señal en ese momento contiene información a la frecuencia de la ondícula. Aplicando una serie de ondas con diferentes frecuencias a la señal, se puede determinar su contenido de frecuencia en cada momento. Esto contrasta con las técnicas estándar de análisis de Fourier, que calculan el contenido de frecuencia de la señal durante un período de tiempo.

El análisis de ondículas cruzadas es una técnica que se desarrolló en la década de 1980 para el análisis simultáneo de dos señales en el dominio de la frecuencia y en el dominio del tiempo. Se utiliza principalmente en campos como la oceanografía (Jevrejeva et al., 2003), la meteorología (Torrence y Compo, 1998) y la econometría (Rua y Nunes, 2009). La técnica también se ha aplicado para estudios en fisiología de la circulación (Kashihara et al., 2009 Keissar et al., 2010). La gran fortaleza del análisis cruzado es que permite estudiar cómo evolucionan las características espectrales con el tiempo. Por tanto, los valores de magnitud, BRS, fase y coherencia pueden determinarse en función del tiempo. Utilizando el análisis espectral cruzado clásico, se obtiene solo un valor único para estos parámetros para cada período de tiempo considerado. En este trabajo, consideramos la interacción BP-IBI como un sistema de lazo abierto, es decir, la variación de IBI se debe a las fluctuaciones de la PA mediante el sistema de control barorreflejo.

Utilizamos la transformación de ondas continuas de MATLAB & # x00AE Wavelet Toolbox (MATLAB & # x00AE R2018b), que es potente y muy fácil de usar. Mantuvimos la mayoría de las configuraciones predeterminadas de MATLAB & # x00AE, utilizando ondas Morse y cuatro octavas con 12 pasos cada una para los valores de frecuencia distribuidos logarítmicamente (49 frecuencias). Para nuestro propósito, se necesitaban principalmente la función cwt y la función wcoherence de MATLAB & # x00AE, para la transformada wavelet unidimensional y para la coherencia wavelet y el espectro cruzado, respectivamente. La función de coherencia se modificó ligeramente para obtener valores no normalizados para el espectro cruzado de ondículas.

El wBRS y el ángulo de fase & # x03D5 entre la presión sistólica y el IBI se calcularon de la siguiente manera:

wcsSS, wcsII y wcsSI son los espectros cruzados de SAP frente a SAP, IBI frente a IBI y SAP frente a IBI, respectivamente. Para un registro con norte latidos, la dimensión de estas matrices complejas es norte & # x00D7 49. A continuación, en notación matricial:

Esto da como resultado tres matrices para wBRS, & # x03D5 y r 2, cada uno con tamaño norte & # x00D7 49. Se descartaron valores donde r 2 & # x003C 0.5, porque para una baja coherencia, el wBRS y el ángulo de fase & # x03D5 no pueden estimarse de manera confiable (De Boer et al., 1985 p. 353). Para norte latidos norte & # x00D7 Se dan 2 valores de intervalos y presión. Por tanto, los datos resultantes en el norte & # x00D7 49 matrices contienen mucha dependencia. Para fines de suavizado, usamos un filtro de promedio móvil con ancho N / 2, es decir, 32 para una grabación de 2000 latidos. La frecuencia aparente en los espectros de ondículas se deriva de la frecuencia de muestreo global, que es uno sobre el IBI promediado. Porque el local la frecuencia de muestreo es uno por encima del local IBI, esta frecuencia aparente variará con el local IBI (como se verá en las Figuras 7A, B).

Para estudiar por separado el rango de frecuencia baja de 0.1 Hz y el rango de frecuencia respiratoria de frecuencia más alta, los valores de wBRS y los valores de ángulo de fase se promediaron en dos rangos de frecuencia del tamaño de una octava: 0.07 & # x20130.15 Hz (LF) y 0.15 & # x20130,3 Hz (HF). Por lo tanto, para el promedio de las frecuencias, se utilizaron rangos de frecuencia ajustados, con Fadj. = F & # x00D7 (IBIlocal/ IBItotal), donde IBIlocal se calcula con un filtro de media móvil con ancho N / 2.

XBRS-Computación

Calculamos la sensibilidad barorrefleja instantánea (xBRS) mediante la correlación cruzada de la presión arterial y el intervalo entre latidos (IBI) en una ventana deslizante de 10 s como describen Westerhof et al. (2004). En resumen: una ventana de 10 s se mueve en pasos de 1 s sobre las señales SAP e IBI, y los valores se vuelven a muestrear a una velocidad de 1 s después de la aplicación de un spline cúbico. Luego, las correlaciones cruzadas de SAP e IBI se calculan en esta ventana con un retraso de 0, 1, 2 y 5 s. El retraso con el valor de correlación cruzada más alto se toma como retraso óptimo & # x03C4. Si este valor es positivo y significativo en pag & # x003C 0.05, el cociente de las desviaciones estándar de IBI y de SAP se toma como el valor local de xBRS. Para obtener más información, consulte también Wesseling et al. (2017).


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Rooke TW, Hirsch AT, Misra S, Sidawy AN, Beckman JA, Findeiss LK, Golzarian J, Gornik HL, Halperin JL, Jaff MR, Moneta GL, Olin JW, Stanley JC, White CJ, White JV, Zierler RE

Diehm C, Allenberg JR, Pittrow D, Mahn M, Tepohl G, Haberl RL, Darius H, Burghaus I, Trampisch HJ

Kennedy M, Solomon C, Manolio TA, Criqui MH, Newman AB, Polak JF, Burke GL, Enright P, Cushman M


BPK 407 Final Exam

2) PVC = ischemic atherosclerotic heart disease
-Caused by the ventricles not being stimulated by the normal passage of the wave of depolarization through the AV node
-Portions of the ventricles become spontaneously depolarized
-Abnormal high amplitude, long duration QRS complex
-PVC often lack P wave associated with atrial depolarization
-Certain medications, including common asthma medications
-Injury to the heart muscle from coronary artery disease, congenital heart disease, high blood pressure or heart failure = ischemic atherosclerotic heart disease
-Remember: exercise test should be stopped if more than 30% of the beats are PVCs or R wave superimposed on normal T wave
-high frequency of PVC in exercise suggest ischemic atherosclerotic heart disease, often involving 2 or 3 major coronary vessels
-Incidence of sudden death due to ventricular fibrillation is 6 to 10 times higher for hear patients with PVC that for normal patients that don't have PVC

3) Ventricular tachycardia = CAD
-Caused by coronary artery disease, high blood pressure, an enlarged heart (cardiomyopathy) or heart valve disease.
-It can develop after a heart attack or after heart surgery b/c of scar tissue that forms on the heart
-If not corrected it may lead to ventricular fibrillation
- Rapid heart rate (> 100 bpm), Broad QRS complexes (> 120 ms) are common characteristics

4) Ventricular fibrillation = CAD
-The hypoxic myocardial cells act like pacemakers causing the ventricles to be stimulated by more than one pacemaker
-Disorganized electrical activity in the ventricles
-Ventricles do not contract in coordinated matter
-Totally irregular appearance on ECG
-Most commonly caused by a heart disorder such as inadequate blood flow to the heart due to coronary artery disease
-Common after patient has had a heart attack
-Brugada syndrome can also cause VF (genetic disease characterized by abnormal ECG)

5) First-Degree Heart Block
-In first-degree heart block, the heart's electrical signals are slowed as they move from the atria to the ventricles
-this results in a longer, flatter line between the P and the R waves on the EKG
Second Degree Heart Block
-electrical signals between the atria and ventricles are slowed to a large degree.
-Some signals don't reach the ventricles.
-On an EKG, the pattern of QRS waves doesn't follow each P wave as it normally would.
Third Degree Heart Block
-In this type of heart block, none of the electrical signals reach the ventricles.
-This type also is called complete heart block or complete AV block.
- The P waves occur at a faster rate, and it isn't coordinated with the QRS waves.
In all these blocks -Conduction through the AV node is impaired or blocked

6)Atrial fibrillation
-Fibrosis of the atria
-High blood pressure is the most common cause
-Others include Overactive thyroid (hyperthyroidism), A blood clot in the lung (pulmonary embolism), Diseases that damage the valves of the heart.


Abstracto

In animals, gas exchange between blood and tissues occurs in narrow vessels, whose diameter is comparable to that of a red blood cell. Red blood cells must deform to squeeze through these narrow vessels, transiently blocking or occluding the vessels they pass through. Although the dynamics of vessel occlusion have been studied extensively, it remains an open question why microvessels need to be so narrow. We study occlusive dynamics within a model microvascular network: the embryonic zebrafish trunk. We show that pressure feedbacks created when red blood cells enter the finest vessels of the trunk act together to uniformly partition red blood cells through the microvasculature. Using mathematical models as well as direct observation, we show that these occlusive feedbacks are tuned throughout the trunk network to prevent the vessels closest to the heart from short-circuiting the network. Thus occlusion is linked with another open question of microvascular function: how are red blood cells delivered at the same rate to each micro-vessel? Our analysis shows that tuning of occlusive feedbacks increase the total dissipation within the network by a factor of 11, showing that uniformity of flows rather than minimization of transport costs may be prioritized by the microvascular network.


Introducción

State-of-the-art basal ganglia (BG) computational models (Gurney et al., 2015 Schultz et al., 1997) divide the BG network into two functionally related subsystems. First, the main axis (or 'actor' in machine learning terminology) which corresponds to the BG structures that connect state-encoding thalamo-cortical areas to cortical and brainstem motor centers. Second, the neuromodulators (machine learning's 'critics', e.g., the midbrain dopaminergic neurons and striatal cholinergic interneurons) that adjust activity along the BG main axis by encoding a prediction error signal capable of modulating the efficacy of cortico-striatal transmission (Deffains and Bergman, 2015 Reynolds et al., 2001 Shen et al., 2008).

The input structures of the BG main axis (the striatum and subthalamic nucleus, STN) receive considerable glutamatergic inputs from the cortex and the thalamus. The striatum and STN provide major inhibitory GABAergic and excitatory glutamatergic drive respectively to the external segment of the globus pallidus (GPe) and the BG output structures (internal segment of the globus pallidus and substantia nigra reticulata, GPi/SNr) (Parent and Hazrati, 1995a, 1995b). In return, the GPe emits feedback GABAergic projections to the STN (Carpenter et al., 1981) and the striatum (Hegeman et al., 2016 Mallet et al., 2012) as well as massive feedforward GABAergic projections to the GPi/SNr (Parent and Hazrati, 1995b). Thus, aside from the action of the BG neuromodulators and lateral connectivity, the increase-decrease balance of spiking activity (I/D balance) of pallidal and nigral neurons is fined-tuned by the inhibitory and excitatory drives exerted by the striatum and STN, respectively. However, how these antagonistic drives operate to convey relevant information from the state-encoding thalamo-cortical areas through the central (GPe) and output (GPi and SNr) BG structures to brain motor centers is still unknown.

Many human disorders are caused by malfunctions of the BG neuromodulators which impact neuronal activity along the BG main axis. Traditionally, in Parkinson's disease (PD), it is assumed that degeneration of midbrain dopaminergic neurons leads to striatal dopamine depletion which provokes a cascade of physiological disturbances in the BG main axis, notably the emergence of synchronized oscillatory activity in the BG and cortical networks (Levy et al., 2002 Nini et al., 1995 Oswal et al., 2013). These abnormal oscillations likely compromise information flow through the BG main axis and result in the release of abnormal commands by BG output structures.

Despite evidence of subthalamic dopamine depletion in PD and its role in the pathophysiology of the disease (Francois et al., 2000 Galvan et al., 2014 Rommelfanger and Wichmann, 2010), the striatum remains the main site of dopamine depletion in human patients and animal models of PD. In addition, the striatum is much larger than the STN (10 7 vs. 10 5 neurons in non-human primates, respectively, Hardman et al., 2002). Nevertheless, the STN, not the striatum, is the prime target for deep brain stimulation (DBS) of human patients with advanced PD (Limousin et al., 1998 Odekerken et al., 2016). Moreover, it has been shown that STN-DBS abolishes abnormal synchronized oscillations in the BG network of animal models of PD (Meissner et al., 2005) and human PD patients (Kühn et al., 2008 Wingeier et al., 2006). These findings suggest that STN plays a pivotal role in the release of commands by BG output structures, but the respective influence of the striatum and STN activity on the activity of the BG central and output structures in PD are still unknown.

To tackle these issues, we recorded the neuronal activity of the BG input and downstream (central and output) structures of two monkeys engaged in a classical temporal discounting conditioning task (i.e., normal/healthy state, Figure 1A). In the task, we manipulated the value of 2-s cues (predicting future appetitive, aversive or neutral outcomes) and the delivery time of the outcome (immediate or 6-s delayed). Then, once we completed the recordings in the normal state, we proceeded to record neuronal activity in the BG network of the same two monkeys after systemic induction of PD symptoms (i.e., parkinsonian state) with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). These multi-site recordings in both the normal and parkinsonian states served us to reveal how BG activity propagates along the BG main axis in health and parkinsonism. Moreover, it sheds light on which BG input structure (striatum or STN) is more influential in shaping the activity of the BG downstream structures in the recorded conditions.

Task design and behavioral monitoring.

(A) Temporal discounting classical conditioning task. Each trial started with the presentation of a visual cue (2 s) that predicted the delivery of food (reward/appetitive trials), airpuff (aversive trials) or sound only (neutral trials). Cue offset was immediately followed by the outcome period (immediate outcome condition) or by a 6-s delay period which preceded the outcome period (delayed outcome condition). The outcome period (0.15 s) was followed by a variable inter-trial interval (ITI) of 6–10 s. Trial order and ITI duration were randomized. (B) Animals' task performance. Frequency of licking (top) and blinking (bottom) movements over time are aligned to cue onset (time = 0). Time 2 and 8 s correspond to outcome delivery in the immediate and delayed conditions, respectively. Data were averaged for each session (hundreds of trials) and then across sessions (N = 41 and 30, monkey K and S, respectively). Data were grouped since no significant differences were found between the two monkeys. Solid line and shaded envelope represent mean ± standard error of the mean (SEM), respectively. Color code indicates the cue/outcome value: blue-appetitive, green-neutral and red-aversive.


Discusión

In vivo pulsatile blood velocity data were successfully obtained for five patients using a Doppler flow ComboWire ® (Volcano Corp.), although there were some difficulties to obtain the Doppler flow signal. For example, placing a wire in a suitable position, i.e., as centrally as possible in order to get sufficient signal intensity took several minutes, an inconvenience probably related to the difficulty in choosing the best coronary artery/place to perform an accurate measurement i.e. to measure maximum velocity avoiding any artifact and noises. Patients were selected from those needing coronagraphy according to the ethical board’s policy, and we were limited to the use of sites where coronagraphy had to be performed. Within these restrictions, our goal was to find healthy vessels without stenosis and as straight as possible in order to obtain proper measurements. Nevertheless, some measurements had to be performed close to branches or curves. These practical problems add to the inherent variability between successive human heart beats, perturbed signals due to movements of the patient, etc. Hence, the data obtained were often noisy and additional filtering was needed.

After filtering, the five different in vivo signal measurements were analyzed and post-processed. Results showed that the flow pattern varied widely among patients and confirmed the reported variability of pulsatile flow waveforms observed in the arteries (see, e.g., Mills et al. [22], McDonald [23]). The differences among patients in the magnitude of the velocities as well as in the shape of the curve could be due to intrinsic differences between patients and/or measurement positions.

The main goal was to perform experiments with the simplest settings. First in vitro tests involved straight rigid pipes containing a medium with Newtonian properties. However, the necessary velocity measurements had to be made with blood, which has non-Newtonian behavior. In this study, we extracted detailed information on the physical flow quantities, such as pulsatile flow, WSS and WSSTD from in vivo measurements using the Wormesley’s solution (usually applied to a Newtonian behavior). Our subsequently comparison with numerical non-Newtonian, based on the Carreau model, CFD calculations indeed validated the Womersley’s solution for five patients tested. The mean relative errors for the flow rate vary between 3.2 and 6.3%. As presented, both techniques give rise to similar results with a difference growing for smaller flow rates (Figs. 6, 7, 8). Thus, the Womersley’s solution is a fast method that can be used to provide satisfying approximations especially for the higher flow rates where shear rate is higher than 100 s −1 and where the non-Newtonian behavior becomes negligible. For some cases, where more accurate and detailed analysis is required, is should nevertheless be recommended to use CFD techniques.

Based on the previous results and the Womersley’s solution, the comparison with experimental in vitro data was performed. The results in Table 4 suggest that the concept behind the whole control system is valid. The mean relative error is below 5% for all investigated signals. In addition, we have to bear in mind that we are dealing with very fast control loops. LabVIEW™ executes one cycle for all the input/output signals in a time period of 5 ms (200 Hz). It is much quicker than in most industrial controls of mechanical systems. Also, the system is highly non-linear and depends on external conditions such as medium composition that can vary a lot depending on supplements or serum. Other sources of variability include properties of the equipment such as, for example the diaphragm age, external temperature and so on.

The results obtained for all examined patients with the real-time system are satisfactory. However, the system experienced difficulties to follow rapid flow changes. Therefore, when fast WSS and WSSTD acceleration/deceleration appeared, a large error was generated. This results from the foundations behind the WSS calculation method. It indeed involves the use of derivatives which amplifies the noise particularly at higher frequencies. As the system struggles to reproduce fast changes, the calculated WSS values between the reference flow and measured flow may therefore display significant discrepancies, reflected in the calculated relative error. The same principle may be applied to WSSTD, whose values are function of a second derivative of the flow. Therefore the relative error becomes even higher.

As mentioned earlier, several studies have correlated shear stress perturbations with EC behavior. Recently, other studies have correlated WSSTD with the development of the atherogenic phenotype [8], with for example, WSSTD-induced EC proliferation, upregulation of platelet-derived growth factor (PDGF-A) and monocyte chemoattractant protein-1(MCP-1) and enhanced monocyte binding [8]. Investigations have also demonstrated effects of WSSTD and temporal gradient in shear on EC remodeling [8, 24]. However, the WSSTD signal in these studies was deduced as the result of ramp, step, sinusoidal or impulse laminar flow. In this paper, we calculated WSSTD (Figs. 8, 10, right-hand panel) based on real in vivo and in vitro measurements. Clearly WSSTD signals are not a simple impulse or ramp. Thus, during one heartbeat, there was a wide variety in the signal, with rapid increases and decreases, as well as different magnitudes observed in our patients. How this complex signal variety can influence EC remodeling and gene expression remains an open question, which needs to be addressed in future studies. We can classify signals recorded from the 5 patients into two groups: (1) patients 1 and 5 with two WSSTD peaks at the beginning and end of the cycle and relatively constant values in-between and (2) patients 2, 3, 4 with many random peaks during the whole cycle. This observation raises several questions: Is there a difference between these two groups regarding their influence on EC remodeling? Could one of these two groups reflect patients who develop atherosclerosis without any classical risk factors, such as smoking, fat diet or hypertension?

An important question relates to which values can be set as trigger or threshold values to influence EC functions? In a review [8], three conditions were proposed: (1) WSSTD = 0 Pa/s as steady condition (2) WSSTD = 7.1 Pa/s as low condition and (3) WSSTD = 29.3 Pa/s as high condition. When these conditions were applied to our data (Fig. 8), close to 90% of the WSSTD for patient 1 were between −7 and 7 Pa/s, 41.9% for patient 2 and about 60% for the other patients. Furthermore, almost 10% of WSSTD were in the range of high values for patient 2 this was about two times less than for the other patients. However, that high condition i.e. WSSTD about 29.3 Pa/s appears in a fraction of time. It seems that they are placed at higher frequencies of the signal. This triggers another question: Is it enough to activate ECs function with high frequency and high amplitude stimuli? Feaver et al. [25], demonstrated how the frequency spectrum of the wall shear stress signal can regulate the inflammation including NF-kB activity. By modifying or removing particular frequency harmonics from a carotid wall shear stress signal, they analyzed in vitro the response to human endothelial cells. They found that the frequency spectrum, specifically the 0th and 1st harmonics, is a significant regulator of inflammation. However, is it valid to the whole mechanisms of shear stress mechanotransductions? In the future study, in addition to the mentioned signals stimuli, the spectrum analysis of the WSS and WSSTD signal to the ECs will be considered.

This short analysis shows that it is difficult to agree on a constant value and that the patterns of variability in different patients are considerable. Nevertheless our study in 5 patients provides a limited set of preliminary data to plan in vitro experiments and create a database of patient flow patterns.

The aim of this project was also to provide bioengineers and physicians with a tool that will be able to mimic blood flows and later automatically calculate desired flow properties, such as WSS and WSSTD.

These calculations of the real coronary wall shear stress signals are critical for future development. The challenge will now be to reproduce these signals perfectly in this in vitro system. Meeting this challenge (see above) will also allow two critical observations made in this paper to be exploited: patients can be divided into different groups with different behaviors, which are much more complex than described in previous publications. These observations in turn raise two critical questions: Which of these behaviors is important in triggering the EC pro-atherogenic genes? Do these different behaviors explain why many patients with no risk factors develop a clinical condition and some patients with risk factors do not?


Ver el vídeo: Entrevista - Campaña mundial de medición de la presión arterial (Agosto 2022).