Evaluación de un programa de intervención prenatal en embarazadas con fetos pequeños para la edad gestacional

La prematuridad y el retraso de crecimiento intrauterino constituyen actualmente los problemas más importantes de la Medicina Fetal y de la Neonatología y son las causas más frecuentes de la morbilidad y mortalidad perinatal en los países desarrollados. OBJETIVO. Valorar la eficacia de un programa de intervención de apoyo prenatal (creado ex-novo) dirigido a madres gestantes de fetos Pequeños para la Edad Gestacional (PEG): detectar si este procedimiento mejora el desarrollo físico y neuroconductual del neonato, el estado emocional de la madre y el vínculo entre ambos. METODOLOGÍA. Estudio quasiexperimental tipo ensayo clínico controlado y sin asignación aleatoria de la intervención realizado en el área Materno-fetal de BCNatal (corporación del Servicio de Medicina Maternofetal del Hospital Clínic y el Hospital Sant Joan de Déu de Barcelona). El tamaño final de la muestra fue de 158 embarazadas, de las cuales 65 formaron parte del grupo intervención y 93 formaron parte del grupo control. RESULTADOS. Al finalizar el programa se observa que el feto y el neonato muestran una mayor ganancia de peso y mayor perímetro craneal en el grupo intervención. En cuanto a las capacidades y competencias del neonato, valoradas con la Escala de Brazelton, los del grupo intervención obtienen unos resultados discretamente superiores en casi todos los parámetros estudiados, destacando una mayor capacidad de habituación ante los estímulos auditivos. En relación a la embarazada, los resultados más relevantes al finalizar el programa son una disminución de la ansiedad (valorada con el cuestionario STAI) y una mayor vinculación afectiva materno-filial (valorada con la escala EVAP). CONCLUSIONES. Para las madres gestantes de fetos PEG, el hecho de haber participado en un programa de intervención de apoyo prenatal tiene un resultado beneficioso para ambos, madre e hijo, presentando menos ansiedad materna, mejores condiciones para establecer el vínculo así como una mejora en el desarrollo físico e indicios de mejores capacidades neuroconductuales en el neonato.Prematurity and intrauterine growth restriction are currently the most important problems in Fetal Medicine and Neonatology and also are the most frequent causes of perinatal morbidity and mortality in developed countries.The Objectives were to evaluate the effectiveness of a prenatal support program (created ex-novo) aimed at pregnant mothers of small fetuses for Gestational Age (PEG): to detect if this procedure improves the physical and neurobehavioral development of the neonate, the emotional state of the mother and the bond between them. This was a quasiexperimental study of a controlled clinical trial and without random assignment of the intervention performed in the Maternal-fetal area of BCNatal (Hospital of the Maternal-Fetal Medicine Service of Hospital Clínic and Sant Joan de Déu Hospital in Barcelona). The final sample size was 158 pregnant women, of whom 65 were part of the intervention group and 93 were part of the control group. At the end of the program, it is observed that the fetus and the neonate show a greater weight gain and greater cranial perimeter in the intervention group. As for the abilities and competences of the newborn, evaluated with the Brazelton Scale, those in the intervention group obtained slightly better results in almost all the studied parameters, emphasizing a greater capacity of habituation before the auditory stimuli. In relation to the pregnant woman, the most relevant results at the end of the program are a reduction of anxiety (valued with the STAI questionnaire) and a greater maternal-filial affective attachment (valued with the EVAP scale). In conclusion, for pregnant mothers of PEG fetuses, having participated in a prenatal support intervention program has a beneficial outcome for both mother and child, with less maternal anxiety, better bonding conditions, and improved development physical and signs of better neurobehavioral abilities in the neonate

Payoffs for the non-additive donation game.

<p>Payoffs for the non-additive donation game.</p

Accumulator models of decision-making.

<p>Sensory neuron populations for each decision alternative feed into corresponding accumulators, which must reach a threshold for an appropriate action to be initiated. Lines with arrows denote excitatory inputs, while circles denote inhibitory inputs. Arrowed lines with no target denote activation leakage from populations. (a) race model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone.0043443-Vickers1" target="_blank">[14]</a>. (b) feed-forward inhibition model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone.0043443-Ditterich2" target="_blank">[22]</a>. (c) mutual inhibition model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone.0043443-Usher1" target="_blank">[5]</a>. (d) pooled inhibition model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone.0043443-Wang1" target="_blank">[17]</a>.</p

Dynamics of the mutual inhibition model (<b>equations A1</b> and <b>A2</b>

<p>)<b>.</b> In each panel the curve shows the evolution of the state of the model during a simulation, i.e. different points on the curve correspond to different time instances, and their co-ordinates correspond to levels of the activity of the accumulators at corresponding time. The simulations were performed using the Euler method with an integration constant of 0.001s. In all simulations <i>k</i> = <i>w</i> = 10, <i>I</i>₁ = 4,41, <i>I</i>₂ = 3, <i>c</i> = 0.33 (values of <i>I</i>₁, <i>I</i>₂, <i>c</i> were estimated from behaviour of a sample participant performing motion discrimination task as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone.0043443-Bogacz2" target="_blank">[13]</a>) and the decision threshold was 0.4. The dashed lines indicate the positions in the state space in which one of the accumulators reaches a decision threshold. The arrows indicate the average direction in which the state moves from the point indicated by the arrow's tail, and its length corresponds to the speed of movement (i.e., rate of change) in the absence of noise. The dotted diagonal lines show the positions of the lines to which the state of the system is attracted. (a) Simulation of the model with <i>y</i>₁(0) = <i>y</i>₂(0) = 0. (b) Simulation of the model with <i>y</i>₁(0) = <i>y</i>₂(0) = 0.1. (c) Simulation of the model before stimulus onset (i.e. when <i>I</i>₁ = <i>I</i>₂ = <i>c</i> = 0). The simulation starts at <i>Y</i>₁(0) = <i>Y</i>₂(0) = 0, and the accumulators receive constant input <i>I</i>₀ = 2 for 1s. (d) Simulation of the model with <i>Y</i>₁(0) and <i>Y</i>₂(0) set to the last state in panel c, in which the accumulators receive additional constant input of <i>I</i>₀ = 2.</p

Figure 3

<p>(a) An electrical circuit implementation of the race model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043443#pone-0043443-g001" target="_blank">figure 1a</a>). Noisy inputs for each decision alternative, in the form of fluctuating currents, are accumulated by capacitors. These capacitors continue to accumulate charge, until they reach a specified threshold (assessed by the circuit <i>v</i>). On reaching threshold, the capacitor discharges across a motor, which is taken to be the implementation of the decision reached. Variable capacitor thresholds result in variable inputs to the motor, according to decision type (low threshold, fast but inaccurate decisions result in weak motor movements, while high threshold, slow but accurate decisions result in strong motor movements). In contrast, holding capacitor thresholds constant but varying baseline capacitor charge realises consistent inputs to motors, and hence consistent decision implementation. (b) The circuit of (a), modified such that motor commands are implemented by disinhibiting a motor pathway. This is achieved by using the output from the capacitor corresponding to accumulated evidence for one alternative as the input to a transistor, which acts as a switch on the motor pathway. Since the current crossing a transistor varies as a function of its input, consistent outputs from the capacitor are also desirable in order to implement consistent motor actions.</p

Layout of the full system showing subregions and AVDU placements.

<p>The input to the full system consists of 32x32 ommatidial locations (blue grid), which are processed by AVDUs in three subregions, left (green), right (red) and centre (orange). AVDUs (yellow circles) exist between the location pairs sharing the edge they are located on. The preferred motion direction of each subregion is shown with an arrow. Note that the 32x32 extent of the locations covers a field of view extending 260 degrees horizontally and 180 degrees vertically.</p

Reichardt-Hassenstein detector.

<p>The detector tests whether input at the two locations (top) is correlated in time, with peak response at the time constant <i>τ</i>. M represents multiplication, and—represents subtraction. By taking the difference between the progressive and regressive circuits (in this case the right arm is progressive and the left arm is regressive) the detector gives a response (bottom) from -I to +I, where I is the maximum input to the detector, and a negative value indicates a reverse correlation. The architecture of this detector forms the basis of the retinotopic layers of the model, however the form is modified by the addition of neural dynamics on both arms of the detector. Further details can be found in the text.</p

The two versions of the AVDU detector. Left: with dynamic time constants; right: with delays.

<p>Squares indicate LIN units, circles indicate other operations. In the centre we suggest the corresponding regions of the bee visual system for each stage of the detector. In both versions the input is first temporally filtered in the input layer (first square), then is transmitted to two Reichardt-Hassenstein detectors (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004887#pcbi.1004887.g001" target="_blank">Fig 1</a>). These either differ in the time constants of the LINs (τ₁ and τ₂) or by fixed delays (d₁ and d₂) shown with gray backgrounds. A base time constant of τ_b = 1ms is used otherwise. A further LIN is used to apply the subtraction, having a time constant of τ_R = 5ms, and then the division is performed in the final LIN following summation across all detectors in the array. This final LIN has a time constant of τ_S = 100ms to smooth the output.</p

The average responses of the full detector to different spatial frequencies and different contrasts.

<p>The input has a spatial frequency of 19°, and the model <i>F</i> = 0.25. The response shows a clear velocity tuning that is largely invariant to the spatial frequency or contrast of the stimulus, with the exception of very low and high values of AV where there is greater variance.</p

Centering performance of the model with <i>F</i> = 0.0 and <i>F</i> = 0.25 both agree with experimental data, while performance with <i>F</i> = 0.5 does not.

<p>Experimental data are from Dyhr et al [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004887#pcbi.1004887.ref003" target="_blank">3</a>] for real bees. One wall is held at a constant spatial frequency while the other is varied with sinusoidal patterns. Dashed lines indicate the two points where the spatial frequencies of the two walls are equal, one for each of the two lines. The model error bars show the variance of two runs with differing starting positions in the corridor.</p