A stochastic and integrative model of breathing

Long-term memory in Staphylococcus aureus α-hemolysin ion channel kinetics

The kinetics of an ion channel are classically understood as a random process. However, studies have shown that in complex ion channels, formed by multiple subunits, this process can be deterministic, presenting long-term memory. Staphylococcus aureus α-hemolysin (α-HL) is a toxin that acts as the major factor in Staphylococcus aureus virulence. α-HL is a water-soluble protein capable of forming ion channels into lipid bilayers, by insertion of an amphipathic  β-barrel. Here, the α-HL was used as an experimental model to study memory in ion channel kinetics. We applied the approximate entropy (ApEn) approach to analyze randomness and the Detrended Fluctuation Analysis (DFA) to investigate the existence of long memory in α-HL channel kinetics. Single-channel currents were measured through experiments with α-HL channels incorporated in planar lipid bilayers. All experiments were carried out under the following conditions: 1 M NaCl solution, pH 4.5; transmembrane potential of + 40 mV and temperature 25 ± 1 °C. Single-channel currents were recorded in real-time in the memory of a microcomputer coupled to an A/D converter and a patch-clamp amplifier. The conductance value of the α-HL channels was 0.82 ± 0.0025 nS (n = 128). The DFA analysis showed that the kinetics of α-HL channels presents long-term memory ([Formula: see text] = 0.63 ± 0.04). The ApEn outcomes showed low complexity to dwell times when open (ApEno = 0.5514 ± 0.28) and closed (ApEnc = 0.1145 ± 0.08), corroborating the results of the DFA method.

Memory in Ion Channel Kinetics

Ion channels are transport proteins present in the lipid bilayers of biological membranes. They are involved in many physiological processes, such as the generation of nerve impulses, hormonal secretion, and heartbeat. Conformational changes in the ion channel-forming protein allow the opening or closing of pores to control the ionic flux through the cell membranes. The opening and closing of the ion channel have been classically treated as a random kinetic process, known as a Markov process. Here the time the channel remains in a given state is assumed to be independent of the condition it had in the previous state. More recently, however, several studies have shown that this process is not random but a deterministic one, where both the open and closed dwell-times and the ionic current flowing through the channel are history-dependent. This property is called long memory or long-range correlation. However, there is still much controversy regarding how this memory originates, which region of the channel is responsible for this property, and which models could best reproduce the memory effect. In this article, we provide a review of what is, where it is, its possible origin, and the mathematical methods used to analyze the long-term memory present in the kinetic process of ion channels.

Physiological Basis of Neonatal Aerodigestive Difficulties in Chronic Lung Disease

In the United States, preterm birth rates have steadily increased since 2014. Despite the recent advances in neonatal-perinatal care, more than 40% of very low-birth-weight infants develop chronic lung disease (CLD) and almost 25% have feeding difficulties resulting in delayed achievement of full oral feeds and longer hospital stay. Establishment of full oral feeds, a major challenge for preterm infants, becomes magnified among those on respiratory support and/or with CLD. The strategies to minimize aerodigestive disorders include supporting nonnutritive sucking, developing infant-directed feeding protocols, sensory oromotor stimulation, and early introduction of oral feeds.

The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)

Background

Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension.

Method

We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discrete-feature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/area-under-curve analyses were used to compare performance.

Results

The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [p < 0.001]), at least at the implementation level of a clinically relevant prediction algorithm, may be superior to a discrete feature quantification approach (AUC = 0.87 [p < 0.001]), providing implicit access to a plurality of features and combinations thereof. In addition, the expansion of the approach to include the submission of other physiological data signals, such as heart rate variability, to the network can be readily envisioned.

Conclusion

This pilot study has demonstrated that increased coherence in Arterial Stiffness (AS) variability obtained from the pulse wave analysis of a continuous non-invasive blood pressure device appears to be an effective predictor of hypotension after spinal anesthesia in the obstetrics population undergoing C/S.

This allowed us to predict specific dosing thresholds of phenylephrine required to maintain systolic blood pressure above 90 mmHg.

A Stochastic and Mathematically Integrative Model of the Gender Modulation of Cardiorespiratory Activity

Breath-to-breath interval (BBI) and heartbeat-toheartbeat interval (RRI) variability intrinsically contain a combination of random and temporally scaled characteristics. The objective of this study was to design and test a stochastic and mathematically integrative model (SIM) of cardiorespiratory function that could replicate any genderbased differences in breathing or heartrate variability during a calm, resting state. BBI and RRI sequences were recorded from 12 healthy subjects. Inter-breath and inter-beat memory were estimated with an autocorrelation function, and discrete probability density functions were created by fitting polynomial curves to the normalized histograms of each sequence. The SIM generated an artificial BBI or RRI sequence by constructing a random series of interval values selected from a discrete PDF, and then integrating the series with parameters from the autocorrelation analysis. Fractal scaling was quantified with detrended fluctuation analysis. A significant gender difference was identified in the autocorrelation coefficients of the BBI. The SIM produced artificial BBI and RRI sequences with significant fractal scaling as compared to randomly-shuffled surrogate data (p < 0.001), and with fractal-scaling characteristics similar to the original human data. The SIMgenerated BBI sequences also exhibited the same significant gender-based differences as identified in the human data (p < 0.01). In conclusion, this research demonstrated a stochastic and integrative model that replicated the gender-based differences in fractal scaling in resting human breathing patterns.

A stochastic and mathematically integrative model of the control of human heart rate

The innate variability of the heart rate contains both random and temporally scaled components. The objective of this study was to design a stochastic and mathematically integrative model (SIM) of the control of human heart rate that could replicate the cardiac rhythm during rest and two distinct states of exercise. Heartbeat-to-heartbeat interval (RRI) were recorded from 8 healthy subjects during rest and two exercise levels. Signal memory in RRI sequences was estimated with autocorrelation, and probability density functions (PDFs) were created by fitting polynomial curves to the normalized histograms of the RRI sequences. The SIM generated fictive RRI sequences by randomly selecting RRI values from a PDF and integrating the series using parameters derived from the autocorrelation analysis. Fractal-scaling in the model RRI sequences was quantified with detrended fluctuation analysis. After optimizing the model with the autocorrelation-based and PDF parameters, the SIM was able to produce fictive RRI sequences with significant fractal scaling (statistically assessed with surrogate analysis, p <; 0.001), and with fractal-scaling characteristics similar to the original human data (p = 0.62). Increasing the length of the SIM-generated sequences did not alter the temporal scaling of the model sequences. In conclusion, this research demonstrated a stochastic and integrative model that can replicate the temporally correlated characteristics of the human heart rate.