The output from the peripheral terminals of primary nociceptive neurons, which detect and encode the information regarding noxious stimuli, is crucial in determining pain sensation. The nociceptive terminal endings are morphologically complex structures assembled from multiple branches of different geometry, which converge in a variety of forms to create the terminal tree.The output of a single terminal is defined by the properties of the transducer channels producing the generation potentials and voltage-gated channels, translating the generation potentials into action potential firing. However, in the majority of cases, noxious stimuli activate multiple terminals; thus, the output of the nociceptive neuron is defined by the integration and computation of the inputs of the individual terminals. Here we used a computational model of nociceptive terminal tree to study how the architecture of the terminal tree affects input-output relation of the primary nociceptive neurons. We show that the input-output properties of the nociceptive neurons depend on the length, the axial resistance, and location of individual terminals. Moreover, we show that activation of multiple terminals by capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output. Stimulation of terminals in simulated models of inflammatory or nociceptive hyperexcitability led to a change in the temporal pattern of action potential firing, emphasizing the role of temporal code in conveying key information about changes in nociceptive output in pathological conditions, leading to pain hypersensitivity.
Purpose To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes. Methods The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data. Results Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models. Conclusions Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.
Supplemental Digital Content is Available in the Text.Using in vivo two-photon imaging of the activity of dorsal spinal cord neurons in response to the noxious stimuli applied to inflamed skin, we reveal the encoding principles of enhanced sensory input underlying abnormal pain perception.
The output from the peripheral terminals of primary nociceptive neurons, which detect and encode the information regarding noxious stimuli, is crucial in determining pain sensation. The nociceptive terminal endings are morphologically complex structures assembled from multiple branches of different geometry, which converge in a variety of forms to create the terminal tree. The output of a single terminal is defined by the properties of the transducer channels producing the generation potentials and voltage-gated channels, translating the generation potentials into action potential firing. However, in the majority of cases, noxious stimuli activate multiple terminals; thus, the output of the nociceptive neuron is defined by the integration and computation of the inputs of the individual terminals. Here we used a computational model of nociceptive terminal tree to study how the architecture of the terminal tree affects input-output relation of the primary nociceptive neurons. We show that the input-output properties of the nociceptive neurons depend on the length, the axial resistance, and location of individual terminals. Moreover, we show that activation of multiple terminals by capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output. Stimulation of terminals in simulated models of inflammatory or nociceptive hyperexcitability led to a change in the temporal pattern of action potential firing, emphasizing the role of temporal code in conveying key information about changes in nociceptive output in pathological conditions, leading to pain hypersensitivity.Significance statementNoxious stimuli are detected by terminal endings of the primary nociceptive neurons, which are organized into morphologically complex terminal trees. The information from multiple terminals is integrated along the terminal tree, computing the neuronal output, which propagates towards the CNS, thus shaping the pain sensation. Here we revealed that the structure of the nociceptive terminal tree determines the output of the nociceptive neurons. We show that the integration of noxious information depends on the morphology of the terminal trees and how this integration and, consequently, the neuronal output change under pathological conditions. Our findings help to predict how nociceptive neurons encode noxious stimuli and how this encoding changes in pathological conditions, leading to pain.
Inflammation modifies the input-output properties of peripheral nociceptive neurons, thus leading to hyperalgesia, i.e., changes in the perception of noxious heat stimuli such that the same stimulus produces enhanced pain. The increased nociceptive output enters the superficial dorsal spinal cord (SDH), which comprises the first CNS network integrating the noxious information. Here we used in vivo calcium imaging and a computational approach to investigate how the SDH network in mice encodes the injury-mediated abnormal input from peripheral nociceptive neurons. We show the application of noxious heat stimuli to the mice hind paw in naive conditions before induction of injury affects the activity of 70% of recorded neurons by either increasing or suppressing it. Application of the same noxious heat stimuli to hyperalgesic skin leads to activation of previously non-responded cells and de-suppression of the "suppressed" neurons. We demonstrate that reduction in synaptic inhibition mimics the response to the noxious stimuli in the hyperalgesic conditions. Using a computational model of SDH network, we predict that inflammation-induced "disinhibition"-like changes result in increased activity of excitatory projection neurons. Our model also predicts that the observed "disinhibitory" effect of hyperalgesic stimuli does not require changes in the inhibitory transmission and may result from the SDH networks' intrinsic cytoarchitecture.
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