Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data

BACKGROUND

Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data.

METHODS

We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models.

RESULTS

Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6).

CONCLUSIONS

We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes.

SIGNIFICANCE

Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

2D Wavelet-Scalogram Deep-Learning for Seizures Pattern Identification in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep

Neonatal seizures after an hypoxic-ischemic (HI) event in preterm newborns can contribute to neural injury and cause impaired brain development. Preterm neonatal seizures are often not detected or their occurrence underestimated. Therefore, there is a need to improve knowledge about preterm seizures that can help establish diagnostic tools for accurate identification of seizures and for determining morphological differences. We have previously shown the superior utility of deep-learning algorithms for the accurate identification and quantification of post-HI microscale epileptiform transients (e.g., gamma spikes and sharp waves) in preterm fetal sheep models; before the irreversible secondary phase of cerebral energy failure starts by the bursts of high-amplitude stereotypic evolving seizures (HAS) in the signal. We have previously developed successful deep-learning algorithms that accurately identify and quantify the micro-scale transients, during the latent phase. Building up on our deep-learning strategies, this work introduces a real-time deep-learning-based pattern fusion approach to identify HAS in the 256Hz sampled post-HI data from our preterm fetuses. Here, for the first time, we propose a 17-layer deep convolutional neural network (CNN) classifier fed with 2D wavelet-scalogram (WS) images of the EEG patterns for accurate seizure identification. The WS-CNN classifier was cross-validated over 1812 manually annotated EEG segments during ~6 to 48 hours post-HI recordings. The classifier accurately recognized HAS patterns with 97.19% overall accuracy (AUC = 0.96).Clinical relevance-The promising results from this preliminary work indicate the ability of the proposed WS-CNN pattern classifier to identify HI-related seizures in the neonatal preterm brain using 256Hz EEG; the frequency commonly used clinically for data collection.

Atlas-Free Automatic Segmentation of Sheep Brain MRI

Automated 3D brain segmentation methods have been shown to produce fast, reliable, and reproducible segmentations from magnetic resonance imaging (MRI) sequences for the anatomical structures of the human brain. Despite the extensive experimental research utility of large animal species such as the sheep, there is limited literature on the segmentation of their brains relative to that of humans. The availability of automatic segmentation algorithms for animal brain models can have significant impact for experimental explorations, such as treatment planning and studying brain injuries. The neuroanatomical similarities in size and structure between sheep and humans, plus their long lifespan and docility, make them an ideal animal model for investigating automatic segmentation methods.This work, for the first time, proposes an atlas-free fully automatic sheep brain segmentation tool that only requires structural MR images (T1-MPRAGE images) to segment the entire sheep brain in less than one minute. We trained a convolutional neural network (CNN) model - namely a four-layer U-Net - on data from eleven adult sheep brains (training and validation: 8 sheep, testing: 3 sheep), with a high overall Dice overlap score of 93.7%.Clinical relevance- Upon future validation on larger datasets, our atlas-free automatic segmentation tool can have clinical utility and contribute towards developing robust and fully automatic segmentation tools which could compete with atlas-based tools currently available.

Deep-Learning Markerless Tracking of Infant General Movements using Standard Video Recordings

Monitoring spontaneous General Movements (GM) of infants 6-20 weeks post-term age is a reliable tool to assess the quality of neurodevelopment in early infancy. Abnormal or absent GMs are reliable prognostic indicators of whether an infant is at risk of developing neurological impairments and disorders such as cerebral palsy (CP). Therapeutic interventions are most effective at improving neuromuscular outcomes if administered in early infancy. Current clinical protocols require trained assessors to rate videos of infant movements, a time-intensive task. This work proposes a simple, inexpensive, and broadly applicable markerless pose-estimation approach for automatic infant movement tracking using conventional video recordings from handheld devices (e.g., tablets and mobile phones). We leverage the enhanced capabilities of deep-learning technology in image processing to identify 12 anatomical locations (3 per limb) in each video frame, tracking a baby's natural movement throughout the recordings. We validate the capability of resnet152 and a mobile-net-v2-1 to identify body-parts in unseen frames from a full-term male infant, using a novel automatic unsupervised approach that fuses likelihood outputs of a Kalman filter and the deep-nets. Both deep-net models were found to perform very well in the identification of anatomical locations in the unseen data with high average Percentage of Correct Keypoints (aPCK) performances of >99.65% across all locations.Clinical relevance-Results of this research confirm the feasibility of a low-cost and publicly accessible technology to automatically track infants' GMs and diagnose those at higher risk of developing neurological conditions early, when clinical interventions are most effective.

Household Food Insecurity and Associated Factors among Iranian Patients with Esophageal and Gastric Cancers

Background: Household food insecurity (HFI) which has still been one of the major global public health issues is related to adverse health outcomes in individuals. Therefore, this study aimed to determine the prevalence of HFI and its associated factors in Iranian patients with esophageal and gastric cancers. Methods: The data of this cross-sectional study was obtained from 315 patients with esophageal and gastric cancers who were selected from a gastrointestinal cancer-based cohort study conducted in Firoozgar hospital, in Tehran. Food insecurity (FI) was measured using the Iranian version of the HFI questionnaire that was completed by a trained interviewer. The multivariable logistic regression model was used to determine the independent association of each factor with HFI. A P value lower than 0.05 was considered statistically significant. Results: The mean±SD of participants' age was 63.2±12.6 years and 65.4% were men. Most of the patients (75.8%) suffered from gastric cancer and 24.2% from esophageal cancer. The overall prevalence of FI among participants' households was 35.2%. There was an independent significant association between wealth index (WI) and HFI after the use of the multivariable logistic regression model, in such a way that the odds of FI in the poorest, poor, moderate, and rich patients' households were respectively, 6.41, 5.05, 2.74 and 2.04 times higher compared with the richest households. Conclusion: More than a third of participants' households struggled with FI, which was found to have a higher prevalence in loweconomic households. Therefore, health policymakers should intervene in food-insecure households by developing, establishing, and implementing strategies and control programs to improve affordable food access.

Effectiveness of online practical education on vaccination training in the students of bachelor programs during the Covid-19 pandemic

BACKGROUND

The importance of immunization and the necessity of achieving the goals of the immunization expansion plan and the critical role of undergraduate public health students in attaining these goals in the Covid-19 pandemic is evident. The present study aimed at investigating the effectiveness of using online educational videos on practical learning of vaccination in the apprenticeship stage during covid-19 pandemic: a randomized controlled trial.

MATERIAL AND METHODS

This experimental study was conducted on 120 students (60 interventions and 60 control groups) at Hamadan University of Medical Sciences during 2019-2020. The intervention included training vaccination skills through educational videos based on self-efficacy theory, which was conducted for two weeks each week in two sessions of two hours for the intervention group using an educational video. A researcher-made questionnaire and a performance checklist were used to collect data. Data were analyzed using SPSS-16 software. Paired t-test, independent t-test, and Chi-square.

RESULTS

The mean age of the subjects was 22.41 years, and most of the participants were female students (80%). There were statistically significant differences between the intervention and control groups regarding knowledge (19.17±0.92 vs. 16.03±3.00; P<0.001), self-efficacy (40.84±3.71 vs 33.45±4.83; P = 0.01), attitude (22.56±2.95vs 20.28±3.25; P = 0.01) and performance (27.92±6.00 vs 22.38±5.40; P = 0.01) after the intervention.

CONCLUSION

According to the findings of this study, the use of educational videos for undergraduate students of public health during the apprenticeship period has a positive effect on the practical learning of vaccination. However, it seems that in non-critical times, online education along with face-to-face education will be more effective for practical training.

General function approximation of a class of cascade chaotic fuzzy systems

This paper presents an innovative architecture called cascade chaotic fuzzy system (CCFS) for the function approximation and chaotic modeling. The proposed model can dominate complications in the type-2 fuzzy systems and increase the chaotic performance of a whole framework. The proposed cascade structure is based on combining two or more one-dimensional chaotic maps. The combination provides a new chaotic map with more high nonlinearity than its grain maps. The fusion of cascade chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the CCFS more capable of confronting nonlinear problems. Based on the General Function Approximation and Stone-Weierstrass theorem, we show that the proposed model has the function approximation property. By analyzing the bifurcation diagram and applying the CCFS to the problem of chaotic modeling, the new model is investigated. Simulation results and analysis are demonstrated to illustrate the concept of general function approximation.

The complementary value of intraoperative fluorescence imaging and Raman spectroscopy for cancer surgery: combining the incompatibles

A clear margin is an important prognostic factor for most solid tumours treated by surgery. Intraoperative fluorescence imaging using exogenous tumour-specific fluorescent agents has shown particular benefit in improving complete resection of tumour tissue. However, signal processing for fluorescence imaging is complex, and fluorescence signal intensity does not always perfectly correlate with tumour location. Raman spectroscopy has the capacity to accurately differentiate between malignant and healthy tissue based on their molecular composition. In Raman spectroscopy, specificity is uniquely high, but signal intensity is weak and Raman measurements are mainly performed in a point-wise manner on microscopic tissue volumes, making whole-field assessment temporally unfeasible. In this review, we describe the state-of-the-art of both optical techniques, paying special attention to the combined intraoperative application of fluorescence imaging and Raman spectroscopy in current clinical research. We demonstrate how these techniques are complementary and address the technical challenges that have traditionally led them to be considered mutually exclusive for clinical implementation. Finally, we present a novel strategy that exploits the optimal characteristics of both modalities to facilitate resection with clear surgical margins.

Self-Reported Female Orgasm Following Serial Sacroiliac Joint Injections

Sacroiliac joint (SIJ) dysfunction has been increasingly recognized as the underlying pathology responsible for a significant percentage of cases of chronic lower back pain and radiculopathy. Diagnosis of SIJ dysfunction involves multiple provocation tests followed by serial injections of anesthetic, with significant alleviation of pain indicating that the SIJ is the pain generator. One documented complication of SIJ injections is extravasation of injected material from the SIJ capsule, resulting in unintended symptoms. We report the case of a patient who reported experiencing an orgasm following each of her three diagnostic SIJ injections. We hypothesize that this unusual symptom was caused by extravasation of injected material ventrally to the nearby pudendal nerve, a nerve responsible for sensory innervation of the perineum and a mediator of sexual arousal and orgasm.

Perioperative Outcomes of Minimally Invasive Sacroilliac Joint Fusion Using Hollow Screws Through a Lateral Approach: A Single Surgeon Retrospective Cohort Study

Sacroiliac joint (SIJ) pain is a common cause of lower back pain and a significant source of disability in the United States. There is no consensus on the best surgical treatment for SIJ pain that is not responsive to conservative therapy. Minimally invasive fusion of the SIJ using hollow fenestrated screws from a lateral trajectory is a newer technique for SIJ fusion. This study presents perioperative and patient-reported outcomes amongst 62 patients who underwent SIJ fixation with hollow fenestrated screws. We find that mean disability on the Oswestry disability index improved from 52.2% to 34.9% at one-year post-op. Mean operative time was 34±9 minutes and blood loss was 22±35ml. Only six patients required overnight hospitalization. There were two cases of complications requiring operative intervention. We conclude that SIJ fixation using hollow fenestrated screws is a safe and effective procedure for the fixation of the SIJ. Further investigation is warranted to determine the best surgical treatment for SIJ pain.

Deep Convolutional Neural Networks for the Accurate Identification of High-Amplitude Stereotypic Epileptiform Seizures in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep

Neonatal seizures after birth may contribute to brain injury after an hypoxic-ischemic (HI) event, impaired brain development and a later life risk for epilepsy. Despite neural immaturity, seizures can also occur in preterm infants. However, surprisingly little is known about their evolution after an HI insult or patterns of expression. An improved understanding of preterm seizures will help facilitate diagnosis and prognosis and the implementation of treatments. This requires improved detection of seizures, including electrographic seizures. We have established a stable preterm fetal sheep model of HI that results in different types of post-HI seizures. These including the expression of epileptiform transients during the latent phase (0-6 h) of cerebral energy recovery, and bursts of high amplitude stereotypic evolving seizures (HAS) during the secondary phase of cerebral energy failure (∼6-72 h). We have previously developed successful automated machine-learning strategies for accurate identification and quantification of the evolving micro-scale EEG patterns (e.g. gamma spikes and sharp waves), during the latent phase. The current paper introduces, for the first time, a real-time approach that employs a 15-layer deep convolutional neural network (CNN) classifier, directly fed with the raw EEG time-series, to identify HAS in the 1024Hz and 256Hz down-sampled data in our preterm fetuses post-HI. The classifier was trained and tested using EEG segments during ∼6 to 48 hours post-HI recordings. The classifier accurately identified HAS with 98.52% accuracy in the 1024Hz and 97.78% in the 256Hz data. Clinical relevance-Results highlight the promising ability of the proposed CNN classifier for accurate identification of HI related seizures in the neonatal preterm brain, if further applied to the current 256Hz clinical recordings, in real-world.

Physiologic Decompression of Lumbar Spinal Stenosis Through Anatomic Restoration Using Trans-Kambin Oblique Lateral Posterior Lumbar Interbody Fusion (OLLIF): A Retrospective Analysis

Introduction Lumbar spinal stenosis (LSS) is one of the most common indications for spinal surgery. Traditionally, decompression is achieved by removing bony and ligamentous structures through open surgery. However, recent studies have shown that symptomatic relief can be accomplished in many patients by increasing intervertebral and interpedicular height using fusion alone. In this study, we evaluate whether trans-Kambin oblique lateral lumbar interbody fusion (OLLIF) can effectively and safely relieve symptoms of LSS when an indication for fusion is present. Methods This is a retrospective single surgeon cohort study of 187 patients with LSS who underwent 189 OLLIF procedures between 2012 and August 2, 2019. Inclusion criteria for this study were age >18 years with symptoms of LSS, including pain, sensory, and motor deficits, and an additional indication for fusion, which included spondylolisthesis, degenerative disk disease, disk herniation, or scoliosis. Exclusion criteria were the bony obstruction of the approach, osteogenic spinal canal stenosis, large facet hypertrophy, and listhesis grade II or greater. The primary outcome was a change in the Oswestry Disability Index (ODI) one year after surgery. Secondary outcomes were the resolution of radiculopathy at the first follow-up visit and one year after surgery, complication rates, surgery time, blood loss, and hospital stay. Results ODI improved from 52% pre-op to 37% at the one-year follow-up. At the first follow-up, radiculopathy had resolved in 39% of patients, and 72% of patients experienced improvement of 50% or greater. One year after surgery, radiculopathy had resolved in 52% of patients and 74% experienced improvement of 50% or greater. Single-level surgeries required 56.4±21.5 minutes, with a mean hospital stay of 1.6‑±2.4 days. Nerve irritation occurred in 12% of patients at the first postoperative follow-up and persisted in 6.8% of patients one year after surgery. There was one case each of persistent weakness at one year, infection, and cage subsidence. Conclusion Trans-Kambin OLLIF delivers anatomic restoration of intradiscal and interpedicular distance, which results in physiologic decompression of lumbar spinal stenosis in patients undergoing lumbar fusion for degenerative or herniated disk disease, spondylolisthesis, or scoliosis. Amongst patients with LSS, OLLIF results in significant improvement of radiculopathy and patient-reported disability in the majority of patients with low rates of long-term complications. Unlike other minimally invasive surgery (MIS) fusions, OLLIF can be safely used from T12-S1.

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effective for specific, short windows of time during this evolution of injury. Clinically, we often do not know when an insult may have started, and thus which phase of injury the brain may be experiencing. To improve diagnosis, prognosis and treatment efficacy, we need to establish biomarkers which denote phases of injury. Our pre-clinical research, using preterm fetal sheep, show that micro-scale EEG patterns (e.g. spikes and sharp waves), superimposed on suppressed EEG background, primarily occur during the early recovery from an HI insult (0-6 h), and that numbers of events within the first 2 h are strongly predictive of neural survival. Thus, real-time automated algorithms that could reliably identify EEG patterns in this phase will help clinicians to determine the phases of injury, to help guide treatment options. We have previously developed successful automated machine learning approaches for accurate identification and quantification of HI micro-scale EEG patterns in preterm fetal sheep post-HI. This paper introduces, for the first time, a novel online fusion strategy that employs a high-level wavelet-Fourier (WF) spectral feature extraction method in conjunction with a deep convolutional neural network (CNN) classifier for accurate identification of micro-scale preterm fetal sheep post-HI sharp waves in 1024Hz EEG recordings, along with 256Hz down-sampled data. The classifier was trained and tested over 4120 EEG segments within the first 2 hours latent phase recordings. The WF-CNN classifier can robustly identify sharp waves with considerable high-performance of 99.86% in 1024Hz and 99.5% in 256Hz data. The method is an alternative deep-structure approach with competitive high-accuracy compared to our computationally-intensive WS-CNN sharp wave classifier.

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biomarkers to identify at risk infants. Using our pre-clinical fetal sheep models, we have demonstrated that micro-scale patterns evolve over a profoundly suppressed EEG background within the first 6 hours of recovery, post HI insult. In particular, we have shown that high-frequency micro-scale spike transients (in the gamma frequency band, 80-120Hz) emerge immediately after an HI event, with much higher numbers around 2-2.5 h of the insult, with numbers gradually declining thereafter. We have also shown that the automatically quantified sharp waves in this phase are predictive of neural outcome. Initiation of some neuroprotective treatments within this limited window of opportunity, such as therapeutic hypothermia, optimally reduces neural injury. In clinical practice, it is hard to determine the exact timing of the injury, therefore, reliable automatic identification of EEG transients could be beneficial to help specify the phases of injury. Our team has previously developed successful machine- and deep-learning strategies for the identification of post-HI EEG patterns in an HI preterm fetal sheep model.This paper introduces, for the first time, a novel online fusion approach to train an 11-layers deep convolutional neural network (CNN) classifier using Wavelet-Fourier (WF) spectral features of EEG segments for accurate identification of high-frequency micro-scale spike transients in 1024Hz EEG recordings in our preterm fetal sheep. Sets of robust features were extracted using reverse biorthogonal wavelet (rbio2.8 at scale 7) and considering an 80-120Hz spectral frequency range. The WF-CNN classifier was able to accurately identify spike transients with a reliable high-performance of 99.03±0.86%.Clinical relevance-Results confirm the expertise of the method for the identification of similar patterns in the EEG of neonates in the early hours after birth.

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at risk infants at birth. Using pre-clinical data from our fetal sheep models, we have shown that micro-scale EEG patterns, such as high-frequency spikes and sharp waves, evolve superimposed on a significantly suppressed background during the early hours of recovery (0-6 h), after an HI insult. In particular, we have demonstrated that the number of micro-scale gamma spike transients peaks within the first 2-2.5 hours of the insult and automatically quantified sharp waves in this period are predictive of neural outcome. This period of time is optimal for the initiation of neuroprotection treatments such as therapeutic hypothermia, which has a limited window of opportunity for implementation of 6 h or less after an HI insult. Clinically, it is hard to determine when an insult has started and thus the window of opportunity for treatment. Thus, reliable automatic algorithms that could accurately identify EEG patterns that denote the phase of injury is a valuable clinical tool. We have previously developed successful machine-learning strategies for the identification of HI micro-scale EEG patterns in a preterm fetal sheep model of HI. This paper employs, for the first time, reverse biorthogonal Wavelet-Scalograms (WS) as the inputs to a 17-layer deep-trained convolutional neural network (CNN) for the precise identification of high-frequency micro-scale spike transients that occur in the 80-120Hz gamma band during first 2 h period of an HI insult. The rbio-WS-CNN classifier robustly identified spike transients with an exceptionally high-performance of 99.82%.Clinical relevance-The suggested classifier would effectively identify and quantify EEG patterns of a similar morphology in preterm newborns during recovery from an HI-insult.

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategies play an important role to fight against the adverse conditions and improve survival. Therefore, timing, and more specifically early diagnosis of the illness, is crucially important for an effective treatment. Studies have indicated that vital signals such as heart rate variability (HRV) could provide potential prognostic biological markers that can help with early detection of sepsis before it is clinically diagnosed through its actual symptoms. Therefore, this study employs neonatal and pediatric electrocardiogram (ECG) to extract 52 hourly sets of linear and non-linear features from the HRV, starting from 24 hours prior to the clinical diagnosis of sepsis in patients with positive blood cultures (n=14). Similar sets of features were also obtained from a non-sepsis control group to create an evaluation benchmark (n=14).In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV featuresets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.Clinical relevance- Results suggest potential early diagnosis of sepsis through real-time automatic classification of HRV features as prognostic indicators in clinical ECG recordings.

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategies play an important role to fight against the adverse conditions and improve survival. Therefore, timing, and more specifically early diagnosis of the illness, is crucially important for an effective treatment. Studies have indicated that vital signals such as heart rate variability (HRV) could provide potential prognostic biological markers that can help with early detection of sepsis before it is clinically diagnosed through its actual symptoms. Therefore, this study employs neonatal and pediatric electrocardiogram (ECG) to extract 52 hourly sets of linear and non-linear features from the HRV, starting from 24 hours prior to the clinical diagnosis of sepsis in patients with positive blood cultures (n=14). Similar sets of features were also obtained from a non-sepsis control group to create an evaluation benchmark (n=14).In particular, this study initially demonstrates how the variations within the 24 hours values of specific HRV feature-sets could effectively reveal prognostic information about the evolution of sepsis, prior to the actual clinical diagnosis. Moreover, this study demonstrates that differences in the values of a particular set of features at 22 hours before the actual clinical diagnosis/symptoms can be reliably used to train a convolutional neural network for automatic classification between the individuals in the sepsis and non-sepsis groups with 88.89±7.86% accuracy.

Automatically Identified Micro-scale Sharp-wave Transients in the Early-Latent Phase of Hypoxic-Ischemic EEG from Preterm Fetal Sheep Reveal Timing Relationship to Subcortical Neuronal Survival

Perinatal Hypoxic-Ischemia Encephalopathy (HIE) in newborn infants, due to birth-related circumstances such as oxygen deprivation in brain cells, is caused by the disruption in blood flow through the umbilical cord. Subcortical neuronal loss due to the HIE can lead to cerebral palsy and other chronic neurological conditions. Pre-clinical EEG studies using in utero sheep have demonstrated that particular micro-scale HI transients emerge along a suppressed EEG background during a latent phase of 3-6 hours, after a severe HI insult. Whilst the nature of these micro-scale transients is not well understood, it has been hypothesized that such transients may be signatures of the evolving hypoxic-ischemic brain injury, possessing the potential to be served as the diagnosis biomarkers for the injury. Cerebral hypothermia is optimally neuroprotective only if administered within the first 2-3 hours post HI insult. Using data from a cohort of in utero preterm fetal sheep (n=5, at 0.7 of gestational age), this paper indicates how the number of automatically quantified micro-scale sharp wave transients from asphyxiated preterm fetal sheep, statistically correlate to the amount of NeuN-positive neurons measured in caudate nucleus of striatum. Different temporal window sizes of 2hrs, 1hr, ½hr and 10mins within the early phase of the latent phase are examined using our developed Wavelet Type-2 Fuzzy classifier for sharp detection. Analyses were narrowed down to 10min intervals to assess where exactly in time the occurrence of the HI micro-scale sharp waves demonstrate a significant correlation. Signal processing wise, results from the sub-windows indicate a timing trend that highlights a positive correlation, between the number of automatic quantifications and the amount of surviving neurons in the preterm brain, permitting the possibility of a point of care (POC) intervention to stop the spread of injury before it becomes irreversible.

2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep

We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable prognostic biomarkers for Hypoxic-Ischemic Encephalopathy (HIE). Higher number of sharp waves within the first 2 hours from a hypoxic insult is shown to be significantly correlated to subcortical neuronal survival in caudate nucleus of striatum. Cerebral therapeutic hypothermia is also shown to be optimally neuroprotective only if initiated as soon as possible during a short window of opportunity within the first 2-3 hours of HI insult, called the latent phase. Therefore there is an urgent necessity for reliable automated algorithms to robustly identify such biomarkers to help early diagnosis of HIE, in real time at birth, before the optimal window of opportunity for treatment is missed.We have previously introduced successful automated signal processing strategies based on the fusion of wavelet and fuzzy techniques, for real-time identification and quantification of sharp waves along a profoundly suppressed EEG/ECoG background, post HI-insult, during the latent phase of sheep models. This work, in particular, for the first time represents a novel online fusion strategy based on the combination of a deep Convolutional Neural Network (CNN) in conjunction with Wavelet Scalogram (WS) for the real-time identification and classification of micro-scale sharp wave biomarkers within the 1024Hz high resolution ECoG recordings as well as the down-sampled 256Hz signals, from in utero preterm fetal sheep. The WS-CNN classifier highlights ability in the identification of HI sharp waves with remarkable high accuracies of 95.34% for 1024Hz and 94.62% for 256Hz data tested over one hour HI ECoG within the most important interval during the first 2 hours of the latent phase, where experiments have suggested hypothermia is optimally effective.

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Electroencephalogram studies of hypoxic ischemia in fetal and neonatal animal models

Alongside clinical achievements, experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ischemic encephalopathy (HIE). Due to the reasonably fair degree of flexibility with experiments, most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury, following HI insult. This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents, piglets, sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.

Using Porcine Cadavers as an Alternative to Human Cadavers for Teaching Minimally Invasive Spinal Fusion: Proof of Concept and Anatomical Comparison

Training surgeons to perform minimally invasive spinal (MIS) surgery is difficult because there are few realistic alternatives to human cadavers which are expensive and require special handling. In this study we report a protocol for performing an MIS training course on a fresh porcine cadaver. We find that the porcine lumbar spine closely resembles the human spine in terms of the vertebral and discal anatomy. Notable differences include a lower disc height and shallower diameter. We obtained fresh porcine cadavers weighing 40-70 kg from local farmers that had been gutted and bled. We position the cadaver prone on a backboard and set up the operating room with biplanar fluoroscopy. During approach and cage insertion, we found that the tactile feedback obtained is realistic and allows surgeons to familiarize themselves with the procedure. Porcine cadavers were also an excellent tool for practicing pedicle screw fixation due to the larger pedicles. Five training courses involving eight surgeons noted that except for anatomical differences the training course was equivalent to training on human cadavers and unanimously preferred training on porcine cadavers to synthetic foam models. We conclude that porcine cadavers are a useful model for training surgeons in MIS surgery. Routine use of porcine cadavers may increase the availability of MIS surgery training.

Latent Phase Detection of Hypoxic-Ischemic Spike Transients in the EEG of Preterm Fetal Sheep Using Reverse Biorthogonal Wavelets & Fuzzy Classifier

Hypoxic-ischemic (HI) studies in preterms lack reliable prognostic biomarkers for diagnostic tests of HI encephalopathy (HIE). Our group's observations from in utero fetal sheep models suggest that potential biomarkers of HIE in the form of developing HI micro-scale epileptiform transients emerge along suppressed EEG/ECoG background during a latent phase of 6-7h post-insult. However, having to observe for the whole of the latent phase disqualifies any chance of clinical intervention. A precise automatic identification of these transients can help for a well-timed diagnosis of the HIE and to stop the spread of the injury before it becomes irreversible. This paper reports fusion of Reverse-Biorthogonal Wavelets with Type-1 Fuzzy classifiers, for the accurate real-time automatic identification and quantification of high-frequency HI spike transients in the latent phase, tested over seven in utero preterm sheep. Considerable high performance of 99.78 ± 0.10% was obtained from the Rbio-Wavelet Type-1 Fuzzy classifier for automatic identification of HI spikes tested over 42h of high-resolution recordings (sampling-freq:1024Hz). Data from post-insult automatic time-localization of high-frequency HI spikes reveals a promising trend in the average rate of the HI spikes, even in the animals with shorter occlusion periods, which highlights considerable higher number of transients within the first 2h post-insult.

Clinical and Radiological Outcomes of Oblique Lateral Lumbar Interbody Fusion

Oblique lateral lumbar interbody fusion (OLLIF) is a novel operation for fusions of the lumbar spine from T12-S1. In OLLIF, the disk is approached from an oblique lateral angle guided by electrophysiological monitoring and biplanar fluoroscopy; the disk space is accessed through Kambin's triangle. We present perioperative, clinical, patient-reported and radiological outcomes from a series of 303 OLLIF procedures on 568 levels performed by the same surgeon. For a single-level OLLIF, mean surgery time was 56.6 ± 37.7 minutes, with a blood loss of 42.2 ± 31.1 mL, fluoroscopy time of 198.8 ± 87.2 seconds and a hospital stay of 2.2 ± 1.7 days. At the one-year follow-up, 10-point pain scale scores improved from 8.6 ± 1.3 to 4.1 ± 3.0 (p < 0.001). Total Oswestry disability index score improved from 56.6% ± 15.3% to 38.6% ± 21.4% (p < 0.001). At the one-year follow-up, 15 (5%) patients had mild nerve root irritation defined as sensory symptoms and motor weakness better than 4/5. Only one patient had neuropraxia due to weakness (3/5). There was one case (0.3%) of superficial wound infection and one case of bleeding into the psoas major. Reoperation within one year was performed for 14 (4.7%) patients. Interbody fusion was achieved in 98.7% of levels. While OLLIF has previously been described, this study is the first to present clinical, patient-reported, and radiological outcomes of OLLIF. Review of the literature shows that OLLIF produces perioperative outcomes, complication rates, and fusion rates that compare favorably with similar procedures. We establish that OLLIF is a safe, efficient and efficacious procedure for fusions of the lumbar spine.

EEG sharp waves are a biomarker of striatal neuronal survival after hypoxia-ischemia in preterm fetal sheep

The timing of hypoxia-ischemia (HI) in preterm infants is often uncertain and there are few biomarkers to determine whether infants are in a treatable stage of injury. We evaluated whether epileptiform sharp waves recorded from the parietal cortex could provide early prediction of neuronal loss after HI. Preterm fetal sheep (0.7 gestation) underwent acute HI induced by complete umbilical cord occlusion for 25 minutes (n = 6) or sham occlusion (control, n = 6). Neuronal survival was assessed 7 days after HI by immunohistochemistry. Sharp waves were quantified manually and using a wavelet-type-2-fuzzy-logic-system during the first 4 hours of recovery. HI resulted in significant subcortical neuronal loss. Sharp waves counted by the automated classifier in the first 30 minutes after HI were associated with greater neuronal survival in the caudate nucleus (r = 0.80), whereas sharp waves between 2–4 hours after HI were associated with reduced neuronal survival (r = −0.83). Manual and automated counts were closely correlated. This study suggests that automated quantification of sharp waves may be useful for early assessment of HI injury in preterm infants. However, the pattern of evolution of sharp waves after HI was markedly affected by the severity of neuronal loss, and therefore early, continuous monitoring is essential.

Effect of Body Mass Index on Perioperative Outcomes in Minimally Invasive Oblique Lateral Lumbar Interbody Fusion versus Open Fusions: A Multivariant Analysis

Background

Obesity is an increasing public health concern associated with increased perioperative complications and expense in lumbar spine fusions. While open and mini-open fusions such as transforaminal lumbar interbody fusion (TLIF) and minimally invasive TLIF (MIS-TLIF) are more challenging in obese patients, new MIS procedures like oblique lateral lumbar interbody fusion (OLLIF) may improve perioperative outcomes in obese patients relative to TLIF and MIS-TLIF.

Purpose

The purpose of this study is to determine the effects of obesity on perioperative outcomes in OLLIF, MIS-TLIF, and TLIF.

Study design

This is a retrospective cohort study.

Patient sample

We included patients who underwent OLLIF, MIS-TLIF, or TLIF on three or fewer spinal levels at a single Minnesota hospital after conservative therapy had failed. Indications included in this study were degenerative disc disease, spondylolisthesis, spondylosis, herniation, stenosis, and scoliosis.

Outcome measures

We measured demographic information, body mass index (BMI), surgery time, blood loss, and hospital stay.

Methods

We performed summary statistics to compare perioperative outcomes in MIS-TLIF, OLLIF, and TLIF. We performed multivariate regression to determine the effects of BMI on perioperative outcomes controlling for demographics and number of levels on which surgeries were operated.

Results

OLLIF significantly reduces surgery time, blood loss, and hospital stay compared to MIS-TLIF, and TLIF for all levels. MIS-TLIF and TLIF do not differ significantly except for a slight reduction in hospital stay for two-level procedures. On multivariate analysis, a one-point increase in BMI increased surgery time by 0.56 ± 0.47 minutes (p = 0.24) in the OLLIF group, by 2.8 ± 1.43 minutes (p = 0.06) in the MIS-TLIF group, and by 1.7 ± 0.43 minutes (p < 0.001) in the TLIF group. BMI has positive effects on blood loss for TLIF (p < 0.001) but not for OLLIF (p = 0.68) or MIS-TLIF (p = 0.67). BMI does not have significant effects on length of hospital stay for any procedure.

Conclusions

Obesity is associated with increased surgery time and blood loss in TLIF and with increased surgery time in MIS-TLIF. Increased surgery time may be associated with increased perioperative complications and cost. In OLLIF, BMI does not affect perioperative outcomes. Therefore, OLLIF may reduce the disparity in outcomes and cost between obese and non-obese patients.

The Assessment of Fusion Following Sacroiliac Joint Fusion Surgery

Sacroiliac joint fusions (SIJF) have been the subject of many research studies. The technical success of an SIJF is in part determined by whether osseous bridging occurs across the sacroiliac joint (SIJ). However, no validated SIJF assessment method has been described. Our objective was to document previously described SIJF assessment methods and define and validate a detailed assessment system for SIJF. Our results are only intended to establish computed tomography (CT)-based guidelines for SIJF to be used in a subsequent large clinical study to correlate them with clinical outcomes.

The SIJF literature was reviewed to document previous descriptions of SIJF assessments. A detailed system was then developed for assessing SIJF from CT exams. To provide data that can be used to address a range of research questions, the system included assessing bridging bone relative to the SIJ anatomy, bridging bone immediately adjacent to the threaded implants crossing the joint, as well as bridging bone close to but not immediately adjacent to the implants. The system was applied to assessing SIJF from thin-slice CT exams in 19 patients 12 months following surgery. Two experienced radiologists implemented the assessment system, and in the event of a disagreement, an adjudicator was used.

Most prior studies provide very little detail about how SIJF was assessed. Using the new assessment system, the agreement between the primary readers was substantial (0.67 using Gwet’s AC1 statistic). Bridging bone representing a fusion of the SIJ was identified in most patients both immediately adjacent to the threaded implants crossing the joint, as well as distant to the implants.

A detailed radiographic assessment system proved to be applicable to SIJF. The assessment system includes explicit language describing the location and extent of bridging bone across the SIJ. Standardization of the assessment of the SIJFs may allow for a more meaningful comparison of data between studies.

Examining the effect of MgSO4 on sharp wave transient activity in the hypoxic-ischemic fetal sheep model

Hypoxic-ischemic encephalopathy (HIE) due to lack of oxygen is a debilitating disorder experienced by a significant number of preterm infants during birth. Studies show that the brain undergoes different phases of injury following hypoxic insult, but the first 6-8 hours (known as a latent phase) are the key to treatment efficacy. Cerebral hypothermia is one known treatment, and for it to be effective it must be started during the latent phase and continued for several days. In order to determine the effectiveness of treatment it is important to pinpoint the time of insult. Monitoring of sharp wave transient activity in the hypoxic-ischemic (HI) electroencephalogram (EEG) could be a predictor for time of hypoxic insult. Due to practicality, it is optimal if this monitoring is performed automatically. Further, MgSO4 is a drug given to an increasing number of women in labor, due to its neuroprotective properties. This drug may influence transient activity in the HI fetal sheep EEG, leading to further complications in predicting hypoxic insult. This paper explores the effect of MgSO4 on sharp wave transient activity in the EEG of a HI fetal sheep. Demonstrated in this paper is the usage of a Wavelet-Type-II Fuzzy classifier to detect sharp wave transients during the latent phase of a control group fetal sheep and an MgSO4-treated fetal sheep. This detection was performed with an average overall performance of 93.21%±5.49 over 660 minutes of latent phase, post occlusion. There were no significant differences in number of sharp wave transients in the early- and mid-latent phases of injury for both fetal sheep. However, in the late-latent phase the MgSO4-treated fetal sheep had significantly fewer sharp wave transients than the control fetal sheep.

Minimally Invasive Scoliosis Surgery with Oblique Lateral Lumbar Interbody Fusion: Single Surgeon Feasibility Study

BACKGROUND

Degenerative deformities of the spine have traditionally been treated with extensive open surgeries. However, these open procedures are associated with a high degree of surgical morbidity. In this study, we explore whether clinical improvement in patients with spinal deformities can be achieved using a new minimally invasive surgery (MIS) called oblique lateral lumbar interbody fusion (OLLIF). OLLIF is a MIS single surgeon procedure in which the disc is approached through Kambin's triangle. OLLIF can achieve correction of spinal deformities through careful cage placement.

PURPOSE

The purpose of this study is to establish the safety and efficacy of using OLLIF to correct spinal deformities and to collect early outcome data. Collected data includes perioperative outcomes, patient reported outcomes, and radiographic outcomes.

STUDY DESIGN/SETTING

This study is a retrospective review of 37 OLLIF surgeries in 36 patients with symptomatic degenerative spinal deformity. Collected perioperative data included surgery time, blood loss, and hospital stay. Follow-up was conducted at least 150 days post surgery. We recorded complications and patient reported outcomes such as Oswestry Disability Index (ODI) and pain scale. Imaging was conducted pre- and post-surgery. Fusion rates and changes in Cobb angle were also measured.

RESULTS

A total of 37 surgeries that treated 100 vertebral levels were performed. For two and three level procedures, respectively, the mean blood loss was 83 and 178 ml, the average surgery time was 74 and 158 minutes and the average hospital stay was 2.6 and 3.3 days. The patients ambulated within 24 hours in all but two cases. The patients reported pain improvements on the ten-point pain scale from 8.3 to 3.7 (p<0.001) and on the ODI from 53% to 32%. Cobb angles decreased from 16° to 9.3° (p<0.001), amounting to 2.5° of correction per level of surgery. Detailed imaging was reviewed by independent radiologists for 24 cases and 100% interbody fusion was achieved along with 71% right posterolateral and 74% left posterolateral fusion. There were three cases of mild nerve irritation/neuropraxia and no infections.

CONCLUSIONS

OLLIF is a safe and effective MIS technique to correct adult degenerative scoliosis. Unlike alternative procedures, OLLIF is technically less complex than comparable procedures and can safely be used from the thoracolumbar junction to S1.

Minimally Invasive Direct Lateral Interbody Fusion (MIS-DLIF): Proof of Concept and Perioperative Results

BACKGROUND

Minimally invasive direct lateral interbody fusion (MIS-DLIF) is a novel approach for fusions of the lumbar spine. In this proof of concept study, we describe the surgical technique and report our experience and the perioperative outcomes of the first nine patients who underwent this procedure.

STUDY DESIGN/SETTING

In this study we establish the safety and efficacy of this approach. MIS-DLIF was performed on 15 spinal levels in nine patients who failed to respond to conservative therapy for the treatment of a re-herniated disk, spondylolisthesis, or other severe disk disease of the lumbar spine. We recorded surgery time, blood loss, fluoroscopy time, patient-reported pain, and complications.

METHODS

Throughout the MIS-DLIF procedure, the surgeon is aided by biplanar fluoroscopic imaging to place an interbody graft or cage into the disc space through the interpleural space. A discectomy is performed in the same minimally invasive fashion. The procedure is usually completed with posterior pedicle screw fixation.

RESULTS

MIS-DLIF took 44/85 minutes, on average, for 1/2 levels, with 54/112 ml of blood loss, and 0.3/1.7 days of hospital stay. Four of nine patients did not require overnight hospitalization and were discharged two to four hours after surgery. We did not encounter any clinically significant complications. At more than ninety days post surgery, the patients reported a statistically significant reduction of 4.5 points on a 10-point sliding pain scale.

CONCLUSIONS

MIS-DLIF with pedicle screw fixation is a safe and clinically effective procedure for fusions of the lumbar spine. The procedure overcomes many of the limitations of the current minimally invasive approaches to the lumbar spine and is technically straightforward. MIS-DLIF has the potential to improve patient outcomes and reduce costs relative to the current standard of care and therefore warrants further investigation. We are currently expanding this study to a larger cohort and documenting long-term outcome data.

Comparative dynamics of 5-methylcytosine reprogramming and TET family expression during preimplantation mammalian development in mouse and sheep

Despite previous assumption that paternal active DNA demethylation is an evolutionary conserved phenomenon in mammals, emerging studies in other species, particularly sheep, do not support this issue. Recently, ten eleven translocation (TET) enzymes have been suggested as intermediates in genome-wide DNA demethylation through the iterative conversion of five methylcytosine (5mC) into 5-hydroxymethylcytosine (5hmC)/5-formylcytosine/5-carboxylcytosine (5caC) derivatives. This study investigated whether TET enzymes and 5mC derivatives are also involved in dynamic reprogramming of early sheep embryos derived by fertilization. Mouse zygotes and developing embryos were considered as control. Obtained results reported substantial differences in dynamics of parent-of-origin-specific patterns of 5mC reprogramming and generation/dilution of 5mC derivatives (5hmC and 5caC) between mouse and sheep early zygotes. Sheep zygotes reported a gradual and insignificant decrease pattern of parental pronucleus 5mC, which was notably replication independent, coincided with gradual generation of 5hmC and 5caC. Although the expression profiles of TET family of enzymes (Tet1, Tet2, and Tet3), with the main exception being Tet2 at later developmental stages, were similar between mouse and sheep developing embryos. In addition, although the expression level of Tet3 was higher than Tet1 and Tet2 in MII oocytes and zygotes in both mouse and sheep, the expression of Tet3 in mouse was higher than sheep in both MII oocytes and zygotes. The contrasting dynamics of 5mC reprogramming between these two species may be associated with the particular evolutionary differences that exist between developmental program of rodents and ruminants, particularly during peri-implantation stages.

Using type-2 fuzzy logic systems for spike detection in the hypoxic ischemic EEG of the preterm fetal sheep

Perinatal hypoxia is a major cause of brain injury in preterm babies. Thus, neuro-protective treatments play a pivotal role during the first 6-8 hours post hypoxic-ischemic insult. However, at present it is not possible to determine which infants are suffering from hypoxic ischemia. Recent investigations suggest that there are high frequency micro-scale transients exist in the first 6-8 hours of a hypoxic ischemic EEG which could be utilized as the useful benchmarks for the prediction of hypoxia. Type-2 Fuzzy Logic Systems (Type-2 FLS) have the capability to handle inherent uncertainties in nonlinear signals. This paper describes the application of a Type-2 FLS to detect spikes in the preterm fetal sheep electroencephalogram (EEG) after asphyxia in utero. The Type-2 FLS differentiates each detected event in terms of its spikiness and specifies the potential events based on their degree of similarity to an EEG expert definition of a standard spike. An adaptive thresholding method has been employed in order to increase the spike detection ability of the purposed system. The sensitivity and selectivity verify enhanced performance of the Type-2 FLS for spike detection in fetal sheep EEG signals with a 98.1% and 93.7% respectively which are significantly improved in comparison to our previous methods.

Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia

Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.

Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier

Perinatal hypoxic-ischemic encephalopathy (HIE) around the time of birth due to lack of oxygen can lead to debilitating neurological conditions such as epilepsy and cerebral palsy. Experimental data have shown that brain injury evolves over time, but during the first 6-8 hours after HIE the brain has recovered oxidative metabolism in a latent phase, and brain injury is reversible. Treatments such as therapeutic cerebral hypothermia (brain cooling) are effective when started during the latent phase, and continued for several days. Effectiveness of hypothermia is lost if started after the latent phase. Post occlusion monitoring of particular micro-scale transients in the hypoxic-ischemic (HI) Electroencephalogram (EEG), from an asphyxiated fetal sheep model in utero, could provide precursory evidence to identify potential biomarkers of injury when brain damage is still treatable. In our studies, we have reported how it is possible to automatically detect HI EEG transients in the form of spikes and sharp waves during the latent phase of the HI EEG of the preterm fetal sheep. This paper describes how to identify stereotypic evolving micro-scale seizures (SEMS) which have a relatively abrupt onset and termination in a frequency range of 1.8-3Hz (Delta waves) superimposed on a suppressed EEG amplitude background post occlusion. This research demonstrates how a Wavelet Type-II Fuzzy Logic System (WT-Type-II-FLS) can be used to automatically identify subtle abnormal SEMS that occur during the latent phase with a preliminary average validation overall performance of 78.71%±6.63 over the 390 minutes of the latent phase, post insult, using in utero pre-term hypoxic fetal sheep models.

Minimally Invasive Direct Thoracic Interbody Fusion (MIS-DTIF): Technical Notes of a Single Surgeon Study

Background

Minimally invasive direct thoracic interbody fusion (MIS-DTIF) is a new single surgeon procedure for fusion of the thoracic vertebrae below the scapula (T6/7) to the thoracolumbar junction. In this proof of concept study, we describe the surgical technique for MIS-DTIF and report our experience and the perioperative outcomes of the first four patients who underwent this procedure.

Study design/setting

In this study we attempt to establish the safety and efficacy of MIS-DTIF. We have performed MIS-DTIF on six spinal levels in four patients with degenerative disk disease or disk herniation. We recorded surgery time, blood loss, fluoroscopy time, complications, and patient-reported pain.

Methods

Throughout the MIS-DTIF procedure, the surgeon is aided by biplanar fluoroscopic imaging and electrophysiological monitoring. The surgeon approaches the spine with a series of gentle tissue dilations and inserts a working tube that establishes a direct connection from the outside of the skin to the disk space. Through this working tube, the surgeon performs a discectomy and inserts an interbody graft or cage. The procedure is completed with minimally invasive (MI) posterior pedicle screw fixation.

Results

For the single level patients the mean blood loss was 90 ml, surgery time 43 minutes, fluoroscopy time 293 seconds, and hospital stay two days. For the two-level surgeries, the mean blood loss was 27 ml, surgery time 61 minutes, fluoroscopy time 321 seconds, and hospital stay three days. We did not encounter any clinically significant complications. Thirty days post-surgery, the patients reported a statistically significant reduction of 5.3 points on a 10-point sliding pain scale.

Conclusions

MIS-DTIF with pedicle screw fixation is a safe and clinically effective procedure for fusions of the thoracic spine. The procedure is technically straightforward and overcomes many of the limitations of the current minimally invasive (MI) approaches to the thoracic spine. MIS-DTIF has the potential to improve patient outcomes and reduce costs relative to the current standard of care. We are currently expanding this study to a larger cohort and recording long term outcomes and costs.

Superiority of high frequency hypoxic ischemic EEG signals of fetal sheep for sharp wave detection using Wavelet-Type 2 Fuzzy classifiers

There is approximately a 6-8 hour window that exists from when a hypoxic-ischemic insult occurs, in utero, before significant irreversible brain injury occurs in new born infants. The focus of our work is to determine through the electroencephalogram (EEG) if such a hypoxic-ischemic insult has occurred such that neuro-protective treatment can be sought within this period. At present, there are no defined biomarkers in the EEG that are currently being used to help classify if a hypoxic ischemia insult has occurred. However, micro-scale transients in the form of spikes, sharps and slow waves exists that could provide precursory information whether a hypoxic-ischemic insult has occurred or not. In our previous studies we have successfully automatically identified spikes with high sensitivity and selectivity in the conventional 64Hz sampled EEG. This paper details the significant advantage that can be obtained in using high frequency 1024Hz sampled EEG for sharp wave detection over the typically employed 64Hz sampled EEG. This advantage is amplified when a combination of wavelet Type-2 Fuzzy Logic System (WT-Type-2-FLS) classifiers are used to identify the sharp wave transients. By applying WT-Type-2-FLS to the 1024Hz EEG record and to the same down-sampled 64Hz EEG record we demonstrate, how the sharp wave transients detection increases significantly for high resolution 1024Hz EEG over 64Hz EEG. The WT-Type-2-FLS algorithm performance was assessed over 3 standardised time periods within the first 8 hours, post occlusion of a fetal sheep, in utero. 1024Hz EEG results demonstrate the algorithm detected sharps with overall performance rates of 85%, 92%, and 87% in the Early/Mid and Late-latent phases of injury, respectively as compared to 25%, 55% and 31% in the 64Hz EEG. These results demonstrate the power of Wavelet Type-2 Fuzzy Logic System at detecting sharp waves in 1024Hz EEG and suggest that there should be a movement toward recording high frequency EEG for analysis of hypoxic ischemic micro-scale transients that does not occur at present.

Oblique Lateral Lumbar Interbody Fusion (OLLIF): Technical Notes and Early Results of a Single Surgeon Comparative Study

BACKGROUND CONTEXT

 Lower back pain is one of the most prevalent and expensive health conditions in the Western world. The standard treatment, interbody fusion, is an invasive procedure that requires the stripping of muscles and soft tissue, leading to surgical morbidity. Current minimally invasive (MI) spinal fusions are technically demanding and suffer from technical limitations.

PURPOSE

Oblique lumbar lateral interbody fusion (OLLIF) is a new technique for fusion of the lumbar spine that overcomes these complications. Outcome measures include patient demographics, reported outcomes, and surgical outcomes.

STUDY DESIGN/SETTING

 Kambin's Triangle can easily be located as a silent window with an electrophysiological probe. Discectomy is performed through a single access portal with a 10 mm diameter. After a discectomy, the disc space is packed with beta-tricalcium phosphate soaked in autologous bone marrow, aspirated, and the cage is inserted. Finally, a minimally invasive posterior fixation is performed.

METHODS

OLLIF's major innovation is to approach the disc through Kambin's Triangle, aided by bilateral fluoroscopy.

RESULTS

 We present data from 69 consecutive OLLIF surgeries on 128 levels with a control group of 55 consecutive open transformational lumbar interbody fusions (TLIFs) on 125 levels. For a single level OLLIF, the mean surgery time is 69 minutes (min) and blood loss is 29 ml. Surgery time was approximately twice as fast as open TLIF (mean: 135 min) and blood loss is reduced by over 80% compared to TLIF (mean: 355 ml).

CONCLUSIONS

 OLLIF is a minimally invasive fusion that significantly reduces surgery times compared to open surgery. OLLIF overcomes the difficulties of traditional open fusions, making it a safe and technically less demanding surgery than open or minimally invasive TLIF.

Economic Performance of Oblique Lateral Lumbar Interbody Fusion (OLLIF) with a Focus on Hospital Throughput Efficiency

Oblique lateral lumbar interbody fusion (OLLIF) is a minimally invasive lumbar surgery. Differences in resource consumption between open spinal surgeries, transformational lumbar interbody fusions (TLIF) and OLLIF, are not documented. We monetize quantifiable differences in resource utilization between the two procedures. A retrospective review of 124 surgeries was performed (OLLIF=69, TLIF=55). Standard conversion factors were used and values reported based on the levels (1-4) addressed at surgery. One level surgery time (OLLIF 62.9 vs. TLIF 134.9 minutes) and surgical expense (OLLIF $5,253 vs. TLIF $11,264) were reduced in the OLLIF population. Inpatient costs (OLLIF $5,712 vs. TLIF $9,271) and length of stay (LOS) were also reduced (OLLIF 2.6 vs. TLIF 4.2 days). Per case, reduced resource consumption suggests lower total hospital costs. Reduced surgical time and LOS can result in greater patient throughput per operating room and patient bed for OLLIF patients in hospitals that have resourced constrained environments.

Reverse bi-orthogonal wavelets & fuzzy classifiers for the automatic detection of spike waves in the EEG of the hypoxic ischemic pre-term fetal sheep

There exists a 6-8 hour window of opportunity for the treatment of perinatal Hypoxic-Ischemic Encephalopathy (HIE) following the original insult after which significant irreversible brain injury manifests leading to debilitating neurological conditions such as epilepsy and cerebral palsy. At present, there are no identified biomarkers in the electroencephalogram (EEG) that are currently being used to help classify if a HIE insult has occurred or not. However, high frequency micro-scale transients in the form of spikes, sharp waves and slow waves appear in the EEG, post insult, that could provide precursory information whether a HIE insult has occurred or not. This paper describes the superiority of using reverse bi-orthogonal wavelets (RBIO-WT), in the form of the rbio2.8 mother wavelet, in conjunction with a Type-1 Fuzzy Logic System (Type-I FLS) classifier for accurate micro-scale spike wave transient detection in the EEG of Pre-term Fetal Sheep. The algorithm performance for spike detection was assessed over the most critical time period of 25 minutes within the first 8 hours, post occlusion using an in utero fetal sheep model. Obtained results demonstrate that the suggested algorithm detected spikes with a considerably high overall performance of 99.25% using the developed RBIO-WT Type-I FLS.

Glial cell line-derived neurotrophic factor in combination with insulin-like growth factor 1 and basic fibroblast growth factor promote in vitro culture of goat spermatogonial stem cells

Growth factors are increasingly considered as important regulators of spermatogonial stem cells (SSCs). This study investigated the effects of various growth factors (GDNF, IGF1, bFGF, EGF and GFRalpha-1) on purification and colonization of undifferentiated goat SSCs under in vitro and in vivo conditions. Irrespective of the culture condition used, the first signs of developing colonies were observed from day 4 of culture onwards. The number of colonies developed in GDNF + IGF1 + bFGF culture condition was significantly higher than the other groups (p < 0.05). In contrast, the size of colonies developed in GDNF + EGF + LIF culture condition was significantly higher than the other groups (p < 0.05). Immunocytochemical stationing for specific biomarkers of somatic cells (vimentin, alpha-inhibin and α-SMA) and spermatogonial cells (PLZF, THY 1, VASA, alpha-1 integrin, bet-1 integrin and DBA) revealed that both cell types existed in developing colonies, irrespective of the culture condition used. Even though, the relative abundance of VASA, FGFR3, OCT4, PLZF, BCL6B and THY1 transcription factors in GDNF + IGF1 + bFGF treatment group was significantly higher than the other groups (p < 0.05). Additionally, goat SSCs developed in the latter culture condition could colonize within the seminiferous tubules of the germ-cell depleted recipient mice following xenotransplantation. Obtained results demonstrated that combination of GDNF with IGF1 and bFGF promote in vitro culture of goat SSCs while precludes uncontrolled proliferation of somatic cells.