Presumed Neuroglycopenia Caused by Severe Hypoglycemia in Horses.

BackgroundNeuroglycopenia refers to a shortage of glucose in the brain resulting in neuronal dysfunction and death if left untreated. Presumed neuroglycopenia has not been described in horses.ObjectiveTo report neurological signs in horses with presumed neuroglycopenia as the result of severe hypoglycemia.AnimalsNinety horses (hours to 28 years of age) diagnosed with hypoglycemia (blood glucose concentration < 75 mg/dL [< 4.2 mmol/L]).MethodsRetrospective study. Electronic medical records were searched. Signalment, history, complaint, clinical signs, laboratory findings including CSF analysis, electroencephalogram, clinical or definitive diagnosis, and outcome were recorded. Kruskal-Wallis analysis of variance and logistic regression were used to investigate association between blood glucose concentration and data extracted. Statistical significance was set at P < 0.05.ResultsThirty-eight and 52 horses had mild (50-74 mg/dL [2.8-4.1 mmol/L]), and severe hypoglycemia (< 50 mg/dL [< 2.8 mmol/L]), respectively. Most common causes of hypoglycemia included liver and gastrointestinal (40%) disease, sepsis (33%), neoplasia (7%), and insulin-induced (4%). Most common neurologic deficits included obtundation (100%), seizures (42%), and disorientation (22%). CSF-glucose was severely low (mean 2.5 mg/dL [0.1 mmol/L], median 0 mg/dL). Paroxysmal discharges in support of seizures were identified in the occipital (visual) and parietal (closest to temporal-auditory) cortical regions upon EEG examination (8/8 horses).Conclusions and clinical importanceNeuroglycopenia is presumed to occur in horses as the result of severe hypoglycemia. Subclinical seizures, and intermittent blindness and deafness of cortical origin can occur. Severe altered state of consciousness and seizures can be observed at a blood glucose cut-off value of < 42 mg/dL (< 2.3 mmol/L)

Assessment of synchrony in multiple neural spike trains using loglinear point process models

Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS429 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Validation of a Clinical Scoring System for Outcome Prediction in Dogs with Acute Kidney Injury Managed by Hemodialysis.

BackgroundA scoring system for outcome prediction in dogs with acute kidney injury (AKI) recently has been developed but has not been validated.HypothesisThe scoring system previously developed for outcome prediction will accurately predict outcome in a validation cohort of dogs with AKI managed with hemodialysis.AnimalsOne hundred fifteen client-owned dogs with AKI.MethodsMedical records of dogs with AKI treated by hemodialysis between 2011 and 2015 were reviewed. Dogs were included only if all variables required to calculate the final predictive score were available, and the 30-day outcome was known. A predictive score for 3 models was calculated for each dog. Logistic regression was used to evaluate the association of the final predictive score with each model's outcome. Receiver operating curve (ROC) analyses were performed to determine sensitivity and specificity for each model based on previously established cut-off values.ResultsHigher scores for each model were associated with decreased survival probability (P < .001). Based on previously established cut-off values, 3 models (models A, B, C) were associated with sensitivities/specificities of 73/75%, 71/80%, and 75/86%, respectively, and correctly classified 74-80% of the dogs.Conclusions and clinical relevanceAll models were simple to apply and allowed outcome prediction that closely corresponded with actual outcome in an independent cohort. As expected, accuracies were slightly lower compared with those from the previously reported cohort used initially to develop the models

Differences in clinicopathologic variables between Borrelia C6 antigen seroreactive and Borrelia C6 seronegative glomerulopathy in dogs.

BackgroundRapidly progressive glomerulonephritis has been described in dogs that seroreact to Borrelia burgdorferi, but no studies have compared clinicopathologic differences in Lyme-seroreactive dogs with protein-losing nephropathy (PLN) versus dogs with Borrelia-seronegative PLN.Hypothesis/objectivesDogs with Borrelia C6 antigen-seroreactive PLN have distinct clinicopathologic findings when compared to dogs with Borrelia seronegative PLN.AnimalsForty dogs with PLN and Borrelia C6 antigen seroreactivity and 78 C6-seronegative temporally matched dogs with PLN.MethodsRetrospective prevalence case-control study. Clinical information was retrieved from records of dogs examined at the University of California, Davis, Veterinary Medical Teaching Hospital. Histopathologic findings in renal tissue procured by biopsy or necropsy of dogs with PLN were reviewed.ResultsRetrievers and retriever mixes were overrepresented in seroreactive dogs (P < .001). Seroreactive dogs were more likely to have thrombocytopenia (P < .001), azotemia (P = .002), hyperphosphatemia (P < .001), anemia (P < .001), and neutrophilia (P = .003). Hematuria, glucosuria, and pyuria despite negative urine culture were more likely in seroreactive dogs (all P ≤ .002). Histopathologic findings were consistent with immune-complex glomerulonephritis in 16 of 16 case dogs and 7 of 23 control dogs (P = 006). Prevalence of polyarthritis was not different between groups (P = .17).Conclusions and clinical importanceC6 seroreactivity in dogs with PLN is associated with a clinicopathologically distinct syndrome when compared with other types of PLN. Early recognition of this syndrome has the potential to improve outcomes through specific aggressive and early treatment

False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper

FastVentricle: Cardiac Segmentation with ENet

Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advance the state-of-the-art in automated segmentation, which makes FCNs a natural choice for ventricular segmentation. However, FCNs are limited by their computational cost, which increases the monetary cost and degrades the user experience of production systems. To combat this shortcoming, we have developed the FastVentricle architecture, an FCN architecture for ventricular segmentation based on the recently developed ENet architecture. FastVentricle is 4x faster and runs with 6x less memory than the previous state-of-the-art ventricular segmentation architecture while still maintaining excellent clinical accuracy.Comment: 11 pages, 6 figures, Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201

Early tumor response to intraarterial or intravenous administration of carboplatin to treat naturally occurring lower urinary tract carcinoma in dogs.

BackgroundSurvival times and tumor responses associated with malignant neoplasia of the lower urinary tract are poor despite the vast array of current treatments. Therefore, the evaluation of alternative treatments, such as intraarterial administration of chemotherapy (IAC) should be considered.ObjectiveTo describe a technique for superselective catheterization for IAC and to evaluate initial tumor response by ultrasonography after both IAC and intravenous administration of chemotherapy (IVC).AnimalsClient-owned dogs with lower urinary tract neoplasia treated with either IVC (n = 15) or IAC (n = 11).MethodsRetrospective study. An arterial approach via the carotid or femoral artery was utilized to obtain superselective access and administer chemotherapy in the IAC cases. Medical record review was performed, data were recorded, and recorded variables were evaluated statistically.ResultsIntraarterial chemotherapy was successfully administered in all cases. There was a significantly greater decrease in longest unidimensional measurement in the IAC group as compared to the IVC group (P = .013). The IAC group was also significantly more likely to have a tumor response as assessed by modified RECIST guidelines (P = .049). Dogs in the IAC group were significantly less likely to develop anemia (P = .001), lethargy (P = .010) and anorexia (P = .024).Conclusion and clinical importanceThis study demonstrated the feasibility and efficacy of performing IAC for lower urinary tract neoplasia. Further investigation is necessary as the follow-up time was short and the impact on long-term outcome and survival was not determined