A systematic review of neuroprotective strategies after cardiac arrest: from bench to bedside (Part I - Protection via specific pathways).

Neurocognitive deficits are a major source of morbidity in survivors of cardiac arrest. Treatment options that could be implemented either during cardiopulmonary resuscitation or after return of spontaneous circulation to improve these neurological deficits are limited. We conducted a literature review of treatment protocols designed to evaluate neurologic outcome and survival following cardiac arrest with associated global cerebral ischemia. The search was limited to investigational therapies that were utilized to treat global cerebral ischemia associated with cardiac arrest. In this review we discuss potential mechanisms of neurologic protection following cardiac arrest including actions of several medical gases such as xenon, argon, and nitric oxide. The 3 included mechanisms are: 1. Modulation of neuronal cell death; 2. Alteration of oxygen free radicals; and 3. Improving cerebral hemodynamics. Only a few approaches have been evaluated in limited fashion in cardiac arrest patients and results show inconclusive neuroprotective effects. Future research focusing on combined neuroprotective strategies that target multiple pathways are compelling in the setting of global brain ischemia resulting from cardiac arrest

Highly accurate model for prediction of lung nodule malignancy with CT scans

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://bioinformatics.astate.edu/NoduleX

The quest to model chronic traumatic encephalopathy: a multiple model and injury paradigm experience

Chronic neurodegeneration following a history of neurotrauma is frequently associated with neuropsychiatric and cognitive symptoms. In order to enhance understanding about the underlying pathophysiology linking neurotrauma to neurodegeneration, a multi-model preclinical approach must be established to account for the different injury paradigms and pathophysiologic mechanisms. We investigated the development of tau pathology and behavioral changes using a multi-model and multi-institutional approach, comparing the preclinical results to tauopathy patterns seen in post-mortem human samples from athletes diagnosed with chronic traumatic encephalopathy (CTE). We utilized a scaled and validated blast-induced traumatic brain injury model in rats and a modified pneumatic closed-head impact model in mice. Tau hyperphosphorylation was evaluated by western blot and immunohistochemistry. Elevated-plus maze and Morris water maze were employed to measure impulsive-like behavior and cognitive deficits respectively. Animals exposed to single blast (~50 PSI reflected peak overpressure) exhibited elevated AT8 immunoreactivity in the contralateral hippocampus at 1 month compared to controls (q = 3.96, p \u3c 0.05). Animals exposed to repeat blast (six blasts over 2 weeks) had increased AT8 (q = 8.12, p \u3c 0.001) and AT270 (q = 4.03, p \u3c 0.05) in the contralateral hippocampus at 1 month post-injury compared to controls. In the modified controlled closed-head impact mouse model, no significant difference in AT8 was seen at 7 days, however a significant elevation was detected at 1 month following injury in the ipsilateral hippocampus compared to control (q = 4.34, p \u3c 0.05). Elevated-plus maze data revealed that rats exposed to single blast (q = 3.53, p \u3c 0.05) and repeat blast (q = 4.21, p \u3c 0.05) spent more time in seconds exploring the open arms compared to controls. Morris water maze testing revealed a significant difference between groups in acquisition times on days 22–27. During the probe trial, single blast (t = 6.44, p \u3c 0.05) and repeat blast (t = 8.00, p \u3c 0.05) rats spent less time in seconds exploring where the platform had been located compared to controls. This study provides a multi-model example of replicating tau and behavioral changes in animals and provides a foundation for future investigation of CTE disease pathophysiology and therapeutic development

Biological responses to glyphosate drift from aerial application in non-glyphosate-resistant corn

BACKGROUND: Glyphosate drift from aerial application onto susceptible crops is inevitable, yet the biological responses to glyphosate drift in crops are not well characterized. The objectives of this research were to determine the effects of glyphosate drift from a single aerial application (18.3m swath, 866 g AE ha−1) on corn injury, chlorophyll content, shikimate level, plant height and shoot dry weight in non-glyphosate-resistant (non-GR) corn. RESULTS: One week after application (WAA), corn was killed at 3m from the edge of the spray swath, with injury decreasing to 18% at 35.4m downwind. Chlorophyll content decreased from 78% at 6m to 22% at 15.8m, and it was unaffected beyond 25.6m at 1 WAA. Shikimate accumulation in corn decreased from 349% at 0m to 93% at 15.8m, and shikimate levels were unaffected beyond 25.6m downwind. Plant height and shoot dry weight decreased gradually with increasing distance. At a distance of 35.4m, corn height was reduced by 14% and shoot dry weight by 10% at 3WAA. CONCLUSIONS: Corn injury and other biological responses point to the same conclusion, that is, injury from glyphosate aerial drift is highest at the edge of the spray swath and decreases gradually with distance. The LD50 (the lethal distance that drift must travel to cause a 50% reduction in biological response) ranged from 12 to 26m among the biological parameters when wind speed was 11.2 kmh−1 and using a complement of CP-09 spray nozzles on spray aircraft

Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful

Longwave Band-by-band Cloud Radiative Effect and its Application in GCM Evaluation

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM fs radiation code is uniquely valuable for GCM evaluation because (1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and (2) the fractional contribution of each band to the LW broadband CRE (f(sub CRE)) is sensitive to cloud top height but largely insensitive to cloud fraction, presenting thus a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band ]by ]band quantities from collocated AIRS and CERES observations. We present here a study that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs (GFDL AM2, NASA GEOS-5, and CCCma CanAM4) forced by observed SST. The models agree with observation on the annual ]mean LW broadband CRE over the tropical oceans within +/-1W/sq m. However, the differences among these three GCMs in some bands can be as large as or even larger than +/-1W/sq m. Observed seasonal cycles of f(sub CRE) in major bands are shown to be consistent with the seasonal cycle of cloud top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f(sub CRE) agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f(sub CRE) highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands