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The use of gadolinium-based contrast agents (GBCA) is integral to the field of diagnostic magnetic resonance imaging (MRI). Pharmacokinetic evaluation of the plasma clearance of GBCA is required for all new agents or improved formulations, to address concerns over toxicity or unforeseen side effects. Current methods to measure GBCA in plasma lack either a rapid readout or the sensitivity to measure small samples or require extensive processing of plasma, all obstacles in the development and characterization of new GBCA. Here, we quantify the plasma concentration of a labeled analogue of a common clinical GBCA by ligand triplet harvesting and energy transfer. The nonemittive GBCA becomes a "dark donor" to a fluorescent detector molecule, with a lower limit of detection of 10(-7) M in unprocessed plasma. On a time scale of minutes, we determine the plasma clearance rate in the wild-type mouse, using time-resolved fluorescence on a standard laboratory plate reader.

Rats were fed a liquid diet to which an increasing concentration of ethanol was added over a period of 3 weeks; a concentration of 10% ethanol was then maintained for a further 4 weeks. The rats were then returned to a normal ad libitum diet of rat pellets. In order to assess the long-term effects of this ethanol diet, the performance of different groups of rats was assessed 3 and 5 months after the normal diet was resumed. At both times, the ex-ethanol-treated rats showed significantly impaired between-day habituation of exploratory head-dipping at holes that were empty, but normal between-day habituation of head-dipping at the hole with the same object underneath on all 3 days. The ex-ethanol-treated rats also showed a reduced response to the introduction of an object at a previously empty hole and, following this, a subsequent disruption of between-day habituation of head-dipping at this hole. Within-session habituation of head-dipping was unimpaired. There were no deficits in the acquisition or short-term retention of a passive avoidance response, but on retention testing 24 h after training the ex-ethanol-treated rats showed a decreased latency to enter the shocked compartment. On the trials 24 h and 48 h after training the rats tested 5 months after their ethanol treatment showed impaired extinction of the passive avoidance response.

Chronic ethanol consumption dose-dependently affects both incidence and prognosis of ischemic stroke. Our goal was to determine whether the influence of chronic ethanol consumption on ischemic stroke is related to an altered inflammatory profile in the brain. Male C57BL/6J mice were divided into six groups and gavage fed with 0.175, 0.35, 0.7, 1.4, 2.8 g/kg/day ethanol or volume-matched water once a day for 8 weeks. Adhesion molecules, microglial activation, neutrophil infiltration, pro- and anti-inflammatory cytokines/chemokines, blood-brain barrier (BBB) permeability, and matrix metallopeptidases (MMPs) in the cerebral cortex before and following a 90-min unilateral middle cerebral artery occlusion (MCAO)/24-h reperfusion were evaluated. Brain ischemia/reperfusion (I/R) injury was significantly reduced in 0.7 g/kg/day ethanol group (peak blood ethanol concentration: 9 mM) and worsened in 2.8 g/kg/day ethanol group (peak blood ethanol concentration: 37 mM). Baseline E-selectin was downregulated in all ethanol groups, whereas baseline intercellular adhesion molecule-1 (ICAM-1) was only downregulated in 0.35 and 0.7 g/kg/day ethanol groups. Interestingly, baseline vascular cell adhesion molecule-1 (VCAM-1) was upregulated in 0.35, 0.7, and 1.4 g/kg/day ethanol groups. Post-ischemic upregulation of ICAM-1 and E-selectin were suppressed in all ethanol groups. Post-ischemic neutrophil infiltration and microglial activation were significantly less in the low-moderate (0.175-1.4 g/kg/day) ethanol groups but greater in the 2.8 g/kg/day ethanol group compared to the vehicle group. At basal conditions, ethanol increased one pro- and two anti-inflammatory cytokines/chemokines at the 0.7 g/kg/day dose, and 13 pro- and eight anti-inflammatory cytokines/chemokines at the 2.8 g/kg/day dose. After ischemia, 0.7 g/kg/day ethanol suppressed post-ischemic pro-inflammatory cytokines/chemokines and enhanced post-ischemic anti-inflammatory cytokines/chemokines. Moreover, 0.7 g/kg/day ethanol significantly reduced baseline MMP-9 activity and alleviated post-ischemic BBB breakdown. On the other hand, 2.8 g/kg/day ethanol worsened post-ischemic BBB breakdown. Our findings suggest that low-moderate ethanol consumption may prevent ischemic stroke and reduce brain I/R injury by suppressing inflammation, whereas heavy alcohol consumption may induce ischemic stroke and worsen brain I/R injury by aggravating inflammation.

Climate change is accelerating plant developmental transitions coordinated with the seasons in temperate environments. To understand the importance of these timing advances for a stable life history strategy, we constructed a full life cycle model of Arabidopsis thaliana. Modelling and field data reveal that a cryptic function of flowering time control is to limit seed set of winter annuals to an ambient temperature window which coincides with a temperature-sensitive switch in seed dormancy state. This coincidence is predicted to be conserved independent of climate at the expense of flowering date, suggesting that temperature control of flowering time has evolved to constrain seed set environment and therefore frequency of dormant and non-dormant seed states. We show that late flowering can disrupt this bet-hedging germination strategy. Our analysis shows that life history modelling can reveal hidden fitness constraints and identify non-obvious selection pressures as emergent features.

AbstractThe aim of this study was to examine the role of fatty acid amide hydrolase (FAAH) on ethanol sensitivity, preference, and dependence. The deletion of FAAH gene or the inhibition of FAAH by carbamoyl‐biphenyl‐3‐yl‐cyclohexylcarbamate (URB597) (0.1 mg/kg) markedly increased the preference for ethanol. The study further reveals that URB597 specifically acts through FAAH and that cannabinoid‐1 (CB1) receptor is critical for N‐arachidonoyl ethanolamide (AEA) mediated ethanol‐reinforced behavior as revealed by lack of URB597 effect in both FAAH and CB1−/− mice compared with vehicle‐treated −/− mice. The FAAH −/− mice displayed a lower sensitivity to hypothermic and sedative effects to acute ethanol challenge. The FAAH −/− mice also exhibited a reduction in the severity of handling‐induced convulsions following withdrawal from chronic ethanol exposure. The CB1 receptor and proenkephalin gene expressions, and CB1 receptor and μ‐opioid (MO) receptor‐mediated G‐protein activation were found to be significantly lower in the caudate‐putamen, nucleus accumbens core and shell of FAAH −/− than +/+ mice. Interestingly, the MO receptor‐stimulated G‐protein signaling was greater in the striatum of FAAH −/− than +/+ mice following voluntary ethanol consumption. These findings suggest that an elevation in the AEA content and its action on the limbic CB1 receptor and MO receptor might contribute to ethanol‐reinforced behavior. Treatment with drugs that decrease AEA tone might prove useful in reducing excessive ethanol consumption.

Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.