Xenon Pretreatment May Prevent Early Memory Decline after Isoflurane Anesthesia and Surgery in Mice

Postoperative cognitive decline (POCD) is a common complication following surgery, but its aetiology remains unclear. We hypothesized that xenon pretreatment prevents POCD by suppressing the systemic inflammatory response or through an associated protective signaling pathway involving heat shock protein 72 (Hsp72) and PI3-kinase. Twenty-four hours after establishing long-term memory using fear conditioning training, C57BL/6 adult male mice (n = 12/group) received one of the following treatments: 1) no treatment group (control); 2) 1.8% isoflurane anesthesia; 3) 70% xenon anesthesia; 4) 1.8% isoflurane anesthesia with surgery of the right hind leg tibia that was pinned and fractured; or 5) pretreatment with 70% xenon for 20 minutes followed immediately by 1.8% isoflurane anesthesia with the surgery described above. Assessments of hippocampal-dependent memory were performed on days 1 and 7 after treatment. Hsp72 and PI3-kinase in hippocampus, and plasma IL-1β, were measured using western blotting and ELISA respectively, from different cohorts on day 1 after surgery. Isoflurane induced memory deficit after surgery was attenuated by xenon pretreatment. Xenon pretreatment prevented the memory deficit typically seen on day 1 (P = 0.04) but not on day 7 (P = 0.69) after surgery under isoflurane anesthesia, when compared with animals that underwent surgery without pretreatment. Xenon pretreatment modulated the expression of Hsp72 (P = 0.054) but had no significant effect on PI3-kinase (P = 0.54), when compared to control. Xenon pretreatment also reduced the plasma level increase of IL-1β induced by surgery (P = 0.028). Our data indicated that surgery and/or Isoflurane induced memory deficit was attenuated by xenon pretreatment. This was associated with a reduction in the plasma level of IL-1β and an upregulation of Hsp72 in the hippocampus

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy

Background: The prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear.Objectives: This study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM.Methods: A total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively.Results: A total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy ( ≥4.06 ) were more likely to experience SCD and CVD (all P &lt; 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent ( &lt;15% ). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent ( ≥15% ), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P &lt; 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P &lt; 0.001) by multivariable Cox regression analysis.Conclusions: LV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094)</div

ADAR2-dependent RNA editing of GluR2 is involved in thiamine deficiency-induced alteration of calcium dynamics

BACKGROUND: Thiamine (vitamin B1) deficiency (TD) causes mild impairment of oxidative metabolism and region-selective neuronal loss in the central nervous system (CNS). TD in animals has been used to model aging-associated neurodegeneration in the brain. The mechanisms of TD-induced neuron death are complex, and it is likely multiple mechanisms interplay and contribute to the action of TD. In this study, we demonstrated that TD significantly increased intracellular calcium concentrations [Ca2+]i in cultured cortical neurons. RESULTS: TD drastically potentiated AMPA-triggered calcium influx and inhibited pre-mRNA editing of GluR2, a Ca2+-permeable subtype of AMPA receptors. The Ca2+ permeability of GluR2 is regulated by RNA editing at the Q/R site. Edited GluR2 (R) subunits form Ca2+-impermeable channels, whereas unedited GluR2 (Q) channels are permeable to Ca2+ flow. TD inhibited Q/R editing of GluR2 and increased the ratio of unedited GluR2. The Q/R editing of GluR2 is mediated by adenosine deaminase acting on RNA 2 (ADAR2). TD selectively decreased ADAR2 expression and its self-editing ability without affecting ADAR1 in cultured neurons and in the brain tissue. Over-expression of ADAR2 reduced AMPA-mediated rise of [Ca2+]i and protected cortical neurons against TD-induced cytotoxicity, whereas down-regulation of ADAR2 increased AMPA-elicited Ca2+ influx and exacerbated TD-induced death of cortical neurons. CONCLUSIONS: Our findings suggest that TD-induced neuronal damage may be mediated by the modulation of ADAR2-dependent RNA Editing of GluR2

PyScribe–Learning to describe python code

peer reviewedCode comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled PyScribe, which extends the Transformer model to a new encoder-decoder-based framework. Particularly, the triplet position is designed and integrated into the node-level and edge-level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two-stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe, we resort to a large dataset of code-comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state-ofthe-art by achieving an average BLEU score (i.e., av-BLEU) of (Formula presented.) 0.28

Study on pathological and clinical characteristics of chronic HBV infected patients with HBsAg positive, HBV DNA negative, HBeAg negative

AimsStudy of clinical characteristics of hepatitis B virus deoxyribonucleic acid (HBV DNA)-negative, hepatitis B surface antigen (HBsAg)-positive, hepatitis B e antigen (HBeAg)-negative patients based on liver histopathology.MethodsWe retrospectively enrolled patients with chronic HBV infection diagnosis at Beijing Ditan Hospital from May 2008 to November 2020. To study the differences between patients with significant hepatic histopathology and those without significant hepatic histopathology. And to study the independent factors of significant hepatic histopathology.Results85 HBV DNA-negative and HBeAg-negative patients were 37.90 ± 10.30 years old, 23.50% of patients with grade of inflammation (G) &gt;1, 35.30% of patients with liver fibrosis stage (S) &gt;1, 44.70% patients were diagnosed with significant hepatic histopathology. Compared to the no significant hepatic histopathology group, another group had older age (41.70 ± 10.70 vs 34.80 ± 8.87 years, t=-3.28, P=0.002), higher total bilirubin (TBIL) [14.9(10.3, 22.4) vs 11(8.9, 14.4) μmol/L, z=-2.26, P=0.024], lower cholinesterase (CHE) (t=-2.86, P=0.005, 7388.00 ± 2156.00 vs 8988.00 ± 2823.00 U/L) and lower platelet (PLT) (t=2.75, P=0.007, 157.00 ± 61.40 vs 194.00 ± 61.00 10^9/L). Abnormal ALT patients are more likely to have significant hepatic histopathology (z=5.44, P=0.020, 66.70% vs 337.50%). G had significant correlation with CHE (P=0.008, r=-0.23), alanine aminotransferase (ALT) (P=0.041, r=0.18), aspartate aminotransferase (AST) (P=0.001, r=0.29). S had significant correlation with TBIL (P = 0.008, r = 0.23), age (P &lt; 0.001, r = 0.32), international normalized ratio (INR) (P = 0.04, r = 0.23), CHE (P &lt; 0.001, r = -0.30), PLT (P &lt; 0.001, r = -0.40) and prothrombin time activity (PTA) (P = 0.046, r = -0.22). Multivariate logistic analysis indicated only age (95%CI=1.014~1.130, OR=1.069, P=0.013) was an impact factor for significant hepatic histopathology. The cutoff point of age was 34.30 years.ConclusionsA large proportion of chronic HBV infection patients with HBeAg-negative and HBV DNA-negative still have chronic hepatitis. Age is an independent factor for significant hepatic histopatholog

Interactive Reinforced Feature Selection with Traverse Strategy

In this paper, we propose a single-agent Monte Carlo-based reinforced feature selection method, as well as two efficiency improvement strategies, i.e., early stopping strategy and reward-level interactive strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a given downstream machine learning task. Enormous research has been done to improve its effectiveness and efficiency. Recently, the multi-agent reinforced feature selection (MARFS) has achieved great success in improving the performance of feature selection. However, MARFS suffers from the heavy burden of computational cost, which greatly limits its application in real-world scenarios. In this paper, we propose an efficient reinforcement feature selection method, which uses one agent to traverse the whole feature set and decides to select or not select each feature one by one. Specifically, we first develop one behavior policy and use it to traverse the feature set and generate training data. And then, we evaluate the target policy based on the training data and improve the target policy by Bellman equation. Besides, we conduct the importance sampling in an incremental way and propose an early stopping strategy to improve the training efficiency by the removal of skew data. In the early stopping strategy, the behavior policy stops traversing with a probability inversely proportional to the importance sampling weight. In addition, we propose a reward-level and training-level interactive strategy to improve the training efficiency via external advice. What’s more, we propose an incremental descriptive statistics method to represent the state with low computational cost. Finally, we design extensive experiments on real-world data to demonstrate the superiority of the proposed method

Prediction Model of Pumpkin Rootstock Seedlings Based on Temperature and Light Responses

Temperature and light are the key factors that affect the quality of pumpkin rootstock seedlings’ growth process. Responses to temperature and light are an important basis for optimizing the greenhouse environment. In order to determine the quantitative effects of temperature and light on the growth and development of pumpkin (Cucurbita moschata cv. RTWM6018) rootstock seedlings, relationships between temperature, light, and pumpkin rootstock seedlings growth were established using regression analysis. The results indicated that the daily average temperature had a significant negative correlation with the development time of pumpkin rootstock seedlings, and the shoot dry weight of pumpkin rootstock seedlings increased within a certain range of the daily light integral (DLI). We established a prediction model of pumpkin rootstock seedling quality indicators (hypocotyl length, stem diameter, shoot dry weight, root dry weight, root shoot ratio, and seedling quality index) based on thermal effectiveness and photosynthetic photon flux density (TEP). The coefficient of determinations (R2) of the hypocotyl length and seedling quality index prediction models of pumpkin rootstock seedlings, based on accumulated TEP, were 0.707 and 0.834, respectively. The hypocotyl length and seedling quality index prediction models of pumpkin rootstock seedlings, based on accumulated TEP, were y1 = 0.001 x2 − 0.180 x + 13.057 and y2 = 0.008 x0.722, respectively, which could be used for predicting the growth of pumpkin rootstock seedlings grown under different temperature and light conditions

Characteristic Analysis of Digital Emulsion Relief Valve Based on the Hydraulic Loading System

In the hydraulic loading system, the performance of digital relief valve plays an important role in the dynamic response of load. However, the research on large-flow emulsion relief valve is still far from perfect. In this paper, digital relief valve is taken as the research object. Based on pilot-operated relief valve, a digital control scheme using a linear stepping motor is adopted to regulate the working pressure of relief valve. The structure of relief valve is analyzed and optimized from the aspects of dynamic and internal flow field characteristics to obtain a good working performance. To obtain its accurate working characteristic, the structural model and digital control system of relief valve are established by AMESim and Simulink, respectively, for electrohydraulic cosimulation. The results show that digital relief valve has a better characteristic of real-time dynamic pressure regulation. Therefore, the digital control system could improve the dynamic performance of relief valve, and the design of digital relief valve structure is reasonable and feasible. The simulation method employed in this paper provides a better theoretical basis and reference for the comprehensive research of digital large-flow emulsion relief valves based on the hydraulic loading system

Xenon pretreatment may prevent early memory decline after isoflurane anesthesia and surgery in mice.

Postoperative cognitive decline (POCD) is a common complication following surgery, but its aetiology remains unclear. We hypothesized that xenon pretreatment prevents POCD by suppressing the systemic inflammatory response or through an associated protective signaling pathway involving heat shock protein 72 (Hsp72) and PI3-kinase. Twenty-four hours after establishing long-term memory using fear conditioning training, C57BL/6 adult male mice (n = 12/group) received one of the following treatments: 1) no treatment group (control); 2) 1.8% isoflurane anesthesia; 3) 70% xenon anesthesia; 4) 1.8% isoflurane anesthesia with surgery of the right hind leg tibia that was pinned and fractured; or 5) pretreatment with 70% xenon for 20 minutes followed immediately by 1.8% isoflurane anesthesia with the surgery described above. Assessments of hippocampal-dependent memory were performed on days 1 and 7 after treatment. Hsp72 and PI3-kinase in hippocampus, and plasma IL-1β, were measured using western blotting and ELISA respectively, from different cohorts on day 1 after surgery. Isoflurane induced memory deficit after surgery was attenuated by xenon pretreatment. Xenon pretreatment prevented the memory deficit typically seen on day 1 (P = 0.04) but not on day 7 (P = 0.69) after surgery under isoflurane anesthesia, when compared with animals that underwent surgery without pretreatment. Xenon pretreatment modulated the expression of Hsp72 (P = 0.054) but had no significant effect on PI3-kinase (P = 0.54), when compared to control. Xenon pretreatment also reduced the plasma level increase of IL-1β induced by surgery (P = 0.028). Our data indicated that surgery and/or Isoflurane induced memory deficit was attenuated by xenon pretreatment. This was associated with a reduction in the plasma level of IL-1β and an upregulation of Hsp72 in the hippocampus