No abstract

<b><i>Introduction:</i></b> Anemia is a common condition encountered in acute ischemic stroke, and only a few pieces of evidence has been produced suggesting its possible association with short-term mortality have been produced. The study sought to assess whether admission anemia status had any impact on short-term clinical outcome among oldest-old patients with acute ischemic stroke. <b><i>Materials and Methods:</i></b> A retrospective review of Electronic Medical Recording System was performed in 2 tertiary hospitals. Data, from the oldest-old patients aged &#x3e; = 80 years consecutively admitted with a diagnosis of acute ischemic stroke between January 1, 2015, and December 31, 2019, were analyzed. Admission hemoglobin was used as indicator for anemia and severity. Univariate and multivariate regression analyses were used to compare in-hospital mortality and length of in-hospital stay in different anemia statuses and normal hemoglobin patients. <b><i>Results:</i></b> A total of 705 acute ischemic stroke patients were admitted, and 572 were included in the final analysis. Of included patients, 240 of them were anemic and 332 nonanemic patients. A statistical difference between the 2 groups was found in in-hospital mortality (<i>p</i> &#x3c; 0.001). After adjustment for baseline characteristics, the odds ratio value of anemia for mortality were 3.91 (95% confidence intervals (CI) 1.60–9.61, <i>p</i> = 0.003) and 7.15 (95% CI: 1.46–34.90, <i>p</i> = 0.015) in moderate and severely anemic patients, respectively. Similarly, length of in-hospital stay was longer in anemic patients (21.64 ± 6.17 days) than in nonanemic patients (19.08 ± 5.48 days, <i>p</i> &#x3c; 0.001). <b><i>Conclusions:</i></b> Increased severity of anemia may be an independent risk factor for increased in-hospital mortality and longer length of stay in oldest-old patients with acute ischemic stroke.

Glucagon (GCGN) plays a key role in glucose and amino acid (AA) metabolism by increasing hepatic glucose output. AA strongly stimulate GCGN secretion which regulates hepatic AA degradation by ureagenesis. Although increased fasting GCGN levels cause hyperglycemia GCGN has beneficial actions by stimulating hepatic lipolysis and improving insulin sensitivity through alanine induced activation of AMPK. Indeed, stimulating prandial GCGN secretion by isocaloric high protein diets (HPDs) strongly reduces intrahepatic lipids (IHLs) and improves glucose metabolism in type 2 diabetes mellitus (T2DM). Therefore, the role of GCGN and circulating AAs in metabolic improvements in 31 patients with T2DM consuming HPD was investigated. Six weeks HPD strongly coordinated GCGN and AA levels with IHL and insulin sensitivity as shown by significant correlations compared to baseline. Reduction of IHL during the intervention by 42% significantly improved insulin sensitivity [homeostatic model assessment for insulin resistance (HOMA-IR) or hyperinsulinemic euglycemic clamps] but not fasting GCGN or AA levels. By contrast, GCGN secretion in mixed meal tolerance tests (MMTTs) decreased depending on IHL reduction together with a selective reduction of GCGN-regulated alanine levels indicating greater GCGN sensitivity. HPD aligned glucose metabolism with GCGN actions. Meal stimulated, but not fasting GCGN, was related to reduced liver fat and improved insulin sensitivity. This supports the concept of GCGN-induced hepatic lipolysis and alanine- and ureagenesis-induced activation of AMPK by HPD.

Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources.