CircRNA-mediated regulation of brown adipose tissue adipogenesis

Adipose tissue represents a candidate target for the treatment of metabolic illnesses, such as obesity. Brown adipose tissue (BAT), an important heat source within the body, promotes metabolic health through fat consumption. Therefore, the induction of white fat browning may improve lipid metabolism. Currently, the specific roles of circRNA in BAT and white adipose tissue (WAT) remain elusive. Herein, we conducted circRNA expression profiling of mouse BAT and WAT using RNA-seq. We identified a total of 12,183 circRNAs, including 165 upregulated and 79 downregulated circRNAs between BAT and WAT. Differentially expressed (DE) circRNAs were associated with the mitochondrion, mitochondrial part, mitochondrial inner membrane, mitochondrial envelope, therefore, these circRNAs may affect the thermogenesis and lipid metabolism of BAT. Moreover, DE circRNAs were enriched in browning- and thermogenesis-related pathways, including AMPK and HIF-1 signaling. In addition, a novel circRNA, circOgdh, was found to be highly expressed in BAT, formed by back-splicing of the third and fourth exons of the Ogdh gene, and exhibited higher stability than linear Ogdh. circOgdh was mainly distributed in the cytoplasm and could sponge miR-34a-5p, upregulating the expression of Atgl, a key lipolysis gene, which enhanced brown adipocyte lipolysis and suppressed lipid droplet accumulation. Our findings offer in-depth knowledge of the modulatory functions of circRNAs in BAT adipogenesis

Metabolic health phenotype better predicts subclinical atherosclerosis than body mass index-based obesity phenotype in the non-alcoholic fatty liver disease population

BackgroundNon-alcoholic fatty liver disease (NAFLD), especially lean NAFLD is associated with an increased risk of atherosclerotic cardiovascular disease (CVD). It is not currently known which clinical phenotypes of NAFLD contribute most to individual subclinical atherosclerosis risk. We examined the relationship between body mass index (BMI), the metabolically healthy status, and subclinical atherosclerosis in the NAFLD population.MethodsData from asymptomatic NAFLD subjects who participated in a routine health check-up examination were collected. Participants were stratified by BMI (cutoff values: 24.0–27.9 kg/m2 for overweight and ≥28.0 kg/m2 for obesity) and metabolic status, which was defined by Adult Treatment Panel III criteria. Subclinical atherosclerosis was evaluated by brachial-ankle pulse wave velocity (baPWV) in 27,738 participants and by carotid plaque in 14,323 participants.ResultsWithin each BMI strata, metabolically unhealthy subjects had a significantly higher prevalence of subclinical atherosclerosis than metabolically healthy subjects, whereas fewer differences were observed across subjects within the same metabolic category. When BMI and metabolic status were assessed together, a metabolically unhealthy status was the main contributor to the association of clinical phenotypes with the subclinical atherosclerosis burden (all p < 0.001). When BMI and metabolic abnormalities were assessed separately, the incidence of subclinical disease did not increase across BMI categories; however, it increased with an increase in the number of metabolic abnormalities (0, 1, 2 and ≥3).ConclusionA metabolically healthy status in NAFLD patients was closely correlated with subclinical atherosclerosis, beyond that of the BMI-based obesity phenotype. The application of metabolic phenotyping strategies could enable more precise classification in evaluating cardiovascular risk in NAFLD

Serum Creatinine Level: A Supplemental Index to Distinguish Duchenne Muscular Dystrophy from Becker Muscular Dystrophy

Background. To improve assessment of dystrophinopathy, the aim of this study was to identify whether serum creatinine (Crn) level reflects disease severity. Methods. Biochemical, Vignos score, and genetic data were collected on 212 boys with dystrophinopathy. Results. Serum Crn level had a strong inverse correlation with Vignos score by simple correlation ( = −0.793) and partial correlation analysis after adjustment for age, height, and weight ( = −0.791; both < 0.01). Serum Crn level was significantly higher in patients with in-frame than out-of-frame mutations ( = −4.716, < 0.01) and in Becker muscular dystrophy (BMD) patients than Duchenne muscular dystrophy (DMD) patients at ages 4, 5, 7, and 9 yr (all < 0.0125). After adjusting for age, height, and weight, BMD patients still had a significantly higher serum Crn level than DMD patients ( = 7.140, = 6.277, < 0.01). Conclusions. Serum Crn level reflected disease severity and may serve as a supplemental index to distinguish DMD from BMD in clinical practice

Constructing prediction models for excessive daytime sleepiness by nomogram and machine learning: A large Chinese multicenter cohort study

ObjectiveAlthough risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson’s disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).Materials and methodsA total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.ResultsAt the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson’s Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan–Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.ConclusionIn this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy

Effect of sarcopenia on survival of patients with cirrhosis: A meta-analysis

The association between sarcopenia and prognosis in patients with cirrhosis remains to be determined. In this study, we aimed to quantify the association between sarcopenia and the risk of mortality in patients with cirrhosis, by sex, underlying liver disease etiology, and severity of hepatic dysfunction.PubMed, Web of Science, EMBASE, and major scientific conference sessions were searched without language restriction through 13 January 2021 with additional manual search of bibliographies of relevant articles. Cohort studies of ?100 patients with cirrhosis and ?12 months of follow-up that evaluated the association between sarcopenia, muscle mass and the risk of mortality were included.22 studies with 6965 patients with cirrhosis were included. The pooled prevalence of sarcopenia in patients with cirrhosis was 37.5% overall (95% CI 32.4%-42.8%), higher in male patients, patients with alcohol associated liver disease (ALD), patients with CTP grade C, and when sarcopenia was defined in patients by lumbar 3- skeletal muscle index (L3-SMI). Sarcopenia was associated with the increased risk of mortality in patients with cirrhosis (adjusted-hazard ratio [aHR] 2.30, 95% CI 2.01-2.63), with similar findings in sensitivity analysis of cirrhosis patients without HCC (aHR 2.35, 95% CI 1.95-2.83) and in subgroup analysis by sex, liver disease etiology, and severity of hepatic dysfunction. The association between quantitative muscle mass index and mortality further supports the poor prognosis for patients with sarcopenia (aHR 0.95, 95% CI 0.93-0.98). There was no significant heterogeneity in all analyses.Sarcopenia was highly and independently associated with higher risk of mortality in patients with cirrhosis.The prevalence of sarcopenia and its association with death in patients with cirrhosis remain unclear. This meta-analysis indicated that sarcopenia affected about one-third of patients with cirrhosis and up to 50% in patients with ALD or Child's class C cirrhosis. Sarcopenia was independently associated with about 2-fold higher risk of mortality in patients with cirrhosis. The mortality rate increased with greater severity or longer period of having sarcopenia. Increasing awareness about the importance of sarcopenia in patients with cirrhosis among stakeholders must be prioritized

Characteristics and Driving Mechanisms of Understory Vegetation Diversity Patterns in Central and Southern China

Large-scale forest restoration projects significantly reduce the net rates of forest loss. However, as a key component of forest restoration, planted forests have failed to restore biodiversity. China has implemented a large-scale afforestation program, which includes pure planted forests in particular, leading to various changes in ecosystem processes. Despite this, a comprehensive analysis of understory vegetation diversity patterns in these pure planted forests is still lacking. This study aimed to analyze the data on understory vegetation diversity from three typical pure and natural forest ecosystems of Hunan ecological forests to reveal their diversity patterns. The results revealed no significant difference in the understory diversity index between natural and pure forest types, although natural forests had a bigger species pool. The Zipf–Mandelbrot model was a better fit for species abundance distribution. The fitted results suggested that both environmental filtering and neutral processes affected the species abundance distribution and pure understory communities during restoration succession. Natural forests had the most stable understory diversity structure, whereas pure Phyllostachys heterocycla (Carr.) Mitford forests had the least stable structure. Multivariate regression tree analysis identified indicator species for each community. The gradient boosting model indicated that isothermality and slope direction were the most important factors affecting diversity. The β-diversity analysis showed that community establishment in the four forest types was affected via different mechanisms. The findings of this study have significant implications for understanding the impact of afforestation on the mechanisms for maintaining diversity

Predicting the Distributions of <i>Morus notabilis</i> C. K. Schneid under Climate Change in China

As one of the common mulberry tree species, Morus notabilis C. K. Schneid plays a significant role in various industries such as silkworm rearing, papermaking, and medicine due to its valuable mulberry leaves, fruits, and wood. This study utilizes the maximum entropy (MaxEnt) model to predict the potential distribution of M. notabilis in China under future environmental changes. By integrating the relative percentage contribution score of environmental factors with jackknife test analysis, important variables influencing the distribution of M. notabilis were identified along with their optimal values. The results indicate that Annual Precipitation (bio12), Precipitation of Driest Month (bio14), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio5–bio6) (bio7), Precipitation of Warmest Quarter (bio18), and Precipitation of Coldest Quarter (bio19) are the primary environmental variables affecting its potential distribution. Currently, M. notabilis exhibits high suitability over an area spanning 11,568 km2, while medium suitability covers 34,244 km2. Both current and future suitable areas for M. notabilis are predominantly concentrated in Sichuan, Yunnan, and Guizhou provinces, as well as Chongqing city in southwest China. Under the SSP5-8.5 scenario representing high greenhouse gas concentrations by 2050s and 2090s, there is an increase in high suitability area by 2952 km2 and 3440 km2, with growth rates reaching 25.52% and 29.74%, respectively. Notably, these two scenarios exhibit substantial expansion in suitable habitats for this species compared to others analyzed within this study period

Improving Image Super-Resolution Based on Multiscale Generative Adversarial Networks

Convolutional neural networks have greatly improved the performance of image super-resolution. However, perceptual networks have problems such as blurred line structures and a lack of high-frequency information when reconstructing image textures. To mitigate these issues, a generative adversarial network based on multiscale asynchronous learning is proposed in this paper, whereby a pyramid structure is employed in the network model to integrate high-frequency information at different scales. Our scheme employs a U-net as a discriminator to focus on the consistency of adjacent pixels in the input image and uses the LPIPS loss for perceptual extreme super-resolution with stronger supervision. Experiments on benchmark datasets and independent datasets Set5, Set14, BSD100, and SunHays80 show that our approach is effective in restoring detailed texture information from low-resolution images