Targeting HIF2α-ARNT hetero-dimerisation as a novel therapeutic strategy for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a destructive disease of the pulmonary vasculature often leading to right heart failure and death. Current therapeutic intervention strategies only slow disease progression. The role of aberrant hypoxia-inducible factor (HIF)2α stability and function in the initiation and development of pulmonary hypertension (PH) has been an area of intense interest for nearly two decades. Here we determine the effect of a novel HIF2α inhibitor (PT2567) on PH disease initiation and progression, using two pre-clinical models of PH. Haemodynamic measurements were performed, followed by collection of heart, lung and blood for pathological, gene expression and biochemical analysis. Blood outgrowth endothelial cells from idiopathic PAH patients were used to determine the impact of HIF2α-inhibition on endothelial function. Global inhibition of HIF2a reduced pulmonary vascular haemodynamics and pulmonary vascular remodelling in both su5416/hypoxia prevention and intervention models. PT2567 intervention reduced the expression of PH-associated target genes in both lung and cardiac tissues and restored plasma nitrite concentration. Treatment of monocrotaline-exposed rodents with PT2567 reduced the impact on cardiovascular haemodynamics and promoted a survival advantage. In vitro, loss of HIF2α signalling in human pulmonary arterial endothelial cells suppresses target genes associated with inflammation, and PT2567 reduced the hyperproliferative phenotype and overactive arginase activity in blood outgrowth endothelial cells from idiopathic PAH patients. These data suggest that targeting HIF2α hetero-dimerisation with an orally bioavailable compound could offer a new therapeutic approach for PAH. Future studies are required to determine the role of HIF in the heterogeneous PAH population

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF

Language Ability Accounts for Ethnic Difference in Mathematics Achievement

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills

Response of Electric Field in Terrestrial Magnetosphere to Interplanetary Shock

Electric field impulses generated by interplanetary shocks can cause a series of dynamic processes in the Earth's magnetosphere and were previously explained by either fast-mode wave propagation or flow related to compression of the magnetopause. Based on a Space Weather Modeling Framework simulation, we suggest a new scenario in which the evolution of the impulse is due to both the propagation of the fast-mode wave and the compression of the magnetopause, which can explain the simulation and observations in previous related studies. The onset of the electric field impulse is determined by the propagation of the fast-mode wave in the magnetosphere while the peak of the impulse is determined by the propagation of the compression of the magnetopause. The new understanding of the impulse is important for the generation of subsequent ultralow frequency waves through the coupling of the fast-mode to Alfv&eacute;n waves and field line resonances and related radiation-belt electron acceleration. &nbsp;</p

Identification and functional prediction of long non-coding RNAs related to oxidative stress in the jejunum of piglets

Objective Oxidative stress (OS) is a pathological process arising from the excessive production of free radicals in the body. It has the potential to alter animal gene expression and cause damage to the jejunum. However, there have been few reports of changes in the expression of long noncoding RNAs (lncRNAs) in the jejunum in piglets under OS. The purpose of this research was to examine how lncRNAs in piglet jejunum change under OS. Methods The abdominal cavities of piglets were injected with diquat (DQ) to produce OS. Raw reads were downloaded from the SRA database. RNA-seq was utilized to study the expression of lncRNAs in piglets under OS. Additionally, six randomly selected lncRNAs were verified using quantitative real-time polymerase chain reaction (qRT–PCR) to examine the mechanism of oxidative damage. Results A total of 79 lncRNAs were differentially expressed (DE) in the treatment group compared to the negative control group. The target genes of DE lncRNAs were enriched in gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathways. Chemical carcinogenesis-reactive oxygen species, the Foxo signaling pathway, colorectal cancer, and the AMPK signaling pathway were all linked to OS. Conclusion Our results demonstrated that DQ-induced OS causes differential expression of lncRNAs, laying the groundwork for future research into the processes involved in the jejunum’s response to OS

Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis

Rheumatoid arthritis (RA) is characterized by chronic progressive symmetrical synovitis and destruction of multiple joints. Glucocorticoids (GCs) are widely used in the treatment of RA. However, their adverse effects can be serious. Escin, which is isolated from Aesculus hippocastanum L., has been reported to have anti-inflammatory effects. We investigated the anti-RA effect of Escin combined with low dose GCs (dexamethasone, Dex) and the underlying mechanism. Adjuvant-induced RA rats and lipopolysaccharides (LPS)-injured RAW264.7 cells were used to investigate the anti-RA effects of Escin combined with low dose Dex in vivo and in vitro. The results showed that Escin combined with low-dose Dex significantly decreased arthritic index, serum IL-6 and TNF-α levels, reduced paw swelling, and ameliorated the joint pathology and immune organ pathology. Gene chip results revealed that Nr3c1 (GR) expression was significantly altered, and that GR was activated by Escin and low dose Dex in vivo and in vitro. Additionally, Escin combined with low dose Dex also significantly increased GR mRNA expression. However, when GR expression was suppressed by its specific inhibitor, the anti-RA effect of Escin combined with low-dose Dex was abolished. The data in this study demonstrated that Escin combined with Dex reduced the dose of Dex, and exerted significant anti-RA effects, which could also reduce the adverse effects of Dex. This combination might result from GR activation. This study might provide a new combination of drugs for the treatment of RA

Evaluation of Genetic Susceptibility Loci for Chronic Hepatitis B in Chinese: Two Independent Case-Control Studies

BACKGROUND: A recent genome-wide scan has identified two genetic variants in the HLA-DP region strongly associated with hepatitis B infection in Japanese. This study evaluates the effects of these risk variants in Chinese, where the HBV infection is the most popular in the world. METHODS AND FINDINGS: We have assessed the relationship between these two single nucleotide polymorphisms (rs3077 and rs9277535) and chronic hepatitis B infection in two independent case-control studies. The first population in Chinese Han included 736 patients and 782 spontaneously recovered controls. The second set was established in Chinese Zhuang minority of 177 patients and 208 controls. Both A alleles of rs3077 and rs9277535 significantly deceased the risk to CHB in Chinese Han (OR = 0.540, 95%CI: 0.464-0.628, P = 4.068×10(-16) and OR = 0.696, 95%CI: 0.601-0.806, P = 1.062×10(-6), respectively). Conceivably, rs9277535 was found to be associated with decreased risk of the disease in Chinese Zhuang, with an OR of 0.606 (95%CI, 0.441-0.833, P = 0.002). CONCLUSION: Chronic hepatitis B susceptibility loci in HLA-DP region (rs3077 and rs9277535) identified by genome-wide scan in Japanese population were validated in Chinese population. These findings might provide clues to develop screening and surveillance strategies

Role of Information Quality for Value Co-Creation in B2B Service Orchestration Process

Value co-creation is at the core of B2B service orchestration. While advances in information technology provide new means to get customers involved in the service orchestration process, little is known about the mechanisms through which information quality relates to the process that enables value co-creation in B2B services. Drawing on the IS success model and the resource management perspective, we propose that information quality, the key resource generated from use of an IS, positively affects two important service capabilities — service flexibility and service efficiency — which enhance relational value. We argue that modal variety moderates the efficacy of information quality in developing these service capabilities. Survey and archival data collected from a Fortune 100 logistic service provider on its relationships with major customers are used in the empirical testing, which provides general support to the proposed research model