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The Quaking protein (QKI) belongs to the STAR protein family and plays a significant role in the development of the nervous system. It serves as a crucial regulator in the processes of tumor progression and cardiovascular system development. Within the central nervous system, QKI has been associated with the onset and progression of numerous neuropsychiatric disorders, including schizophrenia, depression, ataxia, and Alzheimer’s disease. In malignant tumors, the methylation of the QKI promoter inhibits its expression. QKI primarily involves in the generation, stability, and selective splicing of non-coding RNA, as well as in mRNA translation. The role of QKI in the tumor microenvironment should not be overlooked. Especially in Glioblastoma Multiforme (GBM), although QKI is not the primary mutation, it still plays a vital role in maintaining the stemness of GBM. However, the mechanisms and further studies on this topic demand extensive basic and clinical trials.

To address the challenges posed by the randomness and volatility of multi-energy loads in integrated energy systems for ultra-short-term accurate load forecasting, this paper proposes an ultra-short-term multi-energy load forecasting method based on multi-dimensional coupling feature mining and multi-task learning. Firstly, a method for mining multi-dimensional coupling characteristics of multi-energy loads is proposed, integrating multiple correlation analysis methods. By constructing coupling features of multi-energy loads and using them as input features of the model, the complex coupling relationships between multi-energy loads are effectively quantified. Secondly, an ultra-short-term multi-energy load forecasting model based on multi-task learning and a temporal convolutional network is constructed. In the prediction model construction phase, the potential complex coupling characteristics between multiple loads can be fully explored, and the potential temporal associations and long-term dependencies within data can be extracted. Then, the multi-task learning loss function weight optimization method based on homoscedastic uncertainty is used to optimize the forecasting model, realizing automatic tuning of the loss function weight parameters and further improving the prediction performance of the model. Finally, an experimental analysis is conducted using the integrated energy system of Arizona State University in the United States as an example. The results show that the proposed forecasting method has higher prediction accuracy than other prediction methods.

The traditional median filter can handle the image salt and pepper noise better. However, when the noise intensity is large, it is often necessary to enlarge the filter window to ensure the denoising effect, but the enlarged window may also cause excessive smoothing of the image, loss of texture details, and blurred edges. In view of the strong edge preservation characteristics of variational model denoising, we propose a salt and pepper noise removal method based on the edge-adaptive total variational model. Firstly, the image is segmented into edge regions and non-edge regions by edge detection operators. Secondly, the salt and pepper noise of the image is processed using the median filter and adaptive total variation model, respectively. Lastly, the non-edge regions processed by the median filter and the edge regions processed by the adaptive total variation model are extracted for splicing. The experimental results show that the method cannot only effectively remove salt and pepper noise, but also effectively protect the main edge details of the image.

Parkinson’s disease (PD), the second primary neurodegenerative disease affecting human health, is mainly characterized by dopaminergic neuron damage in the midbrain and the clinical manifestation of movement disorders. Studies have shown that neuroinflammation plays an important role in the progression of PD. Excessively activated microglia produce several pro-inflammatory mediators, leading to damage to the surrounding neurons and finally inducing neurodegeneration. Echinocystic acid (EA) exhibits an anti-inflammatory effect in peripheral tissues. However, whether it inhibited neuroinflammation remains unclear. Therefore, the current study investigates the effect of EA on neuroinflammation and whether it can improve PD symptoms through inhibiting neuroinflammation. In our experiments, we discovered that EA inhibited the production of pro-inflammatory mediators in LPS-exposed BV2 cells. Further mechanism-related studies revealed that EA inhibited inflammation by activating PI3K/Akt and inhibiting NF-κB and MAPK signal pathways in LPS-induced BV2 cells. Research revealed that EA eases microglia-mediated neuron death in SN4741 and SHSY5Y cells. In in vivo studies, the results demonstrated that EA improves weight loss and behavioral impairment in MPTP-induced mice. Further studies have revealed that EA inhibited dopaminergic neuron damage and inflammation in the mice midbrain. In conclusion, our study demonstrated that EA inhibits neuroinflammation and exerts neuroprotective effects by activating PI3K/Akt and inhibiting NF-κB and MAPK signal pathways in vivo and in vitro.

Osteoprotegerin (OPG) is involved in various biological processes, including bone remodeling, vascular calcification and pancreatic β-cell function. Although some clinical studies have shown an increase in serum OPG level during pregnancy, the role of OPG in gestational diabetes mellitus (GDM) is largely unknown. Therefore, we explored the effect of OPG in metabolic homeostasis during pregnancy. We initially evaluated serum OPG levels using ELISA and western blotting techniques on samples from GDM patients. We also assessed OPG expression levels in maternal mice. We then used blastocysts transduced with lentiviruses capable of trophoblast-specific transgene expression to establish placenta-specific OPG knockdown or overexpression mouse models for functional and mechanistic investigation after embryo transplantation. We found that OPG expression was positively associated with GDM in clinical samples, and OPG levels were significantly increased in GDM patient sera and term placenta. Serum OPG was significantly increased in maternal compared to non-pregnant mice, and expression levels of OPG were the highest in placenta compared with other organs, including bone, liver and pancreas. OPG was also significantly increased in pregnant mice fed a high-fat diet (HFD). Placenta-specific OPG knockdown induced glucose intolerance, decreased β-cell proliferation and decreased serum insulin levels, whereas placenta-specific OPG overexpression promoted glucose tolerance and enhanced β-cell proliferation, which increased serum insulin production and decreased fetal weight in HFD-feeding pregnant mice. Placenta-derived OPG (pl-OPG) regulated glucose homeostasis during pregnancy via enhancement of β-cell proliferation, which suggests a potential therapeutic application of OPG for GDM.