Betaine supplementation alleviates corticosterone-induced hepatic cholesterol accumulation through epigenetic modulation of HMGCR and CYP7A1 genes in laying hens.

Excessive corticosterone (CORT) exposure could cause hepatic cholesterol accumulation in chickens and maternal betaine supplementation could decrease hepatic cholesterol deposition through epigenetic modifications in offspring chickens. Nevertheless, it remains uncertain whether providing betaine to laying hens could protect CORT-induced hepatic cholesterol accumulation via epigenetic mechanisms. This study aimed to examine the effects of dietary betaine on plasma and hepatic cholesterol contents, expression of cholesterol metabolic genes, as well as DNA methylation on their promoters in the liver of laying hens exposed to CORT. A total of 72 laying hens at 130 d of age were randomly divided into 3 groups: control (CON), CORT, and CORT+betaine (CORT+BET) groups. The experiment lasted for 35 d. Chickens in CON and CORT groups were fed a basal diet, whereas the CORT+BET group chickens were fed the basal diet supplemented with 0.1% betaine for 35 d. On d 28 of the experiment, chickens in CORT and CORT+BET groups received daily subcutaneous injections of CORT (4.0 mg/kg body weight), whereas the CON group chickens were injected with an equal volume of solvent for 7 d. The results showed that CORT administration led to a significant increase (P < 0.05) in the contents of cholesterol in plasma and liver, associated with activation (P < 0.05) of sterol regulatory element binding transcription factor 2 (SREBP2), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), lecithin-cholesterol acyltransferase (LCAT) and low-density lipoprotein receptor (LDLR) genes expression, and inhibition of cholesterol-7-alpha hydroxylase (CYP7A1) and sterol 27-hydroxylase (CYP27A1) genes expression in the liver compared to the CON. In contrast, CORT-induced up-regulation of HMGCR mRNA and protein abundances and downregulation of CYP7A1 mRNA and protein abundances were completely normalized (P < 0.05) by betaine supplementation. Besides, CORT injection led to significant hypomethylation (P < 0.05) on HMGCR promoter and hypermethylation (P < 0.05) on CYP7A1 promoter. Moreover, dietary betaine rescued (P < 0.05) CORT-induced changes in methylation status of HMGCR and CYP7A1 genes promoters. These results indicate that dietary betaine addition protects laying hens from CORT-induced hepatic cholesterol accumulation via epigenetic modulation of HMGCR and CYP7A1 genes.

Caveolin-1 is essential for the increased release of glutamate in the anterior cingulate cortex in neuropathic pain mice.

Neuropathic pain has a complex pathogenesis. Here, we examined the role of caveolin-1 (Cav-1) in the anterior cingulate cortex (ACC) in a chronic constriction injury (CCI) mouse model for the enhancement of presynaptic glutamate release in chronic neuropathic pain. Cav-1 was localized in glutamatergic neurons and showed higher expression in the ACC of CCI versus sham mice. Moreover, the release of glutamate from the ACC of the CCI mice was greater than that of the sham mice. Inhibition of Cav-1 by siRNAs greatly reduced the release of glutamate of ACC, while its overexpression (induced by injecting Lenti-Cav-1) reversed this process. The chemogenetics method was then used to activate or inhibit glutamatergic neurons in the ACC area. After 21 days of injection of AAV-hM3Dq in the sham mice, the release of glutamate was increased, the paw withdrawal latency was shortened, and expression of Cav-1 in the ACC was upregulated after intraperitoneal injection of 2 mg/kg clozapine N-oxide. Injection of AAV-hM4Di in the ACC of CCI mice led to the opposite effects. Furthermore, decreasing Cav-1 in the ACC in sham mice injected with rAAV-hM3DGq did not increase glutamate release. These findings suggest that Cav-1 in the ACC is essential for enhancing glutamate release in neuropathic pain.

Lateral septum-lateral hypothalamus circuit dysfunction in comorbid pain and anxiety.

Pain and anxiety comorbidities are a common health problem, but the neural mechanisms underlying comorbidity remain unclear. We propose that comorbidity implies that similar brain regions and neural circuits, with the lateral septum (LS) as a major candidate, process pain and anxiety. From results of behavioral and neurophysiological experiments combined with selective LS manipulation in mice, we find that LS GABAergic neurons were critical for both pain and anxiety. Selective activation of LS GABAergic neurons induced hyperalgesia and anxiety-like behaviors. In contrast, selective inhibition of LS GABAergic neurons reduced nocifensive withdrawal responses and anxiety-like behaviors. This was found in two mouse models, one for chronic inflammatory pain (induced by complete Freund's adjuvant) and one for anxiety (induced by chronic restraint stress). Additionally, using TetTag chemogenetics to functionally mark LS neurons, we found that activation of LS neurons by acute pain stimulation could induce anxiety-like behaviors and vice versa. Furthermore, we show that LS GABAergic projection to the lateral hypothalamus (LH) plays an important role in the regulation of pain and anxiety comorbidities. Our study revealed that LS GABAergic neurons, and especially the LSGABAergic-LH circuit, are a critical to the modulation of pain and anxiety comorbidities.

Skin lesion classification using CNNs with grouping of multi-scale attention and class-specific loss weighting.

As one of the most common cancers globally, the incidence of skin cancer has been rising. Dermoscopy-based classification has become the most effective method for the diagnosis of skin lesion types due to its accuracy and non-invasive characteristics, which plays a significant role in reducing mortality. Although a great breakthrough of the task of skin lesion classification has been made with the application of convolutional neural network, the inter-class similarity and intra-class variation in skin lesions images, the high class imbalance of the dataset and the lack of ability to focus on the lesion area all affect the classification results of the model. In order to solve these problems, on the one hand, we use the grouping of multi-scale attention blocks (GMAB) to extract multi-scale fine-grained features so as to improve the model's ability to focus on the lesion area. On the other hand, we adopt the method of class-specific loss weighting for the problem of category imbalance. In this paper, we propose a deep convolution neural network dermatoscopic image classification method based on the grouping of multi-scale attention blocks and class-specific loss weighting. We evaluated our model on the HAM10000 dataset, and the results showed that the ACC and AUC of the proposed method were 91.6% and 97.1% respectively, which can achieve good results in dermatoscopic classification tasks.