Cannabinoid type 2 receptors play a crucial role in social defeat-induced depression.

BACKGROUND: The endocannabinoid system plays a crucial role in regulating mood, but the specific involvement of cannabinoid receptor type 2 (CB2R) in depression remains poorly understood. Similarly, the mechanisms by which electroacupuncture (EA) provides therapeutic benefits for depression are not clearly defined. This research aims to explore the function of CB2R in depression and examine if the therapeutic effects of EA are associated with the hippocampal CB2R system. METHODS: Mice experiencing social defeat stress (SDS) were used to model depression and anxiety behaviors. We quantified hippocampal CB2R and N-arachidonoylethanolamide (AEA) levels. The efficacy of a CB2R agonist, JWH133, in mitigating SDS-induced behaviors was evaluated. Additionally, EA's impact on CB2R and AEA was assessed, along with the influence of CB2R antagonist AM630 on EA's antidepressant effects. RESULTS: SDS led to depressive and anxiety-like behaviors, with corresponding decreases in hippocampal CB2R and AEA. Treatment with JWH133 ameliorated these behaviors. EA treatment resulted in increased CB2R and AEA levels, while AM630 blocked these antidepressant effects. LIMITATIONS: The study mainly focused on the SDS model, which may not entirely reflect other depression models. Besides, further investigation is needed to understand the precise mechanisms by which CB2R and AEA contribute to EA's effects. CONCLUSIONS: The study suggests hippocampal downregulation of CB2R and AEA contributes to depression. Upregulation of CB2R and AEA in response to EA suggests their involvement in EA's antidepressant effects. These findings provide insights into the role of the hippocampal CB2R system in depression and the potential mechanisms underlying EA's therapeutic effects.

Photoredox/Cobalt-Catalyzed Cascade Oxidative Synthesis of 2,5-Disubstituted 1,3,4-Oxadiazoles under Oxidant-Free Conditions.

An efficient oxidant-free, photoredox-mediated cascade cyclization strategy for the synthesis of 1,3,4-oxadiazoles by using an organo acridinium photocatalyst and a cobaloxime catalyst has been developed. Various acylhydrazones have been transformed into the corresponding 1,3,4-oxadiazole products in up to 96% yield, and H2 is the only byproduct. Mechanistic experiments and density functional theory (DFT) calculation studies indicate carbon-centered radicals rather than oxygen-centered radicals as π-radicals produced by the oxidation of photoexcited Mes-Acr+* along with deprotonation, which is responsible for this transformation. The practical utility of this method is highlighted by the one-pot gram-scale synthesis starting directly from commercially available aldehydes and acylhydrazides.

Line graph attention networks for predicting disease-associated Piwi-interacting RNAs.

PIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.

Sirtuin 3 Plays a Critical Role in the Antidepressant- and Anxiolytic-like Effects of Kaempferol.

An estimated 20% of women experience depression at some point during menopause. Hormone replacement therapy (HRT), as the main therapy for depression and other menopausal syndromes, comes with a few undesirable side effects and a potential increase in cancer and cardiovascular risk. Consequently, there is a dire need for the development of new therapies to treat menopausal depression. Oxidative stress combined with the decline in sex hormones might explain the occurrence of psychological symptoms characteristic of menopause. Therefore, antioxidants have been suggested as a promising therapy for aging-associated diseases, such as menopausal depression. As a flavonoid antioxidant, kaempferol might have a potential neuroprotective action. Hence, the study was conducted to assess the potential antidepressant action of kaempferol and clarify the underlying mechanism. The results show that kaempferol has potential beneficial effects on VCD-induced rodent model of menopausal depression and produces antioxidant effects as well as increases the deacetylation of superoxide dismutase 2 (SOD2) and the protein level of Sirtuin3 (Sirt3) in the hippocampus. On the contrary, Sirt3 depletion abrogated the antidepressant- and anxiolytic-like effects as well as antioxidant effects of kaempferol. In conclusion, kaempferol might produce antidepressant effects via upregulating the expression of Sirt3, the major deacetylase in mitochondria, and subsequently activate the mitochondrial antioxidases. These findings shed some light on the use of kaempferol or vegetables and herbs that contain kaempferol as a complementary therapy for menopausal depression.

A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.

Previous studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.

MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning.

Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA-disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA-disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA-disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA-disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA-disease association and can provide reliable candidates for biological experiments.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.

It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.

The antidepressant and anxiolytic effect of GPER on translocator protein (TSPO) via protein kinase a (PKA) signaling in menopausal female rats.

Postmenopausal depression is mainly caused by the deprivation of ovarian hormones during menopausal transition, it is of great importance to study on the treatment that could effectively relieve symptoms of menopausal depression with fewer side effects. Activation of G-protein-coupled estrogen receptor (GPER) has long been reported to facilitate neuronal plasticity and improve cognition in animals. Meanwhile, it could participate in regulation of intracellular signaling pathways through the characteristic of GPER, ameliorate intracellular mitochondrial function and oxidative stress. However, the impact of GPER on regulating estrogen deprived-depressant and anxious behaviors is still largely unknown. Here we used the ovariectomized female rats to imitate the condition of menopause. Owing to the lateral ventricle administration of G-1 which specifically react with GPER receptor intracerebrally, Ovariectomized (OVX) female rats showed depressive- or anxiety-like phenotypes with attenuated mitochondrial function. In addition, G-1 facilitated PKA activation, which further accelerated TSPO phosphorylation and alleviated menopausal depression- and anxiety-like behaviors. Moreover, PKA inhibitor PKI could partially antagonized the anti-anxiety and anti-depression effects of G-1. Taken together, we concluded that GPER activation might exhibit antidepressant and anxiolytic effect by elevating TSPO phosphorylation via protein kinase A signaling and rescuing the redox status in menopausal female rats.

G-1 exhibit antidepressant effect, increase of hippocampal ERs expression and improve hippocampal redox status in aged female rats.

Postmenopausal depression has been shown to be related to the reduction of ovarian hormones produced as a woman transitions from a menopausal to a post-menopausal stage. What remains to be known is which type of estrogen receptor plays a key role in estrogen neuroprotection, a process that may be mediated by potentiating brain mitochondrial function and inhibiting mitochondria-associated apoptosis. In order to better imitate the condition of postmenopause, we conducted our research on aged female rats. Plasma estrogen levels declined significantly in ovariectomized rats and 16-month-old female rats, while anxiety and depression-like behavior increase. Moreover, ERα, ERß, GPER, Bcl2 and UCP2 expression decreased significantly in hippocampus in female rats following ovariectomy. In our study, the anxiety and depression-like behavior in aged female rats were significantly relieved after the treatment of G-1, the GPER agonist. Furthermore, G-1 could reverse the reduction of ERα, ERß, GPER, Bcl2 and UCP2 expression within the hippocampus. Mitochondrial JC-1 staining indicated that mitochondrial membrane potential increased after G-1 treatment. In addition, total antioxidant capacity (TAC) and superoxide dismutase activity (SOD) were found to be elevated in aged female rats following G-1 treatment. Taken together, estrogen receptors, especially GPER, may activate anti-apoptotic signaling and accelerate mitochondrial function. Therefore, GPER could be the potential therapeutic target for estrogen deficiency-related affective disorders.

[Growth kinetics and phenol degradation of highly efficient phenol-degrading Ochrobactrum sp. CH10].

The strain Ochrobactrum sp. CH10 was a highly efficient phenol degrading bacterial strain isolated from soil in a constructed wetland in Yuan Dynasty Capital City Wall Relics in Beijing. Growth and biodegradation were investigated in details with phenol as the sole carbon and energy source. The best growth and most efficient phenol biodegradation occurred when the strain was cultured in medium containing 400 mg x L(-1) phenol at initial pH of 7.0 and 30 degrees C, with 5% inoculation volume. The phenol degradation rate was around 100% , 92.3 and 82.2% with an initial concentration of 400, 900 and 1 000 mg x L(-1) phenol in 24, 44 and 48 h, respectively. Phenol degradation kinetic studies indicated that the strain followed Haldane's model, and the parameters were: upsilon(max) (maximum specific rate) = 0.126 h(-1), K(s) (half-saturation constant) = 23.53 mg x L(-1) and K(I) (inhibition constant) = 806.1 mg x L(-1). The phenol-limited growth kinetics of CH10 by Andrews's model also followed a similar trend to that of phenol degradation. Among all the strains belonging to Ochrobactrum genus, this strain is the most efficient at present. The strain has a good application potential for the phenolic wastewater treatment.

Ethyl 4-ethyl-amino-3-nitro-benzoate.

In the mol-ecule of the title compound, C(11)H(14)N(2)O(4), a bifurcated intra/intermolecular N-H⋯(O,O) hydrogen bond occurs.The intramolecular component results in a non-planar six-membered ring with a flattened-boat conformation. In the crystal structure, the inter-molecular interaction links the mol-ecules into chains parallel to the b axis.

Ethyl 3-nitro-4-(n-propyl-amino)benzoate.

In the mol-ecule of the title compound, C(12)H(16)N(2)O(4), an intra-molecular N-H⋯O hydrogen bond results in the formation of a six-membered ring having an envelope conformation. In the crystal structure, a bifurcated intra/intermolecular N-H⋯(O,O) hydrogen bond generates inversion dimers.

Methyl 4-anilino-3-nitro-benzoate.

In the mol-ecule of the title compound, C(14)H(12)N(2)O(4), the aromatic rings are oriented at a dihedral angle of 51.50 (4)°. An intra-molecular N-H⋯O inter-action results in the formation of a six-membered ring having an envelope conformation. In the crystal structure, inter-molecular N-H⋯O inter-actions link the mol-ecules into centrosymmetric dimers. π-π contacts between the benzene rings [centroid-centroid distance = 3.708 (1) Å] may further stabilize the structure.

Methyl 4-chloro-3-nitro-benzoate.

In the title compound, C(8)H(6)ClNO(4), the mol-ecules are linked by C-H⋯O inter-actions to form a chain parallel to the a axis. The chains are further connected by slipped π-π stacking between symmetry-related benzene rings, with a centroid-to-centroid distance of 3.646 (2) Šand an inter-planar distance of 3.474 Å, resulting in an offset of 1.106 Å.

Ethyl 4-chloro-3-nitro-benzoate.

In the mol-ecule of the title compound, C(9)H(8)ClNO(4), an intra-molecular C-H⋯O hydrogen bond results in the formation of a planar five-membered ring, which is nearly coplanar with the adjacent six-membered ring, the rings being oriented at a dihedral angle of 4.40 (3)°. In the crystal structure, inter-molecular C-H⋯O hydrogen bonds link the mol-ecules.

5-Bromo-2-methyl-pyridine N-oxide.

In the mol-ecule of the title compound, C(6)H(6)BrNO, the methyl C and oxide O atoms lie in the pyridine ring plane, while the Br atom is displaced by 0.103 (3) Å. In the crystal structure, inter-molecular C-H⋯O hydrogen bonds link the mol-ecules into centrosymmetric dimers.

4-Chloro-3-nitro-benzamide.

In the crystal of the title compound, C(7)H(5)ClN(2)O(3), the molecules are linked by N-H⋯O and C-H⋯O hydrogen bonds. The π-π contact between the benzene rings, [centroid-centroid distance = 3.803 (3) Å] may further stabilize the structure.

Ethyl 4-anilino-3-nitrobenzoate.

In the title compound, C(15)H(14)N(2)O(4), the aromatic rings are oriented at a dihedral angle of 78.33 (3)°. An intra-molecular N-H⋯O hydrogen bond results in a non-planar six-membered ring with a flattened-boat conformation. In the crystal structure, inter-molecular N-H⋯O hydrogen bonds link the mol-ecules. π-π contacts between the phenyl rings and both the phenyl and benzene rings, [centroid-centroid distances = 3.841 (3) and 3.961 (3) Å] may further stabilize the structure.

4-Chloro-3-nitro-benzonitrile.

In the title compound, C(7)H(3)ClN(2)O(2), the Cl, C and N atoms are coplanar with the aromatic ring. In the crystal structure, weak inter-molecular C-H⋯O and C-H⋯N hydrogen bonds link the mol-ecules. The π-π contact between the benzene rings, [centroid-centroid distances = 3.912 (3) Å] may further stabilize the structure.