Media Use Behavior Mediates the Association Between Family Health and Intention to Use Mobile Health Devices Among Older Adults: Cross-Sectional Study.

BACKGROUND: With the advent of a new era for health and medical treatment, characterized by the integration of mobile technology, a significant digital divide has surfaced, particularly in the engagement of older individuals with mobile health (mHealth). The health of a family is intricately connected to the well-being of its members, and the use of media plays a crucial role in facilitating mHealth care. Therefore, it is important to examine the mediating role of media use behavior in the connection between the family health of older individuals and their inclination to use mHealth devices. OBJECTIVE: This study aims to investigate the impact of family health and media use behavior on the intention of older individuals to use mHealth devices in China. The study aims to delve into the intricate dynamics to determine whether media use behavior serves as a mediator in the relationship between family health and the intention to use mHealth devices among older adults. The ultimate goal is to offer well-founded and practical recommendations to assist older individuals in overcoming the digital divide. METHODS: The study used data from 3712 individuals aged 60 and above, sourced from the 2022 Psychology and Behavior Investigation of Chinese Residents study. Linear regression models were used to assess the relationships between family health, media use behavior, and the intention to use mHealth devices. To investigate the mediating role of media use behavior, we used the Sobel-Goodman Mediation Test. This analysis focused on the connection between 4 dimensions of family health and the intention to use mHealth devices. RESULTS: A positive correlation was observed among family health, media use behavior, and the intention to use mHealth devices (r=0.077-0.178, P<.001). Notably, media use behavior was identified as a partial mediator in the relationship between the overall score of family health and the intention to use mHealth devices, as indicated by the Sobel test (z=5.451, P<.001). Subgroup analysis further indicated that a complete mediating effect was observed specifically between family health resources and the intention to use mHealth devices in older individuals with varying education levels. CONCLUSIONS: The study revealed the significance of family health and media use behavior in motivating older adults to adopt mHealth devices. Media use behavior was identified as a mediator in the connection between family health and the intention to use mHealth devices, with more intricate dynamics observed among older adults with lower education levels. Going forward, the critical role of home health resources must be maximized, such as initiatives to develop digital education tailored for older adults and the creation of media products specifically designed for them. These measures aim to alleviate technological challenges associated with using media devices among older adults, ultimately bolstering their inclination to adopt mHealth devices.

Notch1 signalling controls the differentiation and function of myeloid-derived suppressor cells in systemic lupus erythematosus.

Emerging studies have reported the expansion of myeloid-derived suppressor cells (MDSCs) in some autoimmune disorders, such as systemic lupus erythematosus (SLE), but the detailed molecular mechanisms of the aberrant expansion in SLE are still unclear. In the present study, we confirmed that the increased MDSCs positively correlated with disease activity in SLE patients. The suppressive capacity of MDSCs from patients with high activity was lower than that of MDSCs from patients with low activity. Moreover, the potential precursors for MDSCs, common myeloid progenitors (CMPs) and granulocyte-monocyte progenitors (GMPs), were markedly increased in the bone marrow (BM) aspirates of SLE patients. As an important regulator of cell fate decisions, aberrant activation of Notch signalling was reported to participate in the pathogenesis of SLE. We found that the expression of Notch1 and its downstream target gene hairy and enhancer of split 1 (Hes-1) increased markedly in GMPs from SLE patients. Moreover, the Notch1 signalling inhibitor DAPT profoundly relieved disease progression and decreased the proportion of MDSCs in pristane-induced lupus mice. The frequency of GMPs was also decreased significantly in lupus mice after DAPT treatment. Furthermore, the inhibition of Notch1 signalling could limit the differentiation of MDSCs in vitro. The therapeutic effect of DAPT was also verified in Toll-like receptor 7 (TLR7) agonist-induced lupus mice. Taken together, our results demonstrated that Notch1 signalling played a crucial role in MDSC differentiation in SLE. These findings will provide a promising therapy for the treatment of SLE.

Highway Visibility Estimation in Foggy Weather via Multi-Scale Fusion Network.

Poor visibility has a significant impact on road safety and can even lead to traffic accidents. The traditional means of visibility monitoring no longer meet the current needs in terms of temporal and spatial accuracy. In this work, we propose a novel deep network architecture for estimating the visibility directly from highway surveillance images. Specifically, we employ several image feature extraction methods to extract detailed structural, spectral, and scene depth features from the images. Next, we design a multi-scale fusion network to adaptively extract and fuse vital features for the purpose of estimating visibility. Furthermore, we create a real-scene dataset for model learning and performance evaluation. Our experiments demonstrate the superiority of our proposed method to the existing methods.

Phosphorylation of Neurofilament Light Chain in the VLO Is Correlated with Morphine-Induced Behavioral Sensitization in Rats.

Neurofilament light chain (NF-L) plays critical roles in synapses that are relevant to neuropsychiatric diseases. Despite postmortem evidence that NF-L is decreased in opiate abusers, its role and underlying mechanisms remain largely unknown. We found that the microinjection of the histone deacetylase (HDAC) inhibitor Trichostatin A (TSA) into the ventrolateral orbital cortex (VLO) attenuated chronic morphine-induced behavioral sensitization. The microinjection of TSA blocked the chronic morphine-induced decrease of NF-L. However, our chromatin immunoprecipitation (ChIP)-qPCR results indicated that this effect was not due to the acetylation of histone H3-Lysine 9 and 14 binding to the NF-L promotor. In line with the behavioral phenotype, the microinjection of TSA also blocked the chronic morphine-induced increase of p-ERK/p-CREB/p-NF-L. Finally, we compared chronic and acute morphine-induced behavioral sensitization. We found that although both chronic and acute morphine-induced behavioral sensitization were accompanied by an increase of p-CREB/p-NF-L, TSA exhibited opposing effects on behavioral phenotype and molecular changes at different addiction contexts. Thus, our findings revealed a novel role of NF-L in morphine-induced behavioral sensitization, and therefore provided some correlational evidence of the involvement of NF-L in opiate addiction.

MUFFIN: multi-scale feature fusion for drug-drug interaction prediction.

MOTIVATION: Adverse drug-drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. RESULTS: Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/xzenglab/MUFFIN.

Transcriptome-based evaluation and validation of suitable housekeeping gene for quantification real-time PCR under specific experiment condition in teleost fishes.

Quantification real-time PCR (qRT-PCR) is a common method in analysis of gene expression, but the stable reference genes for the normalization analysis have not been appreciated before identifying expression pattern of genes in teleost fishes. In this study, we selected eight candidate reference genes (18S, Actin, EF-1α, 40S, B2M, TUBA, UBCE and GAPDH) basing on transcriptome analysis and the traditional housekeeping genes, and analyzed the stability of the reference genes in spleen, head kidney and head kidney leukocytes (HKL) after pathogen challenge in Schizothorax prenanti (S. prenanti). Three common programs (geNorm, NormFinder and Bestkeeper) were used to evaluate the stability of the candidate reference genes. Two reference genes, Actin and EF-1α presented higher stability, while 18S and GAPDH were the lower stable genes, both in in vitro and in vivo. An important immune gene, toll-like receptor 22a (TLR22a), was selected to validate the stability of the proposed reference genes (Actin and EF-1α) across different experiment treatments. The results reveal that Actin and EF-1α are quite suitable reference genes for the normalization analysis. Otherwise, using the most stable gene Actin to validate the reliable of transcriptome data showed the high correlation between the fold change of transcriptome data and qRT-PCR data. In conclusion, our study not only acquired the suitable reference gene for the qRT-PCR assay under specific experiment condition, but also provided a comprehensive method to evaluate and validate the reference gene based on transcriptome analysis in teleost fishes.

Theoretical study of the mechanism of the manganese catalase KatB.

The mechanism of the H2O2 disproportionation catalyzed by the manganese catalase (MnCat) KatB was studied using the hybrid density functional theory B3LYP and the quantum chemical cluster approach. Compared to the previous mechanistic study at the molecular level for the Thermus thermophilus MnCat (TTC), more modern methodology was used and larger models of increasing sizes were employed with the help of the high-resolution X-ray structure. In the reaction pathway suggested for KatB using the Large chemical model, the O-O homolysis of the first substrate H2O2 occurs through a µ-η1:η1 coordination mode and requires a barrier of 10.9 kcal/mol. In the intermediate state of the bond cleavage, two hydroxides form as terminal ligands of the dimanganese cluster at the Mn2(III,III) oxidation state. One of the two Mn(III)-OH- moieties and a second-sphere tyrosine stabilize the second substrate H2O2 in the second-sphere of the active site via hydrogen bonding interactions. The H2O2, unbound to the metals, is first oxidized into HO2· through a proton-coupled electron transfer (PCET) step with a barrier of 9.5 kcal/mol. After the system switches to the triplet surface, the uncoordinated HO2· replaces the product water terminally bound to the Mn(II) and is then oxidized into O2 spontaneously. Transition states with structural similarities to those obtained for TTC, where µ-η2-OH-/O2- groups play important roles, were found to be higher in energy.

Multiple subtypes of TLR22 molecule from Schizothorax prenanti present the functional diversity in ligand recognition and signal activation.

Evolutionary development has increased the diversity of genotypes and the complexity of gene functions in fish. TLR22 has been identified as a teleost-specific gene, but its functions are tremendously different among different fish species. Whether the functional diversity relates to the difference of genotypes remains poorly understand. In this study, we cloned and identified three TLR22 molecules from Schizothorax prenanti (S. prenanti), named as spTLR22-1, spTLR22-2 and spTLR22-3. The full-length coding regions of spTLR22s are 2841 bp, 2805 bp and 2868 bp and coding 946 aa, 934 aa and 955 aa, respectively. All spTLR22s are composed of multiple leucine-rich repeat (LRR) domains, a transmembrane structure and a Toll/IL-1 receptor (TIR) region. The phylogenetic analysis showed that three spTLR22s were close to Cyprinus carpio TLR22-1, TLR22-2 and TLR22-3, respectively. Among the spTLR22s, they presented not close relationship but remained to belong to TLR22 subfamily. All spTLR22s were ubiquitously expressed in all tested tissues, but the expression levels of spTLR22s were dominant in immune-related tissues, such as gill and spleen. The expression levels of spTLR22-1 and spTLR22-3 were significantly increased after treatment with bacteria, LPS and Poly(I:C). However, spTLR22-2 seems like no response to these treatments. The luciferase reporter assay demonstrated that all spTLR22s could activate NF-κB signaling pathway, but only spTLR22-1 and spTLR22-2 could activate IFN-ß signaling pathway. Interestingly, in the ligand recognition analysis, spTLR22-1 and spTLR22-3 but not spTLR22-2 had the recognized potential to Poly(I:C), and all spTLR22s could not recognize LPS. Both spTLR22-1 and spTLR22-3 significantly up-regulated the expression of anti-viral-related genes (Mx, IFN and ISG15) and down-regulated the expression of anti-inflammatory factor IL-10 after the overexpression in carp EPC cell line, but spTLR22-2 failed to impact the expression of these genes. Moreover, we found that all spTLR22s localized to the intracellular region. Taken together, our results reveal that spTLR22-1 and spTLR22-3 but not spTLR22-2 may be involved into the anti-viral immune response via IFN-ß signaling pathway, and all spTLR22s can activate NF-κB signaling pathway but only spTLR22-1 and spTLR22-3 response to the stimulation of bacteria and LPS.

Exogenous melatonin delays leaves senescence and enhances saline and alkaline stress tolerance in grape seedlings.

Saline and alkaline stress is one of the major abiotic stresses facing agricultural production, which severely inhibits the growth and yield of plant. The application of plant growth regulators can effectively prevent crop yield reduction caused by saline and alkaline stress. Exogenous melatonin (MT) can act as a signaling molecule involved in the regulation of a variety of physiological processes in plants, has been found to play a key role in enhancing the improvement of plant tolerance to abiotic stresses. However, the effects of exogenous MT on saline and alkaline tolerance of table grape seedlings and its mechanism have not been clarified. The aim of this study was to investigate the role of exogenous MT on morphological and physiological growth of table grape seedlings (Vitis vinifera L.) under saline and alkaline stress. The results showed that saline and alkaline stress resulted in yellowing and wilting of grape leaves and a decrease in chlorophyll content, whereas the application of exogenous MT alleviated the degradation of chlorophyll in grape seedling leaves caused by saline and alkaline stress and promoted the accumulation of soluble sugars and proline content. In addition, exogenous MT increased the activity of antioxidant enzymes, which resulted in the scavenging of reactive oxygen species (ROS) generated by saline and alkaline stress. In conclusion, exogenous MT was involved in the tolerance of grape seedlings to saline and alkaline stress, and enhanced the saline and alkaline resistance of grape seedlings to promote the growth and development of the grape industry in saline and alkaline areas.

Enhancing Molecular Property Prediction through Task-Oriented Transfer Learning: Integrating Universal Structural Insights and Domain-Specific Knowledge.

Precisely predicting molecular properties is crucial in drug discovery, but the scarcity of labeled data poses a challenge for applying deep learning methods. While large-scale self-supervised pretraining has proven an effective solution, it often neglects domain-specific knowledge. To tackle this issue, we introduce Task-Oriented Multilevel Learning based on BERT (TOML-BERT), a dual-level pretraining framework that considers both structural patterns and domain knowledge of molecules. TOML-BERT achieved state-of-the-art prediction performance on 10 pharmaceutical datasets. It has the capability to mine contextual information within molecular structures and extract domain knowledge from massive pseudo-labeled data. The dual-level pretraining accomplished significant positive transfer, with its two components making complementary contributions. Interpretive analysis elucidated that the effectiveness of the dual-level pretraining lies in the prior learning of a task-related molecular representation. Overall, TOML-BERT demonstrates the potential of combining multiple pretraining tasks to extract task-oriented knowledge, advancing molecular property prediction in drug discovery.

The present state and challenges of active learning in drug discovery.

Active learning (AL) is an iterative feedback process that efficiently identifies valuable data within vast chemical space, even with limited labeled data. This characteristic renders it a valuable approach to tackle the ongoing challenges faced in drug discovery, such as the ever-expanding explore space and the limitations of labeled data. Consequently, AL is increasingly gaining prominence in the field of drug development. In this paper, we comprehensively review the application of AL at all stages of drug discovery, including compounds-target interaction prediction, virtual screening, molecular generation and optimization, as well as molecular properties prediction. Additionally, we discuss the challenges and prospects associated with the current applications of AL in drug discovery.

Dual-View Learning Based on Images and Sequences for Molecular Property Prediction.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness. Conversely, semantic information in SMILES sequences is explicit but lacks spatial structural details. Therefore, in this study, we focus on and explore the relationship between these two types of representations, proposing a novel multimodal architecture named ISMol. ISMol relies on a cross-attention mechanism to extract information representations of molecules from both images and SMILES strings, thereby predicting molecular properties. Evaluation results on 14 small molecule ADMET datasets indicate that ISMol outperforms machine learning (ML) and deep learning (DL) models based on single-modal representations. In addition, we analyze our method through a large number of experiments to test the superiority, interpretability and generalizability of the method. In summary, ISMol offers a powerful deep learning toolbox for drug discovery in a variety of molecular properties.

Attachment anxiety and cyberbullying victimization in college students: the mediating role of social media self-disclosure and the moderating role of gender.

Backgrounds and purpose: Cyberbullying is a globally prevalent social problem that threatens the wellbeing of young people. Despite a rising call for more research focused on cyberbullying victims, our understanding of the psychological and behavioral risk factors associated with cyberbullying victimization (CV) remains limited, especially among the Chinese population. However, such information is crucial for identifying potential victims and planning targeted educational and protective interventions. In this paper, we report an empirical investigation into how attachment anxiety (AA), social media self-disclosure (SMSD), and gender interplay with each other to influence CV. Methods: Cross-sectional survey data from 845 Chinese college students (Female = 635, Mage = 18.7) were analyzed in SPSS PROCESS using Haye's macro with the bootstrap method. Results: Our data support a moderated mediation model. First, SMSD partially mediates the positive relationship between AA and CV, which suggests individuals with high AA tend to engage in risky and excessive self-disclosure behavior on social media, which, in turn, expose them to an increased risk of cyberbullying. Second, gender moderates the direct AA-CV path and the second stage of the mediation path, making the effect of AA on CV appear more direct in males (i.e., not mediated by SMSD) and more indirect (i.e., fully mediated through SMSD) in females. Conclusion: The results contribute to an ongoing endeavor to better understand the psychological and behavioral mechanisms underlying CV and develop effective strategies to identify and protect vulnerable individuals.

Thallium(III) exposure alters diversity and co-occurrence networks of bacterial and fungal communities and intestinal immune response along the digestive tract in mice.

The gut microbiota, which includes fungi and bacteria, plays an important role in maintaining gut health. Our previous studies have shown that monovalent thallium [Tl(I)] exposure is associated with disturbances in intestinal flora. However, research on acute Tl(III) poisoning through drinking water and the related changes in the gut microbiota is insufficient. In this study, we showed that Tl(III) exposure (10 ppm for 2 weeks) reduced the alpha diversity of bacteria in the ileum, colon, and feces of mice, as well as the alpha diversity of fecal fungi. In addition, principal coordinate analysis showed that Tl(III) exposure had little effect on the bacterial and fungal beta diversity. LEfSe analyses revealed that Tl(III) exposure altered the abundance of intestinal bacteria in the digestive tract and feces. Moreover, Tl(III) exposure had little effect on fungal abundance in the ileum, cecum, and colon, but had a considerable effect on fungal abundance in feces. After Tl(III) exposure, the fungal composition was more disrupted in feces than in the intestinal tract, suggesting that feces can serve as a representative of the gut mycobiota in Tl(III) exposure studies. Intra-kingdom network analyses showed that Tl(III) exposure affected the complexity of bacterial-bacterial and fungal-fungal co-occurrence networks along the digestive tract. The bacterial-fungal interkingdom co-occurrence networks exhibited increased complexity after Tl(III) exposure, except for those in the colon. Additionally, Tl(III) exposure altered the intestinal immune response. These results reveal the perturbation in gut bacterial and fungal diversity, abundance, and co-occurrence network complexity, as well as the gut immune response, caused by Tl(III) exposure.

Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction.

Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.

Clinical features of MOGAD with brainstem involvement in the initial attack versus NMOSD and MS.

OBJECTIVE: To assess the characteristics of Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disorder (MOGAD) with brainstem involvement in the first event (BSIFE) and make comparisons with aquaporin-4-IgG seropositive neuromyelitis optica spectrum disorder (AQP4-IgG-NMOSD) and multiple sclerosis (MS). METHODS: From 2017 to 2022, this study identified MOG-IgG-positive patients with brainstem or both brainstem and cerebellum lesions in the first episode. As a comparison group, AQP4-IgG-NMOSD (n = 30) and MS (n = 30) patients with BSIFE were enroled. RESULTS: Thirty-five patients (35/146, 24.0%) were the BSIFE of MOGAD. Isolated brainstem episodes occurred in 9 of the 35 (25.7%) MOGAD patients, which was similar to MS (7/30, 23.3%) but was lower than AQP4-IgG-NMOSD (17/30, 56.7%, P = 0.011). Pons (21/35, 60.0%), medulla oblongata (20/35, 57.1%) and middle cerebellar peduncle (MCP, 19/35, 54.3%) were the most frequently affected areas. Intractable nausea (n = 7), vomiting (n = 8) and hiccups (n = 2) happened in MOGAD patients, but EDSS of MOGAD was lower than AQP4-IgG-NMOSD (P = 0.001) at the last follow-up. MOGAD patients with or without BSIFE did not significantly differ in terms of the ARR (P = 0.102), mRS (P = 0.823), or EDSS (P = 0.598) at the most recent follow-up. Specific oligoclonal bands appeared in MOGAD (13/33, 39.4%) and AQP4-IgG-NMOSD (7/24, 29.2%) in addition to MS (20/30, 66.7%). Fourteen MOGAD patients (40.0%) experienced relapse in this study. When the brainstem was involved in the first attack, there was an increased likelihood of a second attack occurring at the same location (OR=12.22, 95%CI 2.79 to 53.59, P = 0.001). If the first and second events were both in the brainstem, the third event was likely to occur at the same location (OR=66.00, 95%CI 3.47 to 1254.57, P = 0.005). Four patients experienced relapses after the MOG-IgG turned negative. CONCLUSION: BSIFE occurred in 24.0% of MOGAD. Pons, medulla oblongata and MCP were the most frequently involved regions. Intractable nausea, vomiting and hiccups occurred in MOGAD and AQP4-IgG-NMOSD, but not MS. The prognosis of MOGAD was better than AQP4-IgG-NMOSD. In contrast to MS, BSIFE may not indicate a worse prognosis for MOGAD. When patients with BSIFE, MOGAD tent to reoccur in the brainstem. Four of the 14 recurring MOGAD patients relapsed after the MOG-IgG test turned negative.

Plasma Non-targeted Metabolomics Analysis of Yili Horses Raced on Tracks With Different Surface Hardness.

In this study, the plasma non-targeted metabolomics of Yili horses were characterized before and after exercise on tracks that differed in surface hardness to better understand exercise-related biochemical changes. Blood samples were obtained from eight trained Yili horses before and immediately after exercise. Samples were used for metabolomic analysis by ultra-performance liquid chromatography-Q-EXACTIVE mass spectrometry. In total, 938 significantly different metabolites involving sugar, lipid, and amino acid metabolism were detected in the plasma, with significant increases in glucose, glucoheptanoic acid, lactic acid, malic acid, and methylmalonic acid and significant decreases in creatinine, D-tryptophan, carnitine, and citric acid after exercise. Among these metabolites, acetylcarnitine, tuliposide, vitamin C, and methylmalonic acid showed regular changes in concentration after exercise on tracks that differed in surface hardness, providing new insights into equine exercise physiology. The findings indicated the potential of vitamin C and methylmalonic acid as novel biomarkers of equine locomotor injury.

The 5-HT7 receptors in the VLO contribute to the development of morphine-induced behavioral sensitization in rats.

The 5-hydroxytryptamine 7 receptor (5-HT7R) is one of the most recently cloned serotonin receptors which have been implicated in many physiological and pathological processes including drug addiction. Behavioral sensitization is the progressive process during which re-exposure to drugs intensified the behavioral and neurochemical responses to drugs. Our previous study has demonstrated that the ventrolateral orbital cortex (VLO) is critical for morphine-induced reinforcing effect. The aim of the present study was to investigate the effect of 5-HT7Rs in the VLO on morphine-induced behavioral sensitization and their underlying molecular mechanisms. Our results showed that a single injection of morphine, followed by a low challenge dose could induce behavioral sensitization. Microinjection of the selective 5-HT7R agonist AS-19 into the VLO during the development phase significantly increased morphine-induced hyperactivity. Microinjection of the 5-HT7R antagonist SB-269970 suppressed acute morphine-induced hyperactivity and the induction of behavioral sensitization, but had no effect on the expression of behavioral sensitization. In addition, the phosphorylation of AKT (Ser 473) was increased during the expression phase of morphine-induced behavioral sensitization. Suppression of the induction phase could also block the increase of p-AKT (Ser 473). In conclusion, we demonstrated that 5-HT7Rs and p-AKT in the VLO at least partially contribute to morphine-induced behavioral sensitization.

Integrative Analysis of Morphine-Induced Differential Circular RNAs and ceRNA Networks in the Medial Prefrontal Cortex.

Circular RNAs (circRNAs) are a novel type of non-coding RNAs. Despite the fact that the functional mechanisms of most circRNAs remain unknown, emerging evidence indicates that circRNAs could sponge microRNAs (miRNAs), bind to RNA binding proteins (RBP), and even be translated into protein. Recent research has demonstrated the crucial roles played by circRNAs in neuropsychiatric disorders. The medial prefrontal cortex (mPFC) is a crucial component of drug reward circuitry and exerts top-down control over cognitive functions. However, there is currently limited knowledge about the correlation between circRNAs and morphine-associated contextual memory in the mPFC. Here, we performed morphine-induced conditioned place preference (CPP) in mice and extracted mPFC tissue for RNA-sequencing. Our study represented the first attempt to identify differentially expressed circRNAs (DEcircRNAs) and mRNAs (DEmRNAs) in the mPFC after morphine-induced CPP. We identified 47 significantly up-regulated DEcircRNAs and 429 significantly up-regulated DEmRNAs, along with 74 significantly down-regulated DEcircRNAs and 391 significantly down-regulated DEmRNAs. Functional analysis revealed that both DEcircRNAs and DEmRNAs were closely associated with neuroplasticity. To further validate the DEcircRNAs, we conducted qRT-PCR, Sanger sequencing, and RNase R digestion assays. Additionally, using an integrated bioinformatics approach, we constructed ceRNA networks and identified critical circRNA/miRNA/mRNA axes that contributed to the development of morphine-associated contextual memory. In summary, our study provided novel insights into the role of circRNAs in drug-related memory, specifically from the perspective of ceRNAs.

WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks.

Network representation learning, also known as network embedding, aims to learn the low-dimensional representations of vertices while capturing and preserving the network structure. For real-world networks, the edges that represent some important relationships between the vertices of a network may be missed and may result in degenerated performance. The existing methods usually treat missing edges as negative samples, thereby ignoring the true connections between two vertices in a network. To capture the true network structure effectively, we propose a novel network representation learning method called WalkGAN, where random walk scheme and generative adversarial networks (GAN) are incorporated into a network embedding framework. Specifically, WalkGAN leverages GAN to generate the synthetic sequences of the vertices that sufficiently simulate random walk on a network and further learn vertex representations from these vertex sequences. Thus, the unobserved links between the vertices are inferred with high probability instead of treating them as nonexistence. Experimental results on the benchmark network datasets demonstrate that WalkGAN achieves significant performance improvements for vertex classification, link prediction, and visualization tasks.