Comparative brain-wide mapping of ketamine- and isoflurane-activated nuclei and functional networks in the mouse brain.

Ketamine (KET) and isoflurane (ISO) are two widely used general anesthetics, yet their distinct and shared neurophysiological mechanisms remain elusive. In this study, we conducted a comparative analysis of the effects of KET and ISO on c-Fos expression across the mouse brain, utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the responses of individual brain regions to each anesthetic. Our findings reveal that KET activates a wide range of brain regions, notably in the cortical and subcortical nuclei involved in sensory, motor, emotional, and reward processing, with the temporal association areas (TEa) as a strong hub, suggesting a top-down mechanism affecting consciousness by primarily targeting higher order cortical networks. In contrast, ISO predominantly influences brain regions in the hypothalamus, impacting neuroendocrine control, autonomic function, and homeostasis, with the locus coeruleus (LC) as a connector hub, indicating a bottom-up mechanism in anesthetic-induced unconsciousness. KET and ISO both activate brain areas involved in sensory processing, memory and cognition, reward and motivation, as well as autonomic and homeostatic control, highlighting their shared effects on various neural pathways. In conclusion, our results highlight the distinct but overlapping effects of KET and ISO, enriching our understanding of the mechanisms underlying general anesthesia.

Programmed cell death 4 governs NLRP3-mediated pyroptosis in septic lung disorders.

INTRODUCTION: Sepsis is a pathogenic syndrome of prolonged excessive inflammation and immunosuppression produced by invading pathogens. Programmed cell death 4 (PDCD4) may be implicated in a range of inflammatory lesions, and this study aimed to confirm the involvement of PDCD4 in septic lung injury. MATERIALS AND METHODS: Mice and bronchial epithelial 16HBE cells were separately subjected to CLP and LPS to generate in vivo and in vitro models. Following the level of PDCD4 was determined, the impacts of PDCD4 knockdown on mouse lung injury degree, inflammation, apoptosis, and pyroptosis levels were evaluated. Afterward, cells were treated with the NLRP3 agonist, and the influences of NLRP3 activation on the regulations of PDCD4 knockdown were determined. RESULTS: PDCD4 was elevated following mice developed septic lung injury, PDCD4 knockdown ameliorated septic lung injury and reduced lung inflammation and apoptosis. Moreover, PDCD4 knockdown suppressed NLRP3-mediated pyroptosis, indicating that PDCD4 also mediated pyroptosis. According to cellular models, NLRP3 activation broke the effects of PDCD4 knockdown on cells. CONCLUSIONS: The current study reveals that PDCD4 governs NLRP3-mediated pyroptosis in septic lung injury. PDCD4 is not only related to apoptosis and expands the knowledge of PDCD4 regulation of different cell death modes.

Small G-Protein Rheb Gates Mammalian Target of Rapamycin Signaling to Regulate Morphine Tolerance in Mice.

BACKGROUND: Analgesic tolerance due to long-term use of morphine remains a challenge for pain management. Morphine acts on µ-opioid receptors and downstream of the phosphatidylinositol 3-kinase signaling pathway to activate the mammalian target of rapamycin (mTOR) pathway. Rheb is an important regulator of growth and cell-cycle progression in the central nervous system owing to its critical role in the activation of mTOR. The hypothesis was that signaling via the GTP-binding protein Rheb in the dorsal horn of the spinal cord is involved in morphine-induced tolerance. METHODS: Male and female wild-type C57BL/6J mice or transgenic mice (6 to 8 weeks old) were injected intrathecally with saline or morphine twice daily at 12-h intervals for 5 consecutive days to establish a tolerance model. Analgesia was assessed 60 min later using the tail-flick assay. After 5 days, the spine was harvested for Western blot or immunofluorescence analysis. RESULTS: Chronic morphine administration resulted in the upregulation of spinal Rheb by 4.27 ± 0.195-fold (P = 0.0036, n = 6), in turn activating mTOR by targeting rapamycin complex 1 (mTORC1). Genetic overexpression of Rheb impaired morphine analgesia, resulting in a tail-flick latency of 4.65 ± 1.10 s (P < 0.0001, n = 7) in Rheb knock-in mice compared to 10 s in control mice (10 ± 0 s). Additionally, Rheb overexpression in spinal excitatory neurons led to mTORC1 signaling overactivation. Genetic knockout of Rheb or inhibition of mTORC1 signaling by rapamycin potentiated morphine-induced tolerance (maximum possible effect, 52.60 ± 9.56% in the morphine + rapamycin group vs. 16.60 ± 8.54% in the morphine group; P < 0.0001). Moreover, activation of endogenous adenosine 5'-monophosphate-activated protein kinase inhibited Rheb upregulation and retarded the development of morphine-dependent tolerance (maximum possible effect, 39.51 ± 7.40% in morphine + metformin group vs. 15.58 ± 5.79% in morphine group; P < 0.0001). CONCLUSIONS: This study suggests spinal Rheb as a key molecular factor for regulating mammalian target of rapamycin signaling.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.

Transcriptional Signatures of a Dynamic Epilepsy Process Reveal Potential Immune Regulation.

Epilepsy is a progression of development and advancement over time. However, the molecular features of epilepsy were poorly studied from a dynamic developmental perspective. We intend to investigate the key mechanisms in the process of epilepsy by exploring the roles of stage-specifically expressed genes. By using time-course transcriptomic data of epileptic samples, we first analyzed the molecular features of epilepsy in different stages and divided it into progression and remission stages based on their transcriptomic features. 34 stage-specifically expressed genes were then identified by the Tau index and verified in other epileptic datasets. These genes were then enriched for immune-related biological functions. Furthermore, we found that the level of immune infiltration and mechanisms at different stages were different, which may result from different types of immune cells playing leading roles in distinct stages. Our findings indicated an essential role of immune regulation as the potential mechanism of epilepsy development.

Inverse design of chiral functional films by a robotic AI-guided system.

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction.

Molecular property prediction is a crucial task in various fields and has recently garnered significant attention. To achieve accurate and fast prediction of molecular properties, machine learning (ML) models have been widely employed due to their superior performance compared to traditional methods by trial and error. However, most of the existing ML models that do not incorporate 3D molecular information are still in need of improvement, as they are mostly poor at differentiating stereoisomers of certain types, particularly chiral ones. Also,routine featurization methods using only incomplete features are hard to obtain explicable molecular representations. In this paper, we propose the Stereo Molecular Graph BERT (SMG-BERT) by integrating the 3D space geometric parameters, 2D topological information, and 1D SMILES string into the self-attention-based BERT model. In addition, nuclear magnetic resonance (NMR) spectroscopy results and bond dissociation energy (BDE) are integrated as extra atomic and bond features to improve the model's performance and interpretability analysis. The comprehensive integration of 1D, 2D, and 3D information could establish a unified and unambiguous molecular characterization system to distinguish conformations, such as chiral molecules. Intuitively integrated chemical information enables the model to possess interpretability that is consistent with chemical logic. Experimental results on 12 benchmark molecular datasets show that SMG-BERT consistently outperforms existing methods. At the same time, the experimental results demonstrate that SMG-BERT is generalizable and reliable.

The effect of 17beta-estradiol plus norethisterone acetate on anthropometric indices: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: Little evidence exists on the effect of 17beta-estradiol plus norethisterone acetate on all the anthropometric indices. Hence, this systematic review and meta-analysis of Randomized Controlled Trials was conducted to give an evidence-based report on the effect of 17beta-estradiol plus norethisterone acetate on anthropometric indices. METHODS: The literature search was executed in databases including PubMed/Medline, Web of Science, Scopus, Embase, and Google Scholar to recognize clinical trials that examined the influence of 17beta-estradiol plus norethisterone acetate on obesity indices from database inception to Jan 2023. RESULTS: Combined findings were generated from 20 eligible articles. The meta-analysis showed that body weight (Weighted Mean Difference (WMD): -0.47 kg, 95% CI: -1.32, 0.37, p = 0.274), body fat (WMD: 0.16 kg, 95% CI: -1.26, 1.59, p = 0.821), WHR (WMD: 0.001 kg, 95% CI: -0.006, 1.15, p = 0.872), and LBM (WMD: -0.02 kg, 95% CI: -1.19, 1.15, p = 0.970) were not modified in DHEA group compared to the control, but BMI levels were significantly reduced in 17beta-estradiol plus norethisterone acetate group (WMD: -0.15 kg/m2, 95% CI: -0.30, -0.008, p = 0.039). Moreover, based on intervention duration (months), a more significant reduction in BMI was found in trials that were performed on studies with ˃3 months duration (WMD: -0.176 kg/m2) than studies with ≤ 3 months (WMD: 0.05 kg/m2). CONCLUSION: Administration of 17beta-estradiol plus norethisterone acetate for more than 3 months results in a decrease in BMI, which helps to reduce cardiovascular disease risk.

Distinctive roles of L-type calcium channels subtypes within the dorsal hippocampus in formation of morphine withdrawal-induced aversion in rats.

Although the negative effects coming along with opiate withdrawal are in part modulated by L-type calcium channels (LTCCs), the distinctive physiological properties and functions of LTCCs subtypes suggest differential roles of subtypes during withdrawal. The present study aimed to examine the contributions of LTCC subtypes, Cav1.2 and Cav1.3, within the dorsal hippocampus (DH) in naloxone-precipitated morphine withdrawal using the conditioned place aversion (CPA) paradigm. Firstly, we injected the non-specific LTCCs antagonist verapamil into the DH of morphine-dependent rats before conditioning an environment with naloxone-precipitated withdrawal. Our results showed that verapamil blocked the acquisition of CPA. Then, to explore the molecular mechanisms of LTCCs subtypes during withdrawal, we measured the protein expression of Cav1.2 and Cav1.3 in morphine-dependent rats under different conditions. In morphine-dependent rats, conditioning with withdrawal increased Cav1.2 expression in the membrane, while only acute naloxone injection increased the membrane expression of Cav1.3. To further determine the causal roles of LTCCs subtypes in the withdrawal process, we used Cav1.2 siRNA or Cav1.3 shRNA to knock down the expression of subtypes and detected the effects on CPA and somatic withdrawal signs in morphine-dependent rats. Cav1.2 siRNA, but not Cav1.3 shRNA, inhibited the acquirement of CPA and relieved somatic withdrawal symptoms. Together, our findings reveal that Cav1.2, but not Cav1.3 plays an important role in mediating morphine withdrawal, suggesting this subtype may serve as a potential therapeutic target for the treatment of negative effects in opiate dependence.

Risk factors of lymph node metastasis in patients with T1 stage colorectal cancer-a retrospective cohort study based on the Surveillance, Epidemiology, and End Results database.

Background: Patients with T1 stage early colorectal cancer (CRC) can be treated with radical surgery or endoscopic surgery. Endoscopic surgery has a number of advantages, including minimal trauma and a rapid recovery. However, it cannot remove regional lymph nodes to assess whether there is lymph node metastasis. Thus, the analysis of the risk factors of lymph node metastasis in patients with T1 stage CRC is of great significance in the selection of appropriate treatment methods. Although previous studies have explored the risk factors for lymph node metastasis in T1 stage CRC patients, the number of cases were relatively insufficient, and further exploration is necessary. Methods: A total of 2,085 patients who had been pathologically diagnosed with CRC from 2015 to 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. Among the patients, 324 had lymph node metastasis. A multivariate logistic regression analysis was conducted to analyze the risk factors of lymph node metastasis in patients with T1 stage CRC. Next, we established a prediction model to predict lymph node metastasis in patients with T1 stage CRC. Results: The results of the multivariate logistic regression analysis showed that age at diagnosis, rectosigmoid cancer, poorly differentiated or undifferentiated tumor cells, and distant metastasis were independent factors of lymph node metastasis in patients with T1 stage CRC (P<0.05). This study used the R4.0.3 statistical software for the statistical analysis. The data set was randomly divided into a training set and verification set. The training set comprised 1,460 patients, and the verification set comprised 625 patients. The area under the receiver operating characteristic curve (AUC) of the training set was 0.675 [95% confidence interval (CI): 0.635-0.714], and the AUC of the verification set was 0.682 (95% CI: 0.617-0.747). In the validation set, the model was tested by the Hosmer-Lemeshow Goodness-of-Fit Test (χ2=4.018, P=0.855), and the results showed that the model was reliable at predicting lymph node metastasis in patients with T1 stage CRC. Conclusions: For CRC patients with high risk factors of lymph node metastasis, endoscopic physicians should carefully evaluate the advantages and disadvantages of the endoscopic surgery before deciding whether to perform this surgery.

Research on nonlinear response analysis of micro-cracks under vibro-acoustic modulation.

In view of the complex nonlinear interaction mechanism between acoustic waves and damage in vibration sound modulation technology, this paper derives the kinematic equilibrium equation for linear elastic materials with cracks undergoing infinitesimal deformation using structural mechanics theory. The weak form of the equation is derived by applying the principle of virtual work to calculate the virtual work due to nonlinear changes in crack spacing. This paper also explains the physical origin of high harmonic and sideband signals in the system displacement solution. In addition, a three-dimensional contact model of micro-cracks is constructed to describe the nonlinear effect of contact sound on the crack surface caused by relevant displacement fields. To verify the correctness of the model, two indicators, the modulation index and the damage index, are used to evaluate the simulation results. The results indicate that the interface contact under micro-crack opening and closing motions causes additional nonlinear frequencies and that the nonlinear response increases with excitation amplitude while being relatively sensitive to micron-level cracks. Finally, experimental research is conducted, which confirms the theoretical derivation, and the reliability of the model has been verified.

Spatiotemporal expression patterns of genes coding for plasmalemmal chloride transporters and channels in neurological diseases.

Neuronal voltage changes which are dependent on chloride transporters and channels are involved in forming neural functions during early development and maintaining their stability until adulthood. The intracellular chloride concentration maintains a steady state, which is delicately regulated by various genes coding for chloride transporters and channels (GClTC) on the plasmalemma; however, the synergistic effect of these genes in central nervous system disorders remains unclear. In this study, we first defined 10 gene clusters with similar temporal expression patterns, and identified 41 GClTC related to brain developmental process. Then, we found 4 clusters containing 22 GClTC were enriched for the neuronal functions. The GClTC from different clusters presented distinct cell type preferences and anatomical heterogeneity. We also observed strong correlations between clustered genes and diseases, most of which were nervous system disorders. Finally, we found that one of the most well-known GClTC, SLC12A2, had a more profound effect on glial cell-related diseases than on neuron-related diseases, which was in accordance with our observation that SLC12A2 was mainly expressed in oligodendrocytes during brain development. Our findings provide a more comprehensive understanding of the temporal and spatial expression characteristics of GClTC, which can help us understand the complex roles of GClTC in the development of the healthy human brain and the etiology of brain disorders.

Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model's success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory.

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m3 to 12.283 µg/m3 compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.

Studying Synaptic Connectivity and Strength with Optogenetics and Patch-Clamp Electrophysiology.

Over the last two decades the combination of brain slice patch clamp electrophysiology with optogenetic stimulation has proven to be a powerful approach to analyze the architecture of neural circuits and (experience-dependent) synaptic plasticity in such networks. Using this combination of methods, originally termed channelrhodopsin-assisted circuit mapping (CRACM), a multitude of measures of synaptic functioning can be taken. The current review discusses their rationale, current applications in the field, and their associated caveats. Specifically, the review addresses: (1) How to assess the presence of synaptic connections, both in terms of ionotropic versus metabotropic receptor signaling, and in terms of mono- versus polysynaptic connectivity. (2) How to acquire and interpret measures for synaptic strength and function, like AMPAR/NMDAR, AMPAR rectification, paired-pulse ratio (PPR), coefficient of variance and input-specific quantal sizes. We also address how synaptic modulation by G protein-coupled receptors can be studied with pharmacological approaches and advanced technology. (3) Finally, we elaborate on advances on the use of dual color optogenetics in concurrent investigation of multiple synaptic pathways. Overall, with this review we seek to provide practical insights into the methods used to study neural circuits and synapses, by combining optogenetics and patch-clamp electrophysiology.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Effect of Gender on Serum Leptin in Type 2 Diabetes Mellitus: A System Review and Meta-Analysis.

Objective: To assess the effect of gender factors on serum leptin levels in patients with diabetes mellitus. Methods: To remove any studies that indicated a relationship between leptin-based inflammatory variables and the prevalence of type 2 diabetes in particular patient categories, a comprehensive search of all articles published between July 2019 and June 2021 was performed on PubMed/MEDLINE, Web of Science, Scopus, and EBSCO Host, including Academic Search Premier, Africa-Wide Information, and Cumulative Index to Nursing and Allied Health Literature. A summary description of the combined analysis across multiple centers, regions, and continents will help us better understand the effect of gender on serum leptin levels in patients with diabetes. The meta-analysis was performed using RevMan 5.2 software on the literature that satisfied the inclusion and exclusion criteria. Results: Plasma CRP levels in women with type 2 diabetes were found to be no different from those in males with type 2 diabetes, with an OR of 0.12, 95 percent confidence interval (CI) of 0.12 to 0.12, P = 0.01. There was no statistically significant difference in the plasma level of interleukin-6 (IL-6) between women with type 2 diabetes and males with type 2 diabetes However, the "inverted funnel" diagram is asymmetrical, indicating a publication bias in the included studies, despite the fact that there was no statistically significant difference in abnormal leptin levels between men with type 2 diabetes and women patients (OR = -0.69, 95 percent CI (0.88, 1.00), P < 0.05). Conclusion: Gender factors did not affect the level of inflammatory factors and leptin level in type 2 diabetes.

Highly efficient ethanol vapour detection using g-C3N4/ZnO micro flower-like heterostructural composites.

This work proposes precursor pyrolysis, ultrasonic exfoliation and hydrothermal methods as well as high-temperature calcination strategies to fabricate heterostructured g-C3N4/ZnO composites with excellent ethanol vapour sensing properties. The structure, composition and morphology of the as-prepared g-C3N4/ZnO composites were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), field-emission scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). Then, the sensing properties of the g-C3N4/ZnO composites for ethanol (C2H5OH) were studied, and g-C3N4 doping with different mass ratios was used to control the gas-sensing properties of the composites. Compared with pure ZnO and g-C3N4, the performance of g-C3N4 with 1% doping content is the best, and the gas sensing activity of the 1% g-C3N4/ZnO composite is greatly improved at the optimal working temperature (280 °C). The response to 100 ppm ethanol reaches 81.4, which is 3.7 times that of the pure ZnO-based sensor under the same conditions. In addition, the sensor has good selectivity as well as fast response and recovery speeds (24 s and 63 s, respectively). Finally, a reasonable gas sensing enhancement mechanism is proposed, and it is believed that the constructed g-C3N4/ZnO micro flower-like heterostructure and the distinct positions of the valence and conduction bands of ZnO and g-C3N4 lead to the obtained sensor exhibiting a large specific surface area and increased conductivity, thereby improving the g-C3N4/ZnO-based sensor sensing performance.

Alterations of Dopamine Receptors and the Adaptive Changes of L-Type Calcium Channel Subtypes Regulate Cocaine-Seeking Habit in Tree Shrew.

The putamen (Put) is necessary for habitual actions, while the nucleus caudate (Cd) is critical for goal-directed actions. However, compared with the natural reward (such as sucrose)-seeking habit, how drug-related dysfunction or imbalance between the Put and Cd is involved in cocaine-seeking habit, which is not easy to bias behavior to goal-directed actions, is absent. Therefore, in our present study, in comparison with sucrose-habitual behavior, we evaluated the distinctive changes of the two subtypes of dopamine (DA) receptors (D1R and D2R) in cocaine-seeking habitual behavior animals. Moreover, the adaptive changes of Cav1.2 and Cav1.3, as prime downstream targets of D1R and D2R respectively, were also assessed. Our results showed that a similar percentage of the animals exhibited habitual seeking behavior after cocaine or sucrose variable-interval self-administration (SA) training in tree shrews. In addition, compared with animals with non-habitual behavior, animals with cocaine habitual behavior showed higher D1Rs and Cav1.2 expression in the Put accompanied with lower D2Rs and Cav1.3 expression in the Cd. However, after sucrose SA training, animals with habitual behavior only showed lower membrane expression of D2R in the Put than animals with non-habitual behavior. These results suggested that the upregulation of D1Rs-Cav1.2 signaling may lead to hyper-excitability of the Put, and the inactivation of D2Rs-Cav1.3 signaling may result in depressed activity in the Cd. This imbalance function between the Put and Cd, which causes an inability to shift between habits and goal-directed actions, may underlie the compulsive addiction habit.

Microwave-assisted in situ ring-opening polymerization of ε-caprolactone in the presence of modified halloysite nanotubes loaded with stannous chloride.

Polycaprolactone (PCL) has been widely applied for its excellent physicochemical properties, but it also has common problems with biopolymers. It is important to investigate energy-efficient polymerization crafts and composite catalytic systems in the ring-opening polymerization (ROP) of ε-caprolactone (ε-CL) to prepare high-performance PCL matrix composites. In this study, a composite catalytic system of modified halloysite nanotubes loaded with stannous chloride (APTES-P-h-HNTs-SnCl2) was successfully synthesized via hydroxylation, calcination, silane coupling agent modification and physical loading. It was used to catalyze the microwave-assisted in situ ROP of ε-CL to synthesize PCL matrix nanocomposites with modified halloysite nanotubes (PCL-HNTs). The structure, morphology, polymerization, thermal properties and electrochemical performance of products were subsequently investigated. The results show that PCL-HNTs have been successfully synthesized with connected petal-like and porous structures. Compared with PCL, the film-forming and thermal properties of PCL-HNTs have been significantly improved. Moreover, PCL-HNTs have a potential application value in the field of solid polymer electrolytes (SPEs).