Downregulation of lysine-specific histone demethylase 1A (KDM1A/LSD1) in medial prefrontal cortex facilitates chronic stress-induced pain and emotional dysfunction in female mice.

Chronic primary pain, characterized by overlapping symptoms of chronic pain, anxiety, and depression, is strongly associated with stress and is particularly prevalent among females. Recent research has convincingly linked epigenetic modifications in the medial prefrontal cortex (mPFC) to chronic pain and chronic stress. However, our understanding of the role of histone demethylation in the mPFC in chronic stress-induced pain remains limited. In this study, we investigated the function of lysine-specific histone demethylase 1A (KDM1A/LSD1) in the context of chronic overlapping pain comorbid with anxiety and depression in female mice. We employed a chronic variable stress model to induce pain hypersensitivity in the face and hindpaws, as well as anxiety-like and depression-like behaviors, in female mice. Our findings revealed that chronic stress led to a downregulation of KDM1A mRNA and protein expression in the mPFC. Notably, overexpressing KDM1A in the mPFC alleviated the pain hypersensitivity, anxiety-like behaviors, and depression-like behaviors in female mice, without affecting basal pain responses or inducing emotional distress. Conversely, conditional knockout of KDM1A in the mPFC exacerbated pain sensitivity and emotional distress specifically in females. In summary, this study highlights the crucial role of KDM1A in the mPFC in modulating chronic stress-induced overlapping pain, anxiety, and depression in females. Our findings suggest that KDM1A may serve as a potential therapeutic target for treating chronic stress-related overlap pain and associated negative emotional disorders.

Advantages of unilateral percutaneous kyphoplasty for osteoporotic vertebral compression fractures-a systematic review and meta-analysis.

Data from English randomized controlled trials comparing unilateral versus bilateral PKP for the treatment of OVCFs were retrieved and analyzed, and the results showed that unilateral PKP is a better choice for the treatment of patients with OVCFs, which will provide a reliable clinical rationale for the treatment of OVCFs. PURPOSE: To investigate the advantages of unilateral percutaneous kyphoplasty (PKP) for the treatment of osteoporotic vertebral compression fractures(OVCFs). METHODS: The systematic evaluation program met all program requirements (CRD 42023422383) by successfully passing the PROSPERO International Prospective Systematic Evaluation Registry. Researchers searched the references of English-language randomized controlled trials comparing unilateral and bilateral PKP for the treatment of osteoporotic vertebral compression fractures published between 2010 and 2023 and manually searched for known primary and review articles. The study statistically analyzed data from all the included literature, which primarily included time to surgery, visual pain score(VAS) and Oswestry disability index(ODI) at postoperative follow-up time points, polymethylmethacrylate (PMMA, bone cement) injection dose, cement leakage, radiation dose, and improvement in kyphotic angle. RESULTS: This meta-analysis searched 416 articles published from 2010 to 2023 based on keywords, and 18 articles were finally included in this study. The results of the forest plot showed that unilateral PKP operative time, amount of bone cement used, and radiation dose to the patient were significantly reduced (p < 0.01, p < 0.01, and p < 0.01, respectively), and unilateral and bilateral PKP had comparable cement leakage (p = 0.49, 95% CI = 0.58-1.30), and there was no significant difference in the kyphotic angle between unilateral and bilateral PKP (p = 0.42, 95% CI = - 2.29-0.96). During follow-up, there was no significant difference in pain relief between unilateral and bilateral PKP (p = 0.70, 95% CI = - 0.09-0.06), nor was there a significant difference in ODI (p = 0.27, 95% CI = - 0.35-1.24). CONCLUSIONS: There is no difference in clinical efficacy between unilateral PKP and bilateral PKP, but unilateral PKP has a shorter operative time, a lower incidence of cement leakage, a lower amount of cement, and a lower radiation dose to the patient and operator. Unilateral PKP is a better option for patients with OVCFs.

High throughput identification of carbonyl compounds in natural organic matter by directional derivatization combined with ultra-high resolution mass spectrometry.

Carbonyl compounds are important components of natural organic matter (NOM) with high reactivity, so that play a pivotal role in the dynamic transformation of NOM. However, due to the lack of effective analytical methods, our understanding on the molecular composition of these carbonyl compounds is still limited. Here, we developed a high-throughput screening method to detect carbonyl molecules in complex NOM samples by combining chemical derivatization with electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR-MS). In six different types of dissolved organic matter (DOM) samples tested in this study, 20-30 % of detected molecules contained at least one carbonyl group, with relative abundance accounted for 45-70 %. These carbonyl molecules displayed lower unsaturation level, lower molecular weight, and higher oxidation degree compared to non-carbonyl molecules. More importantly, the measured abundances of carbonyl molecules were consistent with the results of 13C nuclear magnetic resonance (NMR) analysis. Based on this method, we found that carbonyl molecules can be produced at DOM-ferrihydrite interface, thus playing a role in shaping the molecular diversity of DOM. This method has broad application prospects in screening carbonyl compounds from complex mixtures, and the same strategy can be used to directional identification of molecules with other functional groups as well.

Luteolin and its analog luteolin-7-methylether from Leonurus japonicus Houtt suppress aromatase-mediated estrogen biosynthesis to alleviate polycystic ovary syndrome by the inhibition of tumor progression locus 2.

ETHNOPHARMACOLOGICAL RELEVANCE: Leonurus japonicus Houtt (L. japonicus, Chinese motherwort), known as Yi Mu Cao which means "good for women", has long been widely used in China and other Asian countries to alleviate gynecological disorders, often characterized by estrogen dysregulation. It has been used for the treatment of polycystic ovary syndrome (PCOS), a common endocrine disorder in women but the underlying mechanism remains unknown. AIM OF THE STUDY: The present study was designed to investigate the effect and mechanism of flavonoid luteolin and its analog luteolin-7-methylether contained in L. japonicus on aromatase, a rate-limiting enzyme that catalyzes the conversion of androgens to estrogens and a drug target to induce ovulation in PCOS patients. MATERIALS AND METHODS: Estrogen biosynthesis in human ovarian granulosa cells was examined using ELISA. Western blots were used to explore the signaling pathways in the regulation of aromatase expression. Transcriptomic analysis was conducted to elucidate the potential mechanisms of action of compounds. Finally, animal models were used to assess the therapeutic potential of these compounds in PCOS. RESULTS: Luteolin potently inhibited estrogen biosynthesis in human ovarian granulosa cells stimulated by follicle-stimulating hormone. This effect was achieved by decreasing cAMP response element-binding protein (CREB)-mediated expression of aromatase. Mechanistically, luteolin and luteolin-7-methylether targeted tumor progression locus 2 (TPL2) to suppress mitogen-activated protein kinase 3/6 (MKK3/6)-p38 MAPK-CREB pathway signaling. Transcriptional analysis showed that these compounds regulated the expression of different genes, with the MAPK signaling pathway being the most significantly affected. Furthermore, luteolin and luteolin-7-methylether effectively alleviated the symptoms of PCOS in mice. CONCLUSIONS: This study demonstrates a previously unrecognized role of TPL2 in estrogen biosynthesis and suggests that luteolin and luteolin-7-methylether have potential as novel therapeutic agents for the treatment of PCOS. The results provide a foundation for further development of these compounds as effective and safe therapies for women with PCOS.

C-terminally encoded peptide-like genes are associated with the development of primary root at qRL16.1 in soybean.

Root architecture traits are belowground traits that harness moisture and nutrients from the soil and are equally important to above-ground traits in crop improvement. In soybean, the root length locus qRL16.1 was previously mapped on chromosome 16. The qRL16.1 has been characterized by transcriptome analysis of roots in near-isogenic lines (NILs), gene expression analysis in a pair of lines contrasting with alleles of qRL16.1, and differential gene expression analysis in germplasm accessions contrasting with root length. Two candidate genes, Glyma.16g108500 and Glyma.16g108700, have shown relatively higher expression in longer root accessions than in shorter rooting accessions. The C-terminal domain of Glyma.16g108500 and Glyma.16g108700 is similar to the conserved domain of C-terminally encoded peptides (CEPs) that regulate root length and nutrient response in Arabidopsis. Two polymorphisms upstream of Glyma.16g108500 showed a significant association with primary root length and total root length traits in a germplasm set. Synthetic peptide assay with predicted CEP variants of Glyma.16g108500 and Glyma.16g108700 demonstrated their positive effect on primary root length. The two genes are root-specific in the early stage of soybean growth and showed differential expression only in the primary root. These genes will be useful for improving soybean to develop a deep and robust root system to withstand low moisture and nutrient regimes.

An pair of an atypical NLR encoding genes confer Asian soybean rust resistance in soybean.

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, is a devastating disease that is present in all major soybean-producing regions. The limited availability of resistant germplasm has resulted in a scarcity of commercial soybean cultivars that are resistant to the disease. To date, only the Chinese soybean landrace SX6907 has demonstrated an immune response to ASR. In this study, we present the isolation and characterization of Rpp6907-7 and Rpp6907-4, a gene pair that confer broad-spectrum resistance to ASR. Rpp6907-7 and Rpp6907-4 encode atypic nucleotide-binding leucine-rich repeat (NLR) proteins that are found to be required for NLR-mediated immunity. Genetic analysis shows that only Rpp6907-7 confers resistance, while Rpp6907-4 regulates Rpp6907-7 signaling activity by acting as a repressor in the absence of recognized effectors. Our work highlights the potential value of using Rpp6907 in developing resistant soybean cultivars.

CRISPR/Cas9-mediated editing of GmDWF1 brassinosteroid biosynthetic gene induces dwarfism in soybean.

KEY MESSAGE: The study on the GmDWF1-deficient mutant dwf1 showed that GmDWF1 plays a crucial role in determining soybean plant height and yield by influencing the biosynthesis of brassinosteroids. Soybean has not adopted the Green Revolution, such as reduced height for increased planting density, which have proven beneficial for cereal crops. Our research identified the soybean genes GmDWF1a and GmDWF1b, homologous to Arabidopsis AtDWF1, and found that they are widely expressed, especially in leaves, and linked to the cellular transport system, predominantly within the endoplasmic reticulum and intracellular vesicles. These genes are essential for the synthesis of brassinosteroids (BR). Single mutants of GmDWF1a and GmDWF1b, as well as double mutants of both genes generated through CRISPR/Cas9 genome editing, exhibit a dwarf phenotype. The single-gene mutant exhibits moderate dwarfism, while the double mutant shows more pronounced dwarfism. Despite the reduced stature, all types of mutants preserve their node count. Notably, field tests have shown that the single GmDWF1a mutant produced significantly more pods than wild-type plants. Spraying exogenous brassinolide (BL) can compensate for the loss in plant height induced by the decrease in endogenous BRs. Comparing transcriptome analyses of the GmDWF1a mutant and wild-type plants revealed a significant impact on the expression of many genes that influence soybean growth. Identifying the GmDWF1a and GmDWF1b genes could aid in the development of compact, densely planted soybean varieties, potentially boosting productivity.

Creating a zero amylose barley with high soluble sugar content by genome editing.

Amylose biosynthesis is strictly associated with granule-bound starch synthase I (GBSSI) encoded by the Waxy gene. Mutagenesis of single bases in the Waxy gene, which induced by CRISPR/Cas9 genome editing, caused absence of intact GBSSI protein in grain of the edited line. The amylose and amylopectin contents of waxy mutants were zero and 31.73%, while those in the wild type were 33.50% and 39.00%, respectively. The absence of GBSSI protein led to increase in soluble sugar content to 37.30% compared with only 10.0% in the wild type. Sucrose and ß-glucan, were 39.16% and 35.40% higher in waxy mutants than in the wild type, respectively. Transcriptome analysis identified differences between the wild type and waxy mutants that could partly explain the reduction in amylose and amylopectin contents and the increase in soluble sugar, sucrose and ß-glucan contents. This waxy flour, which showed lower final viscosity and setback, and higher breakdown, could provide more option for food processing.

[Coal Control and Carbon Reduction Path in Henan Province's Power Industry Under the Carbon Peak and Neutralization Target: A Medium- and Long-term Study].

Achieving peak carbon dioxide emissions and accelerating decarbonization progress in the power industry is of paramount significance to Henan Province's objective of achieving carbon peak and neutrality. In this study, the Carbon Emission-Energy Integrated Model (iCEM) was employed to conduct scenario studies on the coal reduction and carbon reduction paths under the "dual-carbon" goal of Henan's power industry. The results indicated that, by considering measures such as optimizing the power source structure and technological progress, Henan Province's power industry carbon emissions will reach their peak between 2028-2033, with coal consumption in the power industry continuing to grow during the "14th Five-Year Plan" period. With a peak range between 2027-2031, the peak value increased by 1881, 1592, and 11.48 million tce, respectively, compared with that in 2020. To control coal in Henan Province under the constraint of carbon peak goals, it is proposed to develop clean energy sources such as wind and solar power, use more low-carbon or zero-carbon heat sources, increase the proportion of external electricity supply, and enhance energy-saving transformation in coal-fired power plants. Accelerating the elimination of backward units and energy-saving transformation of existing units, accelerating non-fossil energy development, advanced planning for external electricity supply, improving market mechanisms for the exit of coal-fired power plants and peak regulation, increasing system flexibility, and accelerating external policies to ensure clean energy security are effective paths for controlling coal and reducing carbon emissions in Henan's power industry. Additionally, inland nuclear power layout is one of the crucial paths to alleviate coal control pressure in Henan Province and achieve "dual-carbon" goals during the carbon-neutral stage. Therefore, it is imperative to conduct research on demonstrations in advance. Henan Province is highly dependent on energy from other provinces, and the power supply and demand situation in Henan Province will become increasingly tense in the future. It is necessary to support Henan Province from the State Grid and coordinate the construction of inter-provincial and inter-regional power transmission channels.

CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm.

BACKGROUND AND OBJECTIVE: Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis. METHODS: In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin. RESULTS: The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin. CONCLUSIONS: In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

Constructing Chainmail-Structured CoP/C Nanospheres as Highly Active Anodic Electrocatalysts for Oxygen Evolution Reaction.

Constructing highly active and noble metal-free electrocatalysts is significant for the anodic oxygen evolution reaction (OER). Herein, uniform carbon-coated CoP nanospheres (CoP/C) are developed by a direct impregnation coupling phosphorization approach. Importantly, CoP/C only takes a small overpotential of 230 mV at the current density of 10 mA cm-2 and displays a Tafel slope of 56.87 mV dec-1. Furthermore, the intrinsic activity of CoP/C is 21.44 times better than that of commercial RuO2 under an overpotential of 260 mV. In situ Raman spectroscopy studies revealed that a large number of generated Co-O and Co-OH species could facilitate the *OH adsorption, effectively accelerating the reaction kinetics. Meanwhile, the carbon shell with a large number of mesoporous pores acts as the chainmail of CoP, which could improve the active surface area of the catalyst and prevent the Co sites from oxidative dissolution. This work provides a facile and effective reference for the development of highly active and stable OER catalysts.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Unique dissolved organic matter molecules and microbial communities in rhizosphere of three typical crop soils and their significant associations based on FT-ICR-MS and high-throughput sequencing analysis.

Using cucumber, maize, and ryegrass as model plants, the diversity and uniqueness of the molecular compositions of dissolved organic matter (DOM) and the structures of microbial communities in typical crop rhizosphere soils, as well as their associations, were investigated based on high-resolution mass spectrometry combined with high-throughput sequencing. The results showed that the rhizosphere contained 2200 organic molecules that were not identified in the non-rhizosphere soils, as characterized by FT-ICR-MS. The rhizosphere DOM molecules generally contained more N, S, and P heteroatoms, stronger hydrophilicity, and more refractory organic matter, representing high and complex chemical diversity characteristics. 16SrRNA sequencing results demonstrated that Proteobacteria, Actinomycetes and Firmicutes were the dominant flora in the soils. Plant species could significantly change the composition and relative abundance of rhizosphere microbial populations. The microbial community structures of rhizosphere and non-rhizosphere soils showed significant differences at both the phylum and class levels. Multiple interactions between the microorganisms and DOM compositions formed a complex network of relationships. There were strong and remarkable positive or negative couplings between different sizes and categories of DOM molecules and the specific microbial groups (P < 0.05, |R| ≥ 0.9) in the rhizosphere soils as shown by network profiles. The correlations between DOM molecules and microbial groups in rhizosphere soils had plant species specificity. The results above emphasized the relationship between the heterogeneity of DOM and the diversity of microbial communities, and explored the molecular mechanisms of the biochemical associations in typical plant rhizosphere soils, providing a foundation for in-depth understanding of plant-soil-microbe interactions.

Associations Between Temporomandibular Disorders and Brain Imaging-Derived Phenotypes.

OBJECTIVE: Temporomandibular disorders (TMD) affect the temporomandibular joint and associated structures. Despite its prevalence and impact on quality of life, the underlying mechanisms of TMD remain unclear. Magnetic resonance imaging studies suggest brain abnormalities in patients with TMD. However, these lines of evidence are essentially observational and cannot infer a causal relationship. This study employs Mendelian randomisation (MR) to probe causal relationships between TMD and brain changes. METHODS: Genome-wide association study (GWAS) summary statistics for TMD were collected, along with brain imaging-derived phenotypes (IDPs). Instrumental variables were selected from the GWAS summary statistics and used in bidirectional 2-sample MR analyses. The inverse-variance weighted analysis was chosen as the primary method. In addition, false discovery rate (FDR) correction of P value was used. RESULTS: Eleven IDPs related to brain imaging alterations showed significant causal associations with TMD (P-FDR < .05), validated through sensitivity analysis. In forward MR, the mean thickness of left caudal middle frontal gyrus (OR, 0.76; 95% CI, 0.67-0.87; P-FDR = 1.15 × 10-2) and the volume of right superior frontal gyrus (OR, 1.24; 95% CI, 1.10-1.39; P-FDR = 2.26 × 10-2) exerted significant causal effects on TMD. In the reverse MR analysis, TMD exerted a significant causal effect on 9 IDPs, including the mean thickness of the left medial orbitofrontal cortex (ß = -0.10; 95% CI, -0.13 to -0.08; P-FDR = 2.06 × 10-11), the volume of the left magnocellular nucleus (ß = -0.15; 95% CI, -0.22 to -0.09; P-FDR = 3.26 × 10-4), the mean intensity of the right inferior-lateral ventricle (ß = -0.09; 95% CI, -0.14 to -0.04; P-FDR = 2.23 × 10-2), the volume of grey matter in the anterior division of the left superior temporal gyrus (ß = 0.09; 95% CI, 0.04-0.14; P-FDR = 1.69 × 10-2), and so forth. CONCLUSIONS: This study provides genetic evidence supporting the bidirectional causal associations between TMD and brain IDPs, shedding light on potential neurobiological mechanisms underlying TMD development and its relationship with brain structure.

PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning.

Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights. Identifying key compounds in patents is essential to finding novel lead compounds. This study collected a comprehensive data set comprising 1555 patents, encompassing 1000 key compounds, to explore innovative approaches for predicting these key compounds. Our novel PatentNetML framework integrated network science and machine learning algorithms, combining network measures, ADMET properties, and physicochemical properties, to construct robust classification models to identify key compounds. Through a model interpretation and an analysis of three compelling case studies, we showcase the potential of PatentNetML in unveiling hidden patterns and connections within diverse patents. While our framework is pioneering, we acknowledge its limitations when applied to patents that deviate from the assumed central pattern. This work serves as a promising foundation for future research endeavors aimed at efficiently identifying promising drug candidates and expediting drug discovery in the pharmaceutical industry.

Always positive covalent organic nanosheet enabling pH-independent adsorption and removal of Cr(â ¥).

Rapid and highly effective removal of hexavalent chromium (Cr(Ⅵ)) is extremely vital to water resources restoration and environmental protection. To overcome the pH limitation faced by most ionic absorbents, an always positive covalent organic nanosheet (CON) material was prepared and its Cr(VI) adsorption and removal capability was investigated in detail. As-prepared EB-TFB CON (TFB = 1,3,5-benzaldehyde, EB = ethidium bromide) shows strong electropositivity in the tested pH range of 1 ∼ 10, display a pH-independent Cr(VI) removal ability, and work well for Cr(VI) pollution treatment with good anti-interference capability and reusability in a wide pH range covering almost all Cr(VI)-contaminated real water samples, thus eliminating the requirement for pH adjustment. Moreover, the nanosheet structure, which is obtained by a facile ultrasonic-assisted self-exfoliation, endows EB-TFB CON with fully exposed active sites and shortened mass transfer channels, and the Cr(VI) adsorption equilibrium can be reached within 15 min with a high adsorption capacity of 280.57 mg·g-1. The proposed Cr(VI) removal mechanism, which is attributed to the synergetic contributions of electrostatic adsorption, ion exchange and chemical reduction, is demonstrated by experiments and theoretical calculations. This work not only provides a general Cr(VI) absorbent without pH limitation, but also presents a paradigm to prepare ionic CONs with relatively constant surface charges.

Valproate attenuates somatic hyperalgesia induced by orofacial inflammation combined with stress through inhibiting spinal IL-6 and STAT1 phosphorylation.

Temporomandibular disorder (TMD) and fibromyalgia syndrome (FMS) may present as comorbid conditions, but treatment options are ineffective. The purpose of this study was to investigate whether valproate (VPA) attenuates somatic hyperalgesia induced by orofacial inflammation combined with stress, which represents a model of pain associated with TMD and FMS comorbidity, and to explore the potential mechanisms. The results showed that VPA inhibited somatic hyperalgesia induced by orofacial inflammation combined with stress, and down-regulated the interleukin-6 (IL-6) expression in the L4-L5 spinal dorsal horn of female rats. The anti-nociceptive effect of VPA was blocked by single or 5 consecutive day intrathecal administration of recombinant rat IL-6. Orofacial inflammation combined with stress up-regulated the ratio of phosphorylated signal transducer and activator of transcription 1 (p-STAT1) to STAT1 (p-STAT1/STAT1) in the spinal cord. VPA did not affect the STAT1 expression, while it down-regulated the ratio of p-STAT1/STAT1. The expression of STAT3 and the ratio of p-STAT3/STAT3 were not affected by orofacial inflammation combined with stress and VPA treatment. Intrathecal administration of exogenous IL-6 up-regulated the ratio of p-STAT1/STAT1. These data indicate that VPA attenuated somatic hyperalgesia induced by orofacial inflammation combined with stress via inhibiting spinal IL-6 in female rats, and the mechanism may involve the alteration of activation status of spinal STAT1. Thus, VPA may be a new candidate analgesic that targets IL-6 and STAT1 for the treatment of pain associated with the comorbidity of TMD and FMS.

Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep.

MOTIVATION: Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. RESULTS: In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. AVAILABILITY AND IMPLEMENTATION: The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.

Rational Regulation of the Defect Density in Platinum Nanocrystals for Highly Efficient Hydrogen Evolution Reaction.

Constructing structural defects is a promising way to enhance the catalytic activity toward the hydrogen evolution reaction (HER). However, the relationship between defect density and HER activity has rarely been discussed. In this study, a series of Pt/WOx nanocrystals are fabricated with controlled morphologies and structural defect densities using a facile one-step wet chemical method. Remarkably, compared with polygonal and star structures, the dendritic Pt/WOx (d-Pt/WOx) exhibited a richer structural defect density, including stepped surfaces and atomic defects. Notably, the d-Pt/WOx catalyst required 4 and 16 mV to reach 10 mA cm-2, and its turnover frequency (TOF) values are 11.6 and 22.8 times higher than that of Pt/C under acidic and alkaline conditions, respectively. In addition, d-Pt/WOx//IrO2 displayed a mass activity of 5158 mA mgPt -1 at 2.0 V in proton exchange membrane water electrolyzers (PEMWEs), which is significantly higher than that of the commercial Pt/C//IrO2 system. Further mechanistic studies suggested that the d-Pt/WOx exhibited reduced number of antibonding bands and the lowest dz2-band center, contributing to hydrogen adsorption and release in acidic solution. The highest dz2-band center of d-Pt/WOx facilitated the adsorption of hydrogen from water molecules and water dissociation in alkaline medium. This work emphasizes the key role of the defect density in improving the HER activity of electrocatalysts.