Modeling and biological evaluation of pegmolesatide, a novel and potent erythropoiesis-stimulating agent.

Pegmolesatide, a synthetic, polyethylene-glycolylated, peptide-based erythropoiesis-stimulating agent (ESA), has been recently approved in China. Pegmolesatide is derived from the structure of endogenous erythropoietin (EPO), a natural product in mammals. This study compared the in vitro effects and selectivity of pegmolesatide to those of recombinant EPO and carbamylated EPO (CEPO) through computer-aided analyses and biological tests. The findings indicate that pegmolesatide exhibited the same stimulating effect on erythropoiesis as EPO with fewer side effects than EPO and CEPO.

Cross-Modal Retrieval Between 13C NMR Spectra and Structures Based on Focused Libraries.

Library matching by comparing carbon-13 nuclear magnetic resonance (13C NMR) spectra with spectral data in the library is a crucial method for compound identification. In our previous paper, we introduced a deep contrastive learning system called CReSS, which used a library that contained more structures. However, CReSS has two limitations: there were no unknown structures in the library, and a redundant library reduces the structure-elucidation accuracy. Herein, we replaced the oversize traditional libraries with focused libraries containing a small number of molecules. A previously generative model, CMGNet, was used to generate focused libraries for CReSS. The combined model achieved a Top-10 accuracy of 54.03% when tested on 6,471 13C NMR spectra. In comparison, CReSS with a random reference structure library achieved an accuracy of only 9.17%. Furthermore, to expand the advantages of the focused libraries, we proposed SAmpRNN, which is a recurrent neural network (RNN). With the large focused library amplified by SAmpRNN, the structure-identification accuracy of the model increased in 70.0% of the 30 random example cases. In general, cross-modal retrieval between 13C NMR spectra and structures based on focused libraries (CFLS) achieved high accuracy and provided more accurate candidate structures than traditional libraries for compound identification.

Applying the Full Endoscopic Keyhole Technique to CPA Lesions: A Single-Center Study.

In recent years, endoscopy has become an increasingly common tool used during neurosurgical procedures. However, its application in treating cerebellopontine angle (CPA) lesions has not progressed as rapidly. In this study, the authors present their initial experience with surgically treating CPA lesions using a fully endoscopic keyhole retrosigmoid approach. They conducted a retrospective analysis of clinical data from patients who underwent endoscopic keyhole CPA surgery at their center between May 2017 and April 2022. They provide a comprehensive explanation of this method and an overview of the strategies that have been developed to achieve better clinical outcomes. The study included 107 patients, consisting of 10 cases of vestibular schwannoma, 21 cases of epidermoid cyst, 32 cases of trigeminal neuralgia, and 44 cases of hemifacial spasm. The authors analyzed the clinicodemographic details of the patients. Among the 31 tumor cases, gross total resection was achieved in 25 patients (80.6%), while near-total resection was performed in 6 patients (19.4%). In patients with trigeminal neuralgia, facial pain resolved in 31 out of 32 patients (96.9%). Similarly, facial convulsions disappeared or were relieved in all 44 patients (100%) with hemifacial spasms after the operation. Postoperative complications included facial nerve paresis (n=9, 8.4%), with improvement observed in 6 cases during follow-up, transient facial hypoesthesia (n=3, 2.8%), cerebrospinal fluid rhinorrhea (n=3, 2.8%), transient abducens paresis (n=1, 0.9%), and postoperative hemorrhage (n=1, 0.9%). Endoscopy provides improved deep illumination and, combined with close-up observation, enhances the visualization of structures within the CPA region. The fully endoscopic keyhole technique is a safe and effective method for managing CPA lesions.

Calcitonin gene-related peptide-modulated macrophage phenotypic alteration regulates angiogenesis in early bone healing.

OBJECTIVES: This study aimed to investigate the effect of calcitonin gene-related peptide (CGRP) on the temporal alteration of macrophage phenotypes and macrophage-regulated angiogenesis duringearlybonehealing and preliminarily elucidate the mechanism. METHODS: In vivo, the rat mandibular defect models were established with inferior alveolar nerve transection (IANT) or CGRP receptor antagonist injection. Radiographicandhistologic assessments for osteogenesis, angiogenesis, and macrophage phenotypic alteration within bone defects were performed. In vitro, the effect and mechanism of CGRP on macrophage polarization and phenotypic alteration were analyzed. Then the conditioned medium (CM) from CGRP-treated M1 or M2 macrophages was used to culture human umbilical vein endothelial cells (HUVECs), and the CGRP's effect on macrophage-regulated angiogenesis was detected. RESULTS: Comparable changes following IANT and CGRP blockade within bone defects were observed, including the suppression of early osteogenesis and angiogenesis, the prolonged M1 macrophage infiltration and the prohibited transition toward M2 macrophages around vascular endothelium. In vitro experiments showed that CGRP promoted M2 macrophage polarization while upregulating the expression of interleukin 6 (IL-6), a major cytokine that facilitates the transition from M1 to M2-dominant stage, in M1 macrophages via the activation of Yes-associated protein 1. Moreover, CGRP-treated macrophage-CM showed an anabolic effect on HUVECs angiogenesis compared with macrophage-CM and might prevail over the direct effect of CGRP on HUVECs. CONCLUSIONS: Collectively, our results reveal the effect of CGRP on M1 to M2 macrophage phenotypic alteration possibly via upregulating IL-6 in M1 macrophages, and demonstrate the macrophage-regulated pro-angiogenic potential of CGRP in early bone healing.

Targeting the Subpocket Enables the Discovery of Thiadiazole-Pyridazine Derivatives as Glutaminase C Inhibitors.

As glutaminase C (GAC) has become an attractive target for cancer treatment by regulating glutaminolysis, thus, interest in GAC inhibitors has risen in recent years. Herein, a potential binding subpocket comprising basic residues was identified, and through extensive structure-activity relationship studies, promising inhibitors 11 and 39 were identified with robust GAC inhibitory activity and A549 cell antiproliferative activity. X-ray crystallography of the 11-GAC and 27-GAC complexes revealed a novel binding mode against GAC. The potency of 11 and 27 against GACK320A further highlighted the importance of the binding. Notably, compounds 11 and 39 regulated the cellular metabolite, thereby increasing reactive oxygen species by blocking glutamine metabolism. Compound 11 also exhibited excellent antiproliferative activity in the A549 cell xenograft model. We further proved that 11 is a safe GAC allosteric inhibitor. A basic subpocket is proposed that might provide new strategies for the development of novel GAC inhibitors in the future.

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective.

The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.

CMGN: a conditional molecular generation net to design target-specific molecules with desired properties.

The rational design of chemical entities with desired properties for a specific target is a long-standing challenge in drug design. Generative neural networks have emerged as a powerful approach to sample novel molecules with specific properties, termed as inverse drug design. However, generating molecules with biological activity against certain targets and predefined drug properties still remains challenging. Here, we propose a conditional molecular generation net (CMGN), the backbone of which is a bidirectional and autoregressive transformer. CMGN applies large-scale pretraining for molecular understanding and navigates the chemical space for specified targets by fine-tuning with corresponding datasets. Additionally, fragments and properties were trained to recover molecules to learn the structure-properties relationships. Our model crisscrosses the chemical space for specific targets and properties that control fragment-growth processes. Case studies demonstrated the advantages and utility of our model in fragment-to-lead processes and multi-objective lead optimization. The results presented in this paper illustrate that CMGN has the potential to accelerate the drug discovery process.

Zoledronic acid affects the process of Porphyromonas gingivalis infecting oral mucosal epithelial barrier: An in-vivo and in-vitro study.

Zoledronic acid (ZA), one of the commonly used bisphosphonates, is mainly used for bone-metabolic diseases. Studies proved that ZA has adverse effects on oral soft tissues. As the first line of innate immunity, the gingival epithelium could be infected by periodontal pathogens, which is a key process of the initiation of periodontal diseases. Yet, how ZA affects the periodontal pathogens infecting the epithelial barrier remains unclear. This study aimed to investigate the influences of ZA on the process of Porphyromonas gingivalis (P. gingivalis) infecting the gingival epithelial barrier via in-vitro and in-vivo experiments. In the in-vitro experiments, under the condition of different concentrations of ZA (0, 1, 10, and 100 µM), P. gingivalis was used to infect human gingival epithelial cells (HGECs). The infections were detected by transmission electron microscope and confocal laser scanning microscope. Besides, the internalization assay was applied to quantify the P. gingivalis, which infected the HGECs, in the different groups. To evaluate the expression levels of pro-inflammatory cytokines, including interleukin (IL)-1ß, IL-6, and IL-8, by infected HGECs, real-time quantitative reverse transcription-polymerase chain reactions were applied. In the in-vivo experiments, rats were given ZA solution (ZA group) or saline (control group) by tail intravenous injection for 8 weeks. Subsequently, we put ligatures around the maxillary second molars of all the rats and inoculated P. gingivalis to the gingiva every other day from day 1 to day 13. The rats were sacrificed on days 3, 7, and 14 for micro-CT and histological analyses. The in-vitro results manifested that the quantity of P. gingivalis that had infected HGECs increased with the ZA concentrations. Pro-inflammatory cytokines expression by HGECs were significantly increased by 100 µM ZA. In the in-vivo study, compared to the control group, more P. gingivalis was detected in the superficial layer of gingival epithelium in the ZA group. Besides, ZA significantly increased the expression level of IL-1ß on day 14 and IL-6 on days 7 and 14 in gingival tissues. These findings suggest that the oral epithelial tissues of patients who receive high-dose ZA treatment may be more susceptible to periodontal infections, resulting in severe inflammatory conditions.

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge.

Structure elucidation of unknown compounds based on nuclear magnetic resonance (NMR) remains a challenging problem in both synthetic organic and natural product chemistry. Library matching has been an efficient method to assist structure elucidation. However, it is limited by the coverage of libraries. In addition, prior knowledge such as molecular fragments is neglected. To solve the problem, we propose a conditional molecular generation net (CMGNet) to allow input of multiple sources of information. CMGNet not only uses 13C NMR spectrum data as input but molecular formulas and fragments of molecules are also employed as input conditions. Our model applies large-scale pretraining for molecular understanding and fine-tuning on two NMR spectral data sets of different granularity levels to accommodate structure elucidation tasks. CMGNet generates structures based on 13C NMR data, molecular formula, and fragment information, with a recovery rate of 94.17% in the top 10 recommendations. In addition, the generative model performed well in the generation of various classes of compounds and in the structural revision task. CMGNet has a deep understanding of molecular connectivities from 13C NMR, molecular formula, and fragments, paving the way for a new paradigm of deep learning-assisted inverse problem-solving.

Structure-activity relationship study of amidobenzimidazole derivatives as stimulator of interferon genes (STING) agonists.

Stimulator of interferon genes (STING) is a crucial adaptor protein that can regulate the innate immune response by inducing the secretion of type Ι interferons and other cytokines after recognizing endogenous or exogenous DNA. Due to the key role of STING in the innate immune system, the activation of STING pathway is expected to be an efficacious immunotherapeutic tactic to treat cancer. In this study, we performed a structure-activity relationship study of amidobenzimidazole monomer, led to a series of ABZI STING agonist derivatives with potent STING-activating effects. Among them, compound 72, as a representative compound, markedly activated the STING-TBK1-IRF3 signaling pathway and significantly increased the mRNA and protein levels of IFN-ß, CXCL10 and IL-6 in both WT THP-1 cells and human peripheral blood mononuclear cells (hPBMCs). In addition, it was confirmed that compound 72 was highly selective for human STING, specifically targeting human STING signaling and showing no activation of m-STING.

High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning.

Background: Lowering the dose for positron emission tomography (PET) imaging reduces patients' radiation burden but decreases the image quality by increasing noise and reducing imaging detail and quantifications. This paper introduces a method for acquiring high-quality PET images from an ultra-low-dose state to achieve both high-quality images and a low radiation burden. Methods: We developed a two-task-based end-to-end generative adversarial network, named bi-c-GAN, that incorporated the advantages of PET and magnetic resonance imaging (MRI) modalities to synthesize high-quality PET images from an ultra-low-dose input. Moreover, a combined loss, including the mean absolute error, structural loss, and bias loss, was created to improve the trained model's performance. Real integrated PET/MRI data from 67 patients' axial heads (each with 161 slices) were used for training and validation purposes. Synthesized images were quantified by the peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), structural similarity (SSIM), and contrast noise ratio (CNR). The improvement ratios of these four selected quantitative metrics were used to compare the images produced by bi-c-GAN with other methods. Results: In the four-fold cross-validation, the proposed bi-c-GAN outperformed the other three selected methods (U-net, c-GAN, and multiple input c-GAN). With the bi-c-GAN, in a 5% low-dose PET, the image quality was higher than that of the other three methods by at least 6.7% in the PSNR, 0.6% in the SSIM, 1.3% in the NMSE, and 8% in the CNR. In the hold-out validation, bi-c-GAN improved the image quality compared to U-net and c-GAN in both 2.5% and 10% low-dose PET. For example, the PSNR using bi-C-GAN was at least 4.46% in the 2.5% low-dose PET and at most 14.88% in the 10% low-dose PET. Visual examples also showed a higher quality of images generated from the proposed method, demonstrating the denoising and improving ability of bi-c-GAN. Conclusions: By taking advantage of integrated PET/MR images and multitask deep learning (MDL), the proposed bi-c-GAN can efficiently improve the image quality of ultra-low-dose PET and reduce radiation exposure.

Design, synthesis, and biological evaluation of pyrrolopyrimidine derivatives as novel Bruton's tyrosine kinase (BTK) inhibitors.

Developing Bruton's tyrosine kinase (BTK) inhibitors has become a significant focus in recent years because BTK inhibition is an effective approach for the treatment of B-cell malignancies. For covalent BTK inhibitors, low oral bioavailability and low kinase selectivity remain unaddressed issues; thus, more diverse inhibitors with both novel structures and selective on target binding profiles are still needed. Here, four key regions where inhibitors bind to BTK were identified by analyzing the existing crystal structures of BTK complexes. Then, a scaffold-based molecular design work flow was established by integrating fragment-growing method, deep learning-based framework XGraphBoost and molecular docking, leading to four compounds that showed potency against BTK. Optimization of compounds 1 and 2 led to the discovery of the potent BTK inhibitor compound 42 by using in vitro potency and pharmacokinetic (PK) studies to prioritize the compounds. Compound 42 exhibited great BTK inhibition activity (IC50 = 0.7 nM) along with high oral absorption. Moreover, 42 demonstrated excellent kinase selectivity, especially over EGFR kinase, and low toxicity. In a TMD8 xenograft model, 42 significantly inhibited tumor growth (TGI = 104%) at a dosage of 50 mg/kg, indicating its potential as a novel therapeutic option for B-cell lymphomas.

Multi-omics analysis of oral bacterial biofilm on titanium oxide nanostructure modified implant surface: In vivo sequencing-based pilot study in beagle dogs.

Peri-implantitis, the major cause of implant failure, is an inflammatory destructive disease due to the dysbiotic polymicrobial communities at the peri-implant sites. Therefore, it is highly warranted to develop the implant materials with antimicrobial properties and investigate their effects on oral microbiota. However, most of the relevant studies were performed in vitro, and insufficient to provide the comprehensive assessment of the antimicrobial capacity of the implant materials in vivo. Herein, we introduce an innovative approach to evaluate the in vivo antibacterial properties of the most commonly used implant materials, titanium with different nanostructured surfaces, and investigate their antibacterial mechanism via the next-generation sequencing (NGS) technology. We firstly prepared the titanium implants with three different surfaces, i) mechanical polishing (MP), ii) TiO2 nanotubes (NT) and iii) nanophase calcium phosphate embedded to TiO2 nanotubes (NTN), and then characterized them using scanning electron microscopy (SEM), energy-dispersive X-ray spectrometer (EDS), X-ray photoelectron spectroscopy (XPS), confocal laser scanning microscopy (CLSM) and surface hydrophilicity analysis. Afterwards, the implants were placed in the beagle dogs' mouths to replace the pre-extracted premolar and molar teeth for eight weeks through implant surgery. The supra- and sub-mucosal plaques were collected and subjected to 16S rRNA gene/RNA sequencing and data analysis. It was found that the nanostructured surfaces in NT and NTN groups showed significantly increased roughness and decreased water contact angles compared to the MP group, while the XPS data further confirmed the successful modifications of TiO2 nanotubes and the subsequent deposition of nanophase calcium phosphate. Notably, the nanostructured surfaces in NT and NTN groups had limited impact on the diversity and community structure of oral microbiota according to the 16S rRNA sequencing results, and the nanostructures in NTN group could down-regulate the genes associated with localization and locomotion based on Gene Ontology (GO) terms enrichment analysis. Moreover, the differentially expressed genes (DEGs) were associated with microbial metabolism, protein synthesis and bacterial invasion of epithelial cells. Taken together, this study provides a new strategy to evaluate the antibacterial properties of the biomedical materials in vivo via the high-throughput sequencing and bioinformatic approaches, revealing the differences of the composition and functional gene expressions in the supra- and sub-mucosal microbiome.

Comparing Methods for Assessing Reliability.

The usual method for assessing the reliability of survey data has been to conduct reinterviews a short interval (such as one to two weeks) after an initial interview and to use these data to estimate relatively simple statistics, such as gross difference rates (GDRs). More sophisticated approaches have also been used to estimate reliability. These include estimates from multi-trait, multi-method experiments, models applied to longitudinal data, and latent class analyses. To our knowledge, no prior study has systematically compared these different methods for assessing reliability. The Population Assessment of Tobacco and Health Reliability and Validity (PATH-RV) Study, done on a national probability sample, assessed the reliability of answers to the Wave 4 questionnaire from the PATH Study. Respondents in the PATH-RV were interviewed twice about two weeks apart. We examined whether the classic survey approach yielded different conclusions from the more sophisticated methods. We also examined two ex ante methods for assessing problems with survey questions and item nonresponse rates and response times to see how strongly these related to the different reliability estimates. We found that kappa was highly correlated with both GDRs and over-time correlations, but the latter two statistics were less highly correlated, particularly for adult respondents; estimates from longitudinal analyses of the same items in the main PATH study were also highly correlated with the traditional reliability estimates. The latent class analysis results, based on fewer items, also showed a high level of agreement with the traditional measures. The other methods and indicators had at best weak relationships with the reliability estimates derived from the reinterview data. Although the Question Understanding Aid seems to tap a different factor from the other measures, for adult respondents, it did predict item nonresponse and response latencies and thus may be a useful adjunct to the traditional measures.

The temporal shift of peri-implant microbiota during the biofilm formation and maturation in a canine model.

OBJECTIVES: Although the mature peri-implant biofilm composition is well studied, there is very little information on the succession of in vivo dental implant colonization. The aim of this study was to characterize the temporal changes and diversity of peri-implant supra-mucosal and sub-mucosal microbiota during the process of the plaque maturation. MATERIALS AND METHODS: Dental implants (n = 25) were placed in the mandible of 3 beagle dogs. Illumina MiSeq sequencing of the hypervariable V3-V4 region of the 16S rRNA gene amplicons was used to characterize the supra/sub-mucosal microbiota in the peri-implant niches at 1day (T1), 7days (T2), 14days (T3), 21days (T4) and 28days (T5) after Phase Ⅱ surgery of the healing abutment placement. QIIME, Mothur, LEfSe and R-package were used for downstream analysis. RESULTS: A total of 1184 operational taxonomic units (OTUs), assigned into 22 phyla, 264 genera and 339 species were identified. In supra-mucosal niches, the alpha parameters of shannon, sobs and chao1 displayed significant differences between T1 and other time-points. However, in sub-mucosal niches, only sobs, chao1, and ace indexes displayed significant differences between T1 and T3, and T1 and T5. Beta-diversity showed statistically significant difference between T1 and T2, T3, T4, T5 within both sub-mucosal and supra-mucosal plaque. The phyla Bacteroidetes, Proteobacteria and Firmicutes were the most dominant phyla of both sub-mucosal and supra-mucosal niches at all time-points and Firmicutes increased during the maturation of peri-implant plaque. At the genus level, Neisseria decreased significantly after T1 suggesting the establishment of an anaerobic microenvironment. A decrease of Porphyromonas during the formation of sub-mucosal microbial community was also detected. Co-occurrence network analysis exhibited a more complicated co-occurrence relationship of bacterial species in the sub-mucosal niches. Fusobacterium nucleatum, Filifactor villosus, and some other species may play a crucial role in biofilm maturation. CONCLUSIONS: The present results suggested that the development of peri-implant biofilm followed a similar pattern to dental plaque formation. Sub-mucosal biofilm may go through a more complicated procedure of maturation than supra-mucosal biofilm.

Antibacterial Evaluation of Lithium-Loaded Nanofibrous Poly(L-Lactic Acid) Membranes Fabricated via an Electrospinning Strategy.

Lithium (Li) reportedly has anti-bacterial properties. Thus, it is an ideal option to modify barrier membranes used for guided bone regeneration to inhibit the bacterial adhesion. The aims of this study were to fabricate and characterize nanofibrous poly(L-lactic acid) (PLLA) membranes containing Li, and investigate their antibacterial effects on Porphyromonas gingivalis and Actinobacillus actinomycetemcomitans in vitro. Li (5%Li, 10%Li, and 15%Li)-loaded nanofibrous PLLA membranes were fabricated using an electrospinning technique, and characterized via scanning electron microscopy, X-ray photoelectron spectroscopy, X-ray diffraction, a contact angle measuring device, and a universal testing machine. Sustained release of Li ions was measured over a 14-day period and biocompatibility of the Li-PLLA membranes was investigated. Evaluation of bacterial adhesion and antibacterial activity were conducted by bacterial colony counting, LIVE/DEAD staining and inhibition zone method using P.gingivalis and A.actinomycetemcomitans. Of the three Li-loaded membranes assessed, the 10%Li-PLLA membrane had the best mechanical properties and biocompatibility. Adhesion of both P.gingivalis and A.actinomycetemcomitans on Li-PLLA membranes was significantly lower than adhesion on pure PLLA membranes, particularly with regard to the 10%Li and 15%Li membranes. Significant antibacterial activity of Li-PLLA were also observed against according to the inhibition zone test. Given their better mechanical properties, biocompatibility, and antibacterial activity, PLLAs with 10%Li are a better choice for future clinical utilization. The pronounced antibacterial effects of Li-loaded PLLA membranes sets the stage for further application in guided bone regeneration.

Downregulating lncRNA PVT1 Relieves Astrocyte Overactivation Induced Neuropathic Pain Through Targeting miR-186-5p/CXCL13/CXCR5 Axis.

Spinal cord injury (SCI) is one of the main causes leading to neuropathic pain. Here, we aim to explore the molecular mechanism and function of lncRNA PVT1 in neuropathic pain induced by SCI. The expression of lncRNA PVT1, microRNA (miR) - 186-5p was measured via quantitative reverse transcription PCR (qRT-PCR), and the activation of astrocytes (labeled by GFAP) was detected by immunohistochemistry. Western blot was conducted to detect the expression of chemokine ligand 13 (CXCL13), chemokine receptor 5 (CXCR5), cyclooxygenase-2 (COX2), inducible nitric oxide synthase (iNOS) and glial fibrillary acidic protein (GFAP) in spinal cord injury lesions. The levels of inflammatory cytokines (including IL-1ß and IL-6) and MDA in tissues were examined via Enzyme-linked immunosorbent assay (ELISA). In vitro experiments were also conducted in primary cultured astrocyte to explore the response of astrocyte to lipopolysaccharide (LPS). What's more, the PVT1-miR-186-5p interaction was verified via the dual luciferase activity assay and RNA immunoprecipitation (RIP) assay. The results demonstrated that the levels of PVT1, CXCL13 and CXCR5 were upregulated, while miR-186-5p were decreased in SCI rats' spinal cord and LPS-mediated astrocytes. In the SCI model, PVT1 depletion significantly alleviated neuropathic pain, astrocytic activation and reduced the expression of neuroinflammatory factors and proteins. The relevant mechanism studies confirmed that PVT1 is a competitive endogenous RNA (ceRNA) of miR-186-5p, targets and inhibits its expression and promotes the expression of CXCL13/CXCR5, while miR-186-5p targets CXCL13. In conclusion, inhibition of lncRNA PVT1 alleviates neuropathic pain in SCI rats by upregulating miR-186-5p and down-regulating CXCL13/CXCR5. The PVT1/miR-186-5p/CXCL13/CXCR5 axis can be used as a new therapeutic target for neuropathic pain.

Corrigendum to: Who Can You Count On? Understanding the Determinants of Reliability.

[This corrects the article DOI: 10.1093/jssam/smz034.][This corrects the article DOI: 10.1093/jssam/smz034.].

Who Can You Count On? Understanding The Determinants of Reliability.

Using reinterview data from the PATH Reliability and Validity (PATH-RV) study, we examine the characteristics of questions and respondents that predict the reliability of the answers. In the PATH-RV study, 524 respondents completed an interview twice, five to twenty-four days apart. We coded a number of question characteristics and used them to predict the gross discrepancy rates (GDRs) and kappas for each question. We also investigated respondent characteristics associated with reliability. Finally, we fitted cross-classified models that simultaneously examined a range of respondent and question characteristics. Although the different models yielded somewhat different conclusions, in general factual questions (especially demographic questions), shorter questions, questions that did not use scales, those with fewer response options, and those that asked about a noncentral topic produced more reliable answers than attitudinal questions, longer questions, questions using ordinal scales, those with more response options, and those asking about a central topic. One surprising finding was that items raising potential social desirability concerns yielded more reliable answers than items that did not raise such concerns. The respondent-level models and cross-classified models indicated that five adult respondent characteristics were associated with giving the same answer in both interviews-education, the Big Five trait of conscientiousness, tobacco use, sex, and income. Hispanic youths and non-Hispanic black youths were less likely to give the same answer in both interviews. The cross-classified model also found that more words were associated with less reliable answers. The results are mostly consistent with earlier findings but are nonetheless important because they are much less model-dependent than the earlier work. In addition, this study is the first to incorporate such personality traits as needed for cognition and the Big Five personality factors and to examine the relationships among reliability, item nonresponse, and response latency.

Cross-Correlation Algorithm-Based Optimization of Aliasing Signals for Inductive Debris Sensors.

An inductive debris sensor can monitor a mechanical system's debris in real time. The measuring accuracy is significantly affected by the signal aliasing issue happening in the monitoring process. In this study, a mathematical model was built to explain two debris particles' aliasing behavior. Then, a cross-correlation-based method was proposed to deal with this aliasing. Afterwards, taking advantage of the processed signal along with the original signal, an optimization strategy was proposed to make the evaluation of the aliasing debris more accurate than that merely using initial signals. Compared to other methods, the proposed method has fewer limitations in practical applications. The simulation and experimental results also verified the advantage of the proposed method.