Search

Background Thyroid autoantibody is a marker for the diagnosis of Hashimoto's thyroiditis (HT), and B cells are essential in the pathogenesis of HT. Enhancer of Zeste homolog 2 (EZH2), which is an important epigenetic regulator, plays an important role in the regulation of lymphocytes development and function. Objective To investigate EZH2 expression in plasmoblasts and plasma cells in HT, and further explore the therapeutic effect of EZH2 inhibitors in experimental autoimmune thyroiditis (EAT) model. Methods The thyroid tissues from 6 patients who underwent thyroidectomy (3 HT patients with PTC, 3 patients with PTC alone) in Peking University First Hospital between 2010 and 2020 were obtained from the contralateral lobe with thyroid cancer, and screened for the expression of B-lymphocyte-related genes by RNA-seq; thyroid tissues from 16 HT patients and 8 normal thyroid tissues were collected and verified for the the expression of EZH2 in B cells in HT thyroid tissues by immunohistochemistry or immunofluorescence, respectively. Fine-needle aspiration (FNA) samples from patients with HT (n=25), and peripheral blood from patients with HT (n=19) or healthy donors (n=12) were analyzed by flow cytometry to define altered EZH2 expression in plasmablasts and plasma cells. Fifteen seven-week-old NOD.H-2h4 mice were randomly divided into the control (n=5), EAT without injection group (n=5), and EZH2 inhibitor+ GSK126 injection group (10 mg/kg, intraperitoneal injection 3 times /week, n=5). The degree of thyroid inflammation and changes in TgAb levels were observed after 8 weeks. Results RNA sequencing analysis showed that EZH2 and genes associated with the B-cell phenotype such as CD19, CD27, CD38, CD52 were higher expressed in HT hyroid tissues compared with normal thyroid tissues. Immunohistochemical results showed that immunohistochemical staining for EZH2 in 16 HT thyroid tissue specimens was strongly positive with positive cells observed in the GC region, and no positive cells were observed in the staining of 8 normal thyroid tissues. EZH2 staining in HT thyroid tissue was highly expressed in the GC region, and EZH2 was specifically expressed in CD19+ B cells. The results of flow cytometry assay showed that the proportion of CD19+ B cells, plasmablasts and plasma cells in HT FNA samples was higher than that of HD peripheral blood and HT peripheral blood samples (P

Radio frequency fingerprint identification (RFFI) shows great potential as a means for authenticating wireless devices. As RFFI can be addressed as a classification problem, deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance. RFFI is suitable for securing the legacy existing Internet of Things (IoT) networks since it does not require any modifications to the existing end-node hardware and communication protocols. However, most deep learning-based RFFI systems require the collection of a great number of labelled signals for training, which is time-consuming and not ideal, especially for the IoT end nodes that are already deployed and configured with long transmission intervals. Moreover, the long time required to train a neural network from scratch also limits rapid deployment on legacy IoT networks. To address the above issues, two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning. More specifically, they rely on fine-tuning and distance metric learning, respectively, and only require only a small amount of signals from the legacy IoT network. As the dataset used for transfer is small, we propose to apply augmentation in the transfer process to generate more training signals to improve performance. A LoRa-RFFI testbed consisting of 40 commercial-off-the-shelf (COTS) LoRa IoT devices and a software-defined radio (SDR) receiver is built to experimentally evaluate the proposed approaches. The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another IoT network with less than ten signals from each LoRa device. The classification accuracy is over 90%, and the augmentation technique can improve the accuracy by up to 20%.

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.

Background: Galangin, a flavonoid compound, is derived from Alpinia officinarum Hance. Previous studies have shown that galangin can inhibit the proliferation of hepatocellular carcinoma (HCC), but its mechanism is still unclear. This study aims to investigate the potential targets and molecular mechanisms of galangin on HCC through network pharmacology, bioinformatics, molecular docking, and experimental in vitro validation. Methods: In this study, network pharmacology was used to investigate the targets and mechanisms of galangin in the treatment of HCC. AutoDockTools software was used to simulate and calculate the binding of galangin to its core targets. GO and KEGG enrichment analyses were conducted in the DAVID database to explore the main biological functions and signaling pathways impacted by galangin intervention. In addition, bioinformatics was applied to examine the correlation between the differential expressions of the anti-HCC core targets of galangin and the survival of patients with HCC. Finally, the findings obtained from network pharmacology and bioinformatics were verified in cell experiments. Results: A total of 67 overlapping target genes of galangin and HCC were identified. Through the analysis of the protein-protein interaction (PPI) network, 10 hub genes with the highest degree of freedom were identified, including SRC, ESR1, MMP9, CDK4, CCNB1, MMP2, CDK2, CDK1, CHK1, and PLK1. These genes were found to be closely related to the degradation of the extracellular matrix, signal transduction, and the cell cycle. GO and KEGG enrichment analyses revealed that galangin exerts an anti-HCC role by affecting various signaling pathways, including the cell cycle, pathways in cancer, and the PI3K-Akt signaling pathway. The results of molecular docking indicated a significant interaction between galangin and CCNB1, CDK4, CDK1, and PLK1. Bioinformatics analysis revealed that CCNB1, CDK4, CDK1, and PLK1 were upregulated in the liver of patients with HCC at both the mRNA and protein levels. Flow cytometry analysis showed that galangin induced G0/G1 phase arrest and cell apoptosis in HepG2 and Huh7 cells. Additionally, galangin suppressed the expression of key proteins and mRNAs involved in the cell cycle pathway. Conclusions: These results suggest that galangin inhibits the growth of HCC cells by arresting the cell cycle at the G0/G1 phase. [ABSTRACT FROM AUTHOR]

Objective: To establish the quality standard for the compatibility solution of Alpinia officinarum Pogostemon cablin. Methods: Thin layer chromatography was used for qualitative identification for the two indicator components of galangin and pogostone in the compatibility solution of Alpinia officinarum Pogostemon cablin: the mixture of anpetroleum ether ethyl acetate (5:3), (5:1)as the developing solvent system on silica gel G thin layer plate, high efficiency silica gel G thin layer plate; High performance liquid chromatography was used to determine the content of galangin and pogostone: C18 chromatographic column (4.6 mmx250 mm, 5 μm)was used, and the mobile phase was methanol (A) 0.2% phosphoric acid (B), with a detection wavelength of 310 nm and gradient elution. Results:For the two indicator components of galangin and pogostone in the compatibility solution of Alpinia officinarum Pogostemon cablin, the fluorescent spots were clear, without interference from negative controls; Mass concentration was between 10.00-1 000.00 μg/mL and 10.00-640.00 μg/mL, and the linear relationship coefficients with peak area within the range of were R²= 0.999 0, R²= 0.999 9, respectively; The precision RSD was 0.63% and 0.56%, the stability RSD was 1.82% and 0.24%, and the repeatability RSD was 1.30% and 0.89%; The average recovery rates (n=9) were 103.19% (RSD=0.93%) and 100.87% (RSD=0.4%), respectively; The content of these two indicator components should not be less than 233.71 μg/mL and 61.48 μg/mL. Conclusion: A research method has been established for the quality standard of Alpinia officinarum Pogostemon cablin compatibility solution. The precision, accuracy, stability, and repeatability of this method meet the requirements. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing analysis has gained significant attention in visual analysis applications, particularly in segmenting and recognizing remote sensing images. However, the existing research has predominantly focused on single-period RGB image analysis, thus overlooking the complexities of remote sensing image capture, especially in highly vegetated land parcels. In this paper, we provide a large-scale vegetation remote sensing (VRS) dataset and introduce the VRS-Seg task for multi-modal and multi-temporal vegetation segmentation. The VRS dataset incorporates diverse modalities and temporal variations, and its annotations are organized using the Vegetation Knowledge Graph (VKG), thereby providing detailed object attribute information. To address the VRS-Seg task, we introduce VRSFormer, a critical pipeline that integrates multi-temporal and multi-modal data fusion, geometric contour refinement, and category-level classification inference. The experimental results demonstrate the effectiveness and generalization capability of our approach. The availability of VRS and the VRS-Seg task paves the way for further research in multi-modal and multi-temporal vegetation segmentation in remote sensing imagery. [ABSTRACT FROM AUTHOR]

There is an urgent need for a mass population screening tool for diabetes. Skin tissue contains a large number of endogenous fluorophores and physiological parameter markers related to diabetes. We built an excitation‐emission spectrum measurement system with the excited light sources of 365, 395, 415, 430, and 455 nm to extract skin characteristics. The modeling experiment was carried out to design and verify the accuracy of the recovery of tissue intrinsic discrete three‐dimensional fluorescence spectrum. Blood oxygen modeling experiment results indicated the accuracy of the physiological parameter extraction algorithm based on the diffuse reflectance spectrum. A community population cohort study was carried out. The tissue‐reduced scattering coefficient and scattering power of the diabetes were significantly higher than normal control groups. The Gaussian multi‐peak fitting was performed on each excitation‐emission spectrum of the subject. A total of 63 fluorescence features containing information such as Gaussian spectral curve intensity, central wavelength position, and variance were obtained from each person. Logistic regression was used to construct the diabetes screening model. The results showed that the area under the receiver operating characteristic curve of the model for predicting diabetes was 0.816, indicating a high diagnostic value. As a rapid and non‐invasive detection method, it is expected to have high clinical value. [ABSTRACT FROM AUTHOR]

Maternal high-fat diet intake has profound effects on the long-term health of offspring, predisposing them to a higher susceptibility to obesity and metabolic dysfunction-associated steatotic liver disease. However, the detailed mechanisms underlying the role of a maternal high-fat diet in hepatic lipid accumulation in offspring, especially at the weaning age, remain largely unclear. In this study, female C57BL/6J mice were randomly assigned to either a high-fat diet or a control diet, and lipid metabolism parameters were assessed in male offspring at weaning. Gut microbiota analysis and targeted metabolomics of short-chain fatty acids (SCFAs) in these offspring were further performed. Both in vivo and in vitro studies were conducted to explore the role of butyrate in hepatic cholesterol excretion in the liver and HepG2 cells. Our results showed that maternal high-fat feeding led to obesity and dyslipidemia, and exacerbated hepatic lipid accumulation in the livers of offspring at weaning. We observed significant decreases in the abundance of the Firmicutes phylum and the Allobaculum genus, known as producers of SCFAs, particularly butyrate, in the offspring of dams fed a high-fat diet. Additionally, maternal high-fat diet feeding markedly decreased serum butyrate levels and down-regulated ATP-binding cassette transporters G5 (ABCG5) in the liver, accompanied by decreased phosphorylated AMP-activated protein kinase (AMPK) and histone deacetylase 5 (HADC5) expressions. Subsequent in vitro studies revealed that butyrate could induce ABCG5 activation and alleviate lipid accumulation via the AMPK-pHDAC5 pathway in HepG2 cells. Moreover, knockdown of HDAC5 up-regulated ABCG5 expression and promoted cholesterol excretion in HepG2 cells. In conclusion, our study provides novel insights into how maternal high-fat diet feeding inhibits hepatic cholesterol excretion and down-regulates ABCG5 through the butyrate-AMPK-pHDAC5 pathway in offspring at weaning., (© 2024 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.)

Objective: The objective of this research was to investigate the part played by KDR and miRNAs in the development and prognosis of oral squamous cell carcinoma (OSCC)., Methods: The gene and protein expression of KDR in OSCC cell lines and a normal human oral epithelial keratinocyte cell line were detected using real-time PCR and western blot analysis. We evaluated the impact of KDR on the proliferation, metastasis, and migration of OSCC cells using the MTT assay and colony formation assay in the CAL27 cell line. Data on OSCC were retrieved from the TCGA-HNSC and GEO databases, and we identified miRNAs that were differentially expressed between OSCC and normal tissue samples. Furthermore, we predicted their potential target mRNAs. miR-424-5p was identified as a regulator upstream of KDR expression, and dual luciferase reporter assays confirmed binding between KDR and miR-424-5p., Results: KDR overexpression was observed in OSCC samples from various databases. These findings were further validated through in vitro experiments, which established that elevated KDR levels enhance the proliferative potential of OSCC cells. Moreover, miR-424-5p was found to counteract the tumorigenic effects of KDR in OSCC cells, suppressing their proliferation, invasion, and metastasis capabilities., Conclusions: Our research suggests that miR-424-5p and KDR might serve as valuable diagnostic and prognostic markers, as well as potential therapeutic targets, in OSCC.

Cities are treated as global methane (CH 4 ) emission hotspots and the monitoring of atmospheric CH 4 concentration in cities is necessary to evaluate anthropogenic CH 4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH 4 emitter as of China, and although obvious seasonal variations of atmospheric CH 4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH 4 concentrations, year-round CH 4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH 4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH 4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH 4 background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH 4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH 4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH 4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from -13.4 to -6.3 ppb. And the temperature induced seasonal CH 4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Large-scale prospective cohort studies on diabetic foot ulcers risk factor screening in China are limited. Therefore, this prospective cohort study aimed to explore the predictive risk factors for diabetic foot ulcers to provide clinicians with concise and effective clinical indicators for identifying a high-risk diabetic foot and guiding the prevention of diabetic foot ulcers., Methods: Patients with diabetes who visited the Department of Endocrinology of Peking University First Hospital from October 2017 to December 2018 were selected as research participants by convenience sampling. A total of 968 patients were included. After enrollment, a dedicated person collected and recorded all baseline data. A dedicated telephone follow-up was conducted every 12-24 months to evaluate whether the endpoint event had occurred. All patients were followed up for an average of 61 (57-71) months, with 95% of them followed up for more than 60 months. According to the occurrence of endpoint events, they were divided into the DFU and non-DFU groups. The data between the two groups were analyzed using independent-sample t-test, Wilcoxon rank sum test, and chi square test. We used univariate and multivariate logistic regression analysis to analyze the factors that affected the occurrence of diabetic foot ulcers., Results and Conclusions: After the 5-year follow-up, the incidence of diabetic foot was 25.83%. Multivariate logistic regression analysis revealed that body mass index (odds ratio: 1.046; 95% confidence interval: 1.001-1.093), abnormal pinprick sensation (odds ratio: 4.138; 95% confidence interval: 1.292-13.255), history of fungal foot infection (odds ratio: 2.287; 95% confidence interval: 1.517-3.448), abnormal 128-Hz tuning fork test (odds ratio: 2.628; 95% confidence interval: 1.098-6.294), and HbA1c≥ 8% (odds ratio: 1.522; 95% confidence interval: 1.014-2.284) were independent predictors of diabetic foot. Our study highlights clinically relevant indicators that may help to prevent the occurrence of diabetic foot and guide timely interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Su, Yuan, Yuan, Sun, Zhang, Liu, Zhang and Guo.)