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"Yue, Dan"
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Exploratory Study of the Diagnostic Value of Combined Serum β-hCG, Serum Ferritin, and Gestational Age in Intrauterine Infection Among Pregnant Women With Premature Rupture of Membranes
Background:Intrauterine infection poses significant risks to both mother and fetus, especially in cases of premature rupture of membranes (PROM). Early and accurate diagnosis is crucial for timely intervention.Methods:This was a prospective study involving 120 patients with PROM, including 32 cases diagnosed with intrauterine infection and 88 non-infected controls. Parameters such as serum beta-human chorionic gonadotropin (β-hCG), serum ferritin (SF), and gestational age (GA) were evaluated for their diagnostic efficacy using logistic regression and receiver operating characteristic (ROC) analysis.Results:A total of 120 patients were analyzed, with 32 (26.67%) diagnosed with intrauterine infection. Infected patients exhibited significantly higher median β-hCG (43,104.00 vs. 22,375.00 mIU/mL; p < 0.0001) and SF (34.14 vs. 27.81 ng/mL; p = 0.0020), and a shorter mean gestational age (38.63 vs. 37.78 weeks; p = 0.0040). Furthermore, the logistic regression analysis established these as independent predictors, with significant ORs for log10-β-hCG (22.41; p = 0.0010), log10-SF (6.45; p = 0.0300), and gestational age (0.61; p = 0.0300). The combined testing approach, particularly the integration of log10-β-hCG, log10-SF, and GA, showed superior diagnostic efficacy, achieving an ROC area under the curve of 0.78, with significantly enhanced sensitivity and specificity.Conclusions:The combined testing of serum β-hCG, SF, and GA offers a robust tool for the early diagnosis of intrauterine infection in women with PROM. These findings support the use of comprehensive biomarker screening in clinical settings to improve diagnostic accuracy and patient outcomes.
Journal Article
T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures
The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.
In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.
We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.
Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.
Journal Article
Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network
To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.
Journal Article
Role of Baicalin in Anti-Influenza Virus A as a Potent Inducer of IFN-Gamma
Baicalin (BA) is a flavonoid compound purified from Scutellaria baicalensis Georgi and has been shown to possess a potent inhibitory activity against viruses. However, the role of BA in anti-influenza virus has not been extensively studied, and the immunological mechanism of BA in antiviral activity remains unknown. Here, we observed that BA could protect mice from infection by influenza virus A/PR/8/34 (H1N1), associated with increasing IFN-γ production, but presented no effects in IFN-γ or IFN-γ receptor deficient mice. Further study indicated that BA could inhibit A/PR/8/34 replication through IFN-γ in human PBMC. Moreover, BA can directly induce IFN-γ production in human CD4+ and CD8+ T cells and NK cells, and activate JAK/STAT-1 signaling pathway. Collectively, BA exhibited anti-influenza virus A (H1N1) activity in vitro and in vivo as a potent inducer of IFN-γ in major IFN-γ producing cells.
Journal Article
Potential contribution of early endothelial progenitor cell (eEPC)-to-macrophage switching in the development of pulmonary plexogenic lesion
Background
Plexiform lesions, which have a dynamic appearance in structure and cellular composition, are the histological hallmark of severe pulmonary arterial hypertension in humans. The pathogenesis of the lesion development remains largely unknown, although it may be related to local inflammation and dysfunction in early progenitor endothelial cells (eEPCs). We tested the hypothesis that eEPCs contribute to the development of plexiform lesions by differentiating into macrophages in the setting of chronic inflammation.
Methods
The eEPC markers CD133 and VEGFR-2, macrophage lineage marker mannose receptor C-type 1 (MRC1), TNFα and nuclear factor erythroid 2-related factor 2 (Nrf2) in plexiform lesions in a broiler model were determined by immunohistochemistry. eEPCs derived from peripheral blood mononuclear cells were exposed to TNFα, and macrophage differentiation and angiogenic capacity of the cells were evaluated by phagocytotic and Matrigel plug assays, respectively. The role of Nrf2 in eEPC-to-macrophage transition as well as in MRC1 expression was also evaluated. Intratracheal installation of TNFα was conducted to determine the effect of local inflammation on the formation of plexiform lesions.
Results
Cells composed of the early lesions have a typical eEPC phenotype whereas those in more mature lesions display molecular and morphological characteristics of macrophages. Increased TNFα production in plexiform lesions was observed with lesion progression. In vitro studies showed that chronic TNFα challenge directed eEPCs to macrophage differentiation accompanied by hyperactivation of Nrf2, a stress-responsive transcription factor. Nrf2 activation (Keap1 knockdown) caused a marked downregulation in CD133 but upregulation in MRC1 mRNA. Dual luciferase reporter assay demonstrated that Nrf2 binds to the promoter of MRC1 to trigger its expression. In good agreement with the in vitro observation, TNFα exposure induced macrophage differentiation of eEPCs in Matrigel plugs, resulting in reduced neovascularization of the plugs. Intratracheal installation of TNFα resulted in a significant increase in plexiform lesion density.
Conclusions
This work provides evidence suggesting that macrophage differentiation of eEPCs resulting from chronic inflammatory stimulation contributes to the development of plexiform lesions. Given the key role of Nrf2 in the phenotypic switching of eEPCs to macrophages, targeting this molecular might be beneficial for intervention of plexiform lesions.
Journal Article
Biological Activities of p-Hydroxycinnamic Acids in Maintaining Gut Barrier Integrity and Function
It is well established that p-Hydroxycinnamic acids (HCAs), including ferulic, caffeic, sinapic, and p-coumaric acids, possess a characteristic phenylpropanoid C6-C3 backbone and account for about one-third of the phenolic compounds in our diet. HCAs are typically associated with various plant cell wall components, including mono-, di-, and polysaccharides, sterols, polyamines, glycoproteins, and lignins. Interestingly, enzymes produced by intestinal microbes liberate HCAs from these associations. HCAs are completely absorbed in their free form upon ingestion and undergo specific reactions upon absorption in the small intestine or liver. The gut epithelium, composed of intestinal epithelial cells (IECs), acts as a physical barrier against harmful bacteria and a site for regulated interactions between bacteria and the gut lumen. Thus, maintaining the integrity of the epithelial barrier is essential for establishing a physiochemical environment conducive to homeostasis. This review summarizes the protective effects of HCAs on the intestinal barrier, achieved through four mechanisms: preserving tight junction proteins (TJPs), modulating pro-inflammatory cytokines, exerting antioxidant activity, and regulating the intestinal microbiota.
Journal Article
Dye-Encapsulated Metal–Organic Frameworks for the Multi-Parameter Detection of Temperature
Temperature is an important physical parameter and plays a significant role in scientific research, the detection of which cannot be too crucial to study. In order to reduce the interference of the external environment on the detection of temperature and improve the accuracy of the detection results, a multi-parameter detection method using several optical signals was proposed. Here, a novel porous metal–organic framework (MOF), Zn-CYMPN, was synthesized and structurally characterized. Then, fluorescent organic dyes, either DPEE or DPEM, were encapsulated into the pores of Zn-CYMPN independently. The successful synthesis of the composites Zn-CYMPN⊃DPEE or Zn-CYMPN⊃DPEM could easily introduce other fluorescent centers into the original material and made it more convenient to realize multi-parameter temperature detection. More specifically, when the temperature changed, the maximum fluorescent emission wavelength (W) and the maximum optical intensity (I) of the Zn-CYMPN⊃DPEE/DPEM both showed good linear responses with temperature over a wide range, indicating that the composites were highly sensitive thermometers with multi-parameter temperature readouts. In addition, the quantum efficiency and thermal stability of the organic dyes, which bother every researcher, were improved as well.
Journal Article
Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network
A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.
Journal Article