miR-155 promotes Th17 differentiation by targeting FOXP3 to aggravate inflammation in MRSA pneumonia.

BACKGROUND: Previous researches have clarified that miR-155 is increased in methicillin-resistant Staphylococcus aureus (MRSA) pneumonia, and modulates Th9 differentiation. Like Th9 cells, Th17 cells were also a subset of CD4+ T cells and involved in MRSA pneumonia progression. This work aimed to investigate the role and mechanism of miR-155 in Th17 differentiation. METHODS: Bronchoalveolar lavage fluid (BALF) was collected from children with MRSA pneumonia and bronchial foreign bodies. MRSA-infected murine model was established followed by collecting BALF and lung tissues. qRT-PCR, ELISA and flow cytometry were performed to examine the mRNA expression and concentration of IL-17 and the number of Th17 cells in above samples. HE and ELISA were used to evaluate inflammatory responses in lung. Furthermore, CD4+ T cells were isolated from BALF of children for in vitro experiments. After treatments with miR-155 mimic/inhibitor, the roles of miR-155 in Th17/IL-17 regulation were determined. The downstream of miR-155 was explored by qRT-PCR, western blotting, dual luciferase reporter analysis and RIP assay. RESULTS: The levels of IL-17 and the proportion of Th17 cells were increased in children with MRSA pneumonia. A similar pattern was observed in MRSA-infected mice. On the contrary, IL-17 neutralization abolished the activation of Th17/IL-17 induced by MRSA infection. Furthermore, IL-17 blockade diminished the inflammation caused by MRSA. In vitro experiments demonstrated miR-155 positively regulated IL-17 expression and Th17 differentiation. Mechanistically, FOXP3 was a direct target of miR-155. miR-155 inhibited FOXP3 level via binding between FOXP3 and Argonaute 2 (AGO2), the key component of RNA-induced silencing complex (RISC). FOXP3 overexpression reversed elevated IL-17 levels and Th17 differentiation induced by miR-155. CONCLUSIONS: miR-155 facilitates Th17 differentiation by reducing FOXP3 through interaction of AGO2 and FOXP3 to promote the pathogenesis of MRSA pneumonia. IL-17 blockade weakens the inflammation due to MRSA, which provides a nonantibiotic treatment strategy for MRSA pneumonia.

Effective reduction on flame soot via plasma coupled with carbon dioxide.

This study explored the impact of non-thermal plasma and CO2 on the flame soot characteristics within the diffusion flames. We analyzed on flame structures that were diluted with either CO2 or N2, temperature distributions, and soot characteristics, both in the presence and absence of plasma. Due to the higher specific heat capacity of CO2 compared to N2, the optical observations consistently showed lower temperatures in flames diluted with CO2 as compared to those diluted with N2. The inclusion of plasma and carbon dioxide resulted in the lowest soot concentration, indicating that plasma coupled with CO2 has a synergistic inhibitory effect on soot emissions. The findings revealed that when CO2 was used to dilute the flames and the oxygen concentration was low, the soot nanostructure appeared amorphous. Raman results showed that the level of graphitization observed in soot particles from CO2 dilution flames was lower than that from N2 dilution flames. In the presence of plasma and CO2, the soot obtained exhibited the shortest fringe length and the highest fringe tortuosity. Significant correlations were observed between the nanostructure of soot and its reactivity. The combined application of plasma and CO2 proved to be effective in reducing the soot carbonization degree.

SCAC: A Semi-Supervised Learning Approach for Cervical Abnormal Cell Detection.

Cervical abnormal cell detection plays a crucial role in the early screening of cervical cancer. In recent years, some deep learning-based methods have been proposed. However, these methods rely heavily on large amounts of annotated images, which are time-consuming and labor-intensive to acquire, thus limiting the detection performance. In this paper, we present a novel Semi-supervised Cervical Abnormal Cell detector (SCAC), which effectively utilizes the abundant unlabeled data. We utilize Transformer as the backbone of SCAC to capture long-range dependencies to mimic the diagnostic process of pathologists. In addition, in SCAC, we design a Unified Strong and Weak Augment strategy (USWA) that unifies two data augmentation pipelines, implementing consistent regularization in semi-supervised learning and enhancing the diversity of the training data. We also develop a Global Attention Feature Pyramid Network (GAFPN), which utilizes the attention mechanism to better extract multi-scale features from cervical cytology images. Notably, we have created an unlabeled cervical cytology image dataset, which can be leveraged by semi-supervised learning to enhance detection accuracy. To the best of our knowledge, this is the first publicly available large unlabeled cervical cytology image dataset. By combining this dataset with two publicly available annotated datasets, we demonstrate that SCAC outperforms other existing methods, achieving state-of-the-art performance. Additionally, comprehensive ablation studies are conducted to validate the effectiveness of USWA and GAFPN. These promising results highlight the capability of SCAC to achieve high diagnostic accuracy and extensive clinical applications.