Dexmedetomidine (DEX) has been reported to attenuate cecal ligation perforation (CLP)-stimulated acute lung injury (ALI) by downregulating HMGB1 and RAGE . This study aimed to further investigate the specific mechanisms of RAGE and its potential-related mechanisms of DEX on ALI models in vitro and in vivo . The in vitro and in vivo ALI models were established by lipopolysaccharide treatment in MLE-12 cells and CLP in mice, respectively. The effect of DEX on pathological alteration was investigated by HE staining. Thereafter, the myeloperoxidase (MPO) activity and inflammatory cytokine levels were respectively detected to assess the lung injury of mice using commercial kits. The expression levels of HMGB1, RAGE, NF-κB, and pyroptosis-related molecules were detected by RT-qPCR and Western blot. HE staining showed that lung injury, increased inflammatory cell infiltration, and lung permeability was found in the ALI mice, and DEX treatment significantly attenuated lung tissue damage induced by CLP. The MPO activity and inflammatory cytokines (TNF-α, IL-1β, and NLRP3) levels were also significantly reduced after DEX treatment compared with those in the ALI mice. Moreover, DEX activated the HMGB1/RAGE/NF-κB pathway and upregulated the pyroptosis-related proteins. However, the protective DEX effect was impaired by RAGE overexpression in ALI mice and MLE-12 cells. Additionally, DEX treatment significantly suppressed HMGB1 translocation from the nucleus region to the cytoplasm, and this effect was reversed by RAGE overexpression. These findings suggested that DEX may be a useful ALI treatment, and the protective effects on ALI mice may be through the inhibition of HMGB1/RAGE/NF-κB pathway and cell pyroptosis.
The study aimed to identify differentially expressed micrornas (mirnas/mirs) and explore the mechanisms governing impaired memory and learning ability in developing brains exposed to sevoflurane. A total of six 7-day-old male ICR mice were randomly assigned into the sevoflurane anesthesia group (treated with 2.4% sevoflurane) or control group (treated with normal saline solution at the same dose). After 14 days, the mice were subjected to a Morris water maze experiment. Then, the animals were sacrificed and hippocampus tissues were isolated. RNAs in hippocampus tissues were sequenced and the differential miRNA expression profiles were identified by a bioinformatics approach. The learning and memory function of mice were significantly affected by sevoflurane exposure. A total of 18 miRNAs were found to be significantly affected by sevoflurane administration. Their target genes clustered into different functional groups, such as 'dephosphorylation', 'vesicle localization' and the 'Wnt signaling pathway'. mir-101b-3p was closely related with 'chromatin binding' and 'protein serine/threonine kinase activity'. The most represented pathways for mirnas included 'neuroactive ligand-receptor interaction' (miR-1187), 'long-term depression' (miR-425-5p), 'FoxO signaling pathway' (miR-425-5p) and the 'neurotrophin signaling pathway' (miR-467a-3p). miR-467a-3p (degree=89), miR-101b-3p (degree=59), and miR-1187 (degree=51) were the hub nodes in the miRNA regulatory network. The Wnt signaling pathway, miR-467a-3p, miR-1187 and miR-101b-3p may be therapeutic targets for preventing cognitive impairments induced by sevoflurane.
BackgroundInvasive micropapillary carcinoma (IMPC) and secretory carcinoma of the breast (SCB) are relatively rare types of breast cancer. IMPC is usually associated with high incidence of lymphovascular invasion, lymph node metastasis and poor prognosis. While SCB usually carries a relatively favorable prognosis, cases of axillary and distant metastases have been reported. Clinicians generally adopt systemic treatments based on the histopathological findings of the patients to improve the prognosis, but there is currently no consensus on the optimal treatment for these two types of cancer.Case presentationWe treated a 50‐year‐old woman with lung cancer history who presented with a single lump in each breast. Following bilateral breast-conserving surgery, the diagnosis of SCB of the left breast and IMPC of the right breast was confirmed with immunohistochemistry. It is worth noting that the pathological results of left lung adenocarcinoma centered on micropapillary-type was same as the invasive micropapillary component of right breast.ConclusionsWe reported this case of bilateral primary relatively-rare-form breast cancer for its extremely rare occurrence and there are less than 20 cases of SCB reported worldwide till now. It is also significative to distinguish this primary tumor of right breast from metastatic cancer. Our histopathologic diagnosis and synthetical therapy scheme will provide material for SCB and IMPC. To facilitate the diagnosis and prognosis of such relatively rare tumors, more cases will need to be reported.
BackgroundContinuous contrast-enhanced ultrasound (CEUS) video is a challenging direction for radiomics research. We aimed to evaluate machine learning (ML) approaches with radiomics combined with the XGBoost model and a convolutional neural network (CNN) for discriminating between benign and malignant lesions in CEUS videos with a duration of more than 1 min.MethodsWe gathered breast CEUS videos of 109 benign and 81 malignant tumors from two centers. Radiomics combined with the XGBoost model and a CNN was used to classify the breast lesions on the CEUS videos. The lesions were manually segmented by one radiologist. Radiomics combined with the XGBoost model was conducted with a variety of data sampling methods. The CNN used pretrained 3D residual network (ResNet) models with 18, 34, 50, and 101 layers. The machine interpretations were compared with prospective interpretations by two radiologists. Breast biopsies or pathological examinations were used as the reference standard. Areas under the receiver operating curves (AUCs) were used to compare the diagnostic performance of the models.ResultsThe CNN model achieved the best AUC of 0.84 on the test cohort with the 3D-ResNet-50 model. The radiomics model obtained AUCs between 0.65 and 0.75. Radiologists 1 and 2 had AUCs of 0.75 and 0.70, respectively.ConclusionsThe 3D-ResNet-50 model was superior to the radiomics combined with the XGBoost model in classifying enhanced lesions as benign or malignant on CEUS videos. The CNN model was superior to the radiologists, and the radiomics model performance was close to the performance of the radiologists.
With the substantial progress of terrestrial fiber-based quantum networks and satellite-based quantum nodes, airborne quantum key distribution (QKD) is now becoming a flexible bond between terrestrial fiber and satellite, which is an efficient solution to establish a mobile, on-demand and real-time coverage quantum network. However, the boundary layer (BL) normally adhere to the surface of the aircraft when its speed is higher than Mach 0.3. The BL would change local refractive index and energy flux density drastically, thus lowering the coupling efficiency and infidelity of quantum states. Here, we investigate the airborne QKD performance with the BL effects, which has been rarely mentioned in existing research. Through simulations and modeling, we present the relation between divergence angle and secure key rate. With the increase of flight speed v, relative flight altitude h and the shortest projection distance d, the key-rate curve is obviously jitter, and the QKD performance is continuously reduced. Simulation results show that, under several typical circumstances, the BL will affect the communicating distance, the transmission efficiency and the generation of secure key rate in varying degrees, which is helpful for future airborne experimental designs.
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