EpCAM (epithelial cell adhesion molecule) is a type I transmembrane glycoprotein, which was originally identified as a tumor-associated antigen due to its high expression level in rapidly growing epithelial tumors. Germ line mutations of the human EpCAM gene have been indicated as the cause of congenital tufting enteropathy. Previous studies based on cell models have revealed that EpCAM contributes to various biological processes including cell adhesion, signaling, migration and proliferation. Due to the previous lack of genetic animal models, the in vivo functions of EpCAM remain largely unknown. However, EpCAM genetic animal models have recently been generated, and are useful for understanding the functions of EpCAM. The authors here briefly review the functions and mechanisms of EpCAM in physiological processes and different diseases.

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BackgroundHistone deacetylase (HDAC) inhibitors are widely used in clinical investigation as novel drug targets. For example, panobinostat and vorinostat have been used to treat patients with melanoma. However, HDAC inhibitors are small-molecule compounds without a specific target, and their mechanism of action is unclear. Therefore, it is necessary to investigate which HDACs are required for the proliferation and metastasis of melanoma cells.MethodsWe used overexpression and knocking down lentivirus to clarify the influence of HDAC5 and HDAC6 in melanoma development. Also, we introduced stable HDAC5 or HDAC6 knockdown cells into null mice and found that the knockdown cells were unable to form solid tumors. Finally, we tested HDAC5 and HDAC6 expression and sub-location in clinical melanoma tissues and tumor adjacent tissues.ResultsIn this study, and found that HDAC5 and HDAC6 were highly expressed in melanoma cells but exhibited low expression levels in normal skin cells. Furthermore, we knocked down HDAC5 or HDAC6 in A375 cells and demonstrated that both HDAC5 and HDAC6 contributed to the proliferation and metastasis of melanoma cells.ConclusionsThis study demonstrated both HDAC5 and HDAC6 were required for melanoma cell proliferation and metastasis through different signaling pathways.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0753-0) contains supplementary material, which is available to authorized users.

Antibody disulfide bond reduction during monoclonal antibody (mAb) production is a phenomenon that has been attributed to the reducing enzymes from CHO cells acting on the mAb during the harvest process. However, the impact of antibody reduction on the downstream purification process has not been studied. During the production of an IgG2 mAb, antibody reduction was observed in the harvested cell culture fluid (HCCF), resulting in high fragment levels. In addition, aggregate levels increased during the low pH treatment step in the purification process. A correlation between the level of free thiol in the HCCF (as a result of antibody reduction) and aggregation during the low pH step was established, wherein higher levels of free thiol in the starting sample resulted in increased levels of aggregates during low pH treatment. The elevated levels of free thiol were not reduced over the course of purification, resulting in carry‐over of high free thiol content into the formulated drug substance. When the drug substance with high free thiols was monitored for product degradation at room temperature and 2–8°C, faster rates of aggregation were observed compared to the drug substance generated from HCCF that was purified immediately after harvest. Further, when antibody reduction mitigations (e.g., chilling, aeration, and addition of cystine) were applied, HCCF could be held for an extended period of time while providing the same product quality/stability as material that had been purified immediately after harvest. Biotechnol. Bioeng. 2017;114: 1264–1274. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals Inc.

Background: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. Purpose: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. Methods: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infections cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. Results: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists’ performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. Conclusion: A deep learning algorithm-based AI model developed in this study successfully improved radiologists’ performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

A number of studies have shown that baicalein shows high antitumor activity in vitro and in vivo. In this study, the inhibitory effect of baicalein on human cervical cancer HeLa cells was studied in vitro. HeLa cells were treated with high (100 µg/ml) and low (50 µg/ml) doses of baicalein, and cell growth inhibition rates were examined by the MTT assay. The morphological changes of apoptotic cells were observed under the light and electron microscope, while the rate of cell apoptosis was examined by flow cytometry. The expression of apoptosis-related proteins was analyzed by western blot, and caspase-3 activation was examined by a caspase-3 activity assay and spectrophotometry. The results demonstrated that baicalein inhibits the proliferation of HeLa cells and induces apoptosis in a caspase-3-dependent pathway, through downregulation of the B-cell lymphoma 2 (Bcl-2) protein and upregulation of the Bcl-2-associated X protein (Bax), Fas, Fas ligand (FasL) and caspase-8. Thus, we conclude that baicalein induces apoptosis of HeLa cells via the mitochondrial and the death receptor pathways. Cell apoptosis in HeLa cells was most likely promoted by the activation of the proteolytic enzyme caspase-3 in both pathways.

Reducing diet fat or replacing lard oil with soybean oil in high-fat diet alleviates obesity-related inflammation and insulin resistance by attenuating the upregulation of OPN and macrophage infiltration into adipose tissue induced by high-fat diet.

To investigate the dynamic changes of Krebs von den Lungen‐6 (KL‐6) among patients with coronavirus disease 2019 (COVID‐19) and the role of KL‐6 as a noninvasive biomarker for predicting long‐term lung injury, the clinical information and laboratory tests of 166 COVID‐19 patients were collected, and a correlation analysis between KL‐6 and other parameters was conducted. There were 17 (10.2%, 17/166) severe/critical and 149 (89.8%, 149/166) mild COVID‐19 patients in our cohort. Serum KL‐6 was significantly higher in severe/critical COVID‐19 patients than in mild patients (median 898.0 vs. 451.2 U/ml, p < .001). KL‐6 was next confirmed to be a sensitive and specific biomarker for distinguishing mild and severe/critical patients and correlate to computed tomography lung lesions areas. Serum KL‐6 concentration during the follow‐up period (>100 days postonset) was well correlated to those concentrations within 10 days postonset (Pearson r = .867, p < .001), indicating the prognostic value of KL‐6 levels in predicting lung injury after discharge. Finally, elevated KL‐6 was found to be significantly correlated to coagulation disorders, and T cells subsets dysfunctions. In summary, serum KL‐6 is a biomarker for assessing COVID‐19 severity and predicting the prognosis of lung injury of discharged patients.