Publications by authors named "E Shen"

566 Publications

Stability of trivalent human papillomavirus (types 16, 18, 58) recombinant vaccine (Escherichia coli).

Chin Med J (Engl) 2021 Jul 19. Epub 2021 Jul 19.

Quality Department of Beijing Health Guard Biotechnology Inc., BDA, Daxing District, Beijing 100176, China National Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China School of Chemical and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China Research and Development Department of Beijing Health Guard Biotechnology Inc., BDA, Daxing District, Beijing 100176, China.

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http://dx.doi.org/10.1097/CM9.0000000000001659DOI Listing
July 2021

Marriage of Virus-Mimic Surface Topology and Microbubble-Assisted Ultrasound for Enhanced Intratumor Accumulation and Improved Cancer Theranostics.

Adv Sci (Weinh) 2021 07 14;8(13):2004670. Epub 2021 May 14.

Department of Ultrasound in Medicine Shanghai Jiao Tong University Affiliated Sixth People's Hospital State Key Laboratory of Oncogenes and Related Genes Shanghai Jiao Tong University School of Medicine Shanghai 200032 P. R. China.

The low delivery efficiency of nanoparticles to solid tumors greatly reduces the therapeutic efficacy and safety which is closely related to low permeability and poor distribution at tumor sites. In this work, an "intrinsic plus extrinsic superiority" administration strategy is proposed to dramatically enhance the mean delivery efficiency of nanoparticles in prostate cancer to 6.84% of injected dose, compared to 1.42% as the maximum in prostate cancer in the previously reported study. Specifically, the intrinsic superiority refers to the virus-mimic surface topology of the nanoparticles for enhanced nano-bio interactions. Meanwhile, the extrinsic stimuli of microbubble-assisted low-frequency ultrasound is to enhance permeability of biological barriers and improve intratumor distribution. The enhanced intratumor enrichment can be verified by photoacoustic resonance imaging, fluorescence imaging, and magnetic resonance imaging in this multifunctional nanoplatform, which also facilitates excellent anticancer effect of photothermal treatment, photodynamic treatment, and sonodynamic treatment via combined laser and ultrasound irradiation. This study confirms the significant advance in nanoparticle accumulation in multiple tumor models, which provides an innovative delivery paradigm to improve intratumor accumulation of nanotherapeutics.
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http://dx.doi.org/10.1002/advs.202004670DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261514PMC
July 2021

Predicting primary treatment failure using interim FDG-PET scanning in diffuse large B-cell lymphoma.

Eur J Haematol 2021 Jul 8. Epub 2021 Jul 8.

Austin Health, Heidelberg, Australia.

Interim FDG-PET (iPET) in diffuse large B-cell lymphoma (DLBCL) is increasingly practised and used in clinical trials to adapt further therapy. However, the optimum timing and methodology of iPET remains controversial. We retrospectively analysed the iPET results and outcomes of 200 DLBCL patients where FDG-PET was routinely performed at baseline, after 2 cycles (iPET2) and at completion of chemoimmunotherapy. iPET was also performed after 4 cycles (iPET4) where at iPET2, Deauville score (DS) was ≥4. Scans were assessed by blinded expert lymphoma PET physicians for DS, maximum standard uptake value (SUVmax), total metabolic tumour volume (TMTV) and total lesion glycolysis (TLG). Treatment failure was defined as death, progression or refractory disease. 95.5% of patients received R-CHOP. No baseline PET parameter was predicted for EFS or OS independent of the NCCN-IPI. The multivariable analysis at iPET2 showed DS5 (19.5% of cases) predicted treatment failure (HR 6.29, 95% CI 3.01-13.17, P < .001), but DS4 was equivalent to DS1-3. At iPET4, ΔSUVmax < 66% predicted treatment failure (HR 5.49, 95% CI 3.03-9.99, P < .001). By multivariable analysis of all time points, high NCCN-IPI and DS5 at iPET2 were negative predictors of survival. These findings were independent of novel prognostic markers.
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http://dx.doi.org/10.1111/ejh.13684DOI Listing
July 2021

M6A "Writer" Gene : A Favorable Prognostic Biomarker and Correlated With Immune Infiltrates in Rectal Cancer.

Front Oncol 2021 17;11:615296. Epub 2021 Jun 17.

Department of Oncology, Xiangya Hospital, Central South University, Changsha, China.

Rectal cancer (RC) is the leading cause of tumor-related death among both men and women. The efficacy of immunotherapy for rectal cancer is closely related to the immune infiltration level. The N6-methyladenosine (m6A) modification may play a pivotal role in tumor-immune interactions. However, the roles of m6A-related genes in tumor-immune interactions of rectal cancer remain largely unknown. After an evaluation on the expression levels of m6A-related genes and their correlations with the prognosis of rectal cancer patients, we found that was the only gene to be significantly correlated with prognosis in rectal cancer patients. Therefore, we further observed the impact of expression and m6A modification on the immune infiltration in rectal cancer. Our study indicates that low expression of the m6A "writer" gene in rectal cancer may lead to the downregulation of m6A RNA modification, thus reducing the level of immune cell infiltration and resulting in poor prognosis. expression level is an independent prognostic factor in rectal cancer and is positively correlated with the immune infiltration level. Our study identified as a potential target for enhancing immunotherapy efficacy in rectal cancer.
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http://dx.doi.org/10.3389/fonc.2021.615296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247640PMC
June 2021

Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method.

Sci Rep 2021 Jul 2;11(1):13764. Epub 2021 Jul 2.

Sino-French Hoffmann Institute, School of Basic Sciences, The Second Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, Guangzhou Medical University, Guangzhou, 511436, Guangdong, China.

With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10. A new taxonomy (Taxonomy 2) containing 6 levels (Project 2-4) was developed based on skin cytology and pathology, and represents individual diseases arranged in a tree structure with three root nodes representing: (1) Keratinogenic diseases, (2) Melanogenic diseases, and (3) Diseases related to non-keratinocytes and non-melanocytes. The predictive effects of the new taxonomy including accuracy, precision, recall, F1, and Kappa were compared with those of ICD-10 on Diseases of the skin and subcutaneous tissue (Taxonomy 1, Project 1) by Deep Residual Learning method. For each project, 2/3 of the images were included as training group, and the rest 1/3 of the images acted as test group according to the category (class) as the stratification variable. Both train and test groups in the Projects (2 and 3) from Taxonomy 2 had higher F1 and Kappa scores without statistical significance on the prediction of skin disease than the corresponding groups in the Project 1 from Taxonomy 1, however both train and test groups in Project 4 had a statistically significantly higher F1-score than the corresponding groups in Project 1 (P = 0.025 and 0.005, respectively). The results showed that the new taxonomy developed based on cytology and pathology has an overall better performance on predictive effect of skin disease than the ICD-10 on Diseases of the skin and subcutaneous tissue. The level 5 (Project 4) of Taxonomy 2 is better on extension to unknown data of diagnosis system assisted by AI compared to current used classification system from ICD-10, and may have the potential application value in clinic of dermatology.
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http://dx.doi.org/10.1038/s41598-021-92848-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8253798PMC
July 2021
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