No abstract

Treating large established tumors is challenging for dendritic cell (DC)-based immunotherapy. DC activation with tumor cell-derived exosomes (TEXs) carrying multiple tumorassociated antigen can enhance tumor recognition. Adding a potent adjuvant, high mobility group nucleosome-binding protein 1 (HMGN1), boosts DCs' ability to activate T cells and improves vaccine efficiency. Here, we demonstrate that TEXs painted with the functional domain of HMGN1 (TEX-N1ND) via an exosomal anchor peptide potentiates DC immunogenicity. TEX-N1ND pulsed DCs (DC TEX-N1ND) elicit long-lasting antitumor immunity and tumor suppression in different syngeneic mouse models with large tumor burdens, most notably large, poorly immunogenic orthotopic hepatocellular carcinoma (HCC). DC TEX-N1ND show increased homing to lymphoid tissues and contribute to augmented memory T cells. Importantly, N1ND-painted serum exosomes from cancer patients also promote DC activation. Our study demonstrates the potency of TEX-N1ND to strengthen DC immunogenicity and to suppress large established tumors, and thus provides an avenue to improve DC-based immunotherapy.

Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis. The convolutional neural network (CNN) has succeeded in a lot of image classification challenges during the recent years. In this paper, we proposed an approach of mammogram and tomosynthesis classification based on CNNs. We had acquired more than 3000 mammograms and tomosynthesis data with approval from an institutional review board at the University of Kentucky. Different models of CNNs were built to classify both the 2-D mammograms and 3-D tomosynthesis, and every classifier was assessed with respect to truth-values generated by histology results from the biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our outcomes demonstrated that CNN-based models we had built and optimized utilizing transfer learning and data augmentation have good potential for automatic breast cancer detection based on the mammograms and tomosynthesis data.

Objective The aim of this review is to identify and summarize factors that are associated with public attitudes towards people with various disabilities systematically. Methods An electronic search of three databases was performed (Medline, EMBASE and Cochrane) covering the period from 1950 to present. A comprehensive search strategy was developed and the lists of citations were screened for potential eligible studies. Only quantitative studies using valid measurements were included, and the methodological quality of included studies was appraised based on three criteria (sample, measurement, analysis) by two independent reviewers. Results The initial electronic search yielded 995 articles after duplicates removed, and 27 studies met the eligibility criteria were included in the study. Three categories of the factors were found to be associated with the public attitudes, which are related to the attitude provider, disabled people, and society respectively. Specifically, the more people know about disabilities, the more likely they were to have positive attitude; and the frequency and quality of the contact with the disabled are also proved to be influential to the attitudes. Meanwhile, the type of disability is also closely correlated to the public’s attitude towards the disabilities. Conclusion People’s knowledge of the disability and their contact with individuals with disabilities are the main influential factors in public attitudes towards persons with disabilities.

Background Personalized immunotherapy utilizing cancer vaccines tailored to the tumors of individual patients holds promise for tumors with high genetic heterogeneity, potentially enabling eradication of the tumor in its entirety. Methods Here, we demonstrate a general strategy for biological nanovaccines that trigger tailored tumor-specific immune responses for hepatocellular carcinoma (HCC). Dendritic cell (DC)-derived exosomes (DEX) are painted with a HCC-targeting peptide (P47-P), an α-fetoprotein epitope (AFP212-A2) and a functional domain of high mobility group nucleosome-binding protein 1 (N1ND-N), an immunoadjuvant for DC recruitment and activation, via an exosomal anchor peptide to form a “trigger” DEX vaccine (DEXP&A2&N). Results DEXP&A2&N specifically promoted recruitment, accumulation and activation of DCs in mice with orthotopic HCC tumor, resulting in enhanced cross-presentation of tumor neoantigens and de novo T cell response. DEXP&A2&N elicited significant tumor retardation and tumor-specific immune responses in HCC mice with large tumor burdens. Importantly, tumor eradication was achieved in orthotopic HCC mice when antigenic AFP peptide was replaced with the full-length AFP (A) to form DEXP&A&N. Supplementation of Fms-related tyrosine kinase 3 ligand greatly augmented the antitumor immunity of DEXP&A&N by increasing immunological memory against tumor re-challenge in orthotopic HCC mice. Depletion of T cells, cross-presenting DCs and other innate immune cells abrogated the functionality of DEXP&A&N. Conclusions These findings demonstrate the capacity of universal DEX vaccines to induce tumor-specific immune responses by triggering an immune response tailored to the tumors of each individual, thus presenting a generalizable approach for personalized immunotherapy of HCC, by extension of other tumors, without the need to identify tumor antigens.