Designer Glycopeptides for Cytotoxic T Cell–based Elimination of Carcinomas

Tumors express embryonic carbohydrate antigens called tumor-associated carbohydrate antigens (TACA). TACA-containing glycopeptides are appealing cytotoxic T cell (CTL)-based vaccines to prevent or treat cancer because the same sugar moieties are expressed in a variety of tumors, rendering a vaccination strategy applicable in a large population. Here we demonstrate that by using glycopeptides with high affinity for the major histocompatibility complex and glycosylated in a position corresponding to a critical T cell receptor (TcR) contact, it is possible to induce anti-TACA CTL in vivo. In the current study we show that designer glycopeptides containing the Thomsen-Freidenreich (TF) antigen (β-Gal-[1→3]-α-GalNAc-O-serine) are immunogenic in vivo and generate TF-specific CTL capable of recognizing a variety of tumor cells in vitro including a MUC1-expressing tumor. The fine specificity of the TF-specific CTL repertoire indicates that the TcR recognize the glycosylated amino acid residue together with TF in a conventional major histocompatibility complex class I–restricted fashion. These results have high potential for immunotherapy against a broad range of tumors

Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system

Development of risk prediction model for cognitive impairment in patients with coronary heart disease: A study protocol for a prospective, cross-sectional analysis

BackgroundIschemic heart disease and degenerative encephalopathy are two main sources of disease burden for the global elderly population. Coronary heart disease (CHD) and cognitive impairment, as representative diseases, are prevalent and serious illnesses in the elderly. According to recent research, patients with CHD are more likely to experience cognitive impairment and their cognitive ability declines more quickly. Vascular risk factors have been associated with differences in cognitive performance in epidemiological studies, but evidence in patients with CHD is more limited. Inextricably linked between the heart and the brain. Considering the unique characteristics of recurrent cognitive impairment in patients with CHD, we will further study the related risk factors. We tried to investigate the potential predictors of cognitive impairment in patients with CHD through a prospective, cross-sectional study.MethodsThe cross-sectional study design will recruit 378 patients with CHD (≥65 years) from Xiyuan Hospital of China Academy of Chinese Medical Sciences. The subjects' cognitive function is evaluated with MoCA scale, and they are divided into cognitive impairment group and normal cognitive function group according to the score results. Demographic data, disease characteristics (results of coronary CT/ angiography, number of stents implanted, status of diseased vessels), laboratory tests (biochemistry, coagulation, serum iron levels, pulse wave velocity), metabolites (blood samples and intestinal metabolites), and lifestyle (smoking, alcohol consumption, sleep, physical activity) will be assessed as outcome indicators. Compare the two groups and the correlation analysis will be performed on the development of mild cognitive impairment. Mann-Whitney U or X2 test was selected to describe and evaluate the variation, and logistics regression analysis was employed to fit the prediction model. After that, do the calibration curve and decision curve to evaluate the model. The prediction model will be validated by a validation set.DiscussionTo explore the risk factors related to mild cognitive impairment (MCI) in patients with CHD, a new predictive model is established, which can achieve advanced intervention in the occurrence of MCI after CHD. Owing to its cross-sectional study design, the study has some limitations, but it will be further studied by increasing the observation period, adding follow-up data collection or prospective cohort study. The study has been registered with the China Clinical Trials Registry (ChiCTR2200063255) to conduct clinical trials