In contrast with adults, children infected by severe acute respiratory syndrome-corona virus (SARS-CoV) develop milder clinical symptoms. Because of this, it is speculated that children vaccinated with various childhood vaccines might develop cross immunity against SARS-CoV. Antisera and T cells from mice immunised with various vaccines were used to determine whether they developed cross reactivity against SARS-CoV. The results showed no marked cross reactivity against SARS-CoV, which implies that the reduced symptoms among children infected by SARS-CoV may be caused by other factors.

The induction of relatively weak immunity by DNA vaccines in humans can be largely attributed to the low efficiency of transduction of somatic cells. Although formulation with liposomes has been shown to enhance DNA transduction of cultured cells, little, if any, effect is observed on the transduction of somatic tissues and cells. To improve the rate of transduction, DNA vaccine delivery by gene gun and the recently developed electroporation techniques have been employed. We report here that to circumvent requirement for such equipment, amiloride, a drug that is prescribed for hypertension treatment, can accelerate plasmid entry into antigen presenting cells (APCs) both in vitro and in vivo. The combination induced APCs more dramatically in both maturation and cytokine secretion. Amiloride enhanced development of full CD8 cytolytic function including induction of high levels of antigen specific CTL and expression of IFN-γ+perforin+granzymeB+ in CD8+ T cells. Thus, amiloride is a facilitator for DNA transduction into host cells which in turn enhances the efficiency of the immune responses.

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This paper establishes an evaluation system based on the low-carbon intensive land use in Jinan city from 2010 to 2017 and uses a multi-attribute approach named grey fuzzy integral to build the evaluation model. In this model, based on the Mobius transformation coefficient of subjective and objective weights of index factors and the interaction degree between index factors, 2-additive fuzzy measures can be obtained; therefore, evaluation of low-carbon and intensive land use in Jinan city is processed by combining the grey correlation degree and Choquet fuzzy integral. The results show that in the study period, land input intensity, land use degree, land output benefit and land sustainability in Jinan city all show a good upward trend, but the low-carbon land use level of has been in a declining state. Although there is a good development trend of low-carbon and intensive land use in Jinan, the state is not stable. A Low-carbon and intensive land use pattern will not be achieved completely overnight, and it is bound to be a dynamic game process.

Background No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting. Methods A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24–48 months] and matched controls (182 OA knees not meeting the case definition). DeepKOA was developed through 3-dimensional (3D) DenseNet169 to predict KOA progression over 24–48 months based on sagittal intermediate-weighted turbo-spin echo sequences with fat-suppression (SAG-IW-TSE-FS), sagittal 3D dual-echo steady-state water excitation (SAG-3D-DESS-WE) and its axial and coronal multiplanar reformation, and their combined MR images with patient-level labels at baseline, 12, and 24 months to eventually determine the probability of progression. The classification performance of the DeepKOA was evaluated using 5-fold cross-validation. An X-ray-based model and traditional models that used clinical variables via multilayer perceptron were built. Combined models were also constructed, which integrated clinical variables with DeepKOA. The area under the curve (AUC) was used as the evaluation metric. Results The performance of SAG-IW-TSE-FS in predicting OA progression was similar or higher to that of other single and combined sequences. The DeepKOA based on SAG-IW-TSE-FS achieved an AUC of 0.664 (95% CI: 0.585–0.743) at baseline, 0.739 (95% CI: 0.703–0.775) at 12 months, and 0.775 (95% CI: 0.686–0.865) at 24 months. The X-ray-based model achieved an AUC ranging from 0.573 to 0.613 at 3 time points. However, adding clinical variables to DeepKOA did not improve performance (P>0.05). Initial visualizations from gradient-weighted class activation mapping (Grad-CAM) indicated that the frequency with which the patellofemoral joint was highlighted increased as time progressed, which contrasted the trend observed in the tibiofemoral joint. The meniscus, the infrapatellar fat pad, and muscles posterior to the knee were highlighted to varying degrees. Conclusions This study initially demonstrated the feasibility of DeepKOA in the prediction of KOA progression and identified the potential responsible structures which may enlighten the future development of more clinically practical methods.

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