Systematic Review and Meta-analysis of PD-L1 Expression Discordance between Primary Tumor and Lung Cancer Brain Metastasis

Background Novel immunotherapeutic strategies targeting the PD-1/PD-L1 axis are often administered when metastatic tumors show PD-L1 positivity, even in the setting of lung cancer brain metastasis (LCBM). However, biological differences exist between primary tumors and metastatic sites. The objective of this study was to analyze rates of PD-L1 receptor discordance between primary tumors and LCBM. Methods A systematic review of studies of biopsied or resected LCBM evaluating PD-L1 discordance published in the Medline database was performed using PRISMA guidelines. Weighted random effects models were used to calculate pooled estimates. Results Six full-text articles (n=230 patients) with a median of 32 patients in each study (range: 24-73) reported PD-L1 receptor expression analyses of both primary lung tumors and brain metastases and met inclusion criteria. The pooled estimate for tumor cell (TC) PD-L1 receptor discordance between primary tumors and LCBM was 19% (95% CI: 10%-27%). For PD-L1 receptor expression in tumor infiltrating lymphocytes (TIL), the weighted pooled estimate for discordance was 21% (95% CI: 8%-44%). For primary versus LCBM, the positive rates by expression levels of <1%, 1-50%, and >50% were 52% (95% CI: 30-73%) vs. 56% (95% CI: 34-76%), 30% (95% CI: 22-40%) vs. 20% (95% CI: 10-35%), and 15% (95% CI: 6-36%) vs. 22% (95% CI: 15-31%) (p=0.425), respectively. Conclusions PD-L1 discordance occurs in ~20% of LCBM, with the greatest discordance in the 1-50% expression category. Although controversial, confirming discordance might be important for selection of immune checkpoint inhibitor therapy and in the analysis of patterns of failure after treatment.

Can we use Facebook groups to establish social presence in online courses?

In this research, the potential of Facebook groups used in an online course in order to establish social presence was examined. Qualitative research methodology was used in this study. The participants of the study were 12 senior undergraduate students taking the School Experience course online over a period of 12 weeks. A Facebook group where announcements and deep discussions were made and files were shared was used. Facebook group posts as document data; and student interviews as interview data were collected. According to the results, it was found that Facebook groups established social presence. Social presence involves three categories; affective expression, open communication and group cohesion. Paralanguage, emotion, humor and self-disclosure emerged as indicators in the affective expression category. Acknowledgement, agreement, invitation and continuing a thread were found as indicators in the open communication category. Greetings and salutations, vocatives, group reference, social sharing and collaboration were found as indicators in the group cohesion category. A similar study can be conducted by collecting quantitative data from a large number of participants using the social presence scale. Other social networks’ potentials to establish social presence can be examined. Since Facebook groups establish social presence, they can be used to support online or face-to-face courses. Keywords: community of inquiry; Facebook; Facebook group; online course; social presence

Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition

For the problem of features extraction and dimensionality reduction of expression recognition, the paper proposes Gabor Locality Preserving Discriminant Projection (GLPDP) algorithm, which is based on Gabor Wavelet. Firstly, we use Gabor wavelet transform to have an expression feature extraction. Secondly, we improved the locality preserving projection (LPP) algorithm, introducing scatter difference in the LPP objective function to increase divergence constraints among the sample classes and extracts more discriminated features while having the dimensionality reduction. Finally, we use the nearest neighbor classifier to have a classification for expression category. The effectiveness of the proposed methods is validated through the experimental results on JAFFE and Cohn-Kanade Facial expression databases.