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Background 5-aminolevulinic acid (5-ALA) fluorescence-guided resection increases the percentage of complete CNS tumor resections and improves the progression-free survival of IDH-wildtype glioblastoma patients. A small subset of IDH-wildtype glioblastoma shows no 5-ALA fluorescence. An explanation for these cases is missing. In this study, we used DNA methylation profiling to further characterize non-fluorescent glioblastomas. Methods Patients with newly diagnosed and recurrent IDH-wildtype glioblastoma that underwent surgery were analyzed. The intensity of intraoperative 5-ALA fluorescence was categorized as non-visible or visible. DNA was extracted from tumors and genome-wide DNA methylation patterns were analyzed using Illumina EPIC (850k) arrays. Furthermore, 5-ALA intensity was measured by flow cytometry on human gliomasphere lines (BT112 and BT145). Results Of 74 included patients, 12 (16.2%) patients had a non-fluorescent glioblastoma, which were compared to 62 glioblastomas with 5-ALA fluorescence. Clinical characteristics were equally distributed between both groups. We did not find significant differences between DNA methylation subclasses and 5-ALA fluorescence (P = .24). The distribution of cells of the tumor microenvironment was not significantly different between the non-fluorescent and fluorescent tumors. Copy number variations in EGFR and simultaneous EGFRvIII expression were strongly associated with 5-ALA fluorescence since all non-fluorescent glioblastomas were EGFR-amplified (P < .01). This finding was also demonstrated in recurrent tumors. Similarly, EGFR-amplified glioblastoma cell lines showed no 5-ALA fluorescence after 24 h of incubation. Conclusions Our study demonstrates an association between non-fluorescent IDH-wildtype glioblastomas and EGFR gene amplification which should be taken into consideration for recurrent surgery and future studies investigating EGFR-amplified gliomas.

Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data with unknown composition. In biomedical research, cell deconvolution, which is a source separation task, is used to obtain mechanistic and diagnostic insights into human disease. An unmet challenge in cell deconvolution, however, is the scarcity of realistic training data and the strong domain shift observed in synthetic training data that is used in contemporary methods. Here, we hypothesize that simultaneous consistency regularization of the target and training domains will improve deconvolution performance. By adding this novel biologically motivated consistency loss to two deep learning-based deconvolution algorithms, we achieve state-of-the-art performance on both cell fraction and gene expression estimation. Our method, DISSECT, outperforms competing algorithms across several gene expression datasets and can be easily adapted to deconvolve other biomedical data types, as exemplified by our spatial expression deconvolution experiments.

Single cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. Here we present DISCERN, a novel deep generative network that reconstructs missing single cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We used DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+and CD8+Tc2 T helper cells, with a potential role in adverse disease outcome. We utilized T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 81% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single cell sequencing workflows and readily adapted to enhance various other biomedical data types.

Background Improvement in people’s knowledge of HIV/AIDS and reduction in stigma and discrimination have been key factors in the fight against HIV/AIDS. The objective of the present paper is to examine the mediation effect of comprehensive knowledge on people’s attitudes towards HIV positives in India. Methods Data for the study have been drawn from nationally representative India’s National Family Health Survey, 2015-16. Statistical methods of data analysis included odds ratio and logistic regression models. Results Two in every five men and of women with comprehensive knowledge held a positive attitude towards PLHIVs. The people in rural areas and females against their counterparts are less likely (OR: 0.85, p Conclusions There existed a difference between males and females in attaining comprehensive knowledge and having comprehensive positive attitude. The mediation by comprehensive knowledge contributed, though partially, direct effects from gender perspective on comprehensive positive attitude. The findings are programmatically relevant to device content analysis of the material in the information, education and communication strategies. Key messages The mediation effect of comprehensive knowledge on people’s attitudes towards HIV positives has been substantial in India.