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Glaucoma is a leading cause of blindness worldwide. Purpose of this study was to identify molecular markers that were significantly correlated with presence of glaucoma and outcome of glaucoma surgery. To accomplish this, we determined the profiles of pro-inflammatory cytokines in the aqueous humor of 101 glaucoma patients; 31 primary open angle glaucoma (POAG), 38 pseudoexfoliation glaucoma (PEG), and 32 neovascular glaucoma (NVG). We also studied 100 normal subjects as controls. In eyes with POAG or PEG, the level of interleukin (IL)-1α, IL-2, IL-4, IL-8, IL-23, and CCL2 were significantly elevated. In the NVG eyes, many inflammatory cytokines were also highly elevated. IL-8 had the highest odds ratio, and levels of IL-8 and CCL2 were significantly correlated with preoperative IOP or visual field defects in PEG eyes. Principal component analysis showed that IL-8 had the highest association to the IOP-cytokine component, and Cox proportional hazard model indicated that an elevation of IL-8 was a significant risk of filtering surgery failure. Together with modeling of their interactions and prognosis, IL-8 elevation is a significant risk factor both for detecting and managing glaucoma and may serve as a therapeutic target candidate to improve the prognosis of glaucoma surgery.

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Both embodied and symbolic accounts of conceptual organization would predict partial sharing and partial differentiation between the neural activations seen for concepts activated via different stimulus modalities. But cross-participant and cross-session variability in BOLD activity patterns makes analyses of such patterns with MVPA methods challenging. Here, we examine the effect of cross-modal and individual variation on the machine learning analysis of fMRI data recorded during a word property generation task. We present the same set of living and non-living concepts (land-mammals, or work tools) to a cohort of Japanese participants in two sessions: the first using auditory presentation of spoken words; the second using visual presentation of words written in Japanese characters. Classification accuracies confirmed that these semantic categories could be detected in single trials, with within-session predictive accuracies of 80–90%. However cross-session prediction (learning from auditory-task data to classify data from the written-word-task, or vice versa) suffered from a performance penalty, achieving 65–75% (still individually significant at p « 0.05). We carried out several follow-on analyses to investigate the reason for this shortfall, concluding that distributional differences in neither time nor space alone could account for it. Rather, combined spatio-temporal patterns of activity need to be identified for successful cross-session learning, and this suggests that feature selection strategies could be modified to take advantage of this.