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Background: Self‐injurious behaviour (SIB) can be classified as intentional, direct injuring of body tissue usually without suicidal intent. In its non‐suicidal form it is commonly seen as a clinical sign of borderline personality disorder, autism, PTSD, depression, and anxiety affecting a wide range of ages and conditions. In rhesus macaques SIB is most commonly manifested through hair plucking, self‐biting, self‐hitting, and head banging. SIB in the form of self‐biting is observed in approximately 5–15% of individually housed monkeys. Recently, glial cells are becoming recognised as key players in regulating behaviours. Method: The goal of this study was to determine the role of glial activation, including astrocytes, in macaques that had displayed SIB. To this end, we performed immunohistochemistry and next generation sequence of brain tissues from rhesus macaques with SIB. Results: Our studies showed increased vimentin, but not nestin, expression on astrocytes of macaques displaying SIB. Initial RNA Seq analyses indicate activation of pathways involved in tissue remodelling, neuroinflammation and cAMP signalling. Conclusions: Glia are most probably activated in primates with self‐injury, and are therefore potential novel targets for therapeutics. [ABSTRACT FROM AUTHOR]

Introduction: For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta‐analysis at each threshold. A standard meta‐analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, we propose a new method that performs multiple imputation of the missing threshold results using discrete combinations (MIDC). Methods: The new MIDC method imputes missing threshold results by randomly selecting from the set of all possible discrete combinations which lie between the results for 2 known bounding thresholds. Imputed and observed results are then synthesised at each threshold. This is repeated multiple times, and the multiple pooled results at each threshold are combined using Rubin's rules to give final estimates. We compared the NI, SI, and MIDC approaches via simulation. Results: Both imputation methods outperform the NI method in simulations. There was generally little difference in the SI and MIDC methods, but the latter was noticeably better in terms of estimating the between‐study variances and generally gave better coverage, due to slightly larger standard errors of pooled estimates. Given selective reporting of thresholds, the imputation methods also reduced bias in the summary receiver operating characteristic curve. Simulations demonstrate the imputation methods rely on an equal threshold spacing assumption. A real example is presented. Conclusions: The SI and, in particular, MIDC methods can be used to examine the impact of missing threshold results in meta‐analysis of test accuracy studies. [ABSTRACT FROM AUTHOR]