Guest editorial

Purpose – The purpose of this paper is to introduce the special issue of the International Journal of Innovation Science, “Innovation and Entrepreneurship in HEI Sector,” which aims to bring together recent developments and methodological contributions within this field with the challenges that characterize innovation and entrepreneurship in the Higher Education Institutions (HEI) Sector. Design/methodology/approach – This special issue includes a collection of seven papers. A brief explanation of the authors’ contributions is presented to invite a broader readership. Findings – The studies in this special issue provide multilevel perspectives on innovation and entrepreneurship in the HEI sector. Mostly, the papers contribute to a better understanding of this topic. Furthermore, the papers suggest some ‘hot topics’ for a future agenda. Research limitations/implications – These findings have particular relevance for policy-makers, business organizations, and HEIs. It also has wider implications for the development of entrepreneurial policies in cross-national contexts. There are several challenges not included here that merit further insight. Originality/value – This special issue advances our understanding of innovation and entrepreneurship in the HEI sector. To include education and training elements in entrepreneurial support programmes is fundamental to boosting the innovation level and consequently a higher rate of entrepreneurship development.info:eu-repo/semantics/publishedVersio

Classification Criteria for Tubulointerstitial Nephritis With Uveitis Syndrome

PURPOSE: To determine classification criteria for tubulointerstitial nephritis with uveitis (TINU). DESIGN: Machine learning of cases with TINU and 8 other anterior uveitides. METHODS: Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 94 cases of TINU, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for TINU included anterior chamber inflammation and evidence of tubulointerstitial nephritis with either (1) a positive renal biopsy or (2) evidence of nephritis (elevated serum creatinine and/or abnormal urine analysis) and an elevated urine beta-2 microglobulin. The mis-classification rates for TINU were 1.2% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for TINU had a low mis-classification rate and seemed to perform well enough for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved

Classification Criteria for Cytomegalovirus Anterior Uveitis

PURPOSE: To determine classification criteria for cytomegalovirus (CMV) anterior uveitis. DESIGN: Machine learning of cases with CMV anterior uveitis and 8 other anterior uveitides. METHODS: Cases of anterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the anterior uveitides. The resulting criteria were evaluated on the validation set. RESULTS: One thousand eighty-three cases of anterior uveitides, including 89 cases of CMV anterior uveitis, were evaluated by machine learning. The overall accuracy for anterior uveitides was 97.5% in the training set and 96.7% in the validation set (95% confidence interval 92.4, 98.6). Key criteria for CMV anterior uveitis included unilateral anterior uveitis with a positive aqueous humor polymerase chain reaction assay for CMV. No clinical features reliably diagnosed CMV anterior uveitis. The misclassification rates for CMV anterior uveitis were 1.3% in the training set and 0% in the validation set. CONCLUSIONS: The criteria for CMV anterior uveitis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved