Aims The study was designed to test the clinical application of the grading of lid-parallel conjunctival folds (LIPCOF) as a diagnostic test for dry eye. Methods At 12 centres in 11 countries, 272 eyes of 272 dry eye patients (75 men, 197 women) were examined. Their mean age was 52.7±16.2 years. The LIPCOF were graded according to the method of Höh et al. The tear film break-up time (BUT) was measured, and fluorescein staining and the Schirmer 1 test were performed. The subjective symptoms were evaluated by 16 questions. Results The LIPCOF score demonstrated significant positive correlations with age, dry eye disease severity and fluorescein staining (r>0.2, p<0.001), and negative correlations with BUT and results of the Schirmer 1 test (r<–0.2, p<0.001). The LIPCOF score exhibited a significant correlation with the overall subjective symptoms (r=0.250, p<0.001). The sensitivity and specificity of LIPCOF grading for discriminating between normal and dry eyes were best with the cut-off between LIPCOF degrees 1 and 2. Conclusions The displayed medium sensitivity and specificity, and good positive predictive value of the LIPCOF test support the use of LIPCOF grading as a simple, quick and non-invasive dry eye screening tool.

Crowding, i.e., the size of the infrapopulation inhabiting an individual host, is a major component of parasites' environment, which often influences both morphological and life-history characters (the so-called density-dependent characters) in different parasite taxa. Although crowding equals intensity in case of a single parasite individual, mean intensity of the host population does not define mean crowding of the parasite population. Crowding indices are notoriously hard to handle statistically because of the inherently large number of nonindependent values in data. In this study, we aim to investigate the apparently paradox features of crowding indices and to make some proposals and also to introduce statistical methods to calculate confidence intervals and 1-sample and 2-sample tests for mean crowding. All methods described in this study are supported by the freely distributed statistical software Quantitative Parasitology.