PurposeThe purpose of this paper is to offer an example of how ontology, such as CIDOC, can be turned into a format from the perspective of an object. In fact, it illustrates the possible semantic analysis of an object description into a view neutral machine‐interpretable form. The aim is to show that a museum object located in a museum can be described in detail and then related to other information objects located in other memory institutions such as libraries and archives.Design/methodology/approachStudying different documents, all the information about Imam Reza's Zarih 4th was derivated. Then the most important were listed and represented in detail, according to CIDOC CRM entities.FindingsThe figures explicitly reveal the existing semantic correlations between the heterogeneous cultural heritage information in various memory institutions, such as Organization of Libraries, Museums and Documents Centre of Astan Quds Razavi.Originality/valueThe authors capture the knowledge from different resources and relate them in a semantic description with the aid of a semantic conceptual model like CIDOC CRM, to show more effective information integration in a cultural institution such as Organization of Libraries, Museums and Documents Centre of Astan Quds Razavi, in order to provide unified access to collection‐level information.

Inflammation plays an important role in the outcome of patients with cystic fibrosis (CF). It may develop due to cystic fibrosis transmembrane conductance regulator protein dysfunction, pancreatic insufficiency, or prolonged pulmonary infection. Fecal calprotectin (FC) has been used as a noninvasive method to detect inflammation. Therefore, the aim of the current meta-analysis was to investigate the relationship between FC and phenotype severity in patients with CF. In this study, searches were conducted in PubMed, Science Direct, Scopus, and Embase databases up to August 2021 using terms such as “cystic fibrosis,” “intestine,” “calprotectin,” and “inflammation.” Only articles published in English and human studies were selected. The primary outcome was the level of FC in patients with CF. The secondary outcome was the relationship between FC and clinical severity. Statistical analysis was performed using Comprehensive Meta-Analysis software. Of the initial 303 references, only six articles met the inclusion criteria. The mean (95% confidence interval [CI]) level of FC was 256.5 mg/dL (114.1-398.9). FC levels were significantly associated with pancreatic insufficiency (mean, 243.02; 95% CI, 74.3 to 411.6; p =0.005; I 2 =0), pulmonary function (r=–0.39; 95% CI, –0.58 to –0.15; p =0.002; I 2 =60%), body mass index (r=–0.514; 95% CI, 0.26 to 0.69; p <0.001; I 2 =0%), and Pseudomonas colonization (mean, 174.77; 95% CI, 12.5 to 337.02; p =0.035; I 2 =71%). While FC is a reliable noninvasive marker for detecting gastrointestinal inflammation, it is also correlated with the severity of the disease in patients with CF.

Background: Structural holes as a considerable issue in network approach can be tracked in central indices. Objectives: This research aims to survey indices that can measure structural holes. We are looking for the most important scientific authors and their connections in the field of medical genetics to facilitate information flow in the network of medical genetics relations. Methods: First, co-authorship network of Iranian medical genetics scientists (faculty members) was extracted via searching Scopus, Web of Science, and PubMed databases, as a result of which 7451 articles were retrieved. With co-authorship techniques, the most central nodes in the network were picked as highlighted scientists. In the next phase, two other indices (i.e., hypertext induced topics search [HITS] and PageRank) were calculated with the help of Sci2 and compared to redundancy, efficiency and effective size as structural hole indices. Results: There was a significant relationship between the two groups of indices. There were few structural holes in our network because redundancy and constraint were low. Constraint index and centrality indices can be used for extracting structural holes. Conclusions: In confirmation of previous studies, the constraint index can be used as a method for extracting structural holes. Compared to the HITS algorithm, the constraint index works best in this regard. At the same time, the study of HITS and PageRank indicators showed a significant association between the figures derived from the calculation of these two indicators, and each one can be employed to find structural holes.