Keratin 13 is a more specific marker of conjunctival epithelium than keratin 19

PurposeTo evaluate the expression patterns of cytokeratin (K) 12, 13, and 19 in normal epithelium of the human ocular surface to determine whether K13 could be used as a marker for conjunctival epithelium.MethodsTotal RNA was isolated from the human conjunctiva and central cornea. Those transcripts that had threefolds or higher expression levels in the conjunctiva than the cornea were identified using microarray technique. Expression levels of three known signature genes and of two conjunctival genes, K13 and K19 were confirmed by using quantitative real-time PCR (qRT-PCR). Protein expression of K12, K13, and K19 was confirmed by immunostaining with specific antibodies on histologic sections of human sclerocornea that contained the conjunctiva, limbus, and cornea and on impression cytology (IC) specimens of the cornea and conjunctiva from normal donors. Double staining of K13/K12 and K19/K12 on histologic sections and IC specimens was performed.ResultsThere were 337 transcripts that were preferentially expressed in the conjunctiva. K13 and K19 were among the top twenty transcripts in the conjunctiva and this preferential expression pattern of K13 and K19 was confirmed by qRT-PCR. Immunohistochemical studies showed that K13 was expressed at the posterior limbal epithelium and conjunctival epithelium but was totally absent in the cornea. K12 was expressed in the corneal and anterior limbal epithelia except for the basal layer and was absent from the conjunctiva. In contrast, K19 was detected in the corneal, limbal and conjunctival epithelia. Immunostaining of the IC specimens showed K12(+) epithelial cells in the corneal region, K13(+) cells in the conjunctival area, and K19(+) cells in the corneal and conjunctival specimens. Expression of K13 and K12 on the ocular surface was mutually exclusive on both the histologic and IC samples using double immunostaining.ConclusionsK13 is more specific to the conjunctival epithelial cells than K19 and potentially could be used as a marker to identify conjunctival epithelial cells in limbal stem cell deficiency

Phenotypic and Genotypic Characterization of ESBL-, AmpC-, and Carbapenemase-Producing Klebsiella pneumoniae and Escherichia coli Isolates

Objectives: Drug resistance among gram-negative bacteria is a worldwide challenge. Due to the importance of drug-resistant Klebsiella pneumoniae and Escherichia coli strains in hospital-acquired infections, we aimed to determine the phenotypic and genotypic characteristics of ESBL-, AmpC-, and carbapenemase-producing isolates obtained from hospitalized patients in Tehran and Ilam (Iran). Materials and Methods: In total, 90 K. pneumoniae isolates and 65 E. coli isolates were collected from various infections. Phenotypic identification of bacterial isolates was performed using standard methods. Phenotypic screening of ESBL, AmpC, and carbapenemase enzymes was carried out. Detection of ESBL, AmpC, and carbapenemase genes was also performed by the PCR method. Results: Phenotypic detection tests showed that 36 (40) K. pneumoniae and 23 (35.4) E. coli isolates were ESBL producers. Moreover, 18 (20) and 6 (9.2) K. pneumoniae and E. coli isolates were AmpC producers, respectively. Modified Hodge test results indicated that 39 (43.3) K. pneumoniae and 18 (27.7) E. coli isolates produced carbapenemase. Molecular tests showed that 40 of K. pneumoniae and 36.9 of E. coli isolates were ESBL positive. AmpC was detected in 24.4 and 13.8 of K. pneumoniae and E. coli isolates. Carbapenemase was detected in 34 (37.8) K. pneumoniae and 13 (20) E. coli isolates. -Conclusion: In this study, 3 K. pneumoniae isolates simultaneously carried ESBL, AmpC, and carbapenemase genes. Up-to-date strategies such as combination therapy or utilization of new antimicrobial agents might help to combat such drug-resistant organisms. © 2019 The Author(s) Published by S. Karger AG, Basel

Application of Supervised Classification Algorithms in Long-Term Survey of Surface Soil Salinity by Using Remote Sensing: A Case Study of Atabiyeh Plain, Khuzestan Province

The current study aims to find the best methods of using remote sensing and supervised classification algorithms in long-term salinity monitoring of salinity changes in the Atabieh area with an area of 5000 hectares in the west of Khuzestan province. The procedure is based on the separation of different levels of saline soils utilizing information obtained from Landsat 7 and 8 satellite images (2001 to 2015) along with salinity data taken from the study area, and salinity indices including SI1, SI2, SI3, NDSI, IPVI, and VSSI. The results show the expansion of the saline zone trend in the soils of the study area, among which, soils with EC of more than 16 dS m-1 (very saline) have the highest frequency. The area of saline soils has increased significantly over the past 15 years, with a saline land area increasing by more than 90%. The percentage of salinity class is low (S1). According to this study, the only significant index in soil salinity at a 95% confidence level is the SI3 index, which has been able to have a good estimate of the increasing changes in soils in the region. The results of the supervised classification showed that the support vector machine (with an overall accuracy of 95.78 and a kappa coefficient of 0.89) is more accurate. After the vector machine method, the methods of minimum distance, maximum likelihood, and distance of Mahalanobis have the highest accuracy, respectively. Based on salinity maps obtained in years in 2001, 2005, 2010, and 2015, it can be said that the salinity rate in the whole of the study area was progressing and at the same time the salinity area in the middle and high classes increased decreased and on the other hand, the salinity area in the high class in 2001 gradually increased and distributed in 2015 throughout the region