Purpose: To generate a single-cell RNA-sequencing (scRNA-seq) map and construct cell–cell communication networks of mouse corneas. Methods: C57BL/6 mouse corneas were dissociated to single cells and subjected to scRNA-seq. Cell populations were clustered and annotated for bioinformatic analysis using the R package “Seurat.” Differential expression patterns were validated and spatially mapped with whole-mount immunofluorescence staining. Global intercellular signaling networks were constructed using CellChat. Results: Unbiased clustering of scRNA-seq transcriptomes of 14,732 cells from 40 corneas revealed 17 cell clusters of six major cell types: nine epithelial cell, three keratocyte, two corneal endothelial cell, and one each of immune cell, vascular endothelial cell, and fibroblast clusters. The nine epithelial cell subtypes included quiescent limbal stem cells, transit-amplifying cells, and differentiated cells from corneas and two minor conjunctival epithelial clusters. CellChat analysis provided an atlas of the complex intercellular signaling communications among all cell types. Conclusions: We constructed a complete single-cell transcriptomic map and the complex signaling cross-talk among all cell types of the cornea, which can be used as a foundation atlas for further research on the cornea. This study also deepens the understanding of the cellular heterogeneity and heterotypic cell–cell interaction within corneas.

Background: The aim of this study was to evaluate the efficacy of a deep learning system in pterygium grading and recurrence prediction. Methods: This was a single center, retrospective study. Slit-lamp photographs, from patients with or without pterygium, were collected to develop an algorithm. Demographic data, including age, gender, laterality, grading, and pterygium area, recurrence, and surgical methods were recorded. Complex ocular surface diseases and pseudopterygium were excluded. Performance of the algorithm was evaluated by sensitivity, specificity, F1 score, accuracy, and area under the receiver operating characteristic curve. Confusion matrices and heatmaps were created to help explain the results. Results: A total of 237 eyes were enrolled, of which 176 eyes had pterygium and 61 were non-pterygium eyes. The training set and testing set were comprised of 189 and 48 photographs, respectively. In pterygium grading, sensitivity, specificity, F1 score, and accuracy were 80% to 91.67%, 91.67% to 100%, 81.82% to 94.34%, and 86.67% to 91.67%, respectively. In the prediction model, our results showed sensitivity, specificity, positive predictive value, and negative predictive values were 66.67%, 81.82%, 33.33%, and 94.74%, respectively. Conclusions: Deep learning systems can be useful in pterygium grading based on slit lamp photographs. When clinical parameters involved in the prediction of pterygium recurrence were included, the algorithm showed higher specificity and negative predictive value in prediction.

Purpose.Alternariaspp. are an uncommon cause of mycotic keratitis. Previous studies onAlternariakeratitis have generally been limited to case reports. We examined the clinical characteristics ofAlternariakeratitis in this study.Methods.The characteristics and outcomes of 7 patients with culture-provenAlternariakeratitis treated in our hospital were compared with 25 previously reported cases.Results.The risk factors forAlternariakeratitis were trauma in 5 patients and soft contact lenses in 1 patient. Six patients with early diagnosis (<2 weeks) were cured with medical antimicrobial treatment; a patch graft was required in 1 patient with perforation. When incorporated with previous reports onAlternariakeratitis(n=32), 14 (44%) infections followed trauma, 10 (31%) were associated with preexisting corneal disease or previous ocular surgery, and 5 (16%) occurred in soft contact lens wearers. Successful medical treatment was achieved in 23 (72%) patients, including 10 out of 21 eyes (48%) treated with natamycin and/or amphotericin B. Therapeutic penetrating keratoplasty was performed in 9 (28%) cases.Conclusions.Alternariakeratitis is generally associated with specific risk factors and responds to medical treatment when early diagnosis is performed and prompt antifungal treatment is initiated.

Purpose. To provide an objective, quantitative approach for monitoring Fuchs’ endothelial corneal dystrophy (FECD), with Scheimpflug imaging. Design. This is a retrospective case-control pilot study. Methods. The study group consisted of 53 eyes in 27 patients diagnosed with FECD, with normal subjects paired as control. Main outcome measures were corneal thickness, morphological patterns on densitograms, and indices of corneal density including the average area density (mean AD) and the average ratio of Descemet’s membrane density versus area density (DM/AD) in Pentacam Scheimpflug images. Results. There were no significant differences in age and corneal thickness between FECD and normal groups. Morphologically, hanging-hammock patterns were noted on the densitograms of FECD patients, which were different from the high-back chair patterns in normal subjects. Quantitatively, mean AD and DM/AD were both elevated in FECD patients as compared with normal subjects (P=0.01 and 0.025, resp.). In addition, FECD patients with corneal edema had significantly higher mean AD (P=0.018) than those without corneal edema. Conclusions. This pilot study shows that Pentacam system provides an objective, quantitative way to approach FECD corneas. It can assist ophthalmologists in detecting the early change and in monitoring disease progression of FECD. Further studies are needed to consolidate the findings.

We performed molecular identification and antifungal susceptibilities of pathogens and investigated clinical features of 43 culture-proven Fusarium keratitis cases from 2015–2020 in Taiwan. The pathogens were identified by sequencing of their internal transcribed spacer regions of ribosomal DNA and translation elongation factor 1α gene; their antifungal susceptibilities (to seven agents) were determined by broth microdilution method. We also collected clinical data to compare the drug susceptibilities and clinical features of Fusarium solani species complex (FSSC) isolates with those of other Fusarium species complexes (non-FSSC). The FSSC accounted for 76.7% pathogens, among which F. falciforme (32.6%) and F. keratoplasticum (27.9%) were the most common species. Among clinically used antifungal agents, amphotericin B registered the lowest minimal inhibitory concentration (MIC), and the new azoles efinaconazole, lanoconazole and luliconazole, demonstrated even lower MICs against Fusarium species. The MICs of natamycin, voriconazole, chlorhexidine, lanoconazole, and luliconazole were higher for the FSSC than the non-FSSC, but no significant differences were noted in clinical outcomes, including corneal perforation and final visual acuity. In Taiwan, the FSSC was the most common complex in Fusarium keratitis; its MICs for five tested antifungal agents were higher than those of non-FSSC, but the clinical outcomes did not differ significantly.