Acanthamoeba Keratitis in China: Genotypic and Clinical Correlations.

Purpose: To investigate the relationship between Acanthamoeba genotypes, clinical manifestations, and outcomes in Acanthamoeba keratitis (AK) patients. Methods: This retrospective study included 159 culture-confirmed AK patients. Patients' data were collected, including demographics, initial diagnosis, treatments, and clinical features. The genotype of Acanthamoeba was identified through sequencing the Diagnostic Fragment 3 (DF3) region in the small ribosomal subunit RNA genes. The phylogenetic tree was constructed using the ClustalW model and maximum likelihood method. Cases with "poor outcome" were defined based on specific clinical criteria, including corneal perforation, keratoplasty, other eye surgery, duration of anti-amoebic therapy ≥8.0 months, and final visual acuity ≤20/80. "Better outcome" cases were the remainder. The correlation between T4 subtypes, clinical phenotypes, and clinical prognosis were further analyzed. Results: In this study, AK was primarily attributed to the T4A genotype, with a positive correlation between geographical and genetic distances. The primary clinical associated with T4 subtypes was deep stromal infiltration. Results was also showed a significant association between T4 subtypes and clinical outcomes (P = 0.021). Further analysis revealed that T4C was closely associated with a better prognosis (P = 0.040) and T4D with worse outcomes (P = 0.013). Conclusions: In China, AK was predominantly caused by the T4A subtype. Geographical distance positively correlated with genetic distance. Clinical prognosis varied among different subtypes, notably in T4C and T4D. Translational Relevance: This study demonstrated the association between T4 subtypes and clinical phenotypes, as well as the effects of T4 subtypes on clinical prognosis.

Corneal Epithelial Dendritic Cells: An Objective Indicator for Ocular Surface Inflammation in Patients with Obstructive Meibomian Gland Dysfunction?

PURPOSE: To examine whether corneal epithelial dendritic cells (CEDC) could serve as an indicator to distinguish obstructive meibomian gland dysfunction (MGD) with or without ocular surface inflammation (OSI). METHODS: We performed a case-control study on patients with diagnosed obstructive MGD between August 2017 and November 2019. RESULTS: 30 MGD cases and 25 healthy controls were recruited. The classification of MGD patients with and without OSI was based on the tear pro-inflammatory cytokine levels. Compared with the MGD without OSI and the control group, a higher CEDC density was detected in the MGD with OSI subgroup. The presence of >15.6 cells/mm2 CEDC had a sensitivity of 73% and specificity of 75% for the diagnosis of MGD with OSI. CONCLUSIONS: OSI is not present in all patients with obstructive MGD. Evaluation of CEDC density in the central cornea may help identify whether MGD is concomitant with OSI.

Time-Course Transcriptomic Analysis Reveals the Crucial Roles of PANoptosis in Fungal Keratitis.

Purpose: Fungal keratitis (FK) is a serious corneal infection with high morbidity. Host immune responses function as a double-edged sword by eradicating fungal pathogens while also causing corneal damage, dictating the severity, progression, and outcome of FK. However, the underlying immunopathogenesis remains elusive. Methods: Time-course transcriptome was performed to illustrate the dynamic immune landscape in a mouse model of FK. Integrated bioinformatic analyses included identification of differentially expressed genes, time series clustering, Gene Ontology enrichment, and inference of infiltrating immune cells. Gene expression was verified by quantitative polymerase chain reaction (qPCR), Western blot, or immunohistochemistry. Results: FK mice exhibited dynamic immune responses with concerted trends with clinical score, transcriptional alteration, and immune cell infiltration score peaking at 3 days post infection (dpi). Disrupted substrate metabolism, broad immune activation, and corneal wound healing occurred sequentially in early, middle, and late stages of FK. Meanwhile, dynamics of infiltrating innate and adaptive immune cells displayed distinct characteristics. Proportions of dendritic cells showed overall decreasing trend with fungal infection, whereas that of macrophages, monocytes, and neutrophils rose sharply in early stage and then gradually decreased as inflammation resolved. Activation of adaptive immune cells was also observed in late stage of infection. Furthermore, shared immune responses and activation of AIM2-, pyrin-, and ZBP1-mediated PANoptosis were revealed across different time points. Conclusions: Our study profiles the dynamic immune landscape and highlights the crucial roles of PANoptosis in FK pathogenesis. These findings provide novel insights into host responses to fungi and contribute to the development of PANoptosis-targeted therapeutics for patients with FK.

Development and multi-center validation of machine learning model for early detection of fungal keratitis.

BACKGROUND: Fungal keratitis (FK) is a leading cause of corneal blindness in developing countries due to poor clinical recognition and laboratory identification. Here, we aimed to identify the distinct clinical signature of FK and develop a diagnostic model to differentiate FK from other types of infectious keratitis. METHODS: We reviewed the electronic health records (EHRs) of all patients with suspected infectious keratitis in Beijing Tongren Hospital from January 2011 to December 2021. Twelve clinical signs of slit-lamp images were assessed by Lasso regression analysis and collinear variables were excluded. Three models based on binary logistic regression, random forest classification, and decision tree classification were trained for FK diagnosis and employed for internal validation. Independent external validation of the models was performed in a cohort of 420 patients from seven different ophthalmic centers to evaluate the accuracy, specificity, and sensitivity in real world. FINDINGS: Three diagnostic models of FK based on binary logistic regression, random forest classification, and decision tree classification were established and internal validation were achieved with the mean AUC of 0.916, 0.920, and 0.859, respectively. The models were well-calibrated by external validation using a prospective cohort including 210 FK and 210 non-FK patients from seven eye centers across China. The diagnostic model with the binary logistic regression algorithm classified the external validation dataset with a sensitivity of 0.907 (0.774, 1.000), specificity 0.899 (0.750, 1.000), accuracy 0.905 (0.805, 1.000), and AUC 0.903 (0.808, 0.998). INTERPRETATION: Our model enables rapid identification of FK, which will help ophthalmologists to establish a preliminary diagnosis and to improve the diagnostic accuracy in clinic. FUNDING: The Open Research Fund from the National Key Research and Development Program of China (2021YFC2301000) and the Open Research Fund from Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing Tongren Hospital, Beihang University &Capital Medical University (BHTR-KFJJ-202001) supported this study.