BackgroundDry eye is a common disease worldwide, and animal models are critical for the study of it. At present, there is no research about the stability of the extant animal models, which may have negative implications for previous dry eye studies. In this study, we observed the stability of a rabbit dry eye model induced by the topical benzalkonium chloride (BAC) and determined the valid time of this model.Methods and FindingsEighty white rabbits were randomly divided into four groups. One eye from each rabbit was randomly chosen to receive topical 0.1% BAC twice daily for 2 weeks (Group BAC-W2), 3 weeks (Group BAC-W3), 4 weeks (Group BAC-W4), or 5 weeks (Group BAC-W5). Fluorescein staining, Schirmer's tests, and conjunctival impression cytology were performed before BAC treatment (normal) and on days 0, 7, 14 and 21 after BAC removal. The eyeballs were collected at these time points for immunofluorescence staining, hematoxylin and eosin (HE) staining, and electron microscopy. After removing BAC, the signs of dry eye in Group BAC-W2 lasted one week. Compared with normal, there were still significant differences in the results of Schirmer's tests and fluorescein staining in Groups BAC-W3 and BAC-W4 on day 7 (P<0.05) and in Group BAC-W5 on day 14 (P<0.05). Decreases in goblet cell density remained stable in the three experimental groups at all time points (P<0.001). Decreased levels of mucin-5 subtype AC (MUC5AC), along with histopathological and ultrastructural disorders of the cornea and conjunctiva could be observed in Group BAC-W4 and particularly in Group BAC-W5 until day 21.ConclusionsA stable rabbit dry eye model was induced by topical 0.1% BAC for 5 weeks, and after BAC removal, the signs of dry eye were sustained for 2 weeks (for the mixed type of dry eye) or for at least 3 weeks (for mucin-deficient dry eye).

Background Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently Methods Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. Results Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. Conclusion Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

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Background Increasing evidence has been confirmed that small nucleolar RNAs (SnoRNAs) play critical roles in tumorigenesis and exhibit prognostic value in clinical practice. However, there is short of systematic research on SnoRNAs in ovarian cancer (OV). Material/Methods 379 OV patients with RNA‐Seq and clinical parameters from TCGA database and 5 paired clinical OV tissues were embedded in our study. Cox regression analysis was used to identify prognostic SnoRNAs and construct prediction model. SNORic database was adopted to examine the copy number variation of SnoRNAs. ROC curves and KM plot curves were applied to validate the prognostic model. Besides, the model was validated in 5 paired clinical tissues by real‐time PCR, H&E staining and immunohistochemistry. Results A prognostic model was constructed on the basis of SnoRNAs in OV patients. Patients with higher RiskScore had poor clinicopathological parameters, including higher age, larger tumor size, advanced stage and with tumor status. KM plot analysis confirmed that patients with higher RiskScore had poorer prognosis in subgroup of age, tumor size, and stage. 7 of 9 SnoRNAs in the prognostic model had positive correlation with their host genes. Moreover, 5 of 9 SnoRNAs in the prognostic model correlated with their CNVs, and SNORD105B had the strongest correction with its CNVs. ROC curve showed that the RiskScore had excellent specificity and accuracy. Further, results of H&E staining and immunohistochemistry of Ki67, P53 and P16 confirmed that patients with higher RiskScore are more malignant. Conclusions In summary, we identified a nine‐SnoRNAs signature as an independent indicator to predict prognosis of OV, providing a prospective prognostic biomarker and potential therapeutic targets for ovarian cancer.