Purpose: To examine the efficacy and biocompatibility of a new artificial cornea using a rabbit model. Methods: Artificial cornea were transplanted into 20 New Zealand white rabbits. The disc-shaped artificial cornea is of diameter 8.0 mm (the core, clear optical zone diameter is 5.0 mm and that of the peripheral skirt 1.5 mm); of thickness 0.5 mm; and is fabricated from PHEMA, PMMA, and PETTA. Transplantation proceeded in two stages; all rabbits were then observed weekly to 12 weeks. Anterior segment photographs were taken, and anterior segment optical coherence tomography and histological analysis performed, to confirm the biocompatibility of the skirt and the extents of cell proliferation in surrounding tissues. Results: Two rabbits were sacrificed for histological examination in weeks 4 and 8 (one each). Four eyes failed because of surgical errors (artificial corneal decentration or excessively thin flaps). Of the 12 eyes for which surgery was technically successful, six (50.0%) maintained the optical zone structure and transparency to 12 weeks. Histology revealed that cells proliferated in the skirt and bound to surrounding tissues. Six eyes (50.0%) evidenced protrusions of the artificial cornea. Conclusions Transplantation of a new artificial cornea into rabbits met with some success (as confirmed anatomically and optically). However, corneal improvement and new surgical techniques are required to increase the success rate. Also, long-term follow-up is needed.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.