OBJECTIVETo detect FOXL2 gene mutation in a Chinese pedigree affected with blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) type I, and to explore its genotype-phenotype correlation.

METHODSPeripheral blood samples were obtained from 3 patients and 19 healthy members from the pedigree for the isolation of genomic DNA. All exons and flanking sequences of the FOXL2 gene were amplified by PCR with 7 pairs of overlapping primers and sequenced.

RESULTSDNA sequencing indicated that the BPES phenotype in this pedigree was caused by a hotspot c.843_859dup17 mutation. The same mutation was not found among the healthy members of the pedigree.

CONCLUSIONThe c.843_859dup17 frameshift mutation probably underlies the BPES type I in this Chinese pedigree, which may manifest as either BEPS type I or type II.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.