OBJECTIVETo explore the genetic etiology for 11 sporadic patients with neurofibromatosis type 1.

METHODSChip targeting capture and high-throughput sequencing were employed to detect potential mutations of NF1 and NF2 genes among the 11 patients. The data was filtered through multiple mutational databases and in-house whole exome sequence database. Sanger sequencing was used for analysis of family members of the patients.

RESULTSEleven pathogenic variants were found among the 11 patients, which included two splicing mutations, one missense mutation, two nonsense mutations, and six frame-shifting mutations. None of the mutations was recorded by the public database or the in-house database generated from 1775 samples through whole exome sequencing. None of the unaffected parents carried the same mutation. Seven mutations were associated with neurofibromatosis type 1 previously, while the remaining four were discovered for the first time. Prenatal diagnosis of two high-risk pregnancies suggested that neither fetus has inherited the NF1 mutation from their affected parents.

CONCLUSIONIdentification of causative mutations in patients with sporadic-type neurofibromatosis type 1 has provided a basis for genetic counseling. The four novel mutations have enriched the spectrum of NF1 gene mutations.

Objective:To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method, and identify the corresponding risk factors of mortality.Methods:In this retrospective cohort study, a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%), aged(77.8±7.3)years, and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition, 60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age, gender, body mass index, diagnosis, co-morbidity, laboratory indicators, electrocardiogram, echocardiogram, treatment data.These patients were followed up for at least 6 years, and the main adverse cardiovascular and cerebrovascular events(MACCE), including death, were recorded.Finally, the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1, Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results:A total of 329 hospitalized patients were included in this study, the overall median follow-up time was 77.0 months(95% CI: 54.0~84.0), 11 cases lost during follow-up(3.3%), and 151 cases died(45.9%). The analysis found that the areas under the ROC curve for a support vector machine(SVM)model, k-Nearest Neighbor(KNN)model, decision tree model, random forest model, ADABoost model, XGBoost model and logistic regression model were 0.76, 0.75, 0.75, 0.91, 0.86, 0.85 and 0.81, respectively.The random forest model had the highest prediction efficiency, with the accuracy of 0.789 and F1 value of 0.806, which was better than the logistic regression model[the Area Under Receiver Operating Characteristic Curve(AUC): 0.91 vs.0.81, P<0.05]. D-dimer, age, number of MACCE, left ventricular ejection fraction, serum albumin level, anemia, New York Heart Association(NYHA)grade, history of old myocardial infarction, estimated glomerular filtration rate(eGFR)and resting heart rate were important risk factors for predicting long-term mortality. Conclusions:The random forest model based on machine learning method can predict the long-term mortality of patients with atrial fibrillation and coronary heart disease aged 60 years and over, have a good identification ability.Its accuracy is higher than that of the traditional Logistic regression model.Reducing the long-term mortality and improving the long-term outcomes can be achieved by intervening on D-dimer levels, correcting hypoproteinemia and anemia, improving cardiac function and controlling resting ventricular rates.

Mechanical allodynia (MA), including punctate and dynamic forms, is a common and debilitating symptom suffered by millions of chronic pain patients. Some peripheral injuries result in the development of bilateral MA, while most injuries usually led to unilateral MA. To date, the control of such laterality remains poorly understood. Here, to study the role of microglia in the control of MA laterality, we used genetic strategies to deplete microglia and tested both dynamic and punctate forms of MA in mice. Surprisingly, the depletion of central microglia did not prevent the induction of bilateral dynamic and punctate MA. Moreover, in dorsal root ganglion-dorsal root-sagittal spinal cord slice preparations we recorded the low-threshold Aβ-fiber stimulation-evoked inputs and outputs of superficial dorsal horn neurons. Consistent with behavioral results, microglial depletion did not prevent the opening of bilateral gates for Aβ pathways in the superficial dorsal horn. This study challenges the role of microglia in the control of MA laterality in mice. Future studies are needed to further understand whether the role of microglia in the control of MA laterality is etiology-or species-specific.
Mice ; Animals ; Hyperalgesia/metabolism* ; Microglia/metabolism* ; Disease Models, Animal ; Spinal Cord/metabolism* ; Spinal Cord Dorsal Horn/metabolism* ; Ganglia, Spinal/metabolism*