Objective To study the clinical features of hand-foot-mouth disease(HFMD) in neonates.Method From April 2015 to May 2016,the clinical manifestations,laboratory examinations,treatments and prognosis of neonates with HFMD in our hospital were retrospectively analyzed.Result A total of 6 cases of neonatal HFMD were included,with 4 males and 2 females.The ages of 2 patients were ≤7 days and the other 4 patients 8 ～ 28 days.5 patients developed this disease during April to July,while the other one in January.2 cases had a definite contract history of HFMD.4 cases presented with fever and rashes in hand and foot,one case with fever,rash and oral ulcer,and one case with rash in hip and oral ulcer without fever.The nucleic acid test of enterovirus were positive in 4 cases.The symptoms of these neonatal HFMD were mild and recovered after symptom-relieving treatment.Conclusion HFMD in neonates with fever and/or rash should be considered during the HFMD epidemic period.

Objective: To evaluate the application of calcium suppressed (CaSupp) spectral CT technique in evaluating disk position and measuring the thickness of the posterior band of temporomandibular joint (TMJ). Methods: The twenty-three temporomandibular disorder patients [mean age 23(12~62) years, male/female=14/9] were performed with oblique sagittal and coronal proton density weighted imaging (PDWI) and spectral CT scans from February to July, 2019 in Department of Radiology, Hainan Hospital of General Hospital of Chinese PLA, and 45 TMJ joints were evaluated. The subjects were classified into two groups according to the scanning modalities: MRI measurement group and CaSupp spectral-based CT group. The CaSupp technique were applied with the spectral-based CT images and CaSupp images were generated. The oblique sagittal and coronal CaSupp imaged were reformatted by perpendicular to the long axis of the condyle. The TMJ disk positions were evaluated on oblique sagittal and coronal images, and the maximal disk thickness were measured on the oblique sagittal images. Results: The joint position was basically consistent on MRI and CaSupp images for the 45 TMJ joints. The intra-class coefficient value was 0.843 (0.712, 0.914) for the measurement of the posterior band of the TMJ disk between MRI and CaSupp images. Bland-Altman presented that the [95.6% (43/45)] points with the difference located in the 95% agreement interval. Wilcoxon paired text demonstrated that there was no significant different for the thickness of the posterior band between MRI [2.57 (1.76, 3.65) mm] and CaSupp images [2.67 (1.74, 4.56) mm] (P=0.07). Conclusions The CaSupp spectral-based CT could be used to evaluated the TMJ disk position and the thickness of the posterior band.

OBJECTIVES: Firefighters are prone to suffer from psychological trauma and post-traumatic stress disorder (PTSD) in the workplace, and have a poor prognosis after PTSD. Reliable models for predicting PTSD allow for effective identification and intervention for patients with early PTSD. By collecting the psychological traits, psychological states and work situations of firefighters, this study aims to develop a machine learning algorithm with the aim of effectively and accurately identifying the onset of PTSD in firefighters, as well as detecting some important predictors of PTSD onset. METHODS: This study conducted a cross-sectional survey through convenient sampling of firefighters from 20 fire brigades in Changsha, which were evenly distributed across 6 districts and Changsha County, with a total of 628 firefighters. We used the synthetic minority oversampling technique (SMOTE) to process data sets and used grid search to finish the parameter tuning. The predictive capability of several commonly used machine learning models was compared by 5-fold cross-validation and using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, precision, recall, and F1 score. RESULTS: The random forest model achieved good performance in predicting PTSD with an average AUC score at 0.790. The mean accuracy of the model was 90.1%, with an F1 score of 0.945. The three most important predictors were perseverance, forced thinking, and reflective deep thinking, with weights of 0.165, 0.158, and 0.152, respectively. The next most important predictors were employment time, psychological power, and optimism. CONCLUSIONS PTSD onset prediction model for Changsha firefighters constructed by random forest has strong predictive ability, and both psychological characteristics and work situation can be used as predictors of PTSD onset risk for firefighters. In the next step of the study, validation using other large datasets is needed to ensure that the predictive models can be used in clinical setting.
Humans ; Stress Disorders, Post-Traumatic/diagnosis* ; Firefighters/psychology* ; Cross-Sectional Studies ; Algorithms ; Machine Learning