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Severe Care Appointments for Eating Disorders Among Children along with Young people As soon as the Beginning of the actual COVID-19 Outbreak.
BACKGROUND Blood pressure (BP) is currently diagnosed by cuff-based devices, which are inconvenient and provide discontinuous measurements. Photoplethysmography (PPG)-based cuffless techniques have recently been used to accurately estimate both systolic BP (SBP) and diastolic BP (DBP). However, it is difficult to use these SBP and DBP estimations to reflect the personalized traits in the peripheral vascular condition; thus, their accuracy is limited. OBJECTIVE The purpose of this study is to describe a technique that can be distinguished simply among three BP categories (normotensive, prehypertensive, and hypertensive) and reflect individual traits using PPG only. METHODS We measured BP over 120 s using the fingers of 105 subjects. The PPG waveforms varied in size and amplitude over time. Therefore, normalization for uniform features for individual traits was done after the extracted waveforms were divided into multiple windows. The feature is determined by the lowest amplitude in the waveform within each divided window. The features have been applied to distinguish three BP categories using the first-eigenvector (1-EV) and second-eigenvector (2-EV) in linear discriminant analysis. RESULTS The best decision boundary for each BP category was estimated using 1-EV (-0.02 to +0.02) and 2-EV (>+0.02) in the hypertensive category, 1-EV ( less then 0) and 2-EV (⩽+0.02) in the prehypertensive category, and 1-EV (⩾-0.02) and 2-EV (⩽+0.02) in the normotensive category. The overlap range with 1-EV (-0.02 to 0) and 2-EV (⩽+0.02) in particular accurately reflected individual traits. CONCLUSION Discrimination among the three BP categories reflecting individual traits was successfully achieved using PPG. This method could improve limitations of cuff-based techniques.BACKGROUND The aircraft cockpit is a highly intensive human-computer interaction system, and its design directly affects flight safety. OBJECTIVE To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model. METHODS The multi-factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) subjective evaluation, and eye tracking. RESULTS The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real-time monitoring of pilots' mental workload state was realized. CONCLUSION In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.BACKGROUND Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification. OBJECTIVE The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed. METHODS We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder. RESULTS Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 × 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA+SVM algorithm is better. CONCLUSION We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.BACKGROUND Fetal ECG can be obtained in a non-invasive manner to monitor fetal growth status. OBJECTIVE In this study, a fetal heart rate calculation system was proposed, which consists of the fetal ECG recorder (MF-HOLTER) and the fetal ECG monitoring software (FECG-MS). The abdomen electrocardiogram (AECG) of pregnant woman is acquired through the MF-HOLTER. The FECG-MS packs the AECG data and calls the fetal ECG separation algorithm to obtain the separated FECG and the fetal QRS (FQRS) position. The fetal heart rate (FHR) is further obtained by calculating the R-R interval value. At the same time, this study proposed a FQRS position correction algorithm to calculate the correct FHR value. METHOD In order to verify the accuracy of the FHR calculation, the ECG signal of FLUKE's PS320 FETAL SIMULATOR and clinical data were simultaneously tested. RESULTS The accuracy rate is over 98% in processing the simulator's data. In processing clinical data, the FHR values obtained by both the system proposed in this study and Monica AN24 are very close, and the difference is less than 1 bpm. CONCLUSION The results show that the fetal heart rate calculation system is accurate and stable, and has a positive application value and prospect.BACKGROUND The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE To establish models for early prediction and intervention of HDP. METHODS This study used the three types of risk factors and Support Vector Machine (SVM) to establish prediction models of HDP at different gestational weeks. RESULTS The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and ⩾ 35 weeks, it reached more than 92%. CONCLUSION Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.BACKGROUND Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states. OBJECTIVE We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI. METHODS We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems. RESULTS The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system. see more CONCLUSION In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.BACKGROUND DNA methylation is a molecular modification of DNA that is vital and occurs in gene expression. In cancer tissues, the 5'-C-phosphate-G-3'(CpG) rich regions are abnormally hypermethylated or hypomethylated. Therefore, it is useful to find out the diseased CpG sites by employing specific methods. CpG sites are highly correlated with each other within the same gene or the same CpG island. OBJECTIVE Based on this group effect, we proposed an efficient and accurate method for selecting pathogenic CpG sites. METHODS Our method aimed to combine a L1/2 regularized solver and a central node fully connected network to penalize group constrained logistic regression model. Consequently, both sparsity and group effect were brought in with respect to the correlated regression coefficients. RESULTS Extensive simulation studies were used to compare our proposed approach with existing mainstream regularization in respect of classification accuracy and stability. The simulation results show that a greater predictive accuracy was attained in comparison to previous methods. Furthermore, our method was applied to over 20000 CpG sites and verified using the ovarian cancer data generated from Illumina Infinium HumanMethylation 27K Beadchip. In the result of the real dataset, not only the indicators of predictive accuracy are higher than the previous methods, but also more CpG sites containing genes are confirmed pathogenic. Additionally, the total number of CpG sites chosen is less than other methods and the results show higher accuracy rates in comparison to other methods in simulation and DNA methylation data. CONCLUSION The proposed method offers an advanced tool to researchers in DNA methylation and can be a powerful tool for recognizing pathogenic CpG sites.OBJECTIVE Single-photon emission computed tomography (SPECT) as well as dual energy X-ray absorptiometry (DXA) scanners were designed in July 2018 at the Nuclear Medicine Department (NM), of the Taiwan Medical University Hospital. These scanners emit substantial X-rays from the target, which are tungsten, iron. Therefore, patients undergoing SPECT and DXA diagnosis, in addition to medical personnel, are exposed to undesirable photon leakage. METHODS Following administration of radiopharmaceuticals, patients become radioactive sources; thus, it is necessary to evaluate a possible increase in the environmental gamma exposure rates in the NM as a result of the operation of the new scanners. A three month evaluation of environmental radiation in the NM was performed using the accurate and sensitive TLD-100H approach, which gives an error rate less than 10%. RESULTS Detected exposure radiation rates in the NM ranged from 0.12 ± 0.02 to 1.00 ± 0.15 mSv per month, indicating that the imaging room had significantly different radiation rates. The results were compared with previous results, and no significant contribution to the enhancement of environmental gamma radiation was detected, which remained far below the occupational dose recommended by ICRP 60. The minimum detectable dose (MDD) for environmental radiation is also discussed herein to demonstrate the reliability of TLD-100H. CONCLUSION Recommendations were sent to the authorities of AEC-ROC to implement actions that could reduce doses at these high-dose locations to meet the ALARA principle.BACKGROUND Endoscopic endonasal transsphenoidal pituitary surgery is usually difficult and risky. With limited sources of cadaveric skulls, traditional methods of using virtual images to study the surgery are difficult for neurosurgeons and students because the surgery requires spatial imagination and good understanding of the patient's conditions as well as practical experience. The three-dimensional (3D) printing technique has played an important role in clinical medicine due to its advantages of low cost, high-efficiency and customization. OBJECTIVE CT images are used as the source data of 3D printing. The image obtained directly from the CT machine has a limited accuracy, which cannot be printed without processing. Some commercial platforms can help build an accurate model but the cost and customization are not satisfactory. In this case, a tactile, precise and low-cost 3D model is highly desirable. METHODS Five kinds of computer software are used in the manufacturing of medical 3D models and the processing procedure is easily understood and operated.
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