110023 research outputs found
Sort by
資訊電機學院行動商務與多媒體應用學系
No full text[[abstract]]現今社會中，疾病的種類非常多，疾病的傳播途徑相當複雜，其中某些特定的病毒是經由固 定的傳染途徑傳播，例如: 愛滋病毒是透過交換體液來傳播、伊波拉經由破損的皮膚及黏膜、新 冠肺炎則以空氣中的飛沫傳播。[[note]]粘維
Enhancement of Osteoblast Function through Extracellular Vesicles Derived from Adipose-Derived Stem Cells
No full text[[abstract]]Adipose-derived stem cells (ADSCs) are a type of mesenchymal stem cell that is investigated in bone tissue engineering (BTE). Osteoblasts are the main cells responsible for bone formation in vivo and directing ADSCs to form osteoblasts through osteogenesis is a research topic in BTE. In addition to the osteogenesis of ADSCs into osteoblasts, the crosstalk of ADSCs with osteoblasts through the secretion of extracellular vesicles (EVs) may also contribute to bone formation in ADSC-based BTE. We investigated the effect of ADSC-secreted EVs (ADSC-EVs) on osteoblast function. ADSC-EVs (size ? 1000 nm) were isolated from the culture supernatant of ADSCs through ultracentrifugation. The ADSC-EVs were observed to be spherical under a transmission electron microscope. The ADSC-EVs were positive for CD9, CD81, and Alix, but β-actin was not detected. ADSC-EV treatment did not change survival but did increase osteoblast proliferation and activity. The 48 most abundant known microRNAs (miRNAs) identified within the ADSC-EVs were selected and then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The GO analysis revealed that these miRNAs are highly relevant to skeletal system morphogenesis and bone development. The KEGG analysis indicated that these miRNAs may regulate osteoblast function through autophagy or the mitogen-activated protein kinase or Ras-related protein 1 signaling pathway. These results suggest that ADSC-EVs enhance osteoblast function and can contribute to bone regeneration in ADSC-based BTE
比較新型與傳統型神經肌肉逆轉劑於老年膝關節置換術患者麻醉安全之回溯調查
No full text[[abstract]]背景：全球老年人口逐年增加，高齡手術麻醉及甦醒成為重要議題。新型逆轉劑Sugammadex能加速逆轉神經肌肉阻斷並促使一般患者盡早脫離呼吸器，對於老年患者麻醉安全指標缺乏相關研究。
目的：比較使用新型與傳統型神經肌肉逆轉劑對於老年膝關節置換術患者麻醉恢復成效調查。
方法：病歷回溯法自2018年1月1日至2018年8月31日期間，採立意取樣65歲以上行膝關節置換術且接受神經肌肉逆轉劑老年患者，自擬結構式問卷「神經肌肉逆轉劑麻醉恢復病歷回溯表」進行登錄，共收案214例。
結果：不論新型或傳統組皆以女性居多(p<0.05)，其他基本性質未達顯著差異。麻醉照護指標方面，新型組術中接受較多次神經肌肉阻斷劑(Rocuronium)(p<0.05)，但在拔管前吐氣二氧化碳監測(ETCO2)兩組雖達顯著差異但皆於正常範圍內(p<0.05)。新型組不論在麻醉與手術時間 (p<0.05)、給藥至拔管時間(p=0.02)，皆有低於傳統組的趨勢，且新型組在拔管後平均神經肌肉阻斷程度(TOF)為98%，達拔管安全指標。恢復期照護指標方面，新型組意識恢復優於傳統組(p<0.05)。在恢復期合併症方面兩組無論在延遲拔管、低血氧、高血壓、頭暈、嘔吐及喉嚨痛，皆未達顯著差異；新型組在術後整體滿意度得分高於傳統組，呈顯著差異(p<0.05)。
結論：接受新型阻斷劑老年病患能縮短手術麻醉時間、意識恢復及自主呼吸，達到麻醉安全指標。恢復期合併症及術後品質分析與傳統組相近，本研究結果可提供醫護人員在麻醉安全及提升照護品質。
關鍵字：麻醉恢復、神經肌肉逆轉劑、Sugammadex、麻醉安全、老人[[abstract]]Background: The pace of population ageing is much faster than in the past and aging increases the probability of a person to undergo surgery. Elderly patients take more time to recover from general anesthesia therefore generation of muscle reversal agent is important in facilitating anesthesia safety issue.
Objective: To compare the effects of new and traditional neuromuscular reversal agents on anesthesia recovery in elderly patients undergoing knee arthroplasty.
Methods: From January 1, 2018 to August 31, 2018, the medical records were retrospectively sampled for elderly patients over 65 years old who underwent knee arthroplasty and received neuromuscular reversal. Self-made structural questionnaire "neuromuscular reversal" The agent's anesthesia recovery medical record retrospective table was registered, and a total of 214 cases were received.
Results: Population of the new or traditional reversal agents were predominantly female (p < 0.05), and there were no significant differences in the baseline characteristics. In terms of anesthesia care indicators, the new reversal agents received more than one neuromuscular blocker (Rocuronium) (p<0.05), but the two groups of exhaled carbon dioxide monitoring (ETCO2) before extubation showed significant differences but were within the normal range ( p<0.05). The new reversal agents had a lower trend than the traditional group in the time of anesthesia and surgery (p<0.05), and the time of administration to extubation (p=0.02), and the average neuromuscular blockade after extubation in the new reversal agents (TOF) is 98%. In terms of recovery period care indicators, the recovery of new reversal agents consciousness was better than that of the traditional group (p<0.05). There were no significant differences between the two groups in terms of delayed complication, delayed extubation, hypoxemia, hypertension, dizziness, vomiting, and sore throat.. The overall satisfaction score of the new group was higher than that of the traditional group (p<0.05).
Conclusion: Elderly patients receiving new reversal agents can recover consciousness and spontaneous breathing in a short time, and achieve anesthesia safety indicators
Effects of Twenty Hours of Neurofeedback-based Neuropsychotherapy on the Executive Functions and Achievements among ADHD Children
No full text[[abstract]]Objective. Neurofeedback can reduce ADHD symptoms; however, current programs are relatively long, with fewer concerns about executive function (EF). The present study aimed to investigate a 20-hour combined computerized training neurofeedback program. Methods. Fifty ADHD children were randomly assigned to either the experimental group (EXP) or the wait-list control group (CON), who took training after the post-tests. The EF measures were the Tower of London (ToL), Wisconsin Card Sorting Test (WCST), and Comprehensive Nonverbal Attention Test (CNAT). SNAP-IV and questionnaires reported by parents constituted the behavioral measures. Two-way repeated-measures ANOVA and bootstrapping dependent t-tests were also used. Results. The F-tests revealed the interaction effects on ADHD symptoms and math scores. The EXP had increased the ToL scores, decreased the error and perseverative error rates on WCST, as well as the dysexecutive index on CNAT in the t-test. Conclusions. The training effects were related to behavioral symptoms and functions, EFs, and generalized achievement performances. We suggest that future studies could apply to different patients and examine the maintenance of the program
後疫情時代幼兒防疫行為之研究
No full text[[abstract]]COVID-19新冠肺炎席捲全球，造成全球150個國家因疫情而停課，嚴重影響學生學習與健康。本研究旨在了解幼兒在幼兒園內防疫行為之落實現況，研究對象聚焦於2至6歲幼兒，藉由半結構式問卷，訪談6位教保服務人員了解幼兒防疫行為樣態。本研究之結論共有四點：一、幼兒在疫情爆發之後，產生許多日常飲食行為的質變；二、幼兒能落實戴口罩與環境消毒措施，潛移默化中已建立身體動作防疫習慣；三、幼兒能實踐主動保持社交距離的防疫行為，並會主動提醒同學保持社交距離；四、多數幼兒能配合午睡時戴口罩，建議觀察疫情狀況予以調整。本研究提出相關防疫建議，以供相關單位參考，共同守護幼兒健康。[[abstract]]More than 150 countries across the planet were or had been affected by preschool and school closures due to the COVID-19 pandemic. The purpose of this study was to understand the implementation of children's preventive behaviors in preschools. The semi-structured interview was conducted by using a sample from 6 preschool educators in Taiwan. The conclusions for this study were as follows: (1) After the outbreak of the pandemic, children had made many qualitative changes in their daily eating behaviors. (2) The children were able to wear masks and disinfect the environment, and had established the habit of physical movement to prevent the epidemic by implication. (3) Children could take the initiative to keep a social distance and remind their classmates to follow the rules together. (4) Most of the children could wear masks during nap time, and it was recommended to adjust them according to the pandemic situation in the future. Based on the results of our research, some epidemic prevention suggestions were provided for relevant units to jointly protect the health of children
An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ
No full text[[abstract]]Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients
An Efficient Cluster Head Selection for Wireless Sensor Network-based Smart Agriculture Systems
No full text[[abstract]]With the increasing availability of high-resolution satellite and drone images and the Internet of Things (IoT) has begun transforming remote sensing of agriculture by improving accessibility and frequency of updates. Modern IoT-based smart agriculture systems use Wireless Sensor Networks (WSNs) to gather information from an ecosystem that regulates the quantity of water in agricultural fields could be one of these activities. The WSNs remained a challenge to transfer data to drones for analysis purposes. These are composed of tiny sensory architectures organized together to bring efficiency and scalability features to a network. WSN nodes are controlled and managed by a cluster. It is quite difficult to design an efficient leader election protocol. The computation power, storage space, and energy supply of sensor nodes make them unable to frequently switch to a different cluster. The WSN cluster-head election process requires a lot of energy (evaluation and computational process to select the most appropriate node with the least impact on network fragmentation in energy consumption of selected node). Then it is necessary to formulate a mechanism where WSNs utilize the least energy to coordinate with the remote sensing sources. This study presents a cluster election algorithm using the fuzzy logic inference system. It uses a coordinates system to map network nodes and map them based on prioritized scheduling. Lifetime augmentation in wireless sensor networks has always been of great interest. During data transmission from normal sensor nodes to the base station (sink), excess energy is dissipated. Optimizing the energy dissipation of WSNs through the selection of cluster heads is a powerful way to increase the lifespan. By electing more efficient nodes as cluster heads, the proposed method extends the network's lifetime by reducing the number of unimportant communications between nodes. With the utilization of network resources efficiently, the network's lifetime is extended. The proposed algorithm is evaluated with the LEACH (Low Energy Adaptive Clustering Structure) algorithm and FCA method based on the remaining energy and the number of active nodes. The simulation results show that the proposed algorithm utilizes less energy for communication with remote sensory equipment for intelligent agriculture. The performance of the method improved for remaining energy by 9%, the number of active nodes rate by 24%, and indirectly network resource utilization than other states of the art solutions
Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke
No full text[[abstract]]Background: Although carotid sonographic features have been used as predictors of recurrent stroke, few large-scale studies have explored the use of machine learning analysis of carotid sonographic features for the prediction of recurrent stroke.
Methods: We retrospectively collected electronic medical records of enrolled patients from the data warehouse of China Medical University Hospital, a tertiary medical center in central Taiwan, from January 2012 to November 2018. We included patients who underwent a documented carotid ultrasound within 30 days of experiencing an acute first stroke during the study period. We classified these participants into two groups: those with non-recurrent stroke (those who has not been diagnosed with acute stroke again during the study period) and those with recurrent stoke (those who has been diagnosed with acute stroke during the study period). A total of 1,235 carotid sonographic parameters were analyzed. Data on the patients' demographic characteristics and comorbidities were also collected. Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the machine learning methods.
Results: In total, 2,411 patients were enrolled in this study, of whom 1,896 and 515 had non-recurrent and recurrent stroke, respectively. After extraction, 43 features of carotid sonography (36 carotid sonographic parameters and seven transcranial color Doppler sonographic parameter) were analyzed. For predicting recurrent stroke, CatBoost achieved the highest area under the curve (0.844, CIs 95% 0.824–0.868), followed by the Light Gradient Boosting Machine (0.832, CIs 95% 0.813–0.851), random forest (0.819, CIs 95% 0.802–0.846), support-vector machine (0.759, CIs 95% 0.739–0.781), logistic regression (0.781, CIs 95% 0.764–0.800), and decision tree (0.735, CIs 95% 0.717–0.755) models.
Conclusion: When using the CatBoost model, the top three features for predicting recurrent stroke were determined to be the use of anticoagulation medications, the use of NSAID medications, and the resistive index of the left subclavian artery. The CatBoost model demonstrated efficiency and achieved optimal performance in the predictive classification of non-recurrent and recurrent stroke
Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19
No full text[[abstract]]Background
The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible.
Method
For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer.
Results
A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database