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Background Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing (e.g., rotating, scaling, and translating training images) to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables. Results This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%. Conclusions Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images.

Background Recent studies on acute mountain sickness (AMS) have used fixed-location and fixed-time measurements of environmental and physiological variable to determine the influence of AMS-associated factors in the human body. This study aims to measure, in real time, environmental conditions and physiological variables of participants in high-altitude regions to develop an AMS risk evaluation model to forecast prospective development of AMS so its onset can be prevented. Results Thirty-two participants were recruited, namely 25 men and 7 women, and they hiked from Cuifeng Mountain Forest Park parking lot (altitude: 2300 m) to Wuling (altitude: 3275 m). Regression and classification machine learning analyses were performed on physiological and environmental data, and Lake Louise Acute Mountain Sickness Scores (LLS) to establish an algorithm for AMS risk analysis. The individual R 2 coefficients of determination between the LLS and the measured altitude, ambient temperature, atmospheric pressure, relative humidity, climbing speed, heart rate, blood oxygen saturation (SpO 2 ), heart rate variability (HRV), were 0.1, 0.23, 0, 0.24, 0, 0.24, 0.27, and 0.35 respectively; incorporating all aforementioned variables, the R 2 coefficient is 0.62. The bagged trees classifier achieved favorable classification results, yielding a model sensitivity, specificity, accuracy, and area under receiver operating characteristic curve of 0.999, 0.994, 0.998, and 1, respectively. Conclusion The experiment results indicate the use of machine learning multivariate analysis have higher AMS prediction accuracies than analyses utilizing single varieties. The developed AMS evaluation model can serve as a reference for the future development of wearable devices capable of providing timely warnings of AMS risks to hikers.

Background As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum. Results We present a MsCWT image-based deep learning machine for AF differentiation. For the training, validation, and test sets, we achieved average accuracies of 97.94%, 97.84%, and 91.32%; and overall F1 scores of 97.13%, 96.86%, and 89.41% respectively. Moreover, AUC ROC curves of over 0.99 were obtained for all classes in the training and validation sets; and were over 0.9679 for the test set. Conclusions Training deep learning machines for the classification of AF with MsCWT-based images demonstrated to yield favorable outcomes and achieved superior performance amongst studies utilizing the same dataset. Though minimal, the conversion of signals into wavelet form with MsCWT may drastically improve outcomes not only in future ECG signal studies; but all signal-based diagnostics.

To investigate the role of thyroid transcription factor-1 (TTF-1) and tumor differentiation in resected lung adenocarcinoma. A total of 520 patients with clinical early stage lung adenocarcinoma who underwent surgical resection were reviewed retrospectively. Clinical data and outcomes were evaluated with an average follow-up of 117 months. The results were validated via lung cancer cell line studies. The clinical parameters did not differ between relapse and nonrelapse patients. Exceptions were tumor differentiation, lymphovascular space invasion, F 18 -fluorodeoxyglucose maximum standard uptake value, tumor size, and pathological stage ( p  < 0.001). Poor tumor differentiation was the independent prognostic factor (odds ratio: 2.937, p  = 0.026). The expression of TTF-1 was correlated with tumor differentiation in resected lung adenocarcinoma patients ( p  < 0.001). Five-year survival was 60.0% for score 1 TTF-1 expression patients, 80.1% for score 2 TTF-1 expression patients, and 86.1% for score 3 TTF-1 expression group patients. The lung cancer cell line study of knockdown and overexpression of TTF-1 revealed TTF-1 mediated High Mobility Group AT-Hook 2 (HMGA2) protein involved with epithelium-mesenchymal transformation. The chromatin immunoprecipitation revealed TTF-1 regulated HMGA2 via direct binding. TTF-1/HMGA2 axis was associated with tumor differentiation and mediated the aggressiveness of the tumor and prognosis.

Objective With the increasing prevalence of Parkinson's disease (PD) and the development of PD-based acoustic recording databases, this study aims to evaluate the feasibility of using an ensemble-based machine learning (ML) approach to detect PD across diverse acoustic datasets. Methods We utilized three publicly available PD speech datasets—MIU (Sakar), UEX (Carrón), and UCI (Little)—to build ML models incorporating a hybrid preprocessing framework. This framework includes a scaling phase (using RobustScaler), a sampling phase (employing random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and random undersampling (RUS)), and an ML classifier selection phase (featuring eXtreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost)). Performance was evaluated using accuracy, precision, recall, and F1-score metrics. Additionally, we conducted SHAP (SHapley Additive exPlanations) analysis to identify the most significant PD-related acoustic features. Results The optimal combination of preprocessing and classification techniques varied across datasets. However, the highest classification performance was generally achieved using RobustScaler for scaling, a combination of ROS, SMOTE, and RUS for sampling, and XGBoost or AdaBoost for classification. The best-performing model on the MIU dataset achieved accuracy of 97.37%, precision of 96.07%, and F1-score of 96.57%. The UEX and UCI datasets achieved perfect classification with 100% accuracy, precision, and recall. SHAP analysis revealed that Mel-frequency cepstral coefficients were consistently among the most influential PD-related acoustic features. Conclusions Our findings confirm the feasibility of an ensemble-based approach for PD detection using acoustic recordings, highlighting the importance of dataset-specific preprocessing strategies. This study ranks impactful PD-related acoustic features, offering guidance for future voice-based PD screening tools.

During the COVID-19 pandemic, which emerged in 2020, many patients were treated in isolation wards because of the high infectivity and long incubation period of COVID-19. Therefore, monitoring systems have become critical to patient care and to safeguard medical professional safety. The user interface is very important to the surveillance system; therefore, we used web technology to develop a system that can create an interface based on user needs. When the surveillance scene needs to be changed, the surveillance location can be changed at any time, effectively reducing the costs and time required, so that patients can achieve timely and appropriate goals of treatment. ZigBee was employed to develop a monitoring system for intensive care units (ICUs). Unlike conventional GUIs, the proposed GUI enables the monitoring of various aspects of a patient, and the monitoring interface can be modified according to the user needs. A simulated ICU environment monitoring system was designed to test the effectiveness of the system. The simulated environment and monitoring nodes were set up at positions consistent with the actual clinical environments to measure the time required to switch between the monitoring scenes or targets on the GUI. A novel system that can construct ZigBee-simulated graphical monitoring interfaces on demand was proposed in this study. The locations of the ZigBee monitoring nodes in the user interface can be changed at any time. The time required to deploy the monitoring system developed in this study was 4 min on average, which is much shorter than the time required for conventional methods (131 min). The system can effectively overcome the limitations of the conventional design methods for monitoring interfaces. This system can be used to simultaneously monitor the basic physiological data of numerous patients, enabling nursing professionals to instantly determine patient status and provide appropriate treatments. The proposed monitoring system can be applied to remote medical care after official adoption.

ZigBee wireless sensor devices possess characteristics of small size, light weight, low power consumption, having up to 65535 nodes in a sensor network, in theory. Therefore, the ZigBee wireless sensor network (WSN) is very suitable for use in developing monitoring and control (MC) applications, such as remote healthcare, industrial control, fire detection, environmental monitoring, and so on. This dissertation is directed towards the research on the issues of communication extension and backup, encountered in creating ZigBee-based MC systems for military storerooms, together with providing associated solutions. We design an embedded gateway that possesses wired network (Ethernet) and wireless communication (GSM) backup capability. The gateway can not only easily extend the monitoring distance of the ZigBee-based MCS, but can also solve the problem that some military zones do not have wire networks or possess communication blind spots. The results of this dissertation have been practically applied in constructing a paradigm monitoring system of a military storeroom. It is believed that the research results could be a useful reference for developing ZigBee-based MCSs in the future.

PurposeTo determine whether a deep learning approach using generative adversarial networks (GANs) is beneficial for the classification of retinal conditions with Optical coherence tomography (OCT) images.MethodsOur study utilized 84,452 retinal OCT images obtained from a publicly available dataset (Kermany Dataset). Employing GAN, synthetic OCT images are produced to balance classes of retinal disorders. A deep learning classification model is constructed using pretrained deep neural networks (DNNs), and outcomes are evaluated using 2082 images collected from patients who visited the Department of Ophthalmology and the Department of Endocrinology and Metabolism at the Tri-service General Hospital in Taipei from January 2017 to December 2021.ResultsThe highest classification accuracies accomplished by deep learning machines trained on the unbalanced dataset for its training set, validation set, fivefold cross validation (CV), Kermany test set, and TSGH test set were 97.73%, 96.51%, 97.14%, 99.59%, and 81.03%, respectively. The highest classification accuracies accomplished by deep learning machines trained on the synthesis-balanced dataset for its training set, validation set, fivefold CV, Kermany test set, and TSGH test set were 98.60%, 98.41%, 98.52%, 99.38%, and 84.92%, respectively. In comparing the highest accuracies, deep learning machines trained on the synthesis-balanced dataset outperformed deep learning machines trained on the unbalanced dataset for the training set, validation set, fivefold CV, and TSGH test set.ConclusionsOverall, deep learning machines on a synthesis-balanced dataset demonstrated to be advantageous over deep learning machines trained on an unbalanced dataset for the classification of retinal conditions.

Aim： SMXZF （a combination of ginsenoside Rbl, ginsenoside Rgl, schizandrin and DT-13） derived from Chinese traditional medicine formula ShengMai preparations） is capable of alleviating cerebral ischemia-reperfusion injury in mice. In this study we used network pharmacology approach to explore the mechanisms of SMXZF in the treatment of cardio-cerebral ischemic diseases. Methods： Based upon the chemical predictors, such as chemical structure, pharmacological information and systems biology functional data analysis, a target-pathway interaction network was constructed to identify potential pathways and targets of SMXZF in the treatment of cardio-cerebral ischemia. Furthermore, the most related pathways were verified in TNF-α-treated human vascular endothelial EA.hy926 cells and H202-treated rat PC12 cells. Results： Three signaling pathways including the NF-κB pathway, oxidative stress pathway and cytokine network pathway were demonstrated to be the main signaling pathways. The results from the gene ontology analysis were in accordance with these signaling pathways. The target proteins were found to be associated with other diseases such as vision, renal and metabolic diseases, although they exerted therapeutic actions on cardio-cerebral ischemic diseases. Furthermore, SMXZF not only dose-dependently inhibited the phosphorylation of NF-κB, p50, p65 and IKKαβ in TNF-α-treated EA.hy926 cells, but also regulated the Nrf2/HO-1 pathway in H2O2- treated PC12 cells. Conclusion： NF-κB signaling pathway, oxidative stress pathway and cytokine network pathway are mainly responsible for the therapeutic actions of SMXZF against cardio-cerebral ischemic diseases.

Functional CsPbI 3 perovskite phases are not stable at ambient conditions and spontaneously convert to a non-perovskite δ phase, limiting their applications as solar cell materials. We demonstrate the preservation of a black CsPbI 3 perovskite structure to room temperature by subjecting the δ phase to pressures of 0.1 – 0.6 GPa followed by heating and rapid cooling. Synchrotron X-ray diffraction and Raman spectroscopy indicate that this perovskite phase is consistent with orthorhombic γ-CsPbI 3 . Once formed, γ-CsPbI 3 could be then retained after releasing pressure to ambient conditions and shows substantial stability at 35% relative humidity. First-principles density functional theory calculations indicate that compression directs the out-of-phase and in-phase tilt between the [PbI 6 ] 4− octahedra which in turn tune the energy difference between δ- and γ-CsPbI 3 , leading to the preservation of γ-CsPbI 3 . Here, we present a high-pressure strategy for manipulating the (meta)stability of halide perovskites for the synthesis of desirable phases with enhanced materials functionality. Inorganic lead halide perovskites are structurally unstable, which prevents their application in solar cells. Here the authors synthesize, using high pressure and temperature, a perovskite CsPbI 3 phase that is metastably preserved to ambient conditions through a structural deformation induced at high pressure.