Assessment of short-term effects of thoracic radiotherapy on the cardiovascular parasympathetic and sympathetic nervous systems

BackgroundPrior research suggests that cardiovascular autonomic dysfunction might be an early marker of cardiotoxicity induced by antitumor treatment and act as an early predictor of cardiovascular disease-related morbidity and mortality. The impact of thoracic radiotherapy on the parasympathetic and sympathetic nervous systems, however, remains unclear. Therefore, this study aimed to evaluate the short-term effects of thoracic radiotherapy on the autonomic nervous system, using deceleration capacity (DC), acceleration capacity (AC) of heart rate, and heart rate variability (HRV) as assessment tools.MethodsA 5 min electrocardiogram was collected from 58 thoracic cancer patients before and after thoracic radiotherapy for DC, AC, and HRV analysis. HRV parameters employed included the standard deviation of the normal-normal interval (SDNN), root mean square of successive interval differences (RMSSD), low frequency power (LF), high frequency power (HF), total power (TP), and the LF to HF ratio. Some patients also received systemic therapies alongside radiotherapy; thus, patients were subdivided into a radiotherapy-only group (28 cases) and a combined radiotherapy and systemic therapies group (30 cases) for additional subgroup analysis.ResultsThoracic radiotherapy resulted in a significant reduction in DC (8.5 [5.0, 14.2] vs. 5.3 [3.5, 9.4], p = 0.002) and HRV parameters SDNN (9.9 [7.03, 16.0] vs. 8.2 [6.0, 12.4], p = 0.003), RMSSD (9.9 [6.9, 17.5] vs. 7.7 [4.8, 14.3], p = 0.009), LF (29 [10, 135] vs. 24 [15, 50], p = 0.005), HF (35 [12, 101] vs. 16 [9, 46], p = 0.002), TP (74 [41, 273] vs. 50 [33, 118], p < 0.001), and a significant increase in AC (−8.2 [−14.8, −4.9] vs. -5.8 [−10.1, −3.3], p = 0.003) and mean heart rate (79.8 ± 12.6 vs. 83.9 ± 13.6, p = 0.010). Subgroup analysis indicated similar trends in mean heart rate, DC, AC, and HRV parameters (SDNN, RMSSD, LF, HF, TP) in both the radiotherapy group and the combined treatment group post-radiotherapy. No statistically significant difference was noted in the changes observed in DC, AC, and HRV between the two groups pre- and post-radiotherapy.ConclusionThoracic radiotherapy may induce cardiovascular autonomic dysfunction by reducing parasympathetic activity and enhancing sympathetic activity. Importantly, the study found that the concurrent use of systemic therapies did not significantly amplify or contribute to the alterations in autonomic function in the short-term following thoracic radiotherapy. DC, AC and HRV are promising and feasible biomarkers for evaluating autonomic dysfunction caused by thoracic radiotherapy

Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor’s type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time–frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson’s disease severity, surpassing DCLSTM’s 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases

iTCM : toward learning-based thermal comfort modeling via pervasive sensing for smart buildings

For decades, ASHRAE Standard 55 has been using the Fanger's predicted mean vote (PMV) model to evaluate the indoor thermal comfort satisfaction. However, this canonical model has drawbacks in both data inadequacy and lack of inputs from test subjects. In this paper, we propose a learning-based solution for thermal comfort modeling via the emerging machine learning techniques and Internet of Things-based pervasive sensing technologies. First, we build an intelligent thermal comfort management (iTCM) system. It adopts the wireless sensor network to collect environmental data and utilizes the wearable device for vital sign monitoring. In addition, a cloud-based back-end system, with cost efficient deployment fees, is developed for data management and analysis. Second, we implement a black-box neural network (NN), namely the intelligent thermal comfort NN (ITCNN). To evaluate the performance of ITCNN, we compare it with the PMV model, three traditional white-box machine learning approaches and three classical black-box machine learning methods. Our preliminary results show that four black-box methods achieve better performance than the PMV model and the three white-box approaches. The ITCNN achieves the best performance and outperforms the PMV model by on average 13.1% and up to 17.8%. Third, with the iTCM system, we demonstrate a novel deep reinforcement learning-based application by encouraging human behavioral changes to form energy-saving habits for greener, smarter, and healthier building. Finally, we discuss the limitations of this paper and present the plan for our future research.NRF (Natl Research Foundation, S’pore

Water advancing and receding process as a liquid–vapor interface geometrical question

Existing wetting theories have difficulty accurately describing advancing/receding processes on micro-structured surfaces. A strategy is proposed to solve this problem by recognizing it as a liquid–vapor interface geometrical question. The wetting chip method is proposed to realize the microscopic observation of liquid–vapor interface variations. A wetting model based on the liquid–vapor interface shape (LVIS model) is established to describe the analytical relationships between the apparent contact angles, liquid–vapor interface radius, substrate geometry, and chemical nature of liquid. The LVIS model is divided into four typical time points and three transition stages, and its predictions agree with the experimental measurements. In contrast to traditional theories, the apparent contact angles in a quasi-equilibrium state should be separated into advancing and receding processes, and in this state, apparent contact angles vary with changes in the parameters of micro-pillar width and spacing. This strategy has the potential to accurately describe the wetting process on micro-structure surfaces

Generating New Cross‐Relaxation Pathways by Coating Prussian Blue on NaNdF 4

Cross-relaxation among sensitizers is commonly regarded as deleterious in fluorescent materials, although favorable in photothermal agents. Herein, we coated Prussian blue (PB) on NaNdF4 nanoparticles to fabricate core-shell nanocomplexes with new cross relaxation pathways between the ladder-like energy levels of Nd3+ ions and continuous energy band of PB. The photothermal conversion efficiency was improved exceptionally and the mechanism of the enhanced photothermal effect was investigated. In vivo photoacoustic imaging and photothermal therapy demonstrated the potential of the enhanced photothermal agents. Moreover, the concept of generating new cross-relaxation pathways between different materials is proposed to contribute to the design of all kinds of enhanced photothermal agents.Nanyang Technological UniversityThis work was financially supported by an NTU internal grant(M4081851), the National Basic Research Program of China(No.61805118 and 21674048), the Natural Science Founda-tion of Jiangsu Province of China (No.BK20171020), theChina Postdoctoral Science Foundation (No.2018T110488),and the open research fund of the Key Laboratory forOrganic Electronics and Information Displays