Dosimetry and efficiency comparison of knowledge-based and manual planning using volumetric modulated arc therapy for craniospinal irradiation.

BACKGROUND: Craniospinal irradiation (CSI) poses a challenge to treatment planning due to the large target, field junction, and multiple organs at risk (OARs) involved. The aim of this study was to evaluate the performance of knowledge-based planning (KBP) in CSI by comparing original manual plans (MP), KBP RapidPlan initial plans (RPI), and KBP RapidPlan final plans (RPF), which received further re-optimization to meet the dose constraints. PATIENTS AND METHODS: Dose distributions in the target were evaluated in terms of coverage, mean dose, conformity index (CI), and homogeneity index (HI). The dosimetric results of OARs, planning time, and monitor unit (MU) were evaluated. RESULTS: All MP and RPF plans met the plan goals, and 89.36% of RPI plans met the plan goals. The Wilcoxon tests showed comparable target coverage, CI, and HI for the MP and RPF groups; however, worst plan quality was demonstrated in the RPI plans than in MP and RPF. For the OARs, RPF and RPI groups had better dosimetric results than the MP group (P < 0.05 for optic nerves, eyes, parotid glands, and heart). The planning time was significantly reduced by the KBP from an average of 677.80 min in MP to 227.66 min (P < 0.05) and 307.76 min (P < 0.05) in RPI, and RPF, respectively. MU was not significantly different between these three groups. CONCLUSIONS: The KBP can significantly reduce planning time in CSI. Manual re-optimization after the initial KBP is recommended to enhance the plan quality.

Does Music Intervention Improve Anxiety in Dementia Patients? A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Music interventions (MIs) have been widely used to relieve anxiety in dementia in clinical settings. However, limited meta-analysis with randomized controlled trials (RCTs) on this topic has been conducted so far. A systematic search was conducted in four major databases (PubMed, EMBASE, Web of Science, and Cochrane Library) for data provided by RCTs from the inception to February 2023. The search strategy employed the terms "anxiety AND music AND dementia OR Alzheimer's disease". Thirteen RCTs (827 participants) were included. The results showed MI reduced anxiety significantly (SMD = -0.67, p < 0.001), especially for Alzheimer's disease (p = 0.007) and Mixed (p < 0.001)-type dementia. Moreover, significant improvements in agitation (p = 0.021) and depression (p < 0.001) in dementia were observed. Additionally, several psychological mechanisms which may be associated with MI were reviewed comprehensively. In conclusion, our findings support the efficacy of MI in alleviating anxiety symptoms in dementia patients. PROSPERO Registration (ID: CRD42021276646).

A Real-Time Affective Computing Platform Integrated with AI System-on-Chip Design and Multimodal Signal Processing System.

Recently, deep learning algorithms have been used widely in emotion recognition applications. However, it is difficult to detect human emotions in real-time due to constraints imposed by computing power and convergence latency. This paper proposes a real-time affective computing platform that integrates an AI System-on-Chip (SoC) design and multimodal signal processing systems composed of electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) signals. To extract the emotional features of the EEG, ECG, and PPG signals, we used a short-time Fourier transform (STFT) for the EEG signal and direct extraction using the raw signals for the ECG and PPG signals. The long-term recurrent convolution networks (LRCN) classifier was implemented in an AI SoC design and divided emotions into three classes: happy, angry, and sad. The proposed LRCN classifier reached an average accuracy of 77.41% for cross-subject validation. The platform consists of wearable physiological sensors and multimodal signal processors integrated with the LRCN SoC design. The area of the core and total power consumption of the LRCN chip was 1.13 x 1.14 mm2 and 48.24 mW, respectively. The on-chip training processing time and real-time classification processing time are 5.5 µs and 1.9 µs per sample. The proposed platform displays the classification results of emotion calculation on the graphical user interface (GUI) every one second for real-time emotion monitoring.Clinical relevance- The on-chip training processing time and real-time emotion classification processing time are 5.5 µs and 1.9 µs per sample with EEG, ECG, and PPG signal based on the LRCN model.

Amplified spontaneous emission from ZnO in n-ZnO/ZnO nanodots-SiO(2) composite/p-AlGaN heterojunction light-emitting diodes.

This study demonstrates amplified spontaneous emission (ASE) of the ultraviolet (UV) electroluminescence (EL) from ZnO at lambda~380 nm in the n-ZnO/ZnO nanodots-SiO(2) composite/p- Al(0.12)Ga(0.88)N heterojunction light-emitting diode. A SiO(2) layer embedded with ZnO nanodots was prepared on the p-type Al(0.12)Ga(0.88)N using spin-on coating of SiO(2) nanoparticles followed by atomic layer deposition (ALD) of ZnO. An n-type Al-doped ZnO layer was deposited upon the ZnO nanodots-SiO(2) composite layer also by the ALD technique. High-resolution transmission electron microscopy (HRTEM) reveals that the ZnO nanodots embedded in the SiO(2) matrix have diameters of 3-8 nm and the wurtzite crystal structure, which allows the transport of carriers through the thick ZnO nanodots-SiO(2) composite layer. The high quality of the n-ZnO layer was manifested by the well crystallized lattice image in the HRTEM picture and the low-threshold optically pumped stimulated emission. The low refractive index of the ZnO nanodots-SiO(2) composite layer results in the increase in the light extraction efficiency from n-ZnO and the internal optical feedback of UV EL into n-ZnO layer. Consequently, significant enhancement of the UV EL intensity and super-linear increase in the EL intensity, as well as the spectral narrowing, with injection current were observed owing to ASE in the n-ZnO layer.