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Objective Methods Results Conclusion In this study, we investigated the safety and practicability of ultra‐fast track anesthesia (UFTA) for endoscopic thoracic sympathectomy (ETS).A total of 72 patients with palmar hyperhidrosis undergoing ETS were randomly divided into three groups: the UFTA group (group I), the group undergoing single‐lumen tracheal intubation with local infiltration anesthesia technique (group II), and the group undergoing single‐lumen tracheal intubation with routine anesthesia (group III). Mean arterial pressure (MAP) and heart rate (HR) were recorded for all three groups at the following six time points: Before anesthetics administration (T0), the time of intubating or inserting laryngeal mask airway (T1), the time of incising skin (T2), the time of disconnecting of the right sympathetic nerve (T3), the time of disconnecting of the left sympathetic nerve (T4), the time of withdrawing the tracheal tube or laryngeal mask airway (T5), and the time of transferring the patient to a post‐anesthesia care unit (PACU) (T6). The three groups were compared from the following perspectives: surgery duration; anesthesia recovery duration, that is, the duration from discontinuation of anesthesia to extubating the tracheal tube; the dose of propofol and remifentanil per kilogram body mass per unit time interval (the time at the end of the procedure, which lasted from anesthesia induction to incision suturing); and the visual analog scale (VAS) in the resting state in the PACU.Based on pairwise comparisons, the average HR and average MAP values of the three groups differed significantly from T2 to T6 (p < 0.05). As demonstrated by the correlation analysis between remifentanil and propofol with HR and MAP, the doses of the total amount of remifentanil and propofol were lower, and group I used less remifentanil and propofol than group II. No patient in group I experienced throat discomfort following surgery. Patients in groups II and III experienced a range of postoperative discomfort. The VAS scores of groups I and II were significantly lower than those of group III, with group I lower than group II.When utilized in ETS, UFTA can provide effective anesthesia for minor traumas. It is safe, effective, and consistent with the enhanced recovery philosophy of fast‐track surgery departments. [ABSTRACT FROM AUTHOR]

Sensor-based rehabilitation physical training assessment methods have attracted significant attention in refined evaluation scenarios. A refined rehabilitation evaluation method combines the expertise of clinicians with advanced sensor-based technology to capture and analyze subtle movement variations often unobserved by traditional subjective methods. Current approaches center on either body postures or muscle strength, which lack more sophisticated analysis features of muscle activation and coordination, thereby hindering analysis efficacy in deep rehabilitation feature exploration. To address this issue, we present a multimodal network algorithm that integrates surface electromyography (sEMG) and stress distribution signals. The algorithm considers the physical knowledge a priori to interpret the current rehabilitation stage and efficiently handles temporal dynamics arising from diverse user profiles in an online setting. Besides, we verified the performance of this model using a learned-nonuse phenomenon assessment task in 24 subjects, achieving an accuracy of 94.7%. Our results surpass those of conventional feature-based, distance-based, and ensemble baseline models, highlighting the advantages of incorporating multimodal information rather than relying solely on unimodal data. Moreover, the proposed model presents a network design solution for rehabilitation physical training that requires deep bioinformatic features and can potentially assist real-time and home-based physical training work.

Introduction: The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data., Methods: In this paper, a novel U -shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U -shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics., Results: We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement., Conclusion: Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zuo, Li, Shi, Hong, Zhu, Chen, Wu and Guo.)