Evaluation of the clinical efficacy of ultra-fast track anesthesia for endoscopic thoracic sympathectomy of palmar hyperhidrosis.

OBJECTIVE: In this study, we investigated the safety and practicability of ultra-fast track anesthesia (UFTA) for endoscopic thoracic sympathectomy (ETS). METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Temporal Dynamics and Physical Priori Multimodal Network for Rehabilitation Physical Training Evaluation.

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.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis.

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.

Generative AI for brain image computing and brain network computing: a review.

Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.

WMNN: Wearables-Based Multi-Column Neural Network for Human Activity Recognition.

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis.

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven models. Specifically, the label distribution is estimated to regularize the latent space learned by the graph encoder, which can make the learning process stable and the learned representation robust. Also, the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions. The typical topological properties and discriminative features can be preserved entirely. Furthermore, the generated brain functional networks improve the prediction performance using different classifiers, which can be applied to analyze other cognitive diseases. Attempts on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed model can generate good brain functional networks. The classification results show adding generated data can achieve the best accuracy value of 85.33%, sensitivity value of 84.00%, specificity value of 86.67%. The proposed model also achieves superior performance compared with other related augmented models. Overall, the proposed model effectively improves cognitive disease diagnosis by generating diverse brain functional networks.