Your preoperative rehabilitation assistant: A study protocol for the impact of a telemedicine-supported preoperative home rehabilitation program on the prognosis of patients undergoing thoracoscopic surgery.

Background: Reduced cardiorespiratory fitness levels are associated with increased short-term complications after surgery, and potentially exert long-lasting effects on the postoperative lives, work and educational pursuits of patients. Currently, research suggests that lifestyle interventions, such as preoperative physical exercise undertaken by patients themselves, may improve patients' cardiopulmonary fitness and reduce post-operative complications. This study aims to investigate the effectiveness and feasibility of a remote medical supervision model for prehabilitation exercise in patients undergoing thoracoscopic lung tumour resection surgery. Methods/Design: All enrolled patients will participate in a 4-week pre-operative exercise intervention to improve their cardiorespiratory fitness. During this period, patients will wear wearable devices and exercise at home based on exercise prescriptions. The exercise prescription comprises aerobic exercise (three times a week or more), muscle strengthening exercise (twice a week or more), and respiratory muscle exercise (once a day). The primary aim is to investigate whether baseline VO2max could be improved following a 4-week preoperative exercise program. Secondary objectives include changes in forced expiratory volume in 1 s and forced vital capacity, degree of acceptance of the technology, quality of life, handgrip strength, postoperative complication rate and length of hospital stay. Discussion: This study aims to evaluate the influence of preoperative prehabilitation exercises in a telemedicine active supervision mode in patients undergoing thoracoscopic lung tumour resection. As such, results of this trial might have some impact on future implementations of group- and home-based prehabilitation exercises in lung cancers. Trial registration: This study was approved by the Medical Ethics Committee of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology (approval number: TJ-IRB20220564) with registration at ClinicalTrials.gov (identifier: NCT05608759).

Prediction of lung adenocarcinoma prognosis and diagnosis with a novel model anchored in circadian clock-related genes.

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients' data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

Efficacy of ultrasound-guided second serratus anterior plane block on postoperative quality of recovery and analgesia after video-assisted thoracic surgery: a randomized, triple-blind, placebo-controlled study.

Background: Despite widespread application of minimally invasive video-assisted thoracic surgery (VATS), postoperative pain following this procedure is still a constant clinical challenge. Serratus anterior plane (SAP) block is one of the regional analgesic techniques with promising outcomes. However, due to the limited duration of action, optimal analgesia is often not achieved with a single injection. We tested whether in patients who have been subjected to routine SAP block under preoperative anesthesia, the addition of a second SAP block 24 hours after surgery, improves quality of recovery, lowers postoperative opioid consumption, and reduces the prevalence of chronic pain. Methods: The present study is a single institutional, prospective, randomized, triple-blinded, placebo-controlled study. Ninety patients undergoing VATS from January 2022 to April 2022 were randomized at 1:1 ratio to receive ultrasound-guided second SAP block with 15 mL 0.375% ropivacaine (SAP block group) or 15 mL normal saline (control group) 24 hours after both groups received routine SAP block with 15 mL 0.375% ropivacaine. The primary outcome was quality of patient recovery, measured using 40-item quality of recovery questionnaire (QoR-40) at postoperative day 2 (POD 2). Secondary outcomes included: postoperative pain scores at rest, postoperative opioid consumptions, number of times that patient controlled analgesia (PCA) pump button was pressed, perioperative complications and adverse effects, prevalence of chronic pain at 2nd and 3rd month postoperatively, and length of hospital stay (LOS). Results: A total of 83 patients completed the study: 43 patients in SAP block group and 40 patients in the control group. The global QoR-40 scores on POD 2 and POD 3 were significantly higher among SAP block group patients (180.07±11.34, 182.09±8.20) compared with the control group (172.18±6.15, 177.50±6.94) (P=0.01, P=0.008) respectively. Postoperative pain scores, opioid consumptions and incidence of postoperative nausea and vomiting were significantly lower among patients in SAP block group versus control group. There were no statistically significant differences in perioperative complications and LOS between the two groups. The prevalence of chronic pain at the 2nd and 3rd month postoperatively for patients in SAP block group and control group was 16.3%, 14%, and 32.5%, 27.5% respectively. Conclusions: In patients undergoing VATS, application of ultrasound-guided second SAP block 24 hours after surgery improved postoperative quality of life, reduced opioid consumption and related side effects, and lowered the prevalence of chronic pain.

A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network.

As a result of long-term pressure from train operations and direct exposure to the natural environment, rails, fasteners, and other components of railway track lines inevitably produce defects, which have a direct impact on the safety of train operations. In this study, a multiobject detection method based on deep convolutional neural network that can achieve nondestructive detection of rail surface and fastener defects is proposed. First, rails and fasteners on the railway track image are localized by the improved YOLOv5 framework. Then, the defect detection model based on Mask R-CNN is utilized to detect the surface defects of the rail and segment the defect area. Finally, the model based on ResNet framework is used to classify the state of the fasteners. To verify the robustness and effectiveness of our proposed method, we conduct experimental tests using the ballast and ballastless railway track images collected from Shijiazhuang-Taiyuan high-speed railway line. Through a variety of evaluation indexes to compare with other methods using deep learning algorithms, experimental results show that our method outperforms others in all stages and enables effective detection of rail surface and fasteners.

Research on track fastener positioning method based on local unidirectional template matching.

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

AFI-Net: Attention-Guided Feature Integration Network for RGBD Saliency Detection.

This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net.

Data on photovoltaic system using different perturb and observe methods under fast multi-changing solar irradiances.

This article presents the data on photovoltaic (PV) system used different perturb and observe (P&O) methods under fast multi-changing solar irradiances. The mathematical modeling of the PV system and tangent error P&O method was discussed in our previous study entitled "A novel tangent error maximum power point tracking algorithm for photovoltaic system under fast multi-changing solar irradiances" by Peng et al. (2018) [1]. The data provided in this paper can be used directly without having to spend weeks to simulate the output performance. In addition, it is easy to apply the results for comparison with other algorithms (Kollimalla et al., 2014; Belkaid et al., 2016; Chenchen et al., 2015; Jubaer and Zainal, 2015) [2,3,4,5], and develop a new method for practical application.