This study aimed to investigate the effectiveness of neuromuscular electrical stimulation (NMES) blended with early rehabilitation on the diaphragm and skeletal muscle in sufferers on mechanical ...ventilation (MV).
This is a prospective randomized controlled study. Eighty patients on MV for respiratory failure were divided into a study group (40 cases) and a control group (40 cases) randomly. The study group adopted a treatment method of NMES combined with early rehabilitation and the control group adopted the method of early rehabilitation only. The diaphragmatic excursion (DE), diaphragmatic thickening fraction (DTF), variation of thickness of intercostal muscles (TIM), variation of thickness of rectus abdominis (TRA), and variation of the cross-sectional area of rectus femoris (CSA-RF) were measured to evaluate the therapeutic effect by ultrasound before and after intervention at the first day of MV, the 3rd and 7th day of intervention and the day discharged from ICU.
No significant difference was found in the general demographic information and ultrasound indicators between the two groups before treatment (all P > 0.05). After treatment, the variation of DTF (0.15 ± 0.05% vs. 0.12 ± 0.04%, P = 0.034) was significantly higher in the study group than that in the control group on the day discharged from ICU. The variation of TRA (0.05 ± 0.09% vs. 0.10 ± 0.11%, P = 0.029) and variation of CSA-RF (0.13 ± 0.07% vs. 0.19 ± 0.08%, P < 0.001) in the study group were significantly lower than that in the control group. The duration of MV in the study group was significantly shorter than that in the control group 109.5 (88.0, 213.0) hours vs. 189.5 (131.5, 343.5) hours, P = 0.023. The study group had better muscle strength score than the control group at discharge (52.20 ± 11.70 vs. 44.10 ± 15.70, P = 0.011).
NMES combined with early rehabilitation therapy is beneficial in reducing muscle atrophy and improving muscle strength in mechanically ventilated patients. This treatment approach may provide a new option for patients to choose a rehabilitation program; however, more research is needed to fully evaluate the effectiveness of this treatment option.
The influence of grain boundary segregation on corrosion behaviour of AA5083 alloy remains unclear. In the present work, the segregation in both as-received and sensitized AA5083 alloys was examined ...via transmission electron microscopy (TEM). The influence of TEM specimen preparation methods on grain boundary segregation detection was explored. It is revealed that focussed ion beam (FIB) is reliable for the detection of grain boundary segregation. Further, the influence of grain boundary segregation on intergranular corrosion (IGC) of AA5083 alloy was examined. It is indicated that Mg segregation at grain boundary is decisive for IGC development in AA5083-H116 alloy, which also benefits IGC development in sensitized AA5083-H116 alloy by increasing the continuity of grain boundary micro-chemistry.
With the explosive growth of mobile computing, new modes of human-computer interaction (HCI) are emerging and becoming feasible. Compared to vision-based systems that require lighting, radio ...frequency (RF)-based hand motion detection systems are becoming more popular in HCI applications. In real RF hand motion detection scenarios, the line-of-sight between the transmitter (Tx) and receiver (Rx) is usually blocked. Hence, shadowing significantly affects the detection accuracy. To design better RF hand motion detection systems, we propose a simple diffraction and interference model (DIM) to interpret the received signal strength (RSS) variation caused by hand motions in the shadowing scenario. Based on theories of knife-edge diffraction and mutual radio interference, DIM provides a simple theoretical foundation for analyzing the RSS variation with hand size, signal frequency, and Tx-Rx distance. Furthermore, a model-based RF hand motion detection system benefiting from DIM is presented. Unlike existing systems that require a large number of motion features to train a motion classifier, the model-based system achieves training-free motion classification, which has potential for hand motion detection on a real-time basis. Empirical data collected from a vector network analyzer validate our system as well as demonstrate a simple diffraction model can help hand motion detection processing for commonly growing HCI applications.
BCG is widely used for cancer treatment, where macrophages play an important role. However, the mechanism of BCG affecting macrophages remains poorly understood. In this study, we used BCG to ...stimulate myeloid-derived macrophages lacking HIF-1α, the levels of TNF-α, IL-1β, CD86 of macrophages and their effects on the growth of tumor cells MCA207 and B16-F10 were detected. We found that the absence of HIF-1α prevents BCG-stimulated macrophages from polarizing towards the M (BCG) and attenuating its killing effect on tumor cells. In addition, we demonstrated that the tumors of mice lacking HIF-1α in macrophages were significantly increased by the experiment of mice transplantation. Our study provides relevant evidence for exploring the mechanism of the BCG vaccine in the prevention and treatment of related diseases.
► In situ study on atmospheric corrosion under thin electrolyte layer. ► In situ study on atmospheric corrosion under direct current electric field. ► Effect of direct current electric field on ...atmospheric corrosion mechanism.
A thin layer electrochemical cell was successfully developed to study the atmospheric corrosion behavior of copper film in printed circuit board (PCB-Cu) under thin electrolyte layer (TEL) and direct current electric field (DCEF) by electrochemical impedance and electrochemical noise analysis. The electrochemical measurements and SEM morphologies after corrosion test indicate that DCEF decreases the corrosion of PCB-Cu under TEL. The corrosion rate and probability of pitting corrosion of PCB-Cu under DCEF decrease due to the electric migration of aggressive Cl
− ion out of working electrode surface.
This protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer ...(EGC) in routine oesophagogastroduodenoscopy (EGD).
Study design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial.
The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD.
Adults who underwent screening, diagnosis, or surveillance EGD. Randomisation groups: 1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC; 2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC.
Block randomisation, stratified by centre.
Detection rates of gastric neoplasms and EGC.
Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD.
Outcomes are undertaken by blinded assessors.
Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000.
The results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings.
ChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.
We investigated the effects of early rehabilitation therapy on prolonged mechanically ventilated patients after coronary artery bypass surgery (CABG). A total of 106 patients who underwent CABG ...between June 2012 and May 2015 were enrolled and randomly assigned into an early rehabilitation group (53 cases) and a control group (53 cases). The rehabilitation therapy consisted of 6 steps including head up, transferring from supination to sitting, sitting on the edge of bed, sitting in a chair, transferring from sitting to standing, and walking along a bed. The patients received rehabilitation therapy in the intensive care unit (ICU) after CABG in the early rehabilitation group. The control group patients received rehabilitation therapy after leaving the ICU. The results showed that the early rehabilitation therapy could significantly decrease the duration of mechanical ventilation (early rehabilitation group: 8.1 ± 3.3 days; control group: 13.9 ± 4.1 days, P < 0.01), hospital stay (early rehabilitation group: 22.0 ± 3.8 days; control group: 29.1 ± 4.6 days, P < 0.01), and ICU stay (early rehabilitation group: 11.7 ± 3.2 days; control group: 18.3 ± 4.2 days, P < 0.01) for patients requiring more than 72 hours prolonged mechanical ventilation. The results of Kaplan-Meier analysis showed that the proportions of patients remaining on mechanical ventilation in the early rehabilitation group were larger than that in the control group after 7 days of rehabilitation therapy (logrank test: P < 0.01). The results provide evidence for supporting the application of early rehabilitation therapy in patients requiring prolonged mechanical ventilation after CABG.
Background:
Changes in gastric mucosa caused by Helicobacter pylori (H. pylori) infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that ...computer-aided diagnosis (CAD) systems have great potential in the diagnosis of H. pylori infection, their explainability remains a challenge.
Objective:
We aim to develop an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) and giving diagnostic basis under endoscopy.
Design:
A case–control study.
Methods:
We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for H. pylori infection. EADHI’s performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing H. pylori infection.
Results:
The system extracted mucosal features for diagnosing H. pylori infection with an overall accuracy of 78.3% 95% confidence interval (CI): 76.2–80.3. The accuracy of EADHI for diagnosing H. pylori infection (91.1%, 95% CI: 85.7–94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6–95.7) in external test. Mucosal edema was the most important diagnostic feature for H. pylori positive, while regular arrangement of collecting venules was the most important H. pylori negative feature.
Conclusion:
The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs.
Plain language summary
An explainable AI system for Helicobacter pylori with good diagnostic performance
Helicobacter pylori (H. pylori) is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by H. pylori infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify H. pylori infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in H. pylori infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify H. pylori infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% 95% confidence interval (CI): 85.7–94.6, which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6–95.7) in external tests. The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past H. pylori infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs.
Wireless information networks have become a necessity of our day-to-day life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of ...wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics.
Following our previous research, the correlation between the micro-chemistry of grain boundary and the distribution of stored energy in AA2024-T3 alloy is investigated, using the combination of ...transmission Kikuchi diffraction and transmission electron microscopy. It is found that the difference of dislocation density, namely stored energy, between two neighboring grains significantly affects the micro-chemistry of the grain boundary. Further, it is revealed that intergranular corrosion development in the AA2024-T3 alloy is mainly attributed to the combined effect of grain boundary chemistry and stored energy distribution.
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