A comprehensive comparison of molecular and phenotypic profiles between hepatitis B virus (HBV)-infected and non-HBV-infected hepatocellular carcinoma by multi-omics analysis.

Hepatitis B virus (HBV) infection is a major etiology of hepatocellular carcinoma (HCC). An interesting question is how different are the molecular and phenotypic profiles between HBV-infected (HBV+) and non-HBV-infected (HBV-) HCCs? Based on the publicly available multi-omics data for HCC, including bulk and single-cell data, and the data we collected and sequenced, we performed a comprehensive comparison of molecular and phenotypic features between HBV+ and HBV- HCCs. Our analysis showed that compared to HBV- HCCs, HBV+ HCCs had significantly better clinical outcomes, higher degree of genomic instability, higher enrichment of DNA repair and immune-related pathways, lower enrichment of stromal and oncogenic signaling pathways, and better response to immunotherapy. Furthermore, in vitro experiments confirmed that HBV+ HCCs had higher immunity, PD-L1 expression and activation of DNA damage response pathways. This study may provide insights into the profiles of HBV+ and HBV- HCCs, and guide rational therapeutic interventions for HCC patients.

Multi-step validation of a Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis prediction system integrating multi-modal data: A multi-center study.

BACKGROUND AND STUDY AIMS: The impact of various categories of information on the prediction of Post Endoscopic Retrograde Cholangiopancreatography Pancreatitis (PEP) remains uncertain. We aimed to comprehensively investigate the risk factors associated with PEP by constructing and validating a model incorporating multi-modal data through multiple steps. PATIENTS AND METHODS: A total of 1,916 cases underwent ERCP were retrospectively collected from multiple centers for model construction. Through literature research, 49 electronic health record (EHR) features and one image feature related to PEP were identified. The EHR features were categorized into baseline, diagnosis, technique, and prevent strategies, covering pre-ERCP, intra-ERCP, and peri-ERCP phases. We first incrementally constructed models 1-4 incorporating these four feature categories, then added the image feature into models 1-4 and developed models 5-8. All models underwent testing and comparison using both internal and external test sets. Once the optimal model was selected, we conducted comparison among multiple machine learning algorithms. RESULTS: Compared with model 2 incorporating baseline and diagnosis features, adding technique and prevent strategies (model 4) greatly improved the sensitivity (63.89% vs 83.33%, p<0.05) and specificity (75.00% vs 85.92%, p<0.001). Similar tendency was observed in internal and external tests. In model 4, the top three features ranked by weight were previous pancreatitis, NSAIDS, and difficult cannulation. The image-based feature has the highest weight in model 5-8. Lastly, model 8 employed Random Forest algorithm showed the best performance. CONCLUSIONS: We firstly developed a multi-modal prediction model for identifying PEP with clinical-acceptable performance. The image and technique features are crucial for PEP prediction.

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. METHODS: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi-supervised algorithms. Then we selected diagnosis-related features through literature research and developed feature-extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature-extraction models and sole DL model were combined and inputted into seven machine-learning (ML) based fitting-diagnosis models. The optimal model was selected as ENDOANGEL-WD (whitish-diagnosis) and compared with endoscopists. RESULTS: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL-WD. ENDOANGEL-WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). CONCLUSIONS: We developed a novel system ENDOANGEL-WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially.

Mendelian randomization and transcriptomic analysis reveal an inverse causal relationship between Alzheimer's disease and cancer.

BACKGROUND: Alzheimer's disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient. METHODS: Based on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes' upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types. RESULTS: Two-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types. CONCLUSION: AD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases.

Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video).

BACKGROUND AND AIMS: EGD is essential for GI disorders, and reports are pivotal to facilitating postprocedure diagnosis and treatment. Manual report generation lacks sufficient quality and is labor intensive. We reported and validated an artificial intelligence-based endoscopy automatic reporting system (AI-EARS). METHODS: The AI-EARS was designed for automatic report generation, including real-time image capturing, diagnosis, and textual description. It was developed using multicenter datasets from 8 hospitals in China, including 252,111 images for training, 62,706 images, and 950 videos for testing. Twelve endoscopists and 44 endoscopy procedures were consecutively enrolled to evaluate the effect of the AI-EARS in a multireader, multicase, crossover study. The precision and completeness of the reports were compared between endoscopists using the AI-EARS and conventional reporting systems. RESULTS: In video validation, the AI-EARS achieved completeness of 98.59% and 99.69% for esophageal and gastric abnormality records, respectively, accuracies of 87.99% and 88.85% for esophageal and gastric lesion location records, and 73.14% and 85.24% for diagnosis. Compared with the conventional reporting systems, the AI-EARS achieved greater completeness (79.03% vs 51.86%, P < .001) and accuracy (64.47% vs 42.81%, P < .001) of the textual description and completeness of the photo-documents of landmarks (92.23% vs 73.69%, P < .001). The mean reporting time for an individual lesion was significantly reduced (80.13 ± 16.12 seconds vs 46.47 ± 11.68 seconds, P < .001) after the AI-EARS assistance. CONCLUSIONS: The AI-EARS showed its efficacy in improving the accuracy and completeness of EGD reports. It might facilitate the generation of complete endoscopy reports and postendoscopy patient management. (Clinical trial registration number: NCT05479253.).

A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).

BACKGROUND: White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data. METHODS: WL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability. RESULTS: Model 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159). CONCLUSIONS: The multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.

Effect of neuromuscular electrical stimulation combined with early rehabilitation therapy on mechanically ventilated patients: a prospective randomized controlled study.

BACKGROUND: 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). METHOD: 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. RESULTS: 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). CONCLUSION: 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.

Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios.

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.

Problem Solving and Emotion Coping Styles for Social Anxiety: A Meta-analysis of Chinese Mainland Students.

Studies of how positive and negative coping styles affect social anxiety show mixed results. Hence, our two meta-analyses determined the overall effect sizes of problem solving-focused coping (PSC) styles and emotion-focused coping (EFC) styles on social anxiety in mainland China (PSC: k = 49 studies, N = 34,669; EFC: k = 52, N = 36,531). PSC was negatively linked to social anxiety (- .198), and EFC was positively linked to social anxiety (.223). In years with more national income, PSC's and EFC's effect sizes were larger. PSC's effect sizes were smaller among rural students (vs. urban students), larger among older students (university, high school, middle school), and larger in cross-sectional (vs. longitudinal) studies. When using SAD (vs. others) social anxiety measures, PSC effect sizes were larger, but EFC effect sizes were smaller. EFC effect sizes were larger in studies with convenience (vs. representative) samples. Gender, single child status, and coping style measurement showed no moderation effects. These findings suggest that using problem solving-focused coping styles rather than emotion-focused may reduce social anxiety, so future experimental studies can test this idea more rigorously.

Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos).

BACKGROUND AND AIMS: Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning-based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE). METHODS: M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice. RESULTS: A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice. CONCLUSIONS: This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.).

Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos).

BACKGROUND AND AIMS: We aimed to develop and validate a deep learning-based system that covers various aspects of early gastric cancer (EGC) diagnosis, including detecting gastric neoplasm, identifying EGC, and predicting EGC invasion depth and differentiation status. Herein, we provide a state-of-the-art comparison of the system with endoscopists using real-time videos in a nationwide human-machine competition. METHODS: This multicenter, prospective, real-time, competitive comparative, diagnostic study enrolled consecutive patients who received magnifying narrow-band imaging endoscopy at the Peking University Cancer Hospital from June 9, 2020 to November 17, 2020. The offline competition was conducted in Wuhan, China, and the endoscopists and the system simultaneously read patients' videos and made diagnoses. The primary outcomes were sensitivity in detecting neoplasms and diagnosing EGCs. RESULTS: One hundred videos, including 37 EGCs and 63 noncancerous lesions, were enrolled; 46 endoscopists from 44 hospitals in 19 provinces in China participated in the competition. The sensitivity rates of the system for detecting neoplasms and diagnosing EGCs were 87.81% and 100%, respectively, significantly higher than those of endoscopists (83.51% [95% confidence interval [CI], 81.23-85.79] and 87.13% [95% CI, 83.75-90.51], respectively). Accuracy rates of the system for predicting EGC invasion depth and differentiation status were 78.57% and 71.43%, respectively, slightly higher than those of endoscopists (63.75% [95% CI, 61.12-66.39] and 64.41% [95% CI, 60.65-68.16], respectively). CONCLUSIONS: The system outperformed endoscopists in identifying EGCs and was comparable with endoscopists in predicting EGC invasion depth and differentiation status in videos. This deep learning-based system could be a powerful tool to assist endoscopists in EGC diagnosis in clinical practice.

A deep learning-based system for real-time image reporting during esophagogastroduodenoscopy: a multicenter study.

BACKGROUND AND STUDY AIMS: Endoscopic reports are essential for the diagnosis and follow-up of gastrointestinal diseases. This study aimed to construct an intelligent system for automatic photo documentation during esophagogastroduodenoscopy (EGD) and test its utility in clinical practice. PATIENTS AND METHODS: Seven convolutional neural networks trained and tested using 210,198 images were integrated to construct the endoscopic automatic image reporting system (EAIRS). We tested its performance through man-machine comparison at three levels: internal, external, and prospective test. Between May 2021 and June 2021, patients undergoing EGD at Renmin Hospital of Wuhan University were recruited. The primary outcomes were accuracy for capturing anatomical landmarks, completeness for capturing anatomical landmarks, and detected lesions. RESULTS: The EAIRS outperformed endoscopists in retrospective internal and external test. A total of 161 consecutive patients were enrolled in the prospective test. The EAIRS achieved an accuracy of 95.2% in capturing anatomical landmarks in the prospective test. It also achieved higher completeness on capturing anatomical landmarks compared with endoscopists: (93.1% vs. 88.8%), and was comparable to endoscopists on capturing detected lesions: (99.0% vs. 98.0%). CONCLUSIONS: The EAIRS can generate qualified image reports and could be a powerful tool for generating endoscopic reports in clinical practice.

The Relationship Between Perceived Parenting Style and Social Anxiety: A Meta-analysis of Mainland Chinese Students.

Previous research on the relationship between parenting style and social anxiety in Chinese youth has been inconsistent, which has made it difficult to consider whether improving parenting may serve as a preventative intervention for social anxiety. The current study aimed to clarify these inconsistencies by examining the strength of the association between positive/negative parenting style and social anxiety among Chinese students and the role of certain moderators in those associations. A meta-analysis was conducted on 53 studies with a total sample of 26,024 Chinese mainland students. Separate analyses were conducted for positive parenting style and social anxiety (N = 24,081), and negative parenting style and social anxiety (N = 24,933). Findings suggest a small negative association exists between positive parenting style and social anxiety, and a small positive association exists between negative parenting style and child social anxiety. Analyses suggested type of social anxiety measures, developmental stage, and gender all moderated the relationships between parenting style and social anxiety. Results clarify the direction of the relationship between parenting and social anxiety amongst Chinese youth and point to particular implications and future directions for policy, practice, and research.

The role of HIF-1α in BCG-stimulated macrophages polarization and their tumoricidal effects in vitro.

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-1ß, 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.

Impact of fenofibrate therapy on serum uric acid concentrations: a review and meta-analysis.

Fenofibrate is a marketed fibric acid derivative for lipid-lowering in patients with lipid disorders. Numerous studies have proven fenofibrate had a certain effect on serum uric acid, here we conducted this study to quantitatively assess the effect of fenofibrate intervention in modulating serum uric acid concentration and the influence on serum creatinine. The PubMed, Embase and Cochrane were systematically searched for randomized controlled trials update to January, 2020. Primary endpoints focused on serum uric acid concentration and serum creatinine concentration. The pooled effects were calculated as weighted mean difference (WMD) by a random-effects model. Finally, 9 studies representing 487 patients were included in the meta-analysis. The meta-analysis demonstrated that fenofibrate significantly reduced serum uric acid levels (WMD -1.32 mg/dL, 95%CI -1.61 to -1.03, p < 0.001) and an elevated level in serum creatinine (WMD 0.09 mg/dL, 95%CI 0.02 to 0.15, p < 0.001) following fenofibrate therapy compared with placebo. The present study provided strong evidence that fenofibrate intervention exerted a significant reduction on serum uric acid and a mild increase on serum creatinine. Meta-analysis suggested that there were no significant association between the serum uric acid lowering effect with either dose or treatment duration. Overall, our meta-analysis ascertained that fenofibrate have potential therapeutic effects in patients with lipid metabolic abnormalities but with mid nephrotoxicity. There is strong evidence to provide future direction of practical application and clinical researches of fenofibrate.

Early rehabilitation relieves diaphragm dysfunction induced by prolonged mechanical ventilation: a randomised control study.

BACKGROUND: Prolonged mechanical ventilation (MV) induces diaphragm dysfunction in patients in the intensive care units (ICUs). Our study aimed to explore the therapeutic efficacy of early rehabilitation therapy in patients with prolonged MV in the ICU. METHODS: Eighty eligible patients who underwent MV for > 72 h in the ICU from June 2019 to March 2020 were enrolled in this prospective randomised controlled trial. The patients were randomly divided into a rehabilitation group (n = 39) and a control group (n = 41). Rehabilitation therapy included six levels of rehabilitation exercises. Diaphragm function was determined using ultrasound (US). RESULTS: Diaphragmatic excursion (DE) and diaphragm thickening fraction (DTF) were significantly decreased in all patients in both groups after prolonged MV (p < 0.001). The rehabilitation group had significantly higher DTF (p = 0.008) and a smaller decrease in DTF (p = 0.026) than the control group after 3 days of rehabilitation training. The ventilator duration and intubation duration were significantly shorter in the rehabilitation group than in the control group (p = 0.045 and p = 0.037, respectively). There were no significant differences in the duration of ICU stay, proportion of patients undergoing tracheotomy, and proportion of recovered patients between the two groups. CONCLUSIONS: Early rehabilitation is feasible and beneficial to ameliorate diaphragm dysfunction induced by prolonged MV and advance withdrawal from the ventilator and extubation in patients with MV. Diaphragm US is suggested for mechanically ventilated patients in the ICU. Trial registration Chinese Clinical Trial Registry, ID: ChiCTR1900024046, registered on 2019/06/23.

Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments.

Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments.

Homology modeling and docking studies of ENPP4: a BCG activated tumoricidal macrophage protein.

BACKGROUND: The 3D structure and functions of ENPP4, a protein expressed on the surface of Bacillus Calmette-Guerin (BCG)-activated macrophages, are unknown. In this study, we analyzed the 3D structure of ENPP4 and determined its tumoricidal effects on MCA207 cells. RESULTS: Homology modeling showed that Arg305, Tyr341, Asn291, and Asn295 are important residues in substrate, adenosine triphosphate (ATP), binding. A molecular dynamics study was also carried out to study the stability of ENPP4 (including zinc atoms) as well as its ligand-enzyme complex. BCG increased ENPP4 expression in macrophages, and specific blocking of ENPP4 in BCG-activated macrophages (BAMs) significantly reduced their cytotoxicity against MCA207 cells. CONCLUSIONS: These results indicate that zinc remains inside the ENPP4 protein, a BCG activated tumoricidal macrophage protein, throughout the simulation. Important information for the design of new inhibitors was obtained.

Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System.

The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection of pavements, the thickness estimation of the thin top surface layer is still a difficult problem due to the limitations of GPR resolution and the similar permittivity of asphalt sub-layers. Besides, the detection of some road defects, including inadequate compaction and delamination at interfaces, require further practical study. In this paper, a newly-developed vehicle-mounted GPR detection system is introduced. We used a horizontal high-pass filter and a modified layer localization method to extract the underground layers. Besides, according to lab experiments and simulation analysis, we proposed theoretical methods for detecting the degree of compaction and delamination at the interface, respectively. Moreover, a field test was carried out and the estimated results showed a satisfactory accuracy of the system and methods.

Early Rehabilitation Therapy Is Beneficial for Patients With Prolonged Mechanical Ventilation After Coronary Artery Bypass Surgery.

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.