[Identification of key genes in Wilms tumor based on high-throughput RNA sequencing and their impacts on prognosis and immune responses]

OBJECTIVE

To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients.

METHODS

High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor.

RESULTS

Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5- and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells.

CONCLUSION

TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Motion states identification of underwater glider based on complex networks and graph convolutional networks

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

BACKGROUND AND OBJECTIVE

Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet.

METHODS

We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance.

RESULTS

We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%.

CONCLUSION

This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.

WS-MTST: Weakly Supervised Multi-Label Brain Tumor Segmentation With Transformers

Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this article, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

Norovirus outbreaks in hospitals in China: a systematic review

BACKGROUND

Norovirus outbreaks in hospitals can potentially impair patient care and result in significant financial expenses. There is currently limited information on hospital norovirus outbreaks in the Chinese mainland.

AIM

To systematically review the published literature to describe the characteristics of norovirus outbreaks in Chinese mainland hospitals to facilitate prompt identification and control of outbreaks.

METHODS

A systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analysis standards. Databases including PubMed, Web of Science, and Chinese Journals Online databases (China National Knowledge Infrastructure (CNKI), Chinese Wan Fang digital database (WANFANG) were searched from inception to July 18th, 2022.

FINDINGS

A total of 41 norovirus Chinese hospital outbreaks occurring before July 18th, 2022 were reported in 32 articles. Most reported outbreaks were from Shanghai and Beijing, and occurred in December and January. Cases were mainly adults. The male:female ratio was 1.22:1. The majority of cases in norovirus outbreaks were hospitalized patients (56.82%); medical staff were affected in 15 outbreaks. Norovirus outbreaks occurred in both private and public hospitals, and in secondary and tertiary care centres, and occurred mainly in internal medicine and geriatric departments. Person-to-person transmission was the primary transmission mode and GII was more prevalent.

CONCLUSION

Norovirus outbreaks in hospitals can affect both patients and healthcare workers, sometimes causing serious financial losses. In order to have a more complete understanding of the disease burden caused by norovirus outbreaks, surveillance needs to be established in hospitals.

[Epidemiological and clinical characteristics of human ocular helaziasis in China from 2011 to 2022 based on bibliometrics]

OBJECTIVE

To understand the clinical and epidemiological characteristics of human ocular thelaziasis patients in China.

METHODS

Case reports regarding human ocular thelaziasis cases in China were retrieved in international and national electronic databases, including CNKI, VIP, CBM, Traditional Chinese Medical Literature Analysis and Retrieval System, Wanfang Database, PubMed and Web of Science from 2011 to 2022. Patients' gender, age, clinical symptoms, treatment, recurrence, site of infections, time of onset, affected eye, affected sites, number of infected Thelazia callipaeda, sex of T. callipaeda and source of infections were extracted for descriptive analyses.

RESULTS

A total of 85 eligible publications were included, covering 101 cases of human ocular thelaziasis, including 57 males (56.44%) and 44 females (43.56%) and aged from 3 months to 85 years. The main clinical manifestations included foreign body sensation (56 case-times, 22.49%), eye itching (38 case-times, 15.26%), abnormal or increased secretions (36 case-times, 14.46%), tears (28 case-times, 11.24%) and eye redness (28 case-times, 11.24%), and conjunctival congestion (50 case-times, 41.67%) was the most common clinical sign. The most common main treatment (99/101, 98.02%) was removal of parasites from eyes using ophthalmic forceps, followed by administration with ofloxacin and pranoprofen. In publications presenting thelaziasis recurrence, there were 90 cases without recurrence (97.83%) and 2 cases with recurrence (2.17%). Of all cases, 51.96% were reported in four provinces of Hubei, Shandong, Sichuan, Hebei and Henan, and ocular thelaziasis predominantly occurred in summer (42.19%) and autumn (42.19%). In addition, 56.45% (35/62) had a contact with dogs.

CONCLUSIONS

The human thelaziasis cases mainly occur in the continental monsoon and subtropical monsoon climate areas such as the Yellow River and the Yangtze River basin, and people of all ages and genders have the disease, with complex clinical symptoms and signs. Personal hygiene is required during the contact with dogs, cats and other animals, and individual protection is required during outdoor activities to prevent thelaziasis.

[Evaluation of the clinical manifestations of COVID-19 in patients with aplastic anemia undergoing immunosuppressive therapy: a prospective cohort study (NICHE)]

Objective: To investigate the clinical features of coronavirus disease 2019 (COVID-19) in patients with aplastic anemia (AA) undergoing immunosuppressive therapy (IST) . Methods: In this prospective cohort study, we collected the demographic and clinical data of patients with AA and COVID-19 from December 1, 2022, to January 31, 2023. We described the clinical features of COVID-19 among patients with AA and evaluated the effects of IST on the signs and severity of COVID-19. Results: A total of 170 patients with AA and COVID-19 were included. The common early symptoms, including fever, dizziness or headache, muscle or body aches, and sore throat, disappeared within 1-2 weeks. Approximately 25% of the patients had persistent fatigue within 2 weeks. Many patients experienced cough after an initial 1-3 days of infection, which lasted for more than 2 weeks. There were no differences in the duration of total fever episodes and maximum body temperature when patients were stratified according to whether or not they underwent IST, by IST duration, or by use of anti-lymphocyte globulin (ALG) (P>0.05). No differences were observed in the occurrence of symptoms in either the early or recovery stages when patients with AA were stratified according to whether or not they underwent IST, or by IST duration (P>0.05). However, patients who received ALG had fewer fever episodes within 1 week after infection (P=0.035) and more sore throat episodes within 2 weeks after infection (P=0.015). There were no other significant differences in clinical symptoms between patients who did and patients who did not receive ALG (P>0.05) . Conclusion: The majority of patients with AA and COVID-19 recovered within 2 weeks of noticing symptoms when treated with IST.

Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net.

[Y-shaped osteotomy in the apical vertebra for treating congenital complex rigid scoliosis:at least 2-year follow-up]

Objective: To investigate the clinical outcome of the coronal Y-shaped osteotomy in the apical vertebra for treating congenital complex rigid scoliosis. Methods: A retrospective analysis was conducted on 66 cases who underwent Y-shaped osteotomy treatment for congenital complex rigid scoliosis in the uppermost vertebra at the Department of Orthopedics,the Second Hospital of Shanxi Medical University from June 2007 to August 2020. There were 19 males and 47 females,with an age of (13.1±5.3) years(range:2 to 30 years).Classification of congenital scoliosis:25 cases (37.9%) were incomplete,13 cases (19.7%) were dysarthritic,and 28 cases (42.4%) were mixed. There were 25 cases (37.9%) with thoracic or rib malformations. 45 cases (68.2%) were complicated with spinal cord malformation.The main radiological indicators included Cobb angle of the curvature,Cobb angle of the local bend,apical vertebral translation (AVT),trunk shift (TS),thoracic trunk shift (TTS),radiographic shoulder height (RSH),coronal balance and sagittal vertebral axis. The preoperative,postoperative immediate,and last follow-up radiological indicators were collected and the operation time,blood loss,hospitalization time,and operation-related complications were recorded. Data were compared by repeated measure ANOVA and paired-t test. Results: All patients underwent surgery successfully. The duration of the first surgery was (221.4±52.8) minutes,and the blood loss during the first surgery was (273.2±41.8) ml. The length of the first hospital stay was (8.8±1.7) days.Unilateral fixation was performed in 19 cases (28.8%),while bilateral fixation was performed in 47 cases (71.2%). The fused segments were 7.5±2.9,and the vertebral pedicle screw density was (68.5±20.6)%. The follow-up time for the 66 patients was (36.7±17.0) months(range:24 to 102 months).The main curve Cobb Angle was improved from (58.5±18.9)°before surgery to (21.1±11.8)°after surgery,and was (23.6±15.3) ° at the last follow-up(F=273.957,P<0.01),with a correction rate of 66.2%. Segmental curve Cobb Angle was improved from (47.9±18.0)° to (16.0±11.3)° after surgery,and was (16.8±12.8) °at the last follow-up (F=270.483,P<0.01)with a correction rate of 69.2%. The AVT,TS,TTS and RSH values improved significantly at the final follow-up (all P<0.05),while coronal balance and sagittal vertical axis were maintained without significant differences between pre-operation and post-operation(both P>0.05). A total of 5 patients underwent staged operation,all of which were residual scoliosis aggravated after the first stage of orthosis operation and had good prognosis after the second stage of operation. Conclusions: Y-shaped osteotomy for the treatment of congenital rigid scoliosis results in good clinical and radiological outcomes without serious complications. This procedure can be considered as an option for the treatment of congenital complex rigid scoliosis.

Applying machine learning methods for the analysis of two-dimensional mass spectra

In a measurement of isomeric yield-ratios in fission, the Phase-Imaging Ion-Cyclotron-Resonance technique, which projects the radial motions of ions in the Penning trap (JYFLTRAP) onto a position-sensitive micro-channel plate detector, has been applied. To obtain the yield ratio, that is the relative population of two states of an isomer pair, a novel analysis procedure has been developed to determine the number of detected ions in each state, as well as corrections for the detector efficiency and decay losses. In order to determine the population of the states in cases where their mass difference is too small to reach full separation, a Bayesian Gaussian Mixture model was implemented. The position-dependent efficiency of the micro-channel plate detector was calibrated by mapping it with 133 Cs+ ions, and a Gaussian Process was trained with the position data to construct an efficiency function that could be used to correct the recorded distributions. The obtained numbers of counts of excited and ground-state ions were used to derive the isomeric yield ratio, taking into account decay losses as well as feeding from precursors.

[Evaluation of the clinical efficacy of minimally invasive endoscopic surgery in the treatment of isolated non-syndromic sagittal synostosis in infants]

The current study aimed to evaluate the early efficacy in infants with isolated non-syndromic sagittal synostosis who underwent minimally invasive endoscopic-assisted surgery. The clinical data of infants with isolated non-syndromic sagittal synostosis who were admitted to the Department of Neurosurgery of the Children's Hospital of Nanjing Medical University and underwent endoscopic-assisted surgery from October 2018 to December 2021 were retrospectively analyzed. All the infants underwent minimally invasive endoscopic-assisted surgery, and were treated with supine sleeping position after surgery. Computer-aided reconstruction technique was used to reconstruct and measure the thin-slice CT scan images of the head before and 3 months after surgery, and the differences in cranial index (CI), cranial cavity volume and angle drawn between the cranial vertex, nasion, and opisthocranion (VNO angle) of preoperative and postoperative groups were analyzed. A total of 103 infants were included in the final analysis, including 85 males and 18 females. The age at surgery was (2.1±0.8) months, and the weight was (6.1±0.9) kg. The postoperative CI was (84±6)%, which increased obviously compared with the pre-operation [(70±5)%] (P<0.001). The cranial volume of post-operation was (947±130) cm³, which was larger than that of the pre-operation [(748±104) cm³] (P<0.001). The VNO angle after surgery was (45±4)°, which showed a significant reduction compared with the pre-operation [(55±4)°] (P<0.001). The correction of head shape was satisfactory. For the treatment of sagittal synostosis in infants, minimally invasive endoscopic-assisted surgery is safe and effective, and in the case of switching from an auxiliary helmet to a supine position, the postoperative correction efficacy of head shape is better.

[CD5L is elevated in the serum of patients with candidemia and promotes disease progression in mouse models]

OBJECTIVE

To investigate the changes of CD5L levels in patients with candidemia and explore the role of CD5L in progression of candidemia.

METHODS

Twenty healthy control individuals, 27 patients with bacteremia and 35 patients with candidemia were examined for serum CD5L levels using ELISA, and the correlations of CD5L level with other serological indicators were analyzed. A C57BL/6 mouse model of candidemia induced by intravenous injection of Candida albicans were treated with intraperitoneal injection of recombinant CD5L protein, and renal histopathological and serological changes were analyzed to assess renal injures. The effects of CD5L treatment on general condition, fungal burden, of survival of the mice were observed, and the changes in serum IL-6 and IL-8 levels of the mice were detected using ELISA.

RESULTS

CD5L levels were significantly elevated in patients with candidemia and positively correlated with WBC, BDG, Scr and PCT levels. The mouse model of candidemia also showed significantly increased serum and renal CD5L levels, and CD5L treatment significantly increased fungal burden in the renal tissue, elevated IL-6 and IL-8 levels in the serum and kidney, aggravated renal tissue damage, and reduced survival rate of candidemia mice.

CONCLUSION

Serum CD5L levels are increased in patients with candidemia, and treatment with CD5L aggravates candidemia in mouse models.

[Clinical features and long-term prognosis of diabetic patients with low or intermediate complexity coronary artery disease post percutaneous coronary intervention]

Objective: To investigate the clinical features and long-term prognostic factors of diabetic patients with low or intermediate complexity coronary artery disease (CAD) post percutaneous coronary intervention (PCI). Methods: This was a prospective, single-centre observational study. Consecutive diabetic patients with SYNTAX score (SS)≤32 undergoing PCI between January and December 2013 in Fuwai hospital were included in this analysis. The patients were divided into two groups based on SS, namely SS≤22 group and SS 23-32 group. Multivariate Cox regression analysis was performed to identify independent factors related to poor 5-year prognosis. The primary outcomes were cardiac death and recurrent myocardial infarction, the secondary outcomes were all cause death and revascularization. Results: Of the 3 899 patients included in the study, 2 888 were men (74.1%); mean age was 59.4±9.8 years. There were 3 450 patients in the SS≤22 group and 449 patients in the SS 23-32 group. Compared with SS≤22 group, the incidence of revascularization was higher in SS 23-32 group (18.9% (85/449) vs. 15.2% (524/3450), log-rank P=0.019). There was no significant difference in all-cause death, cardiac death and recurrent myocardial infarction between the two groups (log-rank P>0.05). Multivariate Cox regression analysis showed that age (HR=1.05, 95%CI 1.02-1.08, P<0.001), chronic obstructive pulmonary disease (HR=3.12, 95%CI 1.37-7.07, P=0.007) and creatinine clearance rate (CCr)<60 ml/min (HR=3.67, 95%CI 2.05-6.58, P<0.001) were independent risk factors for 5-year cardiac death, while left ventricular ejection fraction (HR=0.94, 95%CI 0.91-0.96, P<0.001) was a protective factor. Previous PCI (HR=2.04, 95%CI 1.38-3.00, P<0.001), blood glucose level≥11.1 mmol/L on admission (HR=2.49, 95%CI 1.32-4.70, P=0.005) and CCr<60 ml/min (HR=1.85, 95%CI 1.14-2.99, P=0.012) were independent risk factors for 5-year recurrent myocardial infarction. The SS of 23-32 was independently associated with risk of revascularization (HR=1.54, 95%CI 1.09-2.16, P=0.014), after adjusting for residual SS. Residual SS was not a risk factor for 5-year prognosis. Conclusions: In diabetic patients with low-or intermediate complexity CAD, SS 23-32 is associated with increased risk of 5-year revascularization; the clinical characteristics of the patients are associated with the long-term mortality and recurrent myocardial infarction, but not related to revascularization.

EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

Reinforcement learning for robust stabilization of nonlinear systems with asymmetric saturating actuators

We study the robust stabilization problem of a class of nonlinear systems with asymmetric saturating actuators and mismatched disturbances. Initially, we convert such a robust stabilization problem into a nonlinear-constrained optimal control problem by constructing a discounted cost function for the auxiliary system. Then, for the purpose of solving the nonlinear-constrained optimal control problem, we develop a simultaneous policy iteration (PI) in the reinforcement learning framework. The implementation of the simultaneous PI relies on an actor-critic architecture, which employs actor and critic neural networks (NNs) to separately approximate the control policy and the value function. To determine the actor and critic NNs' weights, we use the approach of weighted residuals together with the typical Monte-Carlo integration technique. Finally, we perform simulations of two nonlinear plants to validate the established theoretical claims.

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition

Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.

Utility of trio-based prenatal exome sequencing incorporating splice-site and mitochondrial genome assessment in pregnancies with fetal ultrasound anomalies: prospective cohort study

OBJECTIVE

To evaluate the utility of trio-based prenatal exome sequencing (pES), incorporating splice-site and mitochondrial genome assessment, in the prenatal diagnosis of fetuses with ultrasound anomalies and normal copy-number variant sequencing (CNV-seq) results.

METHODS

This was a prospective study of 90 ongoing pregnancies with ultrasound anomalies that underwent trio-based pES after receiving normal CNV-seq results, from September 2020 to November 2021, in a single center in China. By using pES with a panel encompassing exome coding and splicing regions as well as mitochondrial genome for fetuses and parents, we identified the underlying genetic causes of fetal anomalies, incidental fetal findings and parental carrier status. Information on pregnancy outcome and the impact of pES findings on parental decision-making was collected.

RESULTS

Of the 90 pregnancies included, 28 (31.1%) received a diagnostic result that could explain the fetal ultrasound anomalies. The highest diagnostic yield was noted for brain abnormalities (3/6 (50.0%)), followed by hydrops (4/9 (44.4%)) and skeletal abnormalities (13/34 (38.2%)). Collectively, 34 variants of 20 genes were detected in the 28 diagnosed cases, with 55.9% (19/34) occurring de novo. Variants of uncertain significance (VUS) associated with fetal phenotypes were detected in six (6.7%) fetuses. Interestingly, fetal (n = 4) and parental (n = 3) incidental findings (IFs) were detected in seven (7.8%) cases. These included two fetuses carrying a de-novo likely pathogenic (LP) variant of the CIC and FBXO11 genes, respectively, associated with neurodevelopmental disorders, and one fetus with a LP variant in a mitochondrial gene. The remaining fetus presented with unilateral renal dysplasia and was incidentally found to carry a pathogenic PKD1 gene variant resulting in adult-onset polycystic kidney, which was later confirmed to be inherited from the mother. In addition, parental heterozygous variants associated with autosomal recessive diseases were detected in three families, including one with additional fetal diagnostic findings. Diagnostic results or fetal IFs contributed to parental decision-making about termination of the pregnancy in 26 families (26/72 (36.1%)), while negative pES results or identification of VUS encouraged 40 families (40/72 (55.6%)) to continue their pregnancy, which ended in a live birth in all cases.

CONCLUSION

Trio-based pES can provide additional genetic information for pregnancies with fetal ultrasound anomalies without a CNV-seq diagnosis. The incidental findings and parental carrier status reported by trio-based pES with splice-site and mitochondrial genome analysis extend its clinical application, but careful genetic counseling is warranted. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Decentralized Neurocontroller Design With Critic Learning for Nonlinear-Interconnected Systems

We consider the decentralized control problem of a class of continuous-time nonlinear systems with mismatched interconnections. Initially, with the discounted cost functions being introduced to auxiliary subsystems, we have the decentralized control problem converted into a set of optimal control problems. To derive solutions to these optimal control problems, we first present the related Hamilton-Jacobi-Bellman equations (HJBEs). Then, we develop a novel critic learning method to solve these HJBEs. To implement the newly developed critic learning approach, we only use critic neural networks (NNs) and tune their weight vectors via the combination of a modified gradient descent method and concurrent learning. By using the present critic learning method, we not only remove the restriction of initial admissible control but also relax the persistence-of-excitation condition. After that, we employ Lyapunov's direct method to demonstrate that the critic NNs' weight estimation error and the states of closed-loop auxiliary systems are stable in the sense of uniform ultimate boundedness. Finally, we separately provide a nonlinear-interconnected plant and an unstable interconnected power system to validate the present critic learning approach.

[Safety of an inactivated 2019-nCoV vaccine (Vero) in adults aged 60 years and older]

Objective: To analyze the safety of an inactivated 2019-nCoV vaccine (Vero cell) in adults aged 60 years and older. Methods: A randomized, double-blind, placebo-controlled clinical study was conducted in May 2020 The eligible residents aged 60 and above were recruited in Renqiu city, Hebei Province. A total of 422 subjects (phase Ⅰ/Ⅱ:72/350) were enrolled. Two doses of the trial vaccine or placebo were randomly administered according to a 0 and 28-day immunization schedule. Subjects were randomly divided into two groups in Phase Ⅰ. Within each group, participants received vaccine or placebo in a ratio of 2∶1. Subjects were randomly divided into four groups in phase Ⅱ to receive low-dose, medium-dose, high-dose vaccine and placebo, respectively, in a ratio of 2∶2∶2∶1. A combination of regular follow-up and active reporting was used to observe adverse reactions within 28 days after vaccination, and compare the incidence rate of adverse reactions in the trial and control groups. Results: 422 subjects were (66.45±4.70) years old, and 48.82% were male (206/422). There were 100, 124, 124 and 74 patients enrolled into the low-dose, medium-dose, high-dose vaccine groups and the placebo group, respectively. One person without the vaccination was removed, and 421 participants who received at least one dose of vaccine were included in the safety analysis. Within 28 days after the first or second dose, a total of 20.67% (87/421) subjects had adverse reactions (both solicitation and non-solicitation). About 76 patients suffered grade 1 adverse reactions [18.05% (76/421)] and 22 patients suffered grade 2 adverse reactions [5.23% (22/421)]. No grade 3 or above adverse reactions occurred. A total of 19.71% (83/421) subjects had solicited adverse reactions. The most common grade 1 adverse reaction was injection site pain, followed by fever and fatigue. The most common grade 2 adverse reactions were fever and fatigue, followed by muscle pain and injection site redness. A total of 2.61% (11/421) subjects had unsolicited adverse reactions. A total of 1.66% (7/421) subjects had serious adverse events after vaccination, and no serious vaccine-related adverse events were reported. Conclusions: The inactivated SARS-CoV-2 vaccine is safe for people aged 60 years and above.

Feasibility of an ultra-low dose contrast media protocol for coronary CT angiography

AIM

To evaluate the feasibility of an ultra-low volume contrast media (CM) protocol for coronary computed tomography angiography (CTA).

MATERIALS AND METHODS

In total, 214 patients receiving coronary CTA were enrolled prospectively and divided into group A (n=107) receiving a conventional dose of CM and group B (n=107) receiving an ultra-low dose. CT values of the right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex artery (LCX) were measured and radiation doses recorded. The image quality was compared between the groups. Changes in renal function indices and proteinuria before, 24, and 72 hours after coronary CTA among those with chronic kidney disease (CKD) were also assessed.

RESULTS

There were significant differences in CT values and radiation doses between groups A and B. In group A, the average RCA, LAD, and LCX CT values were 412.5 ± 79.2, 423.5 ± 73.7, and 422.0 ± 88.1 HU, respectively. In group B, the average RCA, LAD, and LCX CT values were 275.2 ± 16.2, 277.8 ± 16.4, and 278.9 ± 16.5 HU, respectively. The radiation dose in the ultra-low protocol recipients (118.70 ± 18.52 mGy·cm) was significantly lower than that used in conventional coronary CTA (131.75 ± 20.96 mGy·cm). The image quality of group B was comparable to that of group A, satisfying the diagnostic requirement. In patients with mild CKD, there were no significant differences in renal functions after coronary CTA.

CONCLUSION

An ultra-low CM protocol was established for coronary CTA, providing comparable image quality and diagnostic yields but significantly lower radiation dose compared with a conventional protocol. This new protocol might be applicable to patients with mild CKD.

A novel multi-objective optimization framework for urban green-gray infrastructure implementation under impacts of climate change

Frequent urban flooding disasters can cause severe economic and property losses. Accordingly, the construction of sponge city has become critical to alleviating urban flooding. However, the functional and structural integration of Green Infrastructure (GI) and Gray Drainage Facility (GDF) is still a matter of concern. This study proposed a novel implementation framework for GI and GDF synchronization optimization (G-GSOIF) based on the SWMM and SUSTAIN models, and used data from Beilin District in Xi'an, China to verify the effects. The results show that the spatiotemporal integrated optimization design of GI and GDF proves to be effective in stormwater management. The total investment was reduced by 16.7% and economic benefit was increased by 15.4% based on disaster risk control, and the utilization rate of rainwater resources exceeded 40%. The Staged optimization model (SSOM) based on the SUSTAIN model established in the G-GSOIF was demonstrated to effectively cope with the impact of future climate change by adjusting and optimizing the design scheme dynamically in different simulation scenarios. Integrated LID (I-LID) measures are conducive for simulation of large catchment areas, and have the same implementation effect as distributed LID measures. The results of this study could support decision-making for urban stormwater management and sponge city construction.

Exerimental method and preliminary studies of the passive containment water film evaporation mass transfer

For larger containments and higher operation parameters, characteristics of the outside cooling of the PCCS are very important for the analysis on the containment integrity. A preliminary analysis was made and a four-step experimental method was used to numerically analyze the falling water film evaporation for the advanced passive containment. Then, the water flow stability along the outside wall of the containment was studied. The results fit well with those correlations without airflow when the air velocity is less than 5.0 m/s. However, when the air velocity is larger than 5.0 m/s, the influence of the air velocity on the water film will appear and the mean water film thickness will be thicker. Based on the prototype operation parameters, experimental studies were carried and the results were compared with the Dittus-Boelter correlation within the operation ranges. A modification factor was proposed for the conservative application of this correlation for nuclear safety analysis.

Lnc-AC145676.2.1-6-3 can influence STX3-induced abnormal autophagy by sponging hsa-miR-1292-3p in intestinal aGVHD

OBJECTIVE

Intestinal acute graft-versus-host disease (aGVHD) is a serious complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Abnormal autophagy levels in intestinal aGVHD have been confirmed in many studies. LncRNAs exert coregulatory functions and participate in a variety of intracellular regulatory processes. In this study, we investigated how lnc-AC145676.2.1-6-3 regulates dysregulated STX3-related autophagy in aGVHD.

MATERIALS AND METHODS

First, we established a mouse model of aGVHD by transplanting a mononuclear cell suspension from Balb/c donor mice treated with 60Co X-rays into CB6F1 recipient mice. STX3-related indicators were analyzed by Western blotting (WB) and immunohistochemistry which confirmed that STX3 plays an important role in dysregulating autophagy in intestinal aGVHD. TNF-αinduced Caco-2 cells, which is an in vitro model of intestinal barrier dysfunction, were established to verify the effect of STX3. The direct interaction between the partners of lnc-AC145676.2.1-6-3-mediated hsa-miR-1292-3p and STX3 axis was evaluated by the Dual-Luciferase activity assay. We performed PCR, WB, and immunofluorescence in Caco-2 cells to determine whether the abnormal autophagy levels were influenced by lnc-AC145676.2.1-6-3.

RESULTS

The results showed that lnc-AC145676.2.1-6-3 could significantly suppress the number of autophagic vacuoles, the LC3-II/I ratio, and beclin1 levels by increasing STX3 levels.

CONCLUSIONS

Lnc-AC145676.2.1-6-3 may play an important role in intestinal aGVHD by targeting STX3.

An application research for near-surface repository of strontium-90 sorption kinetic model on mudrocks

In this study,90Sr was used as the test radionuclide to characterize the sorption kinetics and effects of initial 90Sr activity and remaining 90Sr in solid concentration were simulated for a near-surface repository. The study focused on the sorption characteristics of radionuclides in unsaturated groundwater environment (or vadose zone) is the important information for investigating the near-surface disposal of intermediate and low-level radioactive waste (ILLW). Moreover, the 90Sr sorption experiments reached equilibrium within 56 h, which fit to the first order sorption kinetic model, and the remaining 90Sr in mudrock samples showed obvious sorption equilibrium hysteresis, which fit to the second order sorption kinetic model. Before reaching the maximum sorption capacity, the sorption rate constant increases with 90Sr increasing; the distribution coefficient (Kd) of 56 h decreases with the remaining 90Sr decreasing. In addition, it showed that the slow sorption process dominated before the sorption reaches equilibrium. In fact, a reliable safety assessment methodology for on-going near-surface repository required a lot of the radionuclides parameters with local environment including the radionuclides sorption/desorption rate constant and maximum sorption capacity.

A gradient-based automatic optimization CNN framework for EEG state recognition

The Electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged. In this paper, based on an existing gradient-based NAS algorithm, PC-DARTS, with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

Convolutional neural network based on recurrence plot for EEG recognition

Electroencephalogram (EEG) is a typical physiological signal. The classification of EEG signals is of great significance to human beings. Combining recurrence plot and convolutional neural network (CNN), we develop a novel method for classifying EEG signals. We select two typical EEG signals, namely, epileptic EEG and fatigue driving EEG, to verify the effectiveness of our method. We construct recurrence plots from EEG signals. Then, we build a CNN framework to classify the EEG signals under different brain states. For the classification of epileptic EEG signals, we design three different experiments to evaluate the performance of our method. The results suggest that the proposed framework can accurately distinguish the normal state and the seizure state of epilepsy. Similarly, for the classification of fatigue driving EEG signals, the method also has a good classification accuracy. In addition, we compare with the existing methods, and the results show that our method can significantly improve the detection results.

A Nonlinear Optimal Control Approach for Multi-DOF Brachiation Robots

This paper proposes a nonlinear optimal control approach for mulitple degrees of freedom (DOF) brachiation robots, which are often used in inspection and maintenance tasks of the electric power grid. Because of the nonlinear and multivariable structure of the related state-space model, as well as because of underactuation, the control problem of these robots is nontrivial. The dynamic model of the brachiation robots undergoes first approximate linearization with the use of Taylor series expansion around a temporary operating point which is recomputed at each iteration of the control method. For the approximately linearized model, an H-infinity feedback controller is designed. The linearization procedure relies on the Jacobian matrices of the brachiation robots’ state-space model. The proposed control method stands for the solution of the optimal control problem for the nonlinear and multivariable dynamics of the brachiation robots, under model uncertainties and external perturbations. For the computation of the controller’s feedback gains an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control scheme are proven through Lyapunov analysis. The new nonlinear optimal control approach achieves fast and accurate tracking for all state variables of the brachiation robots, under moderate variations of the control inputs.

Value of low-dose spiral CT combined with circulating miR-200b and miR-200c examinations for lung cancer screening in physical examination population

OBJECTIVE

  The aim of this study is to investigate the clinical value of low-dose spiral CT (LDCT), plasma miR-200b, and miR-200c combined screening for lung cancer screening in the physical examination population.

PATIENTS AND METHODS

From January 2016 to December 2018, the Physical Examination Center of our hospital underwent low-dose spiral CT lung cancer screening for 10,823 people aged ≥40 years. The quantitative Real Time-Polymerase Chain Reaction (qRT-PCR) was used to detect the relative expressions of miR-200b and miR-200c in plasma, analyze the imaging characteristics of suspicious nodules in the lung and the relative expressions of miR-200b and miR-200c in plasma.

RESULTS

A total of 2,919 pulmonary nodules were detected in the 10823 physical examination population, with a total detection rate of 26.97%, including 1523 males and 1396 females. 1081 positive nodules were detected with a detection rate of 9.99%. According to the Lung-RADS classification, the number of type 2 nodules was the highest, with a detection rate of 22.13%. Meanwhile, the rate of type 3 nodules was 3.15%, and the rate of type 4 nodules was 1.69%. The sensitivity, accuracy, and negative predictive value of LDCT, miR-200b, and miR-200c in the diagnosis of lung cancer were significantly improved compared with the individual tests, which were 94.74%, 90.16%, and 95.88%, respectively.

CONCLUSIONS

Low-dose spiral CT combined with plasma miR-200b and miR-200c for lung cancer screening in the physical examination population can help to detect lung cancer patients with early symptoms that are not significant, and achieve early diagnosis and early treatment.

Degree of peripheral Thyroxin Deiodination and recurrent cardiovascular events in euthyroid patients undergoing PCI: five-year findings from a large single-center cohort study

Background

In euthyroid patients undergoing percutaneous coronary intervention (PCI), it is still unclear whether thyroxin deiodination level can predict the recurrence of cardiovascular events (CVEs). Using free triiodothyronine to free thyroxine (FT3/FT4) ratio, a marker of peripheral thyroxin deiodination, we aim to investigate its association with recurrent long-term adverse events in this population.

Methods

3549 euthyroid patients with prior CVEs history undergoing PCI were consecutively enrolled in our study and subsequently divided into three FT3/FT4 ratio tertiles (T1&lt;2.41, n=1170; 2.41≤T2&lt;2.75, n=1198; T3&gt;2.75, n=1181). The primary endpoint was major adverse cardiovascular and cerebrovascular event (MACCE), a composite of all-cause death, myocardial infarction, stroke and revascularization. The secondary endpoints were all-cause death and cardiac death.

Results

The median follow-up time was 5 years. The incidence of all-cause death, cardiac death and MACCE were significantly higher among patients in the lowest FT3/FT4 tertile (P&lt;0.05). After adjustment of confounding factors, decreased FT3/FT4 ratio was independently associated with an increased risk of all-cause death (HR 1.82, 95% CI 1.13–2.93, P=0.014), cardiac death (HR 1.90, 95% CI 1.04–3.46, P=0.036) and MACCE (HR 1.33, 95% CI 1.10–1.60, P=0.003).

Conclusions

In euthyroid patients undergoing PCI, FT3/FT4 ratio, a surrogate marker of peripheral thyroxin deiodination, demonstrates a strong association with long-term recurrent cardiovascular events. Routine assessment of FT3/FT4 ratio might be a simple and effective tool for risk stratification in this specific patient population.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Key Research and Development Program of China (No. 2016YFC1301300, 2016YFC1301301); National Natural Science Foundation of China (No. 81770365)

Prognostic utility of fibrinogen in patients with coronary artery disease and prediabetes or diabetes following percutaneous coronary intervention: five-year findings from a large single-center cohort

Background

The prognosis for patients with coronary artery disease (CAD) remains unfavorable despite advances in treatment. Fibrinogen (FIB) is an independent risk factor for mortality and cardiovascular events in general population. However, the relationship between FIB and long-term mortality among CAD patients undergoing PCI is less investigated, especially in individuals concomitated with diabetes mellitus (DM) and prediabetes (Pre-DM).

Methods

6140 patients with CAD undergoing PCI were consecutively enrolled in our study and subsequently divided into three groups according to FIB levels (FIB-L, FIB-M, FIB-H). These patients were further grouped by glycemic metabolism state [normoglycemia (NG), Pre-DM, DM]. The primary endpoint was all-cause mortality. The secondary endpoint was cardiac mortality.

Results

FIB was positively associated with hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) both in CAD patients with and without DM. During a median follow-up time of 5.1 years, elevated FIB was significantly associated with long-term mortality from all-cause (adjusted HR: 1.86; 95% CI: 1.28–2.69; P=0.001) and cardiac specific (adjusted HR: 1.82; 95% CI: 1.15–2.89; P=0.011). Similarly, patients with DM but not Pre-DM had increased risk of all-cause and cardiac mortality (all P&lt;0.05). When grouped by both FIB levels and glycemic metabolism state, diabetic patients with medium and high FIB levels had higher risk of mortality [(adjusted HR: 2.57; 95% CI: 1.12–5.89), (adjusted HR: 3.04; 95% CI: 1.35–6.82), all P&lt;0.05]. Notably, prediabetic patients with high FIB also had higher mortality risk (adjusted HR: 2.27; 95% CI: 1.01–5.12).

Conclusion

FIB was strongly associated with long-term all-cause and cardiac mortality among CAD patients undergoing PCI, especially in persons concomitated with DM and Pre-DM, indicating FIB test may help identify high-risk individuals in this specific patient population.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Key Research and Development Program of China (No. 2016YFC1301300, 2016YFC1301301); National Natural Science Foundation of China (No. 81770365)

High urinary excretion rate of glucose attenuates serum uric acid level in type 2 diabetes with normal renal function

AIMS/INTRODUCTION

The relationship between urinary excretion rate of glucose (UEGL) and uric acid (UA) metabolism in adults with type 2 diabetes (T2D) remains unclear to date. This study aimed to investigate the relationships of UEGL with serum UA (SUA), urinary excretion rate of uric acid (UEUA), and renal clearance of uric acid (CLUA) in adults with T2D. We hypothesised that high UEGL increases UA excretion, which in turn leads to lower SUA.

MATERIALS AND METHODS

This was a cross-sectional study of 635 inpatients with T2D recruited between 2018 and 2019. The relationships of UEGL with UEUA, CLUA, and hyperuricaemia were assessed using analysis of covariance and multivariate regression analysis.

RESULTS

Patients in the higher quartile of UEGL tended to have lower SUA levels than those in the lower quartile. In contrast, patients in the higher quartile of UEGL tended to have higher CLUA (p for trend < 0.0001), and a similar trend was observed for UEUA. In adjusted multivariable linear regression model, UEGL was negatively correlated with SUA (β = - 0.023, 95% CI - 0.034 to - 0.013, p < 0.0001). However, positive correlations of UEGL with UEUA (β = 0.046, 95% CI 0.018-0.074, p = 0.001) and CLUA (β = 0.063, 95% CI 0.042-0.085, p < 0.0001) were found. Furthermore, consistent significant inverse associations were observed between quartiles of UEGL and hyperuricaemia in the adjusted multivariate logistic regression model.

CONCLUSIONS

A high UEGL level was positively correlated with UEUA and CLUA. Moreover, it was inversely associated with SUA level, and a consistently increased UEGL level reduced the risk of hyperuricaemia in patients with T2D.

COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83% and F1-score of 98.71% on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

Attention-Based Parallel Multiscale Convolutional Neural Network for Visual Evoked Potentials EEG Classification

Electroencephalography (EEG) decoding is an important part of Visual Evoked Potentials-based Brain-Computer Interfaces (BCIs), which directly determines the performance of BCIs. However, long-time attention to repetitive visual stimuli could cause physical and psychological fatigue, resulting in weaker reliable response and stronger noise interference, which exacerbates the difficulty of Visual Evoked Potentials EEG decoding. In this state, subjects' attention could not be concentrated enough and the frequency response of their brains becomes less reliable. To solve these problems, we propose an attention-based parallel multiscale convolutional neural network (AMS-CNN). Specifically, the AMS-CNN first extract robust temporal representations via two parallel convolutional layers with small and large temporal filters respectively. Then, we employ two sequential convolution blocks for spatial fusion and temporal fusion to extract advanced feature representations. Further, we use attention mechanism to weight the features at different moments according to the output-related interest. Finally, we employ a full connected layer with softmax activation function for classification. Two fatigue datasets collected from our lab are implemented to validate the superior classification performance of the proposed method compared to the state-of-the-art methods. Analysis reveals the competitiveness of multiscale convolution and attention mechanism. These results suggest that the proposed framework is a promising solution to improving the decoding performance of Visual Evoked Potential BCIs.

Clinical efficacy of mouse nerve growth factor plus nimodipine in neonatal intracranial hemorrhage and its effect on plasma PAF, CNP, MMP-2, and neurological function

OBJECTIVE

To investigate the clinical efficacy of combination of mouse nerve growth factor (NGF) and nimodipine in the treatment of neonatal intracranial hemorrhage (NICH) and its effect on plasma platelet-activating factor (PAF), C-type natriuretic peptide (CNP), matrix metalloproteinase-2 (MMP-2), and neurological function.

PATIENTS AND METHODS

A total of 90 infants with severe ICH admitted to our hospital from December 2016 to December 2018 were enrolled for retrospective study. According to different treatment schemes, they were assigned into 2 groups: group A (n=40) treated with mouse NGF plus nimodipine; group B (n=50) treated with nimodipine. The recovery time, serum indexes (PAF, MMP-2, CNP), neurological function (neonatal behavioral neurological assessment (NBNA) score), complications, and total effective rate of patients were recorded, and the satisfaction degree of family members was statistically analyzed.

RESULTS

Patients in group A showed shorter recovery time, down-regulated PAF and MMP-2, evidently up-regulated CNP, and significantly increased NBNA score after one/two weeks of treatment, as well as fewer complications, higher total effective rate and higher satisfaction of family members.

CONCLUSIONS

To sum up, the combination of mouse NGF and nimodipine achieves good clinical efficacy in NICH, which down-regulates plasma PAF and MMP-2, up-regulates CNP, and improves neurological function. Therefore, it is suitable for clinical promotion.

MiR-127-3p inhibits proliferation of ovarian cancer in rats through down-regulating MAPK4

OBJECTIVE

To reveal the anti-tumor effect of micro ribonucleic acid (miR)-127-3p on epithelial ovarian cancer (EOC).

PATIENTS AND METHODS

The expression of miR-127-3p in 7 kinds of EOC cell lines and 10 cases of clinical samples of EOC patients was detected via quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR). OVCAR-3 and Caov-3 cell lines were transfected with lentiviruses to overexpress endogenous miR-127-3p. Then, the anti-tumor effect of miR-127-3p on EOC cells was explored through the in vitro cell proliferation assay, bufalin sensitivity assay, wound healing assay, and invasion assay. In addition, whether the mitogen-activated protein kinase 4 (MAPK4) gene is a downstream target of miR-127-3p in EOC was verified via Dual-Luciferase reporter assay and qRT-PCR. The involvement of MAPK4 in regulating phenotypes of OVCAR-3 and Caov-3 cells was finally explored.

RESULTS

MiR-127-3p was downregulated in both EOC cell lines and EOC tissues (p<0.05). After lentivirus-mediated overexpression of miR-127-3p, in vitro proliferation and invasion of EOC cells were inhibited, and the sensitivity to bufalin was enhanced (p<0.05). MiR-127-3p directly regulated MAPK4 gene in EOC. Moreover, the upregulation of MAPK4 inhibited the anti-tumor effect of miR-127-3p on EOC, manifested as the remarkably enhanced cell proliferation and migration (p<0.05), and the weakened sensitivity to bufalin (p<0.01).

CONCLUSIONS

MiR-127-3p exerts an inhibitory effect on EOC cells via regulating MAPK4 level.

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.