[Research Progress in Data Acquisition and Intelligent Sensing Methods for Lumbar Electromyographic Signals].

Population aging trend is taking place in our country, and low back pain is a symptom of neuromuscular diseases of concern in the elderly. Accurately analyzing the disease of low back pain is important for both timely intervention and rehabilitation of patients. As a kind of bioelectrical signal, the acquisition and analysis of lumbar electromyography (EMG) signal is an important direction for the study of low back pain. The study reviews the acquisition of lumbar EMG by different types of sensors, introduces the signal characteristics of needle electrodes, surface electromyography electrodes and array electrodes, describes the use of signal algorithms, points out that wireless sensors and the use of deep learning algorithms are the direction of development, and puts forward prospects for its further development.

The impact of cerebral small vessel disease burden and its imaging markers on gait, postural control, and cognition in Parkinson's disease.

OBJECTIVE: This study aimed to investigate how cerebral small vessel disease (CSVD) burden and its imaging markers are related to alterations in different gait parameters in Parkinson's disease (PD) and whether they affect attention, information processing speed, and executive function when global mental status is relatively intact. METHODS: Sixty-five PD patients were divided into the low CSVD burden group (n = 43) and the high CSVD burden group (n = 22). All patients underwent brain magnetic resonance imaging scans, clinical scale evaluations, and neuropsychological tests, as well as quantitative evaluation of gait and postural control. Multivariable linear regression models were conducted to investigate associations between CSVD burden and PD symptoms. RESULTS: Between-group analysis showed that the high CSVD group had worse attention, executive dysfunction, information processing speed, gait, balance, and postural control than the low CSVD group. Regression analysis revealed that greater CSVD burden was associated with poor attention, impaired executive function, and slow gait speed; white matter hyperintensity was associated with slow gait speed, decreased cadence, increased stride time, and increased stance phase time; the presence of lacune was associated only with poor attention and impaired executive function; enlarged perivascular space in the basal ganglia was associated with gait speed. CONCLUSIONS: CSVD burden may worsen gait, postural control, attention, and executive function in patients with PD, and different imaging markers play different roles. Early management of vascular risks and treatment of vascular diseases provide an alternate way to mitigate some motor and cognitive dysfunction in PD.

Exploring Three Decades of Progress: A Comprehensive Visual Review of Intravoxel Incoherent Motion MRI Research (1988-2021).

BACKGROUND Despite an increasing number of published articles on intravoxel incoherent motion (IVIM) in the past decade, almost all have focused on the technique and clinical applications of IVIM, with little attention to the collective knowledge and scientific analysis of this field. The aim of the present study was to construct a knowledge framework and to explore hotspots and emerging trends concerning use of IVIM in humans. MATERIAL AND METHODS The articles concerning IVIM MRI published from 1988 to 2021 were retrieved from the Science Citation Index Expended of the Web of Science Core Collection on 17, August 2021. The downloaded data were imported into Excel 2016 and CiteSpace V for scientometric analysis. RESULTS A total of 921 articles were included in this study and most of them were published since 2012. China (n=392) was the most productive country and the Philips Healthcare (n=46) was the most productive institution. Christian Federau had the largest number of publications (n=18). An article by Andreou A et al (2013) was the most important reference with the most co-citations (n=100) and centrality (0.06). The 5 hotspots in IVIM were perfusion, diffusion-weighted imaging, intravoxel incoherent motion, apparent diffusion coefficient, and magnetic resonance imaging. The 2 frontier topics were "brain perfusion" and "accuracy". According to the clustering of co-citation analysis, "liver", "diffusion weighting", "pancreas", and "brain" were the main research directions. CONCLUSIONS Scientometric analysis of IVIM literature with CiteSpace software can provide researchers with valuable information about knowledge framework, hotspots, and emerging trends concerning IVIM in humans.

Structure-aware siamese graph neural networks for encounter-level patient similarity learning.

Patient similarity learning has attracted great research interest in biomedical informatics. Correctly identifying the similarity between a given patient and patient records in the database could contribute to clinical references for diagnosis and medication. The sparsity of underlying relationships between patients poses difficulties for similarity learning, which becomes more challenging when considering real-world Electronic Health Records (EHRs) with a large number of missing values. In the paper, we organize EHRs as a graph and propose a novel deep learning framework, Structure-aware Siamese Graph neural Networks (SSGNet), to perform robust encounter-level patient similarity learning while capturing the intrinsic graph structure and mitigating the influence from missing values. The proposed SSGNet regards each patient encounter as a node, and learns the node embeddings and the similarity between nodes simultaneously via Graph Neural Networks (GNNs) with siamese architecture. Further, SSGNet employs a low-rank and contrastive objective to optimize the structure of the patient graph and enhance model capacity. The extensive experiments were conducted on two publicly available datasets and a real-world dataset regarding IgA nephropathy from Peking University First Hospital, in comparison with multiple baseline and state-of-the-art methods. The significant improvement in Accuracy, Precision, Recall and F1 score on the patient encounter pairwise similarity classification task demonstrates the superiority of SSGNet. The mean average precision (mAP) of SSGNet on the similar encounter retrieval task is also better than other competitors. Furthermore, SSGNet's stable similarity classification accuracies at different missing rates of data validate the effectiveness and robustness of our proposal.

Abnormal Pulmonary Function in Early Parkinson's Disease: A Preliminary Prospective Observational Study.

Early Parkinson's disease (PD) may cause respiratory dysfunction; however the findings vary among studies. The aim of the preliminary prospective observational study was to explore the deterioration of pulmonary function at various stages in patients with early PD. A total of 237 patients with PD were screened. Fifty-six patients were included (modified Hoehn and Yahr stage ≤ 2.5). In addition, 56 age-matched healthy controls were also included in the study. Significant differences between the PD and control groups were found in all the investigated lung-function parameters. The maximal voluntary ventilation (MVV) percent predicted was the only parameter that distinguished PD stages (101.1 ± 14.9% vs. 82.8 ± 19.2% vs. 71.4 ± 12.9%, Hoehn and Yahr stages 1.5 vs. 2 vs. 2.5, respectively; p < 0.005). MVV could be the most sensitive parameter for distinguishing the severity of early-stage PD.

Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication.

MOTIVATION: Detecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients. RESULTS: By incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/JianingXi/DriverSub. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism.

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

CD-sACE2 inclusion compounds: An effective treatment for coronavirus disease 2019 (COVID-19).

Coronaviruses are common human viruses and include the severe acute respiratory syndrome coronavirus (SARS-CoV), the middle east respiratory syndrome coronavirus and the SARS-CoV-2. Coronaviruses mainly bind to transmembrane receptor proteins on the human cell membrane through spike proteins (S-proteins), thus releasing the RNA of the virus into the interior of the host cell to cause an infection. In this article, we discuss the mechanism and production of cyclodextrin-soluble angiotensin-converting enzyme 2 (CD-sACE2) inclusion compounds in the treatment of SARS-CoV-2 infections by blocking S-proteins. On the basis of the current research evidence, we believe that CD-sACE2 inclusion compounds have the potential to treat COVID-19. We hope that our article can provide a theoretical basis for later experiments.

Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis.

OBJECTIVE: We aim to summarize reliable evidence of evidence-based medicine for the treatment and prevention of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by analyzing all the published studies on the clinical characteristics of patients with SARS-CoV-2. METHODS: PubMed, Cochrane Library, Embase, and other databases were searched. Several studies on the clinical characteristics of SARS-CoV-2 infection were collected for meta-analysis. RESULTS: Ten studies were included in Meta-analysis, including a total number of 50466 patients with SARS-CoV-2 infection. Meta-analysis shows that, among these patients, the incidence of fever was 0.891 (95% CI: 0.818, 0.945), the incidence of cough was 0.722 (95% CI: 0.657, 0.782), and the incidence of muscle soreness or fatigue was 0.425 (95% CI: 0.213, 0.652). The incidence of acute respiratory distress syndrome (ARDS) was 0.148 (95% CI: 0.046, 0.296), the incidence of abnormal chest computer tomography (CT) was 0.966 (95% CI: 0.921, 0.993), the percentage of severe cases in all infected cases was 0.181 (95% CI: 0.127, 0.243), and the case fatality rate of patients with SARS-CoV-2 infection was 0.043 (95% CI: 0.027, 0.061). CONCLUSION: Fever and cough are the most common symptoms in patients with SARS-CoV-2 infection, and most of these patients have abnormal chest CT examination. Several people have muscle soreness or fatigue as well as ARDS. Diarrhea, hemoptysis, headache, sore throat, shock, and other symptoms are rare. The case fatality rate of patients with SARS-CoV-2 infection is lower than that of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). This meta-analysis also has limitations, so the conclusions of this Meta-analysis still need to be verified by more relevant studies with more careful design, more rigorous execution, and larger sample size.

Polymeric hybrid aerogels and their biomedical applications.

Aerogels are a class of porous materials that possess extremely high specific surface area, high pore volume, high porosity, and variable chemical structures. They have been widely applied in the fields of aerospace, chemical engineering, construction, electrotechnics, and biomedicine. In recent years a great boom in aerogels has been observed, where various new aerogels with novel physicochemical properties and functions have been synthesized. Nevertheless, native aerogels with a single component normally face severe problems such as low mechanical strength and lack of functions. One strategy to solve the problems is to construct hybrid aerogels. In this study, a comprehensive review on polymer based hybrid aerogels is presented, including polymer-polymer, polymer-carbon material, and polymer-inorganic hybrid aerogels, which will be introduced and discussed in view of their chemical structures and hybrid structures. Most importantly, polymeric hybrid aerogels are classified into three different composition levels, which are molecular-level, molecular-aggregate-level, and aggregate-level, due to the fact that hybrid aerogels with the same chemical structures but with different composition levels might show quite different functions or properties. The biomedical applications of these hybrid aerogels will also be reviewed and discussed, where the polymeric components in the hybrid aerogels provide the main contribution. This review would provide creative design principles for aerogels by considering both their chemical and physical structures.

MiR-487a-3p suppresses the malignant development of pancreatic cancer by targeting SMAD7.

OBJECTIVE: To uncover the role of microRNA-487a-3p (miR-487a-3p) in influencing the malignant development of pancreatic cancer and the involvement of its downstream target SMAD7. METHODS: MiR-487a-3p level in 40 pancreatic cancer and paracancerous tissues was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The relationship between miR-487a-3p level and clinical indicators in pancreatic cancer patients was analyzed. Regulatory effects of miR-487a-3p on biological phenotypes of pancreatic cancer cells were assessed. At last, the involvement of miR-487a-3p and its downstream target SMAD7 in pancreatic cancer was determined. RESULTS: MiR-487a-3p was lowly expressed in pancreatic cancer tissues. Pancreatic cancer patients expressing a low level of miR-487a-3p suffered high metastasis rate and poor prognosis. Overexpression of miR-487a-3p markedly attenuated proliferative and migratory capacities in pancreatic cancer cells. SMAD7 was the downstream target of miR-487a-3p, which was highly expressed in pancreatic cancer samples. Overexpression of SMAD7 reversed the regulatory effects of miR-487a-3p on pancreatic cancer cell phenotypes. CONCLUSIONS: MiR-487a-3p is downregulated in pancreatic cancer samples, which is linked to metastasis and prognosis in pancreatic cancer. It inhibits the malignant development of pancreatic cancer by negatively regulating SMAD7.

Deletion of long noncoding RNA EFNA3 aggravates hypoxia-induced injury in PC-12 cells by upregulation of miR-101a.

Long noncoding RNAs (lncRNAs) have been reported to be involved in several neurological pathogenesis conditions including cerebral ischemia. In the current study, the functions of lncRNA EFNA3 on hypoxia-injured rat adrenal pheochromocytoma (PC-12) cells and the underlying molecular mechanism were studied. The expression of lncRNA EFNA3 was silenced by short hairpin RNA transfection, after which the cells were subjected with hypoxia. Cell viability, migration, invasion, and apoptosis were, respectively, determined by trypan blue staining, Transwell assay, annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) double-staining, and Western blot analysis. The cross regulation between lncRNA EFNA3 and miR-101a, as well as between miR-101a and Rho associated coiled-coil containing protein kinase 2 (ROCK2) were detected by performing quantitative real-time polymerase chain reaction, RNA pull-down, RNA immunoprecipitation, luciferase activity assay, and Western blot analysis. Studies showed that lncRNA EFNA3 was highly expressed in response to hypoxia. Deletion of lncRNA EFNA3 significantly aggravated hypoxia-induced injury in PC-12 cells, as the impairment of cell viability, migration, and invasion, and the inducement of apoptosis. LncRNA EFNA3 worked as a sponging molecule for miR-101a and miR-101a suppression-protected PC-12 cells against hypoxia-induced injury even when lncRNA EFNA3 was silenced. ROCK2 was a target gene of miR-101a. ROCK2 overexpression exhibited neuroprotective activities. Besides, ROCK2 overexpression activated Wnt/ß-catenin pathway whereas it deactivated JAK/STAT pathway upon hypoxia. Our study suggests that deletion of lncRNA EFNA3 aggravates hypoxia-induced injury in PC-12 cells by upregulating miR-101a, which further targets ROCK2.

Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables.

Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase; only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our method with those of two commonly used only expression-based breast cancer stratification methods: prediction analysis of microarray 50 (PAM50) and highest variability (HV). The result is that our method outperforms them in identifying subtypes significantly associated with five-year survival and recurrence time. Specifically, our method identified recurrence-associated breast cancer subtypes that were not identified by PAM50 and HV. Additionally, our analysis discovered three survival-associated luminal-A subgroups and two survival-associated luminal-B subgroups. The study indicates that screening clinically relevant gene expressions yields improved breast cancer stratification.

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.

BACKGROUND: Discovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions. RESULTS: To incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods. CONCLUSIONS: In summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.

Protective Effect of miR-374a on Chemical Hypoxia-Induced Damage of PC12 Cells In Vitro via the GADD45α/JNK Signaling Pathway.

To explore the effect of microRNA-374a (miR-374a) on chemical hypoxia-induced pheochromocytoma (PC12) cell damage by mediating growth arrest and the DNA damage-45 alpha (GADD45α)/c-Jun N-terminal kinase (JNK) signaling pathway. PC12 cells were divided into a Control group (no treatment), Model group (treated with CoCl2 for 24 h), negative control (NC) group (transfected with miR-374a negative control sequence and treated with CoCl2 for 24 h), and miR-374a mimic group (transfected with miR-374a mimics and treated with CoCl2 for 24 h). The viability and apoptosis of PC12 cells were determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and flow cytometry, while the mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) content were assessed by Rh123 and dichloro-dihydro-fluorescein diacetate (DCFH-DA) methods. The expression of miR-374a and GADD45α/JNK proteins was detected using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and Western blot. A significant decrease was found in the survival rate, MMP and miR-374a expression, while an increase was shown in the ROS content and GADD45α and p-JNK expression in hypoxic PC12 cells (all P < 0.05). A luciferase reporter gene assay demonstrated that GADD45α is the target gene of miR-374a. When transfected with miR-374a mimics, hypoxic PC12 cells showed an obvious elevation in survival rate and MMP but a great reduction in cell apoptosis and ROS content, as well as in the expression of GADD45α and p-JNK proteins (all P < 0.05). MiR-374a can protect PC12 cells against hypoxia-induced injury by inhibiting the GADD45α/JNK pathway, enhancing cell viability, suppressing oxidative stress, and inhibiting cell apoptosis, thereby becoming a potential therapeutic target for hypoxic damage.

Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics.

The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher's exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10-4 ± 3.06 × 10-5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10-1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data.

PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images.

Vessel density within tumor tissues strongly correlates with tumor proliferation and serves as a critical marker for tumor grading. Recognition of vessel density by pathologists is subject to a strong inter-rater bias, thus limiting its prognostic value. There are many challenges in the task of object detection in pathological images, including complex image backgrounds, dense distribution of small targets, and insignificant differences between the features of the target to be detected and the image background. To address these problems and thus help physicians quantify blood vessels in pathology images, we propose Pathological Images-YOLO (PI-YOLO), an enhanced detection network based on YOLOv7. PI-YOLO incorporates the BiFormer attention mechanism, enhancing global feature extraction and accelerating processing for regions with subtle differences. Additionally, it introduces the CARAFE upsampling module, which optimizes feature utilization and information retention for small targets. Furthermore, the GSConv module improves the ELAN module, reducing model parameters and enhancing inference speed while preserving detection accuracy. Experimental results show that our proposed PI-YOLO network has higher detection accuracy compared to Faster-RCNN, SSD, RetinaNet, YOLOv5 network, and the latest YOLOv7 network, with a mAP value of 87.48%, which is 2.83% higher than the original model. We also validated the performance of this network on the ICPR 2012 mitotic dataset with an F1 value of 0.8678, outperforming other methods, demonstrating the advantages of our network in the task of target detection in complex pathology images.

Corrigendum: PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images.

[This corrects the article DOI: 10.3389/fonc.2024.1347123.].