SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection.

Introduction: Epilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks.

Extreme Fuzzy Broad Learning System: Algorithm, Frequency Principle, and Applications in Classification and Regression.

As an effective alternative to deep neural networks, broad learning system (BLS) has attracted more attention due to its efficient and outstanding performance and shorter training process in classification and regression tasks. Nevertheless, the performance of BLS will not continue to increase, but even decrease, as the number of nodes reaches the saturation point and continues to increase. In addition, the previous research on neural networks usually ignored the reason for the good generalization of neural networks.

Employing broad learning and non-invasive risk factor to improve the early diagnosis of metabolic syndrome.

Metabolic syndrome (MetS) as a multifactorial disease is highly prevalent in countries and individuals. Monitoring the conventional risk factors (CRFs) would be a cost-effective strategy to target the increasing prevalence of MetS and the potential of noninvasive CRF for precisely detection of MetS in the early stage remains to be explored. From large-scale multicenter MetS clinical dataset, we discover 15 non-invasive CRFs which have strong relevance with MetS and first propose a broad learning-based approach named Genetic Programming Collaborative-competitive Broad Learning System (GP-CCBLS) with noninvasive CRF for early detection of MetS.

The Brunner adenoma of the duodenum with positive fecal occult blood and anemia: A case report.

Rationale: Brunner gland adenoma (BGA) is a rare benign duodenal tumor that is an adenomatoid lesion in nature rather than an actual tumor. Patients with different adenoma sizes have various clinical manifestations with nonspecific clinical symptoms. Here, We report a case of BGA with black stool and anemia as the primary manifestations.

Development and validation of a deep-broad ensemble model for early detection of Alzheimer's disease.

Introduction: Alzheimer's disease (AD) is a chronic neurodegenerative disease of the brain that has attracted wide attention in the world. The diagnosis of Alzheimer's disease is faced with the difficulties of insufficient manpower and great difficulty. With the intervention of artificial intelligence, deep learning methods are widely used to assist clinicians in the early recognition of Alzheimer's disease.

Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs.

Rationale And Objectives: Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs.

Materials And Methods: In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included.

Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs.

A Digital Microfluidic RT-qPCR Platform for Multiple Detections of Respiratory Pathogens.

The coronavirus disease 2019 pandemic has spread worldwide and caused more than six million deaths globally. Therefore, a timely and accurate diagnosis method is of pivotal importance for controlling the dissemination and expansions. Nucleic acid detection by the reverse transcription-polymerase chain reaction (RT-PCR) method generally requires centralized diagnosis laboratories and skilled operators, significantly restricting its use in rural areas and field settings.

BLCov: A novel collaborative-competitive broad learning system for COVID-19 detection from radiology images.

With the global outbreak of COVID-19, there is an urgent need to develop an effective and automated detection approach as a faster diagnostic alternative to avoid the spread of COVID-19. Recently, broad learning system (BLS) has been viewed as an alternative method of deep learning which has been applied to many areas. Nevertheless, the sparse autoencoder in classical BLS just considers the representations to reconstruct the input data but ignores the relationship among the extracted features.

Efficient graph convolutional networks for seizure prediction using scalp EEG.

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy patients especially those with refractory epilepsy. At present, a large number of deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks have been used to predict epileptic seizures and have obtained better performance than traditional machine learning methods.

An Image-Based Benchmark Dataset and a Novel Object Detector for Water Surface Object Detection.

Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level.

Histone deacetylase 1 promotes glioblastoma cell proliferation and invasion via activation of PI3K/AKT and MEK/ERK signaling pathways.

Histone deacetylase 1 (HDAC1) plays a crucial role in cancer progression and development. This enzyme has been confirmed to be a key regulator of tumor biology functions, such as tumor cell proliferation, migration and invasion. However, HDAC1 expression in glioma remains controversial, and its specific function and molecular mechanism in glioblastoma is poorly understood.

MicroRNA-200b inhibits pituitary tumor cell proliferation and invasion by targeting PKCα.

The present study aimed to investigate the expression of miR-200b and protein kinase Cα (PKCα) in pituitary tumors and to determine whether miR-200b may inhibit proliferation and invasion of pituitary tumor cells. The regulation of PKCα expression was targeted in order to find novel targets for the treatment of pituitary tumors. In total, 53 pituitary tumor tissue samples were collected; these included 28 cases of invasive pituitary tumors and 25 cases of non-invasive tumors, in addition to 5 normal pituitaries.

Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering.

To achieve better performance in multifocus image fusion problems, a new regional approach based on superpixels and superpixel-based mean filtering is proposed in this paper. First, a fast and effective segmentation method is adopted to generate the superpixels over a clarity-enhanced average image. By averaging the clarity information in each superpixel, we make the initial decision map of fusion by regionally selecting sharper superpixels in different source images.

MicroRNA-34a inhibits cell proliferation and induces cell apoptosis of glioma cells via targeting of Bcl-2.

Glioblastoma is a highly malignant brain tumor, characterized by the poor prognosis and high recurrence rates. Despite therapeutic strategies including surgery, radiotherapy and chemotherapy, the median survival of patients is only 14.6 months.

[Effects of probiotics on blood glucose levels and clinical outcomes in patients with severe craniocerebral trauma].

Objective: To investigate the effects of probiotics on blood glucose levels and clinical outcomes in patients suffering from severe craniocerebral trauma.

Methods: A prospective randomized control study was conducted. Fifty-two severe craniocerebral trauma patients admitted to intensive care unit (ICU) were randomized into experimental or control group (each n=26).

Effects of early hyperbaric oxygen therapy on clinical outcome in postoperative patients with intracranial aneurysm.

Objective: To investigate the effects of hyperbaric oxygen (HBO2) in postoperative patients with intracranial aneurysm.

Methods: A total of 120 patients who underwent clipping of intracranial aneurysm of the anterior circulation were randomized into the HBO2 group (n = 60) or the Control group (n = 60). Compared with the Control group, patients in the HBO2 group received additional HBO2 therapy, which was initiated within one to three days as soon as they were deemed clinically stable, for at least 20 sessions (one session per day).