Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography.

Purpose: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN).

A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model.

Epilepsy is a fairly common condition that affects the brain and causes frequent seizures. The sudden and recurring epilepsy brings a series of safety hazards to patients, which seriously affects the quality of their life. Therefore, real-time diagnosis of electroencephalogram (EEG) in epilepsy patients is of great significance.

KLF4 loss in hepatocellular carcinoma: Improving prognostic prediction and correlating immune infiltrates.

Although the molecular mechanisms of Krüpple-like factor 4 (KLF4) as a tumor suppressor in HCC tumorigenesis have been thoroughly examined, its clinical application in terms of precise prognostication and its influence on tumor immune microenvironment in patients with HCC require further investigation. : Bioinformatics and immunohistochemistry (IHC) were used to validate KLF4 expressions in a tissue microarray (TMA) containing HCC samples. Using Cox regression models, independent prognostic factors were identified and employed in the development of nomograms.

Relations between physical activity and hippocampal functional connectivity: Modulating role of mind wandering.

Physical activity is critical for maintaining cognitive and brain health. Previous studies have indicated that the effect of physical activity on cognitive and brain function varies between individuals. The present study aimed to examine whether mind wandering modulated the relations between physical activity and resting-state hippocampal functional connectivity.

MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation.

Automated medical image segmentation for organs or lesions plays an essential role in clinical diagnoses and treatment plannings. However, training an accurate and robust segmentation model is still a long-standing challenge due to the time-consuming and expertise-intensive annotations for training data, especially 3-D medical images. Recently, self-supervised learning emerges as a promising approach for unsupervised visual representation learning, showing great potential to alleviate the expertise annotations for medical images.

Second primary malignancies in cervical cancer and endometrial cancer survivors: a population-based analysis.

Background: We evaluated the relative attribution and interactions of treatment and patient-related risk factors for second primary malignancies (SPMs) in cervical and endometrial cancer survivors.

Methods: Stage I-III cervical and endometrial cancer survivors' data from the Surveillance, Epidemiology, and End Results (SEER) registry between January 1988 and December 2015 were analyzed. The standardized incidence ratio (SIR), excess absolute risk (EAR), and corresponding 95% confidence interval (95% CI) values were calculated.

A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection.

In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because labeling requires numerous neurologists. This paper proposes a one-step semi-supervised epilepsy detection system to reduce the labeling cost by fully utilizing the unlabeled data.

Relations of Creativity to the Interplay Between High-order Cognitive Functions: Behavioral and Neural Evidence.

As a high-order cognitive ability, creativity is viewed as the result of complex interplay between a set of mental processes. However, previous studies have mainly tested one-to-one mutual relations between creativity and other cognitive abilities. It lacks studies to examine whether creativity is related to the interaction between cognitive systems.

Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study.

Background: Nonalcoholic fatty liver disease (NAFLD) is a public health challenge and significant cause of morbidity and mortality worldwide. Early identification is crucial for disease intervention. We recently proposed a nomogram-based NAFLD prediction model from a large population cohort.

The stage-specific roles of radiotherapy and chemotherapy in nodular lymphocyte predominant Hodgkin lymphoma patients: a propensity score-matched analysis of the SEER database.

Background: The stage-specific roles of radiotherapy (RT) alone, chemotherapy alone, and combined RT and chemotherapy (CRT) for patients with nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) have not been adequately evaluated.

Methods: We analyzed patients with all stages of NLPHL enrolled in the Surveillance, Epidemiology, and End Results (SEER) registry from January 2000 to December 2015. Propensity score (PS) analysis with 1:1 matching (PSM) was performed to ensure the well-balanced characteristics of the comparison groups.

Radiotherapy for patients with stage IV classical Hodgkin lymphoma: a propensity-matched analysis of the surveillance, epidemiology, and end results database.

Background The survival advantage of radiotherapy for patients with stage IV classic Hodgkin lymphoma (HL) has not been adequately evaluated. Methods We analyzed patients with stage IV HL enrolled from the Surveillance, Epidemiology, and End Results (SEER) registry from January 2000 to December 2012. Propensity score (PS) analysis with 1:2 matching was performed to ensure well-balanced characteristics of the comparison groups.

Comparison of Treatment Modalities for Locally Advanced Gastric Cancer: A Propensity Score Matching Analysis.

: A consensus regarding optimum treatment strategies for locally advanced gastric cancer (LAGC) has not yet been reached. We aimed to evaluate the efficacy of various treatment modalities for LAGC and provided clinicians salvage options under real-world situation. : Medical charts of patients with LAGC who underwent radical resection plus adjuvant chemotherapy or chemoradiotherapy from July 2003 to December 2014 were included.

CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city.

Objectives: To characterize the chest computed tomography (CT) findings of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) according to clinical severity. We compared the CT features of common cases and severe cases, symptomatic patients and asymptomatic patients, and febrile and afebrile patients.

Methods: This was a retrospective analysis of the clinical and thoracic CT features of 120 consecutive patients with confirmed SARS-CoV-2 pneumonia admitted to a tertiary university hospital between January 10 and February 10, 2020, in Wuhan city, China.

Surgery to the primary tumor is associated with improved survival of patients with metastatic esophageal cancer: propensity score-matched analyses of a large retrospective cohort.

The survival advantage of surgery to the primary tumor for patients with distant metastatic esophageal cancer has not been adequately evaluated. This study aims to investigate the role of surgery to the primary tumor in distant metastatic esophageal cancer and to evaluate possible different effects of surgery on survival of esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). This study included a cohort of 4,367 metastatic esophageal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database, registered from January 2004 to December 2014.

Palliative Gastrectomy and Survival in Patients With Metastatic Gastric Cancer: A Propensity Score-Matched Analysis of a Large Population-Based Study.

Objectives: The role of palliative gastrectomy in the management of metastatic gastric cancer remains inadequately clarified.

Methods: We analyzed patients with metastatic gastric cancer enrolled in the Surveillance, Epidemiology, and End Results registry from January 2004 to December 2012. Propensity score (PS) analysis with 1:1 matching and the nearest neighbor matching method was performed to ensure well-balanced characteristics of the groups of patients who undergone gastrectomy and those without gastrectomy.

Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress.

A single-camera gaze tracking system under natural light.

Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility.

Radiotherapy Improves the Survival of Patients With Metastatic Cervical Cancer: A Propensity-Matched Analysis of SEER Database.

Objective: To demonstrate whether radiotherapy has an effect on the survival of patients with stage IVb (M1) cervical cancer, as it has not been adequately clarified.

Methods: We analyzed International Federation of Gynecology and Obstetrics (FIGO) stage M1 cervical cancer diagnosed in patients between 1992 and 2013 using population-based data from the Surveillance, Epidemiology, and End Results registry. Propensity score (PS) analysis with 1:1 matching and the nearest neighbor matching method was performed to ensure well-balanced characteristics of comparison groups.

Super Nonlinear Electrodeposition-Diffusion-Controlled Thin-Film Selector.

Selector elements with high nonlinearity are an indispensable part in constructing high density, large-scale, 3D stackable emerging nonvolatile memory and neuromorphic network. Although significant efforts have been devoted to developing novel thin-film selectors, it remains a great challenge in achieving good switching performance in the selectors to satisfy the stringent electrical criteria of diverse memory elements. In this work, we utilized high-defect-density chalcogenide glass (GeSbTe) in conjunction with high mobility Ag element (Ag-GST) to achieve a super nonlinear selective switching.

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots.

Memristive devices, having a huge potential as artificial synapses for low-power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability.

Tailoring transient-amorphous states: towards fast and power-efficient phase-change memory and neuromorphic computing.

A new methodology for manipulating transient-amorphous states of phase-change memory (PCM) materials is reported as a viable means to boost the speed, yet reduce the power consumption of PC memories, and is applicable to new forms of PCM-based neuromorphic devices. Controlling multiple-pulse interactions with PC materials may provide an opportunity toward developing a new paradigm for ultra-fast neuromorphic computing.

Enabling an integrated rate-temporal learning scheme on memristor.

Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices.