Lymphoma

Particulate Matter Exposure of Passengers at Bus Stations: A Review.

This review clarifies particulate matter (PM) pollution, including its levels, the factors affecting its distribution, and its health effects on passengers waiting at bus stations. The usual factors affecting the characteristics and composition of PM include industrial emissions and meteorological factors (temperature, humidity, wind speed, rain volume) as well as bus-station-related factors such ...

Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns.

Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative features of cellular processes. Supervised deep learning methods, however, are inherently limited due to the scarcity of annotated data. Unsupervised deep ...

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network.

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of sm...

Genotoxicity evaluation of titanium dioxide nanoparticles using the mouse lymphoma assay and the Ames test.

Titanium dioxide nanoparticles (TiO-NPs) are widely used in the cosmetics, health, and food industries, but their safety and genotoxicity remain a mat...

Central Nervous System Peripheral T Cell Lymphoma Manifesting as Lymphomatosis Cerebri That Was Misdiagnosed as Neuro-Behçet's Disease: A Case Report.

BACKGROUND: Lymphomatosis cerebri (LC) is a unique form of primary central nerves lymphoma (PCNSL), which presents as diffuse infiltration of lymphoma...

Increases in Serial Pretreatment F-FDG PET-CT Metrics Predict Survival in Early Stage Non-Small Cell Lung Cancer Treated With Stereotactic Ablative Radiation Therapy.

PURPOSE: Quantitative changes in positron emission tomography with computed tomography imaging metrics over serial scans may be predictive biomarkers....

The application of machine learning algorithms in understanding the effect of core/shell technique on improving powder compactability.

This study systemically investigated the application of core/shell technique to improve powder compactability. A 28-run Design-of-Experiment (DoE) was...

Measurement and immunophenotyping of pleural fluid EpCAM-positive cells and clusters for the management of non-small cell lung cancer patients.

OBJECTIVES: A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic in...

Non-directed bronchial lavage is a safe method for sampling the respiratory tract in critically ill patient.

Ventilated patients are at risk of acquiring ventilator-associated pneumonia. Various techniques are available for diagnosing ventilator-associated pn...

Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmen...

Translation of morphological and functional musculoskeletal imaging.

In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing...

An evaluation method of risk grades for prostate cancer using similarity measure of cubic hesitant fuzzy sets.

Prostate cancer (PC) is more common cancer in older men. Then, the existing evaluation method of PC risk grades is based on the AJCC (American Joint C...

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

OBJECTIVE: To investigate the classification ability of quantitative radiomics features extracted on non-contrast-enhanced CT (NECT) image for discrim...

Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG.

Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles c...

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to di...

Association of Helicobacter pylori Infection With Colon Cancer and Adenomatous Polyps.

BACKGROUND AND OBJECTIVE: infection is one of the most common chronic bacterial infections in the world, especially in the developing countries. This...

Bipartite synchronization in coupled delayed neural networks under pinning control.

This paper considers the bipartite leader-following synchronization in a signed network composed by an array of coupled delayed neural networks by uti...

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection.

Trustworthiness is a comprehensive quality metric which is used to assess the quality of the services in service-oriented environments. However, trust...

MultiNet PyGRAPPA: Multiple neural networks for reconstructing variable density GRAPPA (a H FID MRSI study).

Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool for mapping metabolite levels across the brain, however, it generally suffers from ...

Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.

OBJECTIVES: Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are st...