Results 151 to 160 of about 6,997,513 (404)

Circulating tumor cells: advancing personalized therapy in small cell lung cancer patients

open access: yesMolecular Oncology, EarlyView.
Small cell lung cancer (SCLC) is an aggressive form of lung cancer that spreads rapidly to secondary sites such as the brain and liver. Cancer cells circulating in the blood, “circulating tumor cells” (CTCs), have demonstrated prognostic value in SCLC, and evaluating biomarkers on CTCs could guide treatment decisions such as for PARP inhibitors ...
Prajwol Shrestha   +6 more
wiley   +1 more source

Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging

open access: yesNeuroImage: Clinical, 2019
Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms.
Julie Ottoy   +15 more
doaj  

Construction of a genetic linkage map of the banana fungal pathogen, Mycosphaerella fijiensis, causal agent of Black Sigatoka disease [PDF]

open access: yes, 2008
The haploid, hemibiotrophic ascomycete fungus Mycosphaerella fijiensis (Morelet) is the causal agent of black Sigatoka, the most economically important disease of banana (Musa spp.). A genetic linkage map of M.
Carlier, Jean   +6 more
core  

A comparative study of circulating tumor cell isolation and enumeration technologies in lung cancer

open access: yesMolecular Oncology, EarlyView.
Lung cancer cells were spiked into donor blood to evaluate the recovery rates of the following circulating tumor cell (CTC) enrichment technologies: CellMag™, EasySep™, RosetteSep™, Parsortix® PR1, and Parsortix® Prototype systems. Each method's advantages and disadvantages are described.
Volga M Saini   +11 more
wiley   +1 more source

Joint Modelling Histology and Molecular Markers for Cancer Classification [PDF]

open access: yesarXiv
Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Although digital pathology has been advancing cancer diagnosis and prognosis, the paradigm in cancer pathology has shifted from purely relying on histology features to ...
arxiv  

Cell‐free and extracellular vesicle microRNAs with clinical utility for solid tumors

open access: yesMolecular Oncology, EarlyView.
Cell‐free microRNAs (cfmiRs) are small‐RNA circulating molecules detectable in almost all body biofluids. Innovative technologies have improved the application of cfmiRs to oncology, with a focus on clinical needs for different solid tumors, but with emphasis on diagnosis, prognosis, cancer recurrence, as well as treatment monitoring.
Yoshinori Hayashi   +6 more
wiley   +1 more source

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles [PDF]

open access: yesarXiv, 2018
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue ...
arxiv  

KRAS and GNAS mutations in cell‐free DNA and in circulating epithelial cells in patients with intraductal papillary mucinous neoplasms—an observational pilot study

open access: yesMolecular Oncology, EarlyView.
This study demonstrates that KRAS and GNAS mutations are more prevalent in patients with resected intraductal papillary mucinous neoplasms (IPMN) compared to those under clinical surveillance. GNAS mutations significantly differ between the two patient cohorts, indicating that their absence may serve as a potential biomarker to support conservative ...
Christine Nitschke   +12 more
wiley   +1 more source

Prediction of overall survival and molecular markers in gliomas via analysis of digital pathology images using deep learning [PDF]

open access: yesarXiv, 2019
Cancer histology reveals disease progression and associated molecular processes, and contains rich phenotypic information that is predictive of outcome. In this paper, we developed a computational approach based on deep learning to predict the overall survival and molecular subtypes of glioma patients from microscopic images of tissue biopsies ...
arxiv  

Home - About - Disclaimer - Privacy