Keyword search (4,164 papers available)

"Diagnosis" Keyword-tagged Publications:

Title Authors PubMed ID
1 Joint enhancement of automatic chest x-ray diagnosis and radiological gaze prediction with multistage cooperative learning Qiu Z; Rivaz H; Xiao Y; 40665596
ENCS
2 Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review Neriya Hegade KP; Bhat RB; Packirisamy M; 40650129
ENCS
3 Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments Vandersteen C; Plonka A; Manera V; Sawchuk K; Lafontaine C; Galery K; Rouaud O; Bengaied N; Launay C; Guérin O; Robert P; Allali G; Beauchet O; Gros A; 37416323
CONCORDIA
4 Primary and Secondary Progressive Aphasia in Posterior Cortical Atrophy Brodeur C; Belley É; Deschênes LM; Enriquez-Rosas A; Hubert M; Guimond A; Bilodeau J; Soucy JP; Macoir J; 35629330
IMAGING
5 X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech Jeancolas L; Petrovska-Delacrétaz D; Mangone G; Benkelfat BE; Corvol JC; Vidailhet M; Lehéricy S; Benali H; 33679361
PERFORM
6 Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines. Shen Y, Khorasani K 32673847
ENCS
7 The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study. Chertkow H, Borrie M, Whitehead V, Black SE, Feldman HH, Gauthier S, Hogan DB, Masellis M, McGilton K, Rockwood K, Tierney MC, Andrew M, Hsiung GR, Camicioli R, Smith EE, Fogarty J, Lindsay J, Best S, Evans A, Das S, Mohaddes Z, Pilon R, Poirier J, Phillips NA, MacNamara E, Dixon RA, Duchesne S, MacKenzie I, Rylett RJ 31309917
PSYCHOLOGY
8 Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis. Yang J, Xie G, Yang Y, Zhang Y, Liu W 31174854
ENCS

 

Title:Microfluidic Liquid Biopsy Minimally Invasive Cancer Diagnosis by Nano-Plasmonic Label-Free Detection of Extracellular Vesicles: Review
Authors:Neriya Hegade KPBhat RBPackirisamy M
Link:https://pubmed.ncbi.nlm.nih.gov/40650129/
DOI:10.3390/ijms26136352
Publication:International journal of molecular sciences
Keywords:cancer diagnosiscancer prognosisexosomesextracellular vesiclesliquid biopsymicrofluidicsminimally invasive diagnosisnano-plasmonic detection
PMID:40650129 Category: Date Added:2025-07-13
Dept Affiliation: ENCS
1 Optical Bio-Microsystems Laboratory, Department of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.

Description:

Cancer diagnosis requires alternative techniques that allow for early, non-invasive, or minimally invasive identification. Traditional methods, like tissue biopsies, are highly invasive and can be traumatic for patients. Liquid biopsy, a less invasive option, detects cancer biomarkers in body fluids such as blood and urine. However, early-stage cancer often presents low biomarker levels, making sensitivity a challenge for integrating liquid biopsy into early diagnosis. Recent studies revealed that extracellular vesicles (EVs) secreted by cells are apt markers for liquid biopsy. Detecting extracellular vesicles (EVs) for liquid biopsy faces challenges like low sensitivity, EV subtype heterogeneity, and difficulty isolating pure populations. Label-free methods, such as plasmonic biosensors and Raman spectroscopy, offer potential solutions by enabling direct analysis without markers, improving accuracy, and reducing complexity. This review paper discusses current challenges in EV-based liquid biopsy for cancer diagnosis and prognosis. It addresses the effective use of microfluidics and nano-plasmonic approaches to address these challenges. Enhancing label-free EV detection in liquid biopsy could revolutionize early cancer diagnosis by offering non-invasive, cost-effective, and rapid testing. This could improve patient outcomes through personalized treatment and ease the burden on healthcare systems.





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