State-of-the-art Hepatocellular Carcinoma Biomarker Detection by Biosensor Technology—a Review

2021 ◽  
Author(s):  
Zihni Onur Uygun
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art ◽  
Biomarker Detection ◽  
Biosensor Technology

State-of-the-art ultrasonography of hepatocellular carcinoma

2006 ◽  
Vol 58 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Jae Young Lee ◽  
Byung Ihn Choi ◽  
Joon Koo Han ◽  
Jeong Min Lee ◽  
Se Hyung Kim
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art

scholarly journals Nucleotide-pair encoding of 16S rRNA sequences for host phenotype and biomarker detection

2018 ◽  
Author(s):  
Ehsaneddin Asgari ◽  
Philipp C. Münch ◽  
Till R. Lesker ◽  
Alice C. McHardy ◽  
Mohammad R.K. Mofrad
Keyword(s):  
Rheumatoid Arthritis ◽  
16S Rrna ◽  
State Of The Art ◽  
Data Representation ◽  
The State ◽  
Biomarker Detection ◽  
New Paradigm ◽  
Phenotype Prediction ◽  
Nucleotide Pair ◽  
Phenotype Classification

ABSTRACTIdentifying combinations of taxa distinctive for microbiome-associated diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on accuracy of microbiome analysis techniques. We propose subsequence based 16S rRNA data analysis, as a new paradigm for microbiome phenotype classification and biomarker detection. This method and software called DiTaxa substitutes standard OTU-clustering or sequence-level analysis by segmenting 16S rRNA reads into the most frequent variable-length subsequences. These subsequences are then used as data representation for downstream phenotype prediction, biomarker detection and taxonomic analysis. Our proposed sequence segmentation called nucleotide-pair encoding (NPE) is an unsupervised data-driven segmentation inspired by Byte-pair encoding, a data compression algorithm. The identified subsequences represent commonly occurring sequence portions, which we found to be distinctive for taxa at varying evolutionary distances and highly informative for predicting host phenotypes. We compared the performance of DiTaxa to the state-of-the-art methods in disease phenotype prediction and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa identified 17 out of 29 taxa with confirmed links to periodontitis (recall= 0.59), relative to 3 out of 29 taxa (recall= 0.10) by the state-of-the-art method. On synthetic benchmark data, DiTaxa obtained full precision and recall in biomarker detection, compared to 0.91 and 0.90, respectively. In addition, machine-learning classifiers trained to predict host disease phenotypes based on the NPE representation performed competitively to the state-of-the art using OTUs or k-mers. For the rheumatoid arthritis dataset, DiTaxa substantially outperformed OTU features with a macro-F1 score of 0.76 compared to 0.65. Due to the alignment- and reference free nature, DiTaxa can efficiently run on large datasets. The full analysis of a large 16S rRNA dataset of 1359 samples required ≈1.5 hours on 20 cores, while the standard pipeline needed ≈6.5 hours in the same setting.AvailabilityAn implementation of our method called DiTaxa is available under the Apache 2 licence at http://llp.berkeley.edu/ditaxa.

scholarly journals DiTaxa: nucleotide-pair encoding of 16S rRNA for host phenotype and biomarker detection

2018 ◽  
Vol 35 (14) ◽  
pp. 2498-2500 ◽  
Author(s):  
Ehsaneddin Asgari ◽  
Philipp C Münch ◽  
Till R Lesker ◽  
Alice C McHardy ◽  
Mohammad R K Mofrad
Keyword(s):  
16S Rrna ◽  
State Of The Art ◽  
Operational Taxonomic Unit ◽  
Supplementary Information ◽  
Biomarker Detection ◽  
New Paradigm ◽  
Nucleotide Pair ◽  
Bowel Diseases ◽  
Benchmark Datasets ◽  
Inflammatory Bowel

Abstract Summary Identifying distinctive taxa for micro-biome-related diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on the accuracy of micro-biome analysis techniques. We propose an alignment- and reference- free subsequence based 16S rRNA data analysis, as a new paradigm for micro-biome phenotype and biomarker detection. Our method, called DiTaxa, substitutes standard operational taxonomic unit (OTU)-clustering by segmenting 16S rRNA reads into the most frequent variable-length subsequences. We compared the performance of DiTaxa to the state-of-the-art methods in phenotype and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa performed competitively to the k-mer based state-of-the-art approach in phenotype prediction while outperforming the OTU-based state-of-the-art approach in finding biomarkers in both resolution and coverage evaluated over known links from literature and synthetic benchmark datasets. Availability and implementation DiTaxa is available under the Apache 2 license at http://llp.berkeley.edu/ditaxa. Supplementary information Supplementary data are available at Bioinformatics online.

Percutaneous treatment of hepatocellular carcinoma: State of the art and innovations

2018 ◽  
Vol 68 (4) ◽  
pp. 783-797 ◽  
Author(s):  
Jean-Charles Nault ◽  
Olivier Sutter ◽  
Pierre Nahon ◽  
Nathalie Ganne-Carrié ◽  
Olivier Séror
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art ◽  
Percutaneous Treatment

scholarly journals State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1194
Author(s):  
Anna Castaldo ◽  
Davide Raffaele De Lucia ◽  
Giuseppe Pontillo ◽  
Marco Gatti ◽  
Sirio Cocozza ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Patient Outcomes ◽  
State Of The Art ◽  
Effective Management ◽  
Lesion Segmentation ◽  
Liver Malignancy ◽  
Modeling And Prediction ◽  
Imaging Characteristics ◽  
Risk Patients ◽  
Improve Patient

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

scholarly journals Radiofrequency combined with immunomodulation for hepatocellular carcinoma: State of the art and innovations

2020 ◽  
Vol 26 (17) ◽  
pp. 2040-2048 ◽  
Author(s):  
Adriano Carneiro da Costa ◽  
Mikael Sodergren ◽  
Kumar Jayant ◽  
Fernando Santa Cruz ◽  
Duncan Spalding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art

scholarly journals High-Performance Passive Plasma Separation on OSTE Pillar Forest

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 355
Author(s):  
Zhiqing Xiao ◽  
Lexin Sun ◽  
Yuqian Yang ◽  
Zitao Feng ◽  
Sihan Dai ◽  
...  
Keyword(s):  
Whole Blood ◽  
High Performance ◽  
State Of The Art ◽  
Lateral Flow ◽  
Protein Recovery ◽  
Biomarker Detection ◽  
Plasma Separation ◽  
Filtration Membrane ◽  
Uniform Microstructure ◽  
Lateral Flow Tests

Plasma separation is of high interest for lateral flow tests using whole blood as sample liquids. Here, we built a passive microfluidic device for plasma separation with high performance. This device was made by blood filtration membrane and off-stoichiometry thiol–ene (OSTE) pillar forest. OSTE pillar forest was fabricated by double replica moldings of a laser-cut polymethylmethacrylate (PMMA) mold, which has a uniform microstructure. This device utilized a filtration membrane to separate plasma from whole blood samples and used hydrophilic OSTE pillar forest as the capillary pump to propel the plasma. The device can be used to separate blood plasma with high purity for later use in lateral flow tests. The device can process 45 μL of whole blood in 72 s and achieves a plasma separation yield as high as 60.0%. The protein recovery rate of separated plasma is 85.5%, which is on par with state-of-the-art technologies. This device can be further developed into lateral flow tests for biomarker detection in whole blood.

scholarly journals Radiomics in hepatocellular carcinoma: A state-of-the-art review

2021 ◽  
Vol 13 (11) ◽  
pp. 1599-1615
Author(s):  
Shan Yao ◽  
Zheng Ye ◽  
Yi Wei ◽  
Han-Yu Jiang ◽  
Bin Song
Keyword(s):  
Hepatocellular Carcinoma ◽  
State Of The Art
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