FTIR microscopic imaging of carcinoma tissue section with 4× and 15× objectives: Practical considerations

2015 ◽  
Vol 4 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Claudia Beleites ◽  
O. Guntinas-Lichius ◽  
G. Ernst ◽  
Jürgen Popp ◽  
Christoph Krafft
Keyword(s):  
Tissue Section ◽  
Microscopic Imaging ◽  
Carcinoma Tissue ◽  
Ftir Microscopic Imaging

FTIR microscopic imaging of collagen and proteoglycan in bovine cartilage

Biopolymers ◽  
2000 ◽  
Vol 62 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Nancy P. Camacho ◽  
Paul West ◽  
Peter A. Torzilli ◽  
Richard Mendelsohn
Keyword(s):  
Microscopic Imaging ◽  
Bovine Cartilage ◽  
Ftir Microscopic Imaging

scholarly journals Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6318
Author(s):  
Jun-Li Xu ◽  
Ana Herrero-Langreo ◽  
Sakshi Lamba ◽  
Mariateresa Ferone ◽  
Amalia G. M. Scannell ◽  
...  
Keyword(s):  
Model Performance ◽  
Predictive Ability ◽  
The Other ◽  
Support Vector ◽  
Bacterial Cells ◽  
Heterogeneous Distribution ◽  
Microscopic Imaging ◽  
Model Generalization ◽  
Reflectance Ftir ◽  
Ftir Microscopic Imaging

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Differentiation of individual human mesenchymal stem cells probed by FTIR microscopic imaging

The Analyst ◽  
2007 ◽  
Vol 132 (7) ◽  
pp. 647 ◽  
Author(s):  
Christoph Krafft ◽  
Reiner Salzer ◽  
Sebastian Seitz ◽  
Christina Ern ◽  
Matthias Schieker
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Human Mesenchymal Stem Cells ◽  
Microscopic Imaging ◽  
Ftir Microscopic Imaging ◽  
Individual Human

Functionalization of MgZnO nanorod films and characterization by FTIR microscopic imaging

2017 ◽  
Vol 409 (27) ◽  
pp. 6379-6386 ◽  
Author(s):  
Yuan Chen ◽  
Qihong Zhang ◽  
Carol Flach ◽  
Richard Mendelsohn ◽  
Elena Galoppini ◽  
...  
Keyword(s):  
Microscopic Imaging ◽  
Ftir Microscopic Imaging

Raman and FTIR microscopic imaging of colon tissue: a comparative study

2008 ◽  
Vol 1 (2) ◽  
pp. 154-169 ◽  
Author(s):  
Christoph Krafft ◽  
Daniela Codrich ◽  
Gloria Pelizzo ◽  
Valter Sergo
Keyword(s):  
Comparative Study ◽  
Colon Tissue ◽  
Microscopic Imaging ◽  
Ftir Microscopic Imaging

The Application of Protein A-Gold Immunocytochemistry to Studies of Protein Secretion

1985 ◽  
Vol 43 ◽  
pp. 426-429
Author(s):  
George H. Herbener ◽  
Antonio Nanci ◽  
Moise Bendayan
Keyword(s):  
Tissue Section ◽  
Colloidal Gold ◽  
Unit Area ◽  
Protein A ◽  
Extracellular Proteins ◽  
Gold Complex ◽  
Second Step ◽  
Igg Antibodies ◽  
Tissue Sections ◽  
Antigenic Sites

Protein A-gold immunocytochemistry is a two-step, post-embedding labeling procedure which may be applied to tissue sections to localize intra- and extracellular proteins. The key requisite for immunocytochemistry is the availability of the appropriate antibody to react in an immune response with the antigenic sites on the protein of interest. During the second step, protein A-gold complex is reacted with the antibody. This is a non- specific reaction in that protein A will combine with most IgG antibodies. The ‘label’ visualized in the electron microscope is colloidal gold. Since labeling is restricted to the surface of the tissue section and since colloidal gold is particulate, labeling density, i.e., the number of gold particles per unit area of tissue section, may be quantitated with ease and accuracy.

Higher CD1a Levels Correlate with PD-L1 Expression and Predict Worse Overall Survival in Triple-Negative Breast Carcinoma

Breast Care ◽  
2021 ◽  
pp. 1-9
Author(s):  
Jian Zheng ◽  
Yuntao Wei ◽  
Xiaoxi Li ◽  
Zhan Shen ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Dendritic Cells ◽  
Lymph Node ◽  
Breast Carcinoma ◽  
Triple Negative ◽  
Carcinoma Tissue ◽  
Kaplan Meier ◽  
Immature Dendritic Cells ◽  
Triple Negative Breast Carcinoma ◽  
High Median

Objective: The aim of this study was to measure the expression of PD-L1, CD1a (a marker for immature dendritic cells), and CD83 (a marker for mature dendritic cells) and further examine the associations of PD-L1, CD83, and CD1a with overall survival (OS) in triple-negative breast carcinoma patients. Methods: PD-L1, CD1a, and CD83 expression in breast carcinoma tissues and CD83 expression in lymph node tissues were examined by immunohistochemistry and tissue microarray in 159 patients. Patients were classified into the low, medium, and high PD-L1, CD1a, and CD83 levels. Pearson χ2 test was used to analyze the correlations between PD-L1, CD1a, and CD83. The Kaplan-Meier method was used to calculate the OS. Multivariate analysis was used to identify determinants of 3- and 5-year OS. Results: 25.1, 25.8, and 49.1% of the patients had low, medium, and high PD-L1 levels, respectively. PD-L1 levels significantly correlated with CD1a (r = 0.30409, p < 0.001) and CD83 levels (r = 0.6146, p < 0.001) in breast carcinoma tissue, as well as CD83 levels (r = 0.17508, p = 0.027) in lymph node. The median OS was 83 months (range 12–106), and the 3- and 5-year OS rates were 94.97% (95% CI 91.57–98.37) and 86.79% (95% CI 81.53–92.06), respectively. Moreover, patients with high median CD1a levels had a significantly lower 5-year OS rate (75.6%) than those with low median CD1a levels (93.5%, p = 0.038). Conclusion: PD-L1, CD1a, and CD83 are variably expressed in triple-negative breast carcinoma tissues, and PD-L1 expression correlates with CD1a and CD83. Higher CD1a levels correlate with PD-L1 expression and predict worse OS in triple-negative breast carcinoma.

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