netquant Search Results


netquant app version 1.3  (MathWorks Inc)
90
MathWorks Inc netquant app version 1.3
Netquant App Version 1.3, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/netquant app version 1.3/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
netquant app version 1.3 - by Bioz Stars, 2026-04
90/100 stars
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netquant  (MathWorks Inc)
90
MathWorks Inc netquant
<t>NETQUANT</t> overview and testing. Basic outline of NET quantification process starting with two-channel image processing, followed by segmentation of cells, analysis of cell properties, detection of cells undergoing NETosis (red stars), and output of data.
Netquant, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/netquant/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
netquant - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
matlab-based automated netquant application  (MathWorks Inc)
90
MathWorks Inc matlab-based automated netquant application
Comparison of NET quantification by hand counting semi-automated quantification and <t>NETQUANT.</t> A : Cell numbers for Image set 1 determined by all three methods, showing significantly fewer cell counts by NETQUANT in the majority of the images. B : NET formation determined by all methods revealed a significant difference between hand counting and NETQUANT as well as the semi-automated method. Statistical analysis was performed by Two-Way ANOVA with Dunnett correction for multiple comparison. Semi-automated and NETQUANT values were compared with hand counting. Data are given as mean ± SD. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).
Matlab Based Automated Netquant Application, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab-based automated netquant application/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based automated netquant application - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier

Image Search Results


NETQUANT overview and testing. Basic outline of NET quantification process starting with two-channel image processing, followed by segmentation of cells, analysis of cell properties, detection of cells undergoing NETosis (red stars), and output of data.

Journal: Frontiers in Immunology

Article Title: NETQUANT: Automated Quantification of Neutrophil Extracellular Traps

doi: 10.3389/fimmu.2017.01999

Figure Lengend Snippet: NETQUANT overview and testing. Basic outline of NET quantification process starting with two-channel image processing, followed by segmentation of cells, analysis of cell properties, detection of cells undergoing NETosis (red stars), and output of data.

Article Snippet: Here, we present NETQUANT, an app for MATLAB that performs immunofluorescence image-based NET quantification and describe its implementation with the aim of delivering a user-friendly freely available tool.

Techniques:

Setup tab for NETQUANT. File path of dataset(s) to be analyzed are entered into set up tab of the software and the file path for the data output is assigned (step 1). The naming conventions defining the channels (step 2), image information (step 3), and channel order (step 4) necessary for the analysis are to be provided by the user. Setting up all fields prior processing the datasets (step 5) is recommended to ensure image conversion in a standardized fashion.

Journal: Frontiers in Immunology

Article Title: NETQUANT: Automated Quantification of Neutrophil Extracellular Traps

doi: 10.3389/fimmu.2017.01999

Figure Lengend Snippet: Setup tab for NETQUANT. File path of dataset(s) to be analyzed are entered into set up tab of the software and the file path for the data output is assigned (step 1). The naming conventions defining the channels (step 2), image information (step 3), and channel order (step 4) necessary for the analysis are to be provided by the user. Setting up all fields prior processing the datasets (step 5) is recommended to ensure image conversion in a standardized fashion.

Article Snippet: Here, we present NETQUANT, an app for MATLAB that performs immunofluorescence image-based NET quantification and describe its implementation with the aim of delivering a user-friendly freely available tool.

Techniques: Software

Segmentation tab for NETQUANT. The segmentation parameters used during analysis include method, sensitivity, iterations, minimum area of an unstimulated neutrophil (step 6). The use of adaptive segmentation is recommended and the settings provided can be left unchanged for most purposes. Control samples are first segmented (step 7), prior to stimulated samples (step 8).

Journal: Frontiers in Immunology

Article Title: NETQUANT: Automated Quantification of Neutrophil Extracellular Traps

doi: 10.3389/fimmu.2017.01999

Figure Lengend Snippet: Segmentation tab for NETQUANT. The segmentation parameters used during analysis include method, sensitivity, iterations, minimum area of an unstimulated neutrophil (step 6). The use of adaptive segmentation is recommended and the settings provided can be left unchanged for most purposes. Control samples are first segmented (step 7), prior to stimulated samples (step 8).

Article Snippet: Here, we present NETQUANT, an app for MATLAB that performs immunofluorescence image-based NET quantification and describe its implementation with the aim of delivering a user-friendly freely available tool.

Techniques: Control

Comparison of NET formation in control and PMA-stimulated neutrophils using NETQUANT. Image datasets from non-stimulated or PMA-stimulated neutrophils were analyzed using NETQUANT. The outputs generated from NETQUANT analysis describing changes in the cell area, nuclear deformation and DNA/NET ratio from control (green) and PMA-stimulated neutrophils (blue) were compared. This type of data are easily available within the app for any analyzed sample. Data are from three independent experiments.

Journal: Frontiers in Immunology

Article Title: NETQUANT: Automated Quantification of Neutrophil Extracellular Traps

doi: 10.3389/fimmu.2017.01999

Figure Lengend Snippet: Comparison of NET formation in control and PMA-stimulated neutrophils using NETQUANT. Image datasets from non-stimulated or PMA-stimulated neutrophils were analyzed using NETQUANT. The outputs generated from NETQUANT analysis describing changes in the cell area, nuclear deformation and DNA/NET ratio from control (green) and PMA-stimulated neutrophils (blue) were compared. This type of data are easily available within the app for any analyzed sample. Data are from three independent experiments.

Article Snippet: Here, we present NETQUANT, an app for MATLAB that performs immunofluorescence image-based NET quantification and describe its implementation with the aim of delivering a user-friendly freely available tool.

Techniques: Comparison, Control, Generated

False discovery rate (FDR) associated with NETQUANT. Bar graphs indicating FDR associated with NETQUANT. NETs were quantified by NETQUANT or manually by an experienced user and were compared to assess FDR. The total FDR was found to be 4.7 ± 0.75% (mean ± SD), with false negatives 4 ± 0.63% and false positives 0.7 ± 0.5%. Data are from three independent experiments.

Journal: Frontiers in Immunology

Article Title: NETQUANT: Automated Quantification of Neutrophil Extracellular Traps

doi: 10.3389/fimmu.2017.01999

Figure Lengend Snippet: False discovery rate (FDR) associated with NETQUANT. Bar graphs indicating FDR associated with NETQUANT. NETs were quantified by NETQUANT or manually by an experienced user and were compared to assess FDR. The total FDR was found to be 4.7 ± 0.75% (mean ± SD), with false negatives 4 ± 0.63% and false positives 0.7 ± 0.5%. Data are from three independent experiments.

Article Snippet: Here, we present NETQUANT, an app for MATLAB that performs immunofluorescence image-based NET quantification and describe its implementation with the aim of delivering a user-friendly freely available tool.

Techniques:

Comparison of NET quantification by hand counting semi-automated quantification and NETQUANT. A : Cell numbers for Image set 1 determined by all three methods, showing significantly fewer cell counts by NETQUANT in the majority of the images. B : NET formation determined by all methods revealed a significant difference between hand counting and NETQUANT as well as the semi-automated method. Statistical analysis was performed by Two-Way ANOVA with Dunnett correction for multiple comparison. Semi-automated and NETQUANT values were compared with hand counting. Data are given as mean ± SD. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Journal: Heliyon

Article Title: Comparison of NET quantification methods based on immunofluorescence microscopy: Hand-counting, semi-automated and automated evaluations

doi: 10.1016/j.heliyon.2023.e16982

Figure Lengend Snippet: Comparison of NET quantification by hand counting semi-automated quantification and NETQUANT. A : Cell numbers for Image set 1 determined by all three methods, showing significantly fewer cell counts by NETQUANT in the majority of the images. B : NET formation determined by all methods revealed a significant difference between hand counting and NETQUANT as well as the semi-automated method. Statistical analysis was performed by Two-Way ANOVA with Dunnett correction for multiple comparison. Semi-automated and NETQUANT values were compared with hand counting. Data are given as mean ± SD. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Article Snippet: Therefore, in the present study, we aimed to compare the evaluation of two sets of immunofluorescence images using three different approaches: (1) hand counting, (2) semi-automated quantification [ ], and (3) the MATLAB-based automated NETQUANT application [ ].

Techniques: Comparison

Comparison of hand counting, NETQUANT, and semi-automated quantification for image set 2. A shows the cell count for the control group with the semi-automated methods giving unrealistically high values for some images. Significant differences by both semi-automated and NETQUANT compared to hand counting were apparent. In B, the pattern remained similar to A, where the semi-automated method showed remarkably higher values of cell counts in several cases. C depicts NET formation values by all three methods, with the semi-automated method showing significantly higher results than both other methods in the control group, even exceeding 100% NET formation. D shows NET formation of the stimulated group where NETQUANT exhibited values within the range of hand counting, while the semi -automated method revealed significant differences. Statistical analysis was performed by Two-Way ANOVA with Dunnett correction for multiple comparison. Semi -automated and NETQUANT values were compared to hand counting. Data are given as mean ± SD. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Journal: Heliyon

Article Title: Comparison of NET quantification methods based on immunofluorescence microscopy: Hand-counting, semi-automated and automated evaluations

doi: 10.1016/j.heliyon.2023.e16982

Figure Lengend Snippet: Comparison of hand counting, NETQUANT, and semi-automated quantification for image set 2. A shows the cell count for the control group with the semi-automated methods giving unrealistically high values for some images. Significant differences by both semi-automated and NETQUANT compared to hand counting were apparent. In B, the pattern remained similar to A, where the semi-automated method showed remarkably higher values of cell counts in several cases. C depicts NET formation values by all three methods, with the semi-automated method showing significantly higher results than both other methods in the control group, even exceeding 100% NET formation. D shows NET formation of the stimulated group where NETQUANT exhibited values within the range of hand counting, while the semi -automated method revealed significant differences. Statistical analysis was performed by Two-Way ANOVA with Dunnett correction for multiple comparison. Semi -automated and NETQUANT values were compared to hand counting. Data are given as mean ± SD. (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Article Snippet: Therefore, in the present study, we aimed to compare the evaluation of two sets of immunofluorescence images using three different approaches: (1) hand counting, (2) semi-automated quantification [ ], and (3) the MATLAB-based automated NETQUANT application [ ].

Techniques: Comparison, Cell Counting, Control