Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.

Effect of Different Configurations on Bubble Cutting and Process Intensification in a Micro-Structured Jet Bubble Column Using Digital Image Analysis

An experimental study was conducted in this work to investigate the effect of different configurations on bubble cutting and process intensification in a micro-structured jet bubble column (MSJBC). Hydrodynamic parameters, including bubble size, flow field, liquid velocity, gas holdup as well as the interfacial area, were compared and researched for a MSJBC with and without mesh. The bubble dynamics and cutting images were recorded by a non-invasive optical measurement. An advanced particle image velocimetry technique (digital image analysis) was used to investigate the influence of different configurations on the surrounding flow field and liquid velocity. When there was a single mesh and two stages of mesh compared with no mesh, the experimental results showed that the bubble size decreased by 22.7% and 29.7%, the gas holdup increased by 5.7% and 9.7%, and the interfacial area increased by more than 34.8% and 43.5%, respectively. Significant changes in the flow field distribution caused by the intrusive effect of the mesh were observed, resulting in separate liquid circulation patterns near the wire mesh, which could alleviate the liquid back-mixing. The mass transfer experiment results on the chemical absorption of CO2 into NaOH enhanced by a mass transfer process show that the reaction time to equilibrium is greatly reduced in the presence of the mesh in the column.

COMPARISON OF EYELID ANGLE MEASURED USING MANUAL AND DIGITAL IMAGE ANALYSIS METHOD

Aim: To compare the eyelid angle measured by using a manual method (Using protractor) and digital image analysis method (Using ImageJ software) at different distances of eye gaze. Methodology: This prospective cross-sectional study was conducted in the preclinical lab at Manipal College of Health Professions. Subjects with no eyelid abnormalities were included in the study and they were asked to fixate at different distances a) at 3 metre (Distance gaze) b) at 70 cm (Intermediategaze) and c) at 40 cm(Near gaze). Using a protractor, the eyelid angle measurements were repeated at various distances which comprised the manual measurement. In the image analysis method, images were captured during distance, intermediate and near gaze using smartphone placed on theside of the face. These images were then analysed using ImageJ software for determining eyelid angle using image analysis method. Palpebral fissure height, Palpebral fissure width, Interpupillary distance, Intercanthal width, Binocular width, Height of open upper lid were some additional anthropometry measurements that were done using meter scale and PD ruler. Results: The mean age of the participants was 20±0.5 years. Anthropometry measurements of the eyelid and Palpebral fissure were done using meter scaleand PD ruler. The mean and standard deviation of the measured parametersare as follows Interpupillary distance: 60.95±2.37 mm, Endo Inter canthal distance: 32.20±2.39 mm, Exo Inter cantal distance: 95.50±3.80 mm, Palpebralfissure height_OD: 12.11±1.32 mm, Palpebral fissure height_OS:12.16±1.46mm, PFW_OD: 32.00±1.10 mm, PFW_OS: 32.11±1.24 mm, Height of upper eyelids_OD: 10.26±1.66 mm and Height of upper eyelids_OS:10.42±1.83 mm. In the right eye, there was no statistically significant difference (p>0.05) between manual protractor method and digital image analysismethod at distance but there was a statistically significant difference (p<0.05)between manual protractor method and digital image analysis method atIntermediate and near. In left eye, there was statistically significant difference(p<0.05) between manual protractor method and digital image analysis method at all three distances. Conclusion: There is a significant difference in eyelid angle measured using manual protractor method and digital image analysis method. The measurement of eyelid angle serves as a critical reference point during cosmetic and reconstructive surgical interventions of the eyelid and accurate measurements are essential for preoperative assessment, surgical planning and postoperative evaluation. Hence more studies on the validation of the anthropometry measurements and eyelid angle using digital image analysis areessential to use digital image analysis in routine eye care practice.

Use of Canopeo for Estimating Green Coverage of Bermudagrass during Postdormancy Regrowth

Objective methods of estimating green coverage using digital image analysis have been used increasingly by turfgrass scientists. The objective of our research was to evaluate the effectiveness of Canopeo, a relatively new smartphone application, for estimating green coverage of bermudagrass (Cynodon dactylon) emerging from winter dormancy, with or without colorants. A field study was conducted on a research ‘U3’ bermudagrass fairway in Stillwater, OK, during Spring 2019 and 2020. The experiment was conducted as a randomized complete block design with three colorant treatments: Endurant Fairway (FW), Endurant Perennial Ryegrass (PR), and an untreated control. Green coverage of the turfgrass canopy was determined weekly from mid-March to early May using a digital camera and ImageJ software, and a smartphone and the Canopeo application. Green coverage estimates from Canopeo correlated strongly (r = 0.91) with those from ImageJ when no colorants were applied. Correlation between Canopeo and ImageJ was diminished under plots treated with colorants. Canopeo is an effective tool for estimating green coverage of living turfgrasses, but additional calibration may be required for acceptable performance when evaluating greenness of colorant-treated turfgrasses.

An Immunohistochemical Evaluation of Tumor-Associated Glycans and Mucins as Targets for Molecular Imaging of Pancreatic Ductal Adenocarcinoma

Targeted molecular imaging may overcome current challenges in the preoperative and intraoperative delineation of pancreatic ductal adenocarcinoma (PDAC). Tumor-associated glycans Lea/c/x, sdi-Lea, sLea, sLex, sTn as well as mucin-1 (MUC1) and mucin-5AC (MU5AC) have gained significant interest as targets for PDAC imaging. To evaluate their PDAC molecular imaging potential, biomarker expression was determined using immunohistochemistry on PDAC, (surrounding) chronic pancreatitis (CP), healthy pancreatic, duodenum, positive (LN+) and negative lymph node (LN−) tissues, and quantified using a semi-automated digital image analysis workflow. Positive expression on PDAC tissues was found on 83% for Lea/c/x, 94% for sdi-Lea, 98% for sLea, 90% for sLex, 88% for sTn, 96% for MUC1 and 67% for MUC5AC, where all were not affected by the application of neoadjuvant therapy. Compared to PDAC, all biomarkers were significantly lower expressed on CP, healthy pancreatic and duodenal tissues, except for sTn and MUC1, which showed a strong expression on duodenum (sTn tumor:duodenum ratio: 0.6, p < 0.0001) and healthy pancreatic tissues (MUC1 tumor:pancreas ratio: 1.0, p > 0.9999), respectively. All biomarkers are suitable targets for correct identification of LN+, as well as the distinction of LN+ from LN− tissues. To conclude, this study paves the way for the development and evaluation of Lea/c/x-, sdi-Lea-, sLea-, sLex- and MUC5AC-specific tracers for molecular imaging of PDAC imaging and their subsequent introduction into the clinic.

Genetic characterization of carrot root shape and size using genome-wide association analysis and genomic-estimated breeding values

Key message The principal phenotypic determinants of market class in carrot—the size and shape of the root—are under primarily additive, but also highly polygenic, genetic control. Abstract The size and shape of carrot roots are the primary determinants not only of yield, but also market class. These quantitative phenotypes have historically been challenging to objectively evaluate, and thus subjective visual assessment of market class remains the primary method by which selection for these traits is performed. However, advancements in digital image analysis have recently made possible the high-throughput quantification of size and shape attributes. It is therefore now feasible to utilize modern methods of genetic analysis to investigate the genetic control of root morphology. To this end, this study utilized both genome wide association analysis (GWAS) and genomic-estimated breeding values (GEBVs) and demonstrated that the components of market class are highly polygenic traits, likely under the influence of many small effect QTL. Relatively large proportions of additive genetic variance for many of the component phenotypes support high predictive ability of GEBVs; average prediction ability across underlying market class traits was 0.67. GWAS identified multiple QTL for four of the phenotypes which compose market class: length, aspect ratio, maximum width, and root fill, a previously uncharacterized trait which represents the size-independent portion of carrot root shape. By combining digital image analysis with GWAS and GEBVs, this study represents a novel advance in our understanding of the genetic control of market class in carrot. The immediate practical utility and viability of genomic selection for carrot market class is also described, and concrete guidelines for the design of training populations are provided.