AI/deep learning-assisted metastasis detection in lymph nodes simplifies assessment for breast and colorectal adenocarcinoma.

Provides consistent positive and negative reference materials for accurate and reproducible IHC assay development and quality control. The MET analysis algorithms for ONCOtopix® quantify MET amplification, offering positive ratio or H-Score outputs to identify negative, intermediate, and positive cores. Combining IHC HDx™ Cell lines with ONCOtopix and the MET APP ensures a high-quality reagent QC system.

Detects and classifies nuclei based on MIF staining expression in the tumor microenvironment (TME) of melanoma. The “10131 – MIF, Melanoma, TME” APP categorizes nuclei as negative, 1+, 2+, or 3+ according to the level of MIF expression around each nucleus, aiding in the evaluation of MIF’s role in promoting cell proliferation, migration, and survival pathways.

Assesses amyloid plaque clearance and cerebral amyloid angiopathy (CAA) in Alzheimer’s disease (AD) models. Using Campbell-Switzer stain, this application visualizes amyloid plaques (black) and CAA (reddish brown), confirmed with immunohistochemical detection of Aβ. It supports research on anti-Aβ immunotherapies, aiming to improve cognitive function and reduce parenchymal amyloid accumulation while minimizing CAA and micro-hemorrhages.

Automatically detects PEG positive areas in tissue and calculates the ratio compared to the total tissue area, indicating tumor growth. This application enhances MRI studies by targeting tumor tissue with nanoparticle-based contrast agents, using IHC staining with diaminobenzidine (DAB).

This application uses insulin-positive area to assess beta-cell mass and turnover in pancreas sections. It measures beta-cell area normalized to either total pancreas area or per islet and calculates beta-cell replication based on the proportion of insulin-positive cells. This automated method aids in understanding pancreas morphology in various health and disease conditions, including pregnancy, insulin resistance, and diabetes.

Calculates beta cell and non-beta cell areas in the endocrine pancreas, utilizing advanced digital platforms, image analysis software, and stereological sampling techniques. This application provides a reliable and efficient quantitative assessment of beta cell dynamics, validated by stereological point counting methods, aiding in the study of glucose homeostasis and metabolic demand adjustments.

Determines beta-cell area by measuring insulin-positive areas normalized to total pancreas area or per islet, and calculates beta-cell replication by identifying insulin-positive cells that are also Ki-67 positive. This automated method aids in assessing beta-cell mass and turnover, enhancing our understanding of pancreas morphology in health and disease conditions such as pregnancy, insulin resistance, and diabetes.

Quantifies protein aggregates in brain regions affected by Huntington’s disease (HD) using digital image analysis based on virtual slides. This method enables high-throughput quantification of aggregates in the cerebral cortex, striatum, and hippocampus, supporting HD research and drug development by providing accurate and high-quality results.

Identify individual muscle fibers in whole slide images and calculate the lesser diameter for each fiber, both stained and unstained. Quantify muscle fiber diameter by myosin fiber type. Assess the clustering of type II fibers in type I myosin predominant muscle as a measure of synaptic denervation and reinnervation, aiding in the study of muscle degeneration, disuse atrophy, and neurogenic atrophy.

Detects and classifies nuclei based on mPGES1 staining expression in the tumor microenvironment (TME) of melanoma. The “10132 – mPGES1, Melanoma, TME” protocol categorizes nuclei as negative, 1+, 2+, or 3+ according to the level of mPGES1 expression around each nucleus, aiding in the assessment of inflammation and hypoxia conditions within various human cancers.

Assesses immune interactions and composition in lung cancer using a biomarker panel (CD8, PD-1, PD-L1, CK, CD68, FoxP3, and CD68). Designed for amplified fluorescence signals unmixed with multispectral imaging, it identifies phenotypes via the Multiplex Phenotyping module.