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To qualitatively aid in the identification by light microscopy of human cells of T-cell lineage, by recognizing CD45RO antigen in normal and pathologic paraffin embedded tissues processed in neutral buffered formalin, 85, or Bouin'a fixative. Positive results aid in the classification of lymphomas as T-cell in origin and must be interpreted by a pathologist within the context of clinical data, gross and microscopic morphological criteria and multiple chemical and immunochemical stains.

ChemMate™ UCHL1 (CD45RO) is comprised of a mouse monoclonal antibody, clone UCHL1, and is of the IgG2a, Kappa light chain class of immunoglobulins. The antibody reacts with the 180kD low molecular weight isoform of CD45 or leukocyte common antigen (LCA/CD45) family. The 180kD glycoprotein occurs on most thymocytes and activated T cells, and on a proportion of resting T cells. Additionally the antibody is reactive with a subpopulation of resting T cells within both the CD4 and CD8 subsets. Granulocytes and monocytes are also reactive with UCHL1 while most normal B cells and NK cells are unreactive. UCHL 1 has been classified as a CD45RO antibody by the Fourth International Workshop on Human Leukocyte Differentiation Antigens.

The provided 510(k) summary for KY115084 (ChemMate™ UCHL1) describes an immunohistochemical reagent, not a "device" in the sense of an automated system or algorithm with performance metrics like sensitivity, specificity, or AUC as a standalone product. Therefore, many of the requested categories (e.g., sample size for test set, number of experts, adjudication method, MRMC studies, standalone performance, training set size) are not applicable or cannot be extracted from this type of regulatory submission for a reagent.

Instead, the submission focuses on demonstrating the immunoreactivity of the antibody by summarizing existing literature and consolidating findings regarding its staining patterns in various normal and pathological tissues. The "performance characteristics" section here refers to the expected biological reactivity of the antibody, not the performance of an AI algorithm or an automated diagnostic device.

Here's an attempt to address the request based on the available information, noting the limitations of the document type:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for a reagent like ChemMate™ UCHL1 are primarily related to its intended use and immunoreactivity. The approval suggests that the reported performance, based on accumulated literature, was deemed acceptable by the FDA for the stated intended use.

Table of Acceptance Criteria and Reported Device Performance:

• Demonstrate reactivity with tissues/cells known to be T-cell lineage.
• Demonstrate lack of significant reactivity with tissues/cells known not to be T-cell lineage (or identify known cross-reactivities). | - Reacts with most thymocytes, activated T cells, and a proportion of resting T cells.
• Reactive with granulocytes and monocytes.
• Unreactive with most normal B cells and NK cells.
• T-Cell Lymphomas: Stains a significant proportion (e.g., 68-79% overall depending on subtype, up to 100% in specific subtypes like Sezary's Syndrome and Lennert's Syndrome).
• B-Cell Lymphomas: Generally unreactive (e.g., 9% overall, 0-8% in most subtypes), but
  reported in some diffuse large cell non-Hodgkin's B-Cell lymphomas (9/50, 18%) and plasmacytomas (47%).
• Hodgkin's Lymphoma: Majority show UCHL1-negative Reed-Sternberg cells, but occasional, scattered positive reactions were mentioned (e.g., 13% overall, up to 33% in some subtypes).
• Normal Tissues: Reactive in lymph node, spleen, tonsil, thymus (T-cell areas), peripheral blood (T-lymphocytes, monocytes, granulocytes), and cytospins.
• Non-Reactive Normal Tissues: Thyroid, lung, breast, pancreas, liver, prostate, kidney, uterus, muscle.
• Negative Non-Lymphoid Pathological Tissues: Generally negative in various adenocarcinomas, squamous cell carcinomas, sarcomas, melanomas, etc. |
  | Specificity (T-cell Lineage) | - Primarily stain T-cells, with limited desired cross-reactivity (e.g., with some monocytes/granulocytes) and clearly identified and characterized non-specific or undesired cross-reactivities (e.g., some non-T-cell lymphomas, non-lymphoid tissues). | - Specifically mentioned reactivity with T-cell areas in lymphoid organs.
• Acknowledged co-expression of CD45RO in some B-cell lymphomas and in some Hodgkin's disease cases/Reed-Sternberg cells, necessitating interpretation in context.
• Identified nonspecific cytoplasmic/nuclear staining in some non-lymphoid tissues/pathologies. |
  | Reproducibility | - Consistent staining results in serial sections of tissue specimens. | - "Consistent staining results have been obtained with run to run and within run antibody testing." |
  | Compatibility with Fixatives | - Reactivity in formally fixed, paraffin-embedded tissues processed in zinc formalin, neutral buffered formalin, Bouin's, or B5 fixative. | - Demonstrated reactivity in both formalin and Bouin's fixed paraffin-embedded tissues (Wieczorek et al.). Others refer to "formalin-fixed paraffin-embedded" and "B-5 fixed paraffin-embedded" tissues throughout the cited literature. |

Additional Information on the Study:

1. Sample size used for the test set and the data provenance:
   The "performance characteristics" are derived from numerous published studies (literature review) rather than a single, dedicated test set for this specific 510(k) submission. Therefore, there isn't a single "sample size" for a test set. The efficacy is based on cumulative evidence from multiple publications, which collectively include hundreds of cases of various normal and pathological tissues.
   Data provenance is from published scientific literature, primarily retrospective clinical studies and case series involving human tissue samples. The countries of origin are not explicitly stated for all cited works but are generally international (e.g., UK, USA, Europe are common sources for such research).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   Not applicable in the context of a literature review. Ground truth was established by the authors of the original scientific publications, typically pathologists, often specializing in hematopathology or dermatopathology, using their diagnostic expertise based on histological criteria, other immunohistochemical markers, and sometimes gene rearrangement studies. The specific number and qualifications of experts for each cited study are not detailed in this 510(k) summary.

3. Adjudication method for the test set:
   Not applicable. Each cited study had its own methods for diagnosis, which would have implicitly included standard clinical diagnostic processes, often involving consensus or review among pathologists.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
   No. This submission is for an immunohistochemical reagent, not an AI-assisted diagnostic system, and therefore, an MRMC study comparing human readers with and without AI assistance is not relevant.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   Not applicable. This is for a reagent; there is no algorithm, and human interpretation by a pathologist is explicitly required ("must be interpreted by a pathologist within the context of clinical data, gross and microscopic morphological criteria and multiple chemical and immunohistochemical stains").

6. The type of ground truth used:
   The ground truth across the various cited studies was primarily expert consensus (pathological diagnosis) based on established histopathological criteria, often supplemented by panels of other immunohistochemical markers, and in some cases, genetic analysis (e.g., gene rearrangement for T-cell lymphomas).

7. The sample size for the training set:
   Not applicable. This is a scientific reagent, not an AI model, so there is no "training set." The antibody's characteristics were determined through conventional scientific research and validation (e.g., animal immunization, hybridoma production, screening, and characterization experiments), not machine learning.

8. How the ground truth for the training set was established:
   Not applicable (no training set).

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