Dataset Information

Mechanosensing drives acuity of ?? T-cell recognition.

ABSTRACT: T lymphocytes use surface [Formula: see text] T-cell receptors (TCRs) to recognize peptides bound to MHC molecules (pMHCs) on antigen-presenting cells (APCs). How the exquisite specificity of high-avidity T cells is achieved is unknown but essential, given the paucity of foreign pMHC ligands relative to the ubiquitous self-pMHC array on an APC. Using optical traps, we determine physicochemical triggering thresholds based on load and force direction. Strikingly, chemical thresholds in the absence of external load require orders of magnitude higher pMHC numbers than observed physiologically. In contrast, force applied in the shear direction ([Formula: see text]10 pN per TCR molecule) triggers T-cell Ca2+ flux with as few as two pMHC molecules at the interacting surface interface with rapid positional relaxation associated with similarly directed motor-dependent transport via [Formula: see text]8-nm steps, behaviors inconsistent with serial engagement during initial TCR triggering. These synergistic directional forces generated during cell motility are essential for adaptive T-cell immunity against infectious pathogens and cancers.

SUBMITTER: Feng Y

PROVIDER: S-EPMC5625899 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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Mechanosensing drives acuity of <i>αβ</i> T-cell recognition.

Feng Yinnian Y Brazin Kristine N KN Kobayashi Eiji E Mallis Robert J RJ Reinherz Ellis L EL Lang Matthew J MJ

Proceedings of the National Academy of Sciences of the United States of America 20170815 39

T lymphocytes use surface [Formula: see text] T-cell receptors (TCRs) to recognize peptides bound to MHC molecules (pMHCs) on antigen-presenting cells (APCs). How the exquisite specificity of high-avidity T cells is achieved is unknown but essential, given the paucity of foreign pMHC ligands relative to the ubiquitous self-pMHC array on an APC. Using optical traps, we determine physicochemical triggering thresholds based on load and force direction. Strikingly, chemical thresholds in the absence ...[more]

PMID: 28811364

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Project description:PurposeVisual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole.DesignCross-sectional study.SubjectsA total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included.MethodsThe notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based algorithm was created to recognize the same VA entities to compare against BERT models.Main outcome measuresModel performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities.ResultsOn the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs.ConclusionsThis study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant performance improvements over a rule-based algorithm. The ability to recognize VA from free-text notes would enable a more detailed characterization of ophthalmology patient cohorts and enhance the development of models to predict ophthalmology outcomes.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Mechanosensing drives acuity of ?? T-cell recognition.

Publications

Mechanosensing drives acuity of <i>αβ</i> T-cell recognition.

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