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1.
Learn Mem ; 30(11): 278-281, 2023 11.
Article in English | MEDLINE | ID: mdl-37852783

ABSTRACT

An in vitro analog of learning that a food is inedible provided insight into mechanisms underlying the learning. Aplysia learn to stop responding to a food when they attempt but fail to swallow it. Pairing a cholinergic agonist with an NO donor or histamine in the Aplysia cerebral ganglion produced significant decreases in fictive feeding in response to the cholinergic agonist alone. Acetylcholine (ACh) is the transmitter of chemoreceptors sensing food touching the lips. Nitric oxide (NO) and histamine (HA) signal failed attempts to swallow food. Reduced responses to the cholinergic agonist after pairing with NO or HA indicate that learning partially arises via a decreased response to ACh in the cerebral ganglion.


Subject(s)
Aplysia , Deglutition , Animals , Aplysia/physiology , Deglutition/physiology , Histamine , Feeding Behavior/physiology , Nitric Oxide/physiology , Cholinergic Agonists
2.
Radiology ; 230(3): 820-3, 2004 Mar.
Article in English | MEDLINE | ID: mdl-14739315

ABSTRACT

PURPOSE: To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation. MATERIALS AND METHODS: A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least two of four radiologists. The CAC system automatically extracted from the digitized mammograms quantitative features that characterized the lesions. The system then used a classification scheme to score the lesions by the likelihood of their malignancy on the basis of these features. The classification scheme was trained with 646 pathologically proved cases (323 malignant), and the results were tested with receiver operating characteristic (ROC) analysis by using the jackknife method. Sensitivity, specificity, positive predictive value, and accuracy were calculated. Category 3 lesions were stratified among BI-RADS categories 2-5 according to CAC-assigned lesion score, and this classification was compared with the results of pathologic analysis. RESULTS: Jackknife analysis of CAC results in the training data set yielded a sensitivity of 94%, specificity of 78%, positive predictive value of 81%, and area under the ROC curve of 0.90. Of the 42 malignant lesions that had been classified at conventional interpretation as probably benign, nine were assigned by the CAC system to BI-RADS category 4, and 29 were assigned to category 5. The CAC system correctly upgraded the BI-RADS classification of these 38 lesions (sensitivity, 90%) and incorrectly upgraded the classification of only 20 benign lesions (specificity, 69%). CONCLUSION: The CAC system scored 38 of the 42 malignant lesions initially assigned to BI-RADS category 3 as BI-RADS category 4 or 5, and thus correctly upgraded the category in 90% of these lesions.


Subject(s)
Breast Neoplasms/classification , Diagnosis, Computer-Assisted , Mammography , Radiographic Image Enhancement , Radiographic Image Interpretation, Computer-Assisted , Radiology Information Systems , Adult , Aged , Aged, 80 and over , Breast/pathology , Breast Neoplasms/diagnostic imaging , Breast Neoplasms/pathology , Female , Fibrocystic Breast Disease/classification , Fibrocystic Breast Disease/diagnostic imaging , Fibrocystic Breast Disease/pathology , Humans , Middle Aged , Precancerous Conditions/classification , Precancerous Conditions/diagnostic imaging , Precancerous Conditions/pathology , Predictive Value of Tests , Probability , ROC Curve , Retrospective Studies , Sensitivity and Specificity
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