Dataset Information

The effect of time of addition of glutamine or nucleosides on proliferation of rat cervical lymph-node T-lymphocytes after stimulation by concanavalin A.

ABSTRACT: 1. Addition of concanavalin A to T-cell lymphocytes from rat cervical lymph nodes increases the activity of glutaminase within 1 h and those of carbamoyl-phosphate synthase II and aspartate transcarbamoylase within 3 h. There was a similar time course for the effects of concanavalin A on rates of glutamine utilization, which was increased within 1 h, and on pyrimidine nucleotide synthesis, which was increased by 40% at 2 h and by 100% at 3 h. 2. A delay in the addition of glutamine to the culture medium after addition of concanavalin A caused a decrease in [3H]thymidine incorporation only after 4-6 h. In the absence of glutamine, delay in addition of guanosine or inosine caused a decrease in [3H]thymidine incorporation only after 6-8 h after the addition of concanavalin A. 3. In contrast, a delay in addition of adenosine or uridine to the culture medium had an immediate effect (i.e. within 2 h) on the rate of incorporation of [3H]thymidine. It is suggested that adenosine and uridine have specific effects on proliferation via specific receptors for these nucleosides in the membrane.

SUBMITTER: Szondy Z

PROVIDER: S-EPMC1151368 | biostudies-other | 1991 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Dataset Information

The effect of time of addition of glutamine or nucleosides on proliferation of rat cervical lymph-node T-lymphocytes after stimulation by concanavalin A.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets