Sentence examples for distinguished t from inspiring English sources

So, within the first year and a half, we had cell-surface markers that distinguished T cells from B cells, which allowed us to separate and study the two cell types.

Flow cytometry was then used to simultaneously evaluate ROS production and distinguish T cells and monocytes present in PBMC cultures by staining with fluorescently labeled CD3 and CD14 antibodies (a,c),n = 3. Parallel experiments were used to evaluate effects of NP treatment on cell viability by PI uptake and flow cytometry (b,d),n = 3.Error bars depict standard error.

In particular, combination analysis of SOX1 and VAMP8 could distinguish T from N/CN with 100% sensitivity and specificity (data not shown).

CD4-CD8-double negative (DN) thymocytes generated from donor hematopoietic stem cells were gated using CD45.2 markers, and plotted with CD45.1 expression to distinguish T cells produced from WT (CD45.1 positive) and Pak2 F/F ;Cd4- Cre (CD45.1 negative) donors (first panel).

By comparing the genotype appeared in H and E, we were able to distinguish T allele from that of E. Marker tests were performed on these eight individuals of the bin set that were genotyped as homozygous for the 'Texas' allele (noted as A), homozygous for the 'Earlygold' allele (B) or heterozygous as MB1-73 (H).

Using this technology, we detected proteins expressed on the surface in single cells that distinguish T-cells among human blood cells.

As a network-based approach, we used PinnacleZ algorithm [ 12] to distinguish T-ALL patients and healthy samples by integrating microarray data with the human PPI network.

Cellular immunoblotting has been validated to have excellent specificity and sensitivity for distinguishing T-cell responses to islet proteins between T1D patients and control subjects (10, 11, 15).

The workshops were designed to test the ability of several different assays, including CI performed in our laboratory, to distinguish T-cell responses to islet proteins of T1D patients from control subjects.

Cross-comparisons between two independent halves of patient dataset revealed that subnetworks are good at distinguishing T-ALL patients from healthy individuals, regardless of the dataset in which they are found.

We have participated in two distinct NIH-supported T-cell validation workshops designed to test the ability of several different assays, including cellular immunoblotting, to distinguish T-cell responses to islet proteins of type 1 diabetic patients from those of control subjects (9, 10).