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Biomedical subjects

S Staats

Publications and source records attributed to S Staats.

8 recordsLinked to original sources
PubMed2000

Immobilization of Notch ligand, Delta-1, is required for induction of notch signaling.

Cell-cell interactions mediated by Notch and its ligands are known to effect many cell fate decisions in both invertebrates and vertebrates. However, the mechanisms involved in ligand induced Notch activation are unknown. Recently it was shown that, in at least some cases, endocytosis of the extracellular domain of Notch and ligand by the signaling cell is required for signal induction in the receptive cell. These results imply that soluble ligands (ligand extracellular domains) although capable of binding Notch would be unlikely to activate it. To test the potential activity of soluble Notch ligands, we generated monomeric and dimeric forms of the Notch ligand Delta-1 by fusing the extracellular domain to either a series of myc epitopes (Delta-1(ext-myc)) or to the Fc portion of human IgG-1 (Delta-1(ext-IgG)), respectively. Notch activation, assayed by inhibition of differentiation in C2 myoblasts and by HES1 transactivation in U20S cells, occurred when either Delta-1(ext-myc) or Delta-1(ext-IgG) were first immobilized on the plastic surface. However, Notch was not activated by either monomeric or dimeric ligand in solution (non-immobilized). Furthermore, both non-immobilized Delta-1(ext-myc) and Delta-1(ext-IgG) blocked the effect of immobilized Delta. These results indicate that Delta-1 extracellular domain must be immobilized to induce Notch activation in C2 or U20S cells and that non-immobilized Delta-1 extracellular domain is inhibitory to Notch function. These results imply that ligand stabilization may be essential for Notch activation.

Cell Communication↗
PubMed1998

The Notch ligand, Jagged-1, influences the development of primitive hematopoietic precursor cells.

We examined the expression of two members of the Notch family, Notch-1 and Notch-2, and one Notch ligand, Jagged-1, in hematopoietic cells. Both Notch-1 and Notch-2 were detected in murine marrow precursors (Lin-Sca-1+c-kit+). The Notch ligand, Jagged-1, was not detected in whole marrow or in precursors. However, Jagged-1 was seen in cultured primary murine fetal liver stroma, cultured primary murine bone marrow stroma, and in stromal cell lines. These results indicate a potential role for Notch-Notch ligand interactions in hematopoiesis. To further test this possibility, the effect of Jagged-1 on murine marrow precursor cells was assessed by coculturing sorted precursor cells (Lin-Sca-1+c-kit+) with a 3T3 cell layer that expressed human Jagged-1 or by incubating sorted precursors with beads coated with the purified extracellular domain of human Jagged-1 (Jagged-1(ext)). We found that Jagged-1, presented both on the cell surface and on beads, promoted a twofold to threefold increase in the formation of primitive precursor cell populations. These results suggest a potential use for Notch ligands in expanding precursor cell populations in vitro.

3T3 Cells↗
PubMed1996

Youthful and older biases as special cases of a self-age optimization bias.

Two concepts of subjective age are measured for two cohorts (college students and older persons with an age range of 50 to 91 years). Functional age (Kastenbaum et al., 1972 Ages-of-Me Scale) shows the typical youthful bias for the older cohort. An older bias is shown for the Best/Ideal Age by the older cohort in comparison to the younger cohort. Taken together, the youthful bias, being like someone of younger chronological age, and the bias of selecting a relatively older age as best represents a "Self Age Optimization Bias." A sub-set of Best Age items dealing with work and career are identified for gender and cohort comparisons.

Adult↗
PubMed1993

Future time perspective, response rates, and older persons: another chapter in the story.

Data quality is compromised when response rates to items vary with age group. Shmotkin (1992) found a 29% nonresponse rate to future-oriented items in persons older than 60 years and suggested future apprehension as a cause. The authors administered similar items to 251 older persons and found fewer instances of nonresponding to future-oriented items. On the basis of the high response rate to an enlarged Cantril ladder measuring future quality of life, presented in interview, the authors question the generality of future apprehension as a determinant of nonresponding. The authors suggest that mode of administration, size of items, and scale complexity, as well as future apprehension, are determinants of nonresponding to future-oriented items and scales.

Age Factors↗
PubMed1993

Subjective age and health perceptions of older persons: maintaining the youthful bias in sickness and in health.

Self-reports of 250 persons fifty years of age and older confirm the increasing bias toward reporting a more youthful age as one increases in years. Optimistic perceptions of health care also maintained in older persons. Results from two subsets of this sample (N = 48) further indicate that the youthful and optimistic bias occurs in older persons with poorer and failing health (N = 23) as well as for persons in stable and good health (N = 25). Given the importance of self-perceptions in quality of life and in determining survivability, and given the indication that such measures are modifiable, it is suggested that future research be aimed at identifying those self-perceptions of health and age that are most susceptible to intervention.

Aged↗
PubMedDate not supplied

Automated testing of arrhythmia monitors using annotated databases.

Arrhythmia-algorithm performance is typically tested using the AHA and MIT/BIH databases. The tools for this test are simulation software programs. While these simulations provide rapid results, they neglect hardware and software effects in the monitor. To provide a more accurate measure of performance in the actual monitor, a system has been developed for automated arrhythmia testing. The testing system incorporates an IBM-compatible personal computer, a digital-to-analog converter, an RS232 board, a patient-simulator interface to the monitor, and a multi-tasking software package for data conversion and communication with the monitor. This system "plays" patient data files into the monitor and saves beat classifications in detection files. Tests were performed using the MIT/BIH and AHA databases. Statistics were generated by comparing the detection files with the annotation files. These statistics were marginally different from those that resulted from the simulation. Differences were then examined. As expected, the differences were related to monitor hardware effects.

Algorithms↗