Project description:BackgroundAlthough tissue microarrays (TMAs) are commonly employed in clinical and basic-science research, there are no guidelines for evaluating the appropriateness of a TMA for a given biomarker and tumor type. Furthermore, TMA performance across multiple biomarkers has not been systematically explored.MethodsA simulated TMA with between 1 and 10 cores was designed to study tumor expression of 6 biomarkers with varied expression patterns (B7-H1, B7-H3, survivin, Ki-67, CAIX, and IMP3) using 100 patients with clear cell renal cell carcinoma (RCC). We evaluated agreement between whole tissue section and TMA immunohistochemical biomarker quantification to assess how many TMA cores are necessary to adequately represent RCC whole tissue section expression. Additionally, we evaluated associations of whole tissue section and TMA expression with RCC-specific death.ResultsThe number of simulated TMA cores necessary to adequately represent whole tissue section quantification is biomarker specific. Although 2-3 cores appeared adequate for B7-H3, Ki-67, CAIX, and IMP3, even as many as 10 cores resulted in poor agreement for B7-H1 and survivin compared to RCC whole tissue sections. While whole tissue section B7-H1 was significantly associated with RCC-specific death, no significant associations were detected using as many as 10 TMA cores, suggesting that TMAs can result in false-negative findings if the TMA is not optimally designed.ConclusionsPrior to TMA analysis, the number of TMA cores necessary to accurately represent biomarker expression on whole tissue sections should be established as there is not a one-size-fits-all TMA. We illustrate the use of a simulated TMA as a cost-effective tool for this purpose.

| S-EPMC2910003 | biostudies-literature

One Size Does Not Fit All: Genetic Prediction of Kawasaki Disease Treatment Response in Diverse Populations.

Project description: Not available

| S-EPMC5661947 | biostudies-literature

Acetylcysteine for paracetamol: Will one size ever fit all?

Project description: Not available

| S-EPMC7082209 | biostudies-literature

Probiotics and COVID-19: one size does not fit all.

Project description: Not available

| S-EPMC7182525 | biostudies-literature

Pharmacogenomics and patient care: one size does not fit all.

Project description:The time is ripe to assess whether pharmacogenomics research--the study of the genetic basis for variation in drug response--has provided important insights into a personalized approach to prescribing and dosing medications. Here, we describe the status of the field and approaches for addressing some of the open questions in pharmacogenomics research and use of genetic testing in guiding drug therapy.

| S-EPMC3963516 | biostudies-literature

Well-being and burnout: One size does not fit all.

Project description:Well-being and burnout are concepts that have become well described throughout emergency medicine. In the past, both well-being and burnout have been defined and addressed as a singular phenomenon, similar for all physicians, regardless of career stage. However, unique stressors may exist for physicians, as a function of their work environment and stage. In this concepts article we present clinician well-being as a dynamic and continuous process, subject to unique factors along the professional lifespan. Specific individual and system-level factors are discussed, ranging from demographic variables, to evolving administrative and professional responsibilities depending on the career stage of a clinician. This detailed description of stressors spanning an emergency physician's professional career may help create more targeted physician well-being and burnout interventions.

| S-EPMC7280727 | biostudies-literature

Intraoperative hypotension and patient outcome: does "one size fit all?".

Project description: Not available

| S-EPMC5508107 | biostudies-literature

Kidney Disease Complexity Manifested: One Biomarker Size Does Not Fit All.

Project description: Not available

| S-EPMC9263414 | biostudies-literature

Visual data mining of biological networks: one size does not fit all.

Project description:High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

| S-EPMC3547662 | biostudies-literature

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