The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7469990 | PMC |
http://dx.doi.org/10.1097/CM9.0000000000000871 | DOI Listing |
J Surg Educ
January 2025
Washington University of St. Louis, Department of Orthopaedic Surgery, St. Louis, Missouri.
Objective: Orthopedic residents are tasked with rapidly acquiring clinical and surgical skills, especially during their PGY-1 year. However, resource constraints and other factors frequently cause skills training to fall short of established guidelines. We aimed to design and evaluate a cross-institutional, month-long curriculum aimed at pooling resources to optimize training.
View Article and Find Full Text PDFJ Nurs Adm
December 2024
Author Affiliation: Assistant Professor, School of Nursing and Healthcare Leadership, University of Washington, Tacoma.
Objective: This study aimed to investigate the mediating role of psychological distress in the relationship between work-family conflict and nurse managers' (NMs') professional and organizational turnover intentions.
Background: Work-family conflict is prevalent among NMs. It can have a significant impact on their intent to leave their organization and the profession.
J Nurs Adm
December 2024
Author Affiliations: Research Associate (Dr Keys), The Center for Health Design, Concord, California; National Senior Director (Dr Fineout-Overholt), Evidence-Based Practice and Implementation Science, at Ascension in St. Louis, MO.
Objective: Relationships among coworker and patient visibility, reactions to physical work environment, and work stress in ICU nurses are explored.
Background: Millions of dollars are invested annually in the building or remodeling of ICUs, yet there is a gap in understanding relationships between the physical layout of nursing units and work stress.
Methods: Using a cross-sectional, correlational, exploratory, predictive design, relationships among variables were studied in a diverse sample of ICU nurses.
J Nurs Adm
December 2024
Author Affiliations: Assistant Professor (Dr Prothero) and Nurse (Sorhus and Huefner), College of Nursing, Brigham Young University, Provo, Utah.
Objective: This study explored nurse leaders' perspectives and experiences in supporting nurses following a serious medical error.
Background: Appropriate support is crucial for nurses following an error. Authentic leadership provides an environment of psychological safety and establishes a patient safety culture.
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!