Spit Camp I Agenda

DAY 1: Lecture and Pre-Analytical Lab Practicum

8:30 am – 2:30 pm: Lecture with Doug Granger

Welcome and Introductions
- Overview of Agenda and Activities
- Theoretical Assumptions and Models
Oral Fluids as Biological Specimens
- Analytes of Interest in Oral Fluid
Saliva Collection, Handling, and Storage
Research Design and Sampling Schemes

12:00 pm – 12:45 pm: Lunch Break

12:45 pm – 2:15 pm: Lecture with Doug Granger

Basics of Salivary Assays
Salivary Analyte Data Issues

Break

2:30 pm – 4:30 pm: Pre-Analytical Lab Practicum with IISBR Lab Team

Introduction to Laboratory equipment and Safety Training

Introduction to the fundamentals of pipetting
Volume Recovery Demonstration

Break

Specimen Collection
Pipetting Practice Session
Sample Prep and Overnight Storage

Break

Before Bed: Assay Review Homework and First Sample Collection (SOS/Bedtime)

DAY 2: Analytical Lab Practicum and Data Discussion

Upon Waking: Second Sample collection (SOS/wake)

30 Min After Waking: Third Sample collection (SOS/+ 30)

8:30 am: Sample Drop Off

9:00 am – 12:00 am: Analytical Lab with IISBR Lab Team

Sample Preparation
Cortisol Assay Training part 1

Break

Cortisol Assay Training part 2

12:00 pm – 12:45 pm: Lunch Break and Discussion with Doug Granger and Jenna Riis

Lunch
Open Discussion

12:45 pm – 2:45 pm: Analytical Lab with IISBR Lab Team

Alpha Amylase Assay Training

Break

2:45 pm – 5:00 pm: Discussion with Doug Granger and IISBR Lab Team

Cortisol Data Discussion
Alpha Amylase Data Discussion
Conclusion
Q&A

5:00 pm – 5:15 pm: End of the Camp with Doug Granger and IISBR Lab Team

**PLEASE NOTE: LAB ATTIRE

While in the laboratory, you will be provided with laboratory coats to wear. No shorts or open-toed shoes are allowed for safety purposes. Please plan your attire accordingly.

DAY 3 (optional): Pre-analytic Data Processing and Basic Statistical Approaches

8:30 am – 10:00 pm: Understanding and importing data reported from the lab - Lecture and guided practice with Jenna Riis

Review of lab-related data measures (e.g., calculating inter- and intra-assay CV, flow rate)
Review of lab notes (e.g., contamination; dilution)
Importing data into a statistical program

Break

10:15 am – 12:00 pm: Examining data quality and characteristics- Lecture with Jenna Riis

12:00 - 12:45 pm: Lunch Break and Discussion with Jenna Riis

12:45 pm – 2:15 pm: Examining data quality and characteristics- Guided Practice with Jenna Riis

Missingness- causes and approaches
Data distributions
Identifying and addressing outliers

Break

2:30 pm – 5:00 pm: Computing and analyzing composite measures- Lecture and Guided Practice with Jenna Riis