diff --git a/README.md b/README.md
index bf1dd54..9bab052 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@ https://aws.amazon.com/blogs/machine-learning/accelerate-analysis-and-discovery-
 Multi-Agents Update:
 -  A Jupyter notebook located in this repository has been developed to walk users through creating these agents
 -  Users deploy AWS CloudFormation infrastructure to their AWS account as before
-- This agent deployment notebook is meant to be run in a SageMaker Notebook environment
+- This agent deployment notebook is meant to be run in a SageMaker Studio environment
 -  Users follow the instructions within the notebook step-by-step to interactively create agents
 
 Now follow the step-by-step instructions below to deploy these Bedrock Agents
@@ -17,9 +17,11 @@ Step 1: Navigate to the agents/ folder in this repository and download the files
 
 Step 2: Click the 'Launch Stack' button located in the [Deployment](#deployment) section to deploy the AWS infrastructure needed to support the agents
 
-Step 3: Create a SageMaker Notebook in your AWS account and upload the agent/ files there
+Step 3: When deploying this template make sure to change the 'GitBranch' parameter to the value 'multi-agent-collaboration' before clicking 'Submit'
 
-Step 4: Follow the step-by-step instructions shown in deploy_agents.ipynb to deploy agents
+Step 4: Go to a SageMaker Studio JupyterLab environment in your AWS account and upload the agent/ files there, use the Python 3 (ipykernel)
+
+Step 5: Follow the step-by-step instructions shown in deploy_agents.ipynb to deploy agents
 
 ## Overview
 The success rate for Phase I oncology clinical trials is significantly low. According to a study published in Nature Reviews Drug Discovery, the overall success rate for oncology drugs from Phase I to approval is around 5%, indicating a high failure rate of approximately 95%. 
diff --git a/streamlitapp/app.py b/streamlitapp/app.py
index c74d5db..93b10f2 100644
--- a/streamlitapp/app.py
+++ b/streamlitapp/app.py
@@ -427,7 +427,7 @@ def response_generator():
         st.success(f"Selected actions: {', '.join(selected_actions)}")
 
 # Main content
-st.title("Clinical Trial Agent")
+st.title("Biomarker Research Agent")
 
 col1, col2 = st.columns([6, 1])
 with col2:
@@ -526,4 +526,4 @@ def response_generator():
         image_placeholder.error(error_msg)
         
     # Add assistant response to chat history
-    #st.session_state.messages.append({"role": "assistant", "content": response})
\ No newline at end of file
+    #st.session_state.messages.append({"role": "assistant", "content": response})