diff --git a/tex/thesis.tex b/tex/thesis.tex
index 4757c1a..a16d82a 100644
--- a/tex/thesis.tex
+++ b/tex/thesis.tex
@@ -2168,7 +2168,7 @@ Venn diagram from the venn R package.
 
 \section{results}
 
-\subsection{DOE shows optimal conditions for expanded potent T cells}
+\subsection{T cells can be grown on DMSs with lower IL2 concentrations}
 
 % TODO this plots proportions look dumb
 \begin{figure*}[ht!]
@@ -2194,6 +2194,10 @@ Venn diagram from the venn R package.
   \label{fig:il2_mod}
 \end{figure*}
 
+% TODO this is not consistent with the next section since the responses are
+% different
+\subsection{DOE shows optimal conditions for expanded potent T cells}
+
 % TODO not all of these were actually use, explain why by either adding columns
 % or marking with an asterisk
 \begin{table}[!h] \centering
@@ -2284,14 +2288,15 @@ process (Fig.1d-e).
 \end{table}
 
 SR models achieved the highest predictive performance (R2>93\%) when using
-multi-omics predictors for all endpoint responses (\cref{tab:mod_results}). SR achieved R2>98\%
-while GBM tree-based ensembles showed leave-one-out cross-validated R2 (LOO-R2)
->95\% for CD4+ and CD4+/CD8+ TN+TCM responses. Similarly, LASSO, PLSR, and SVM
-methods showed consistent high LOO-R2, 92.9\%, 99.7\%, and 90.5\%, respectively,
-to predict the CD4+/CD8+ TN+TCM. Yet, about 10\% reduction in LOO-R2,
-72.5\%-81.7\%, was observed for CD4+ TN+TCM with these three methods. Lastly, SR
-and PLSR achieved R2>90\% while other ML methods exhibited exceedingly variable
-LOO-R2 (0.3\%,RF-51.5\%,LASSO) for CD8+ TN+TCM cells.
+multi-omics predictors for all endpoint responses (\cref{tab:mod_results}). SR
+achieved R2>98\% while GBM tree-based ensembles showed leave-one-out
+cross-validated R2 (LOO-R2) >95\% for CD4+ and CD4+/CD8+ TN+TCM responses.
+Similarly, LASSO, PLSR, and SVM methods showed consistent high LOO-R2, 92.9\%,
+99.7\%, and 90.5\%, respectively, to predict the CD4+/CD8+ TN+TCM. Yet, about
+10\% reduction in LOO-R2, 72.5\%-81.7\%, was observed for CD4+ TN+TCM with these
+three methods. Lastly, SR and PLSR achieved R2>90\% while other ML methods
+exhibited exceedingly variable LOO-R2 (0.3\%,RF-51.5\%,LASSO) for CD8+ TN+TCM
+cells.
 
 % FIGURE the CD4/CD8 model results using SR