University of Iowa

Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h

Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h

Using UV absorption wavelength (λmax) prediction as a testbed across 18,415 solute-solvent pairs (after the Greenman/Song duplicate-handling protocol) with stratified 5-fold cross-validation and two external data sets totaling over 40,000 molecules, this work compares five models spanning four architecture families (Random Forest, XGBoost, a directed message-passing GNN, a BiGRU, and a pretrained Transformer) and finds that Random Forest is statistically indistinguishable from the best deep learning model for screening within known chemical space (RMSE 31.50 ± 1.47 vs 33.15 ± 3.27 nm, p = 0.16) while training in 15 min on a CPU, whereas on 16 novel UV-absorbing compounds the GNN (MAE 26.2 nm), Transformer (26.3 nm), and BiGRU (28.6 nm) all outperform RF (38.

Using UV absorption wavelength (λmax) prediction as a testbed across 18,415 solute-solvent pairs (after the Greenman/Song duplicate-handling protocol) with stratified 5-fold cross-validation and two external data sets totaling over 40,000 molecules, this work compares five models spanning four architecture families (Random Forest, XGBoost, a directed message-passing GNN, a BiGRU, and a pretrained Transformer) and finds that Random Forest is statistically indistinguishable from the best deep learning model for screening within known chemical space (RMSE 31.50 ± 1.47 vs 33.15 ± 3.27 nm, p = 0.16) while training in 15 min on a CPU, whereas on 16 novel UV-absorbing compounds the GNN (MAE 26.2 nm), Transformer (26.3 nm), and BiGRU (28.6 nm) all outperform RF (38.