Epidemiology of Human Parainfluenza Virus Type 3 and Respiratory Syncytial Virus Infections in the Time of Coronavirus Disease 2019: Findings From a Household Cohort in Maryland.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, human parainfluenza type 3 (HPIV-3) and respiratory syncytial virus (RSV) circulation increased as nonpharmaceutical interventions were relaxed. Using data from 175 households (n = 690 members) followed between November 2020 and October 2021, we characterized HPIV-3 and RSV epidemiology in children aged 0-4 years and their households. METHODS: Households with ≥1 child aged 0-4 years were enrolled; members collected weekly nasal swabs (NS) and additional NS with respiratory illnesses (RI). We tested NS from RI episodes in children aged 0-4 years for HPIV-3, RSV, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using reverse-transcriptase polymerase chain reaction (RT-PCR). Among children with HPIV-3 or RSV infection, we tested contemporaneous NS from household members. We compared incidence rates (IRs) of RI with each virus during epidemic periods and identified household primary cases (the earliest detected household infection), and associated community exposures. RESULTS: 41 of 175 (23.4%) households had individuals with HPIV-3 (n = 45) or RSV (n = 46) infections. Among children aged 0-4 years, RI IRs /1000 person-weeks were 8.7 [6.0, 12.2] for HPIV-3, 7.6 [4.8, 11.4] for RSV, and 1.9 [1.0, 3.5] for SARS-CoV-2. Children aged 0-4 years accounted for 35 of 36 primary HPIV-3 or RSV cases. Children attending childcare or preschool had higher odds of primary infection (odds ratio, 10.81; 95% confidence interval, 3.14-37.23). CONCLUSIONS: Among children aged 0-4 years, RI IRs for HPIV-3 and RSV infection were 4-fold higher than for SARS-CoV-2 during epidemic periods. HPIV-3 and RSV were almost exclusively introduced into households by young children.

Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy.

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.

Integrating Multiplexed Imaging and Multiscale Modeling Identifies Tumor Phenotype Transformation as a Critical Component of Therapeutic T Cell Efficacy.

Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here we integrated CODEX multiplexed tissue imaging with multiscale modeling software, to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface.