As the COVID-19 pandemic continues, formulating targeted policy interventions that are informed by differential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics will be of vital importance to national and regional governments. We develop an individual-level model for SARS-CoV-2 transmission that accounts for location-dependent distributions of age, household structure, and comorbidities. We use these distributions together with age-stratified contact matrices to instantiate specific models for Hubei, China; Lombardy, Italy; and New York City, United States. Using data on reported deaths to obtain a posterior distribution over unknown parameters, we infer differences in the progression of the epidemic in the three locations. We also examine the role of transmission due to particular age groups on total infections and deaths. The effect of limiting contacts by a particular age group varies by location, indicating that strategies to reduce transmission should be tailored based on population-specific demography and social structure. These findings highlight the role of between-population variation in formulating policy interventions. Across the three populations, though, we find that targeted “salutary sheltering” by 50% of a single age group may substantially curtail transmission when combined with the adoption of physical distancing measures by the rest of the population.

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.

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A novel fusion gene, LMO7-BRAF, was identified in PTC tumors. The results indicate that the LMO7-BRAF fusion behaves as an oncogenic alteration. This observation expands the spectrum of fusion genes involving kinases in thyroid cancer.

An extreme chronic wound tissue microenvironment causes epigenetic gene silencing. Unbiased whole-genome methylome was studied in the wound-edge (WE) tissue of chronic wound patients.A total of 4689 differentially methylated regions (DMRs) were identified in chronic WE compared to unwounded (UW) human skin. Hypermethylation was more frequently observed (3661 DMRs) in the chronic WE compared to hypomethylation (1028 DMRs). Twenty-six hypermethylated DMRs were involved in epithelial to mesenchymal transition (EMT). Bisulfite sequencing validated hypermethylation of a predicted specific upstream regulator TP53. RNA sequencing analysis was performed to qualify findings from methylome analysis. Analysis of the downregulated genes identified the TP53 signaling pathway as being significantly silenced. Direct comparison of hypermethylation and downregulated genes identified four genes, ADAM17, NOTCH, TWIST1 and SMURF1, that functionally represent the EMT pathway. Single-cell RNA sequencing studies identified that these effects on gene expression were limited to the keratinocyte cell compartment. Experimental murine studies established that tissue ischemia potently induces WE gene methylation and that 5'-azacytidine, inhibitor of methylation, improved wound closure.To specifically address the significance of TP53 methylation, keratinocyte-specific editing of TP53 methylation at the WE was achieved by a tissue nanotransfection (TNT) based CRISPR/dCas9 approach. This work identified that reversal of methylation-dependent keratinocyte gene-silencing represents a productive therapeutic strategy to improve wound closure.