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  1. Abstract Background

    Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics.


    Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP.


    The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescriptionmore »in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians.


    Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.

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  6. Inversion symmetry breaking could lead to the creation of a Rashba–Dresselhauls magnetic field, which plays the key role in spintronic devices. In this work, we propose and develop a composition gradient engineering approach that breaks inversion symmetry into inorganic halide perovskites with strong spin–orbit coupling. We synthesize epitaxial CsPbBr x Cl (3− x ) with Br/Cl composition gradient by a two-step chemical vapor deposition approach. Through optoelectronic measurements, we show the presence of circular photogalvanic effects (CPGEs), evidencing a Rashba-like spin polarized band structure. By spatially resolved photoluminescence spectra, we find that the observed CPGE is likely a cumulative result of inversion symmetry-broken interfaces featured by abrupt and stepwise composition gradient between the pristine and separated daughter phases. Our work suggests an avenue in engineering the spintronic property of halide perovskites for information processing.
  7. Free, publicly-accessible full text available July 1, 2023