RESUMO
Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high-quality experimental data as input. Non-absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to their zero systemic exposure, but their empirical discovery is still time-consuming and costly. Here, a synergistic ML method, integrating small data-driven multi-layer unsupervised learning, in silico quantum-mechanical computations, and minimal wet-lab experiments is devised to identify the finest NODs from massive inorganic materials to achieve multi-objective function (high selectivity, large capacity, and stability). Based on this method, a NH4-form nanoporous zeolite with merlinoite (MER) framework (NH4-MER) is discovered for the treatment of hyperkalemia. In three different animal models, NH4-MER shows a superior safety and efficacy profile in reducing blood K+ without Na+ release, which is an unmet clinical need in chronic kidney disease and Gordon's syndrome. This work provides a synergistic ML method to accelerate the discovery of NODs and other shape-selective materials.
RESUMO
High-quality and high-resolution magnetic resonance (MR) images can provide more details for diagnosis and analyses. Recently, MR images guided neurosurgery has become an emerging technique in clinics. Unlike other medical imaging techniques, it is impossible to achieve both real-time imaging and high image quality in MR imaging. The real-time performance is closely related to the nuclear magnetic equipment itself as well as the collection strategy of the k space data. Optimizing the imaging time cost via the corresponding algorithm is harder than enhancing image quality. Further, in reconstructing low-resolution and noise-rich MR images, getting relatively high-definition and resolution MR images as references are difficult or impossible. In addition, the existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. As a result, severely bad results are inevitable when the modeling assumptions are far apart from the actual situation. To address these problems, we propose a novel adaptive adjustment method based on real MR images via opinion-unaware measurements for real super-resolution (A2OURSR). It can estimate the degree of blur and noise from the test image itself using two scores. These two scores can be considered pseudo labels to train the adaptive adjustable degradation estimation module. Then, the outputs of the above model are used as the inputs of the conditional network to tweak the generated results. Thus, the results can be automatically adjusted via the whole dynamic model. Extensive experimental results show that the proposed A2OURSR is superior to state-of-the-art methods on benchmarks quantitatively and visually.
