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Abstract Blood pressure (BP) shows a seasonal variation with higher levels at lower temperatures. Many hypertensives, however, report on BP disturbances rather in association with acutely changing weather conditions than with absolute temperatures. To date, the impact of changing meteorological parameters on hypertensive episodes remains elusive. We performed a retrospective time series regression analysis on 203,703 patients in three hospitals in Germany between 2010 and 2018, of whom 7362 patients were admitted for hypertensive disease. Numbers of daily admissions for hypertension were associated with metereological data obtained from three nearby weather stations. Data comprised temperature (mean, maximal, minimal and range within 24 h), athmospheric pressure, and precipitation. Changes of these parameters were calculated over a two and three day period. There was an inverse correlation between maximal daily temperature and the number of admissions for hypertensive disease, which remained significant both after adjustment for seasonality and week day in a spline model and in a constrained distributed lag model. A decrease of maximal temperature by 5 °C was associated with a 3% increase of risk for admission for hypertension and vice versa. There were no significant effects of precipitation and athmospheric pressure on the number of admissions. With regard to all observed metereological parameters, neither the change within two, nor within three days was consistently associated with the number of daily admissions. High temperatures are associated with lower numbers of hypertensive episodes requiring hospital admission. In contrast to the subjective perception of many hypertensive patients, however, acutely changing weather conditions are not associated with a higher risk of hypertensive emergency.

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.