Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.

One-stage nipple reconstruction with immediate breast reconstruction using our technique of 3 local flaps on skin envelope flap is possible. This simple, reliable, and rapid technique gives stable aesthetic results over time. Reconstruction may be completed sooner and with fewer procedures. Nipple reconstruction should no longer be considered as a secondary complement to immediate breast reconstruction using deep inferior epigastric perforator or muscle-sparing transverse rectus abdominis myocutaneous flap. Our technique is suitable for patients with ptotic or hypertrophic breasts.

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Research on the Hallstatt and La Tène Periods in Bohemia and Moravia covers a number of important topics. So far out of the main interest is the increasing quantity of foreign artefacts which generally belong to the Vekerzug culture (or through its spreading objects of other Eastern cultures). The authors of this paper believe that their systematic evaluation is essential for progress in this area of research. The volume of individual artefacts and associated contexts is constantly increasing. This is due to systematic research conducted by archaeological institutions, extensive development-led excavations (construction of highways, expansion of industrial zones, etc.), and detector survey carried out by amateurs, which has been monitored with partial success. Systematic scientific research by specialists, however, still lags behind. This paper attempts to partly fill this gap.

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