Therapeutic hypothermia in asphyxiated newborns: selective head cooling vs. whole body cooling — comparison of short term outcomes

Objectives: Therapeutic hypothermia TH became broadly used in the management of the asphyxiated newborns. Although two cooling methods are used, so far the superiority of none of them has been established. The purpose of the study is to compare two cooling methods: selective head cooling (SHC) and whole body cooling (WBC) Material and methods: We conducted a prospective observational study in newborns with HIE. The patients received one of methods: SHC or WBC. The eligibility criteria were similar to previous studies. Stability of cardio-respiratory parameters and short term outcomes were analyzed. Results: 78 neonates with hypoxic-ischemic encephalopathy due to perinatal asphyxia were involved in this study. The SHC group consisted of 51 newborns, the WBC group consisted of 27 patients. Both study groups had similar baseline characteristics and condition at birth. There were no significant differences in hospital course, neurological status and adverse effects associated with cooling procedure between groups. Analyzing the rate of thrombocytopenia and the number of transfusions of blood components no statistically significant differences were found between the groups. Conclusions: Results of our study indicate that two compared methods of TH despite varied target core temperature ranges do not differ significantly according to clinical course and risk of adverse events. Further observations are conducted and we look forward to the results of the long neurodevelopmental care

Use of molecular biology techniques to identify genetically modified organisms (GMOs) in food products – do you know what you eat?

tekst w j. pol. i ang.Ocena poprawności oznaczeń produktów spożywczych pod kątem zawartości genetycznie modyfikowanych organizmów (GMO)

Using simulation to calibrate real data acquisition in veterinary medicine

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine