Expression and co-expression of surface markers of pluripotency on human amniotic cells cultured in different growth media

Objectives: Despite constant advances in the field of biology and medical application of human embryonic stem cells, the molecular mechanism of pluripotency remains largely unknown. So far, definitions of pluripotent stem cells (SC) have been based on a limited number of antigenic markers and have not allowed for unambiguous determination of the homogeneity of each subpopulation. Moreover, the use of some crucial pluripotency markers such as SSEA-3 and SSEA-4 has recently been questioned due to the possibility that the pattern of surface glycans may be changed depending on the content of the cell culture medium. Aim: Quantitative analysis of amniotic SC subpopulations cultured in different media, based on the following pluripotency surface markers: SSEA-3, SSEA-4, TRA-1-60 and TRA-1-81 expression and co-expression. Material and methods: Immunofluorescence and fluorescence microscopy were used to identify and localize S.C. within a normal human placenta at term. The number of SSEA-4+, SSEA-3+, TRA-1-60+ and TRA-1-81+ cells and cells with co-expression of the above mentioned markers, cultured in media containing different protein supplements of animal origin, was counted by flow cytometry. Results and conclusions: Cells with characteristics of embryonic SC were identified in the amniotic epithelium and the chorion, but not in the decidua basalis. Amniotic epithelium contained various types of SC, with SSEA-4+ as the most numerous. Disproportion in the number of SSEA-4+, SSEA-3+, TRA-1-60+ and TRA-1-81+ cells and cells characterized by co-expression of these antigens, as well as lack of quantitative differences between S.C. subpopulations cultured in different media, was observed. In conclusion, the amniotic epithelium is composed of SC at different stages of the development but human amnion might become an alternative source of SSEA-4+ embryonic-like SC. The composition of the evaluated media, characterized by different content of animal-derived proteins, does not influence the number of cells identified within the SC subpopulations

Postoperative Severity Assessment in Sheep

Introduction: Sheep are frequently used in translational surgical orthopedic studies. Naturally, a good pain management is mandatory for animal welfare, although it is also important with regard to data quality. However, methods for adequate severity assessment, especially considering pain, are rather rare regarding large animal models. Therefore, in the present study, accompanying a surgical pilot study, telemetry and the Sheep Grimace Scale (SGS) were used in addition to clinical scoring for severity assessment after surgical interventions in sheep. Methods: Telemetric devices were implanted in a first surgery subcutaneously into four German black-headed mutton ewes (4-5 years, 77-115 kg). After 3-4 weeks of recovery, sheep underwent tendon ablation of the left M. infraspinatus. Clinical scoring and video recordings for SGS analysis were performed after both surgeries, and the heart rate (HR) and general activity were monitored by telemetry. Results: Immediately after surgery, clinical score and HR were slightly increased, and activity was decreased in individual sheep after both surgeries. The SGS mildly elevated directly after transmitter implantation but increased to higher levels after tendon ablation immediately after surgery and on the following day. Conclusion: In summary, SGS- and telemetry-derived data were suitable to detect postoperative pain in sheep with the potential to improve individual pain recognition and postoperative management, which consequently contributes to refinement

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions