Cord Blood Plasma and Placental Mesenchymal Stem Cells-Derived Exosomes Increase Ex Vivo Expansion of Human Cord Blood Hematopoietic Stem Cells While Maintaining Their Stemness.

BACKGROUND: Mesenchymal stem cells (MSCs) have been used for ex vivo expansion of umbilical cord blood (UCB) hematopoietic stem cells (HSCs) to maintain their primitive characters and long-term reconstitution abilities during transplantation. Therapeutic effects of MSCs mainly rely on paracrine mechanisms, including secretion of exosomes (Exos). The objective of this study was to examine the effect of cord blood plasma (CBP)-derived Exos (CBP Exos) and Placental MSCs-derived Exos (MSCs Exos) on the expansion of UCB HSCs to increase their numbers and keep their primitive characteristics. METHODS: CD34+ cells were isolated from UCB, cultured for 10 days, and the expanded HSCs were sub-cultured in semisolid methylcellulose media for primitive colony forming units (CFUs) assay. MSCs were cultured from placental chorionic plates. RESULTS: CBP Exos and MSCs Exos compared with the control group significantly increased the number of total nucleated cells (TNCs), invitro expansion of CD34+ cells, primitive subpopulations of CD34+38+ and CD34+38-Lin- cells (p < 0.001). The expanded cells showed a significantly higher number of total CFUs in the Exos groups (p < 0.01). CONCLUSION: CBP- and placental-derived exosomes are associated with significant ex vivo expansion of UCB HSCs, while maintaining their primitive characters and may eliminate the need for transplantation of an additional unit of UCB.

Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.

Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: "adult caribou", "calf caribou", and "ghost caribou" (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96-0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers' annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.