Bone tissue engineering using adipose‐derived stem cells and endothelial cells: Effects of the cell ratio

The microvascular endothelial network is essential for bone formation and regeneration. In this context, endothelial cells not only support vascularization but also influence bone physiology via cell contact‐dependent mechanisms. In order to improve vascularization and osteogenesis in tissue engineering applications, several strategies have been developed. One promising approach is the coapplication of endothelial and adipose derived stem cells (ADSCs). In this study, we aimed at investigating the best ratio of human umbilical vein endothelial cells (HUVECs) and osteogenic differentiated ADSCs with regard to proliferation, apoptosis, osteogenesis and angiogenesis. For this purpose, cocultures of ADSCs and HUVECs with ratios of 25%:75%, 50%:50% and 75%:25% were performed. We were able to prove that cocultivation supports proliferation whereas apoptosis was unidirectional decreased in cocultured HUVECs mediated by a p‐BAD‐dependent mechanism. Moreover, coculturing ADSCs and HUVECs stimulated matrix mineralization and the activity of alkaline phosphatase (ALP). Increased gene expression of the proangiogenic markers eNOS, Flt, Ang2 and MMP3 as well as sprouting phenomena in matrigel assays proved the angiogenic potential of the coculture. In summary, coculturing ADSCs and HUVECs stimulates proliferation, cell survival, osteogenesis and angiogenesis particularly in the 50%:50% coculture.     

Closing the translation gap: AI applications in digital pathology

Recent advances in artificial intelligence show tremendous promise to improve the accuracy, reproducibility, and availability of medical diagnostics across a number of medical subspecialities. This is especially true in the field of digital pathology, which has recently witnessed a surge in publications describing state-of-the-art performance for machine learning models across a wide range of diagnostic applications. Nonetheless, despite this promise, there remain significant gaps in translating applications for any of these technologies into actual clinical practice. In this review, we will first give a brief overview of the recent progress in applying AI to digitized pathology images, focusing on how these tools might be applied in clinical workflows in the near term to improve the accuracy and efficiency of pathologists. Then we define and describe in detail the various factors that need to be addressed in order to successfully close the “translation gap” for AI applications in digital pathology.     

Therapeutic Use of a Selective S1P1 Receptor Modulator Ponesimod in Autoimmune Diabetes

In the present study, we investigated the therapeutic potential of a selective S1P₁ receptor modulator, ponesimod, to protect and reverse autoimmune diabetes in non-obese diabetic (NOD) mice. Ponesimod was administered orally to NOD mice starting at 6, 10, 13 and 16 weeks of age up to 35 weeks of age or to NOD mice showing recent onset diabetes. Peripheral blood and spleen B and T cell counts were significantly reduced after ponesimod administration. In pancreatic lymph nodes, B lymphocytes were increased and expressed a transitional 1-like phenotype. Chronic oral ponesimod treatment efficiently prevented autoimmune diabetes in 6, 10 and 16 week-old pre-diabetic NOD mice. Treatment withdrawal led to synchronized disease relapse. Ponesimod did not inhibit the differentiation of autoreactive T cells as assessed by adoptive transfer of lymphocytes from treated disease-free NOD mice. In addition, it did not affect the migration, proliferation and activation of transgenic BDC2.5 cells into the target tissue. However, ponesimod inhibited spreading of the T cell responses to islet-specific glucose-6-phosphatase catalytic subunit-related protein (IGRP). Treatment of diabetic NOD mice with ponesimod induced disease remission. However, here again, upon treatment cessation, the disease rapidly recurred. This recurrence was effectively prevented by combination treatment with a CD3 antibody leading to the restoration of self-tolerance. In conclusion, treatment with a selective S1P₁ modulator in combination with CD3 antibody represents a promising therapeutic approach for the treatment of autoimmune diabetes.     

Genetic variation in oxytocin rs2740210 and early adversity associated with postpartum depression and breastfeeding duration

Mothers vary in duration of breastfeeding. These individual differences are related to a variety of demographic and individual maternal factors including maternal hormones, mood and early experiences. However, little is known about the role of genetic factors. We studied single‐nucleotide polymorphisms (SNPs) in the OXT peptide gene (rs2740210; rs4813627) and the OXT receptor gene (OXTR rs237885) in two samples of mothers from the Maternal adversity, Vulnerability and Neurodevelopment study (MAVAN), a multicenter (Hamilton and Montreal, Canada) study following mothers and their children from pregnancy until 7 years of age. Data from the Hamilton site was the primary sample (n = 201) and data from Montreal was the replication sample (n = 151). Breastfeeding duration, maternal mood (measured by the CES‐D scale) and early life adversity (measured by the CTQ scale) were established during 12 months postpartum. In our primary sample, polymorphisms in OXT rs2740210, but not the other SNPs, interacted with early life adversity to predict variation in breastfeeding duration (overall F_8,125 = 2.361, P = 0.021; interaction effect b = ?8.12, t = ?2.3, P = 0.023) and depression (overall F_8,118 = 5.751, P ≤ 0.001; interaction effect b = 6.06, t = 3.13, P = 0.002). A moderated mediation model showed that higher levels of depression mediated the inverse relation of high levels of early life adversity to breastfeeding duration, but only in women possessing the CC genotype [effect a′ = ?3.3401, 95% confidence interval (CI) = ?7.9466 to ?0.0015] of the OXT SNP and not in women with the AA/AC genotype (a′ = ?1.2942, ns). The latter findings (moderated mediation model) were replicated in our Montreal sample (a′ = ?0.277, 95% CI = ?0.7987 to ?0.0348 for CC; a′ = ?0.1820, ns for AA/AC) .