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The presence of antibodies directed against human leukocyte antigens (HLA) expressed on donor cells is a significant risk factor for serious clinical complications after transplantation. The crossmatch assay is one of the most important tests available for the detection of donor-specific antibodies in potential allograft recipients. Early crossmatch methods utilized complement-dependent cytotoxicity, which is useful for detecting the donor-specific anti-HLA antibodies responsible for hyperacute allograft rejection but lacks adequate sensitivity. Consequently, more sensitive crossmatch methods have been developed, ultimately leading to the flow cytometry crossmatch as the currently preferred methodology. Herein, we review the evolution of the crossmatch assay and the most important factors to consider when performing and interpreting the results of this fundamental assay for ensuring the long-term survival of the transplanted organ.

In primary prostate cancer, the common multifocality and heterogeneity are major obstacles in finding robust prognostic tissue biomarkers. The long noncoding RNA SCHLAP1 has been suggested, but its prognostic value has not been investigated in the context of tumor heterogeneity. In the present study, expression of SCHLAP1 was investigated using real-time RT-PCR in a multisampled series of 778 tissue samples from radical prostatectomies of 164 prostate cancer patients (median follow-up time 7.4 y). The prognostic value of SCHLAP1 was evaluated with biochemical recurrence as endpoint. In total, 29% of patients were classified as having high expression of SCHLAP1 in at least one malignant sample. Among these, inter- and intrafocal heterogeneity was detected in 72% and 56%, respectively. High expression of SCHLAP1 was shown to be a predictor of biochemical recurrence in both uni- and multivariable cox regression analyses (P < 0.001 and P = 0.02). High expression of SCHLAP1 was also significantly associated with adverse clinicopathological characteristics, including grade group, high pT stage, invasive cribriform growth/intraductal carcinoma of the prostate, and reactive stroma. In conclusion, high expression of SCHLAP1 in at least one malignant sample is a robust prognostic biomarker in primary prostate cancer. For the first time, high SCHLAP1 expression has been associated with the aggressive histopathologic feature reactive stroma. The expression of SCHLAP1 is highly heterogeneous, and analysis of multiple samples is therefore crucial in determination of the SCHLAP1 status of a patient., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: High renin-angiotensin system (RAS) activity has been associated with a high risk of severe hypoglycaemia in patients with type 1 diabetes and with cognitive deterioration during experimental hypoglycaemia in healthy subjects. The aim of this study was to describe possible differences in cerebral activity during hypoglycaemia and cognitive testing in two groups of healthy men with different basal RAS activity., Methods: Ten healthy men with high RAS activity and 10 with low activity underwent six oxygen-15-labelled water positron emission tomography scans: twice during normoglycaemia, twice during insulin-induced hypoglycaemia and twice during post-hypoglycaemia. During the scans, the subjects performed a computer-based reaction time test., Results: Occipital areas were consistently more activated in the low RAS group than in the high RAS group throughout all three conditions. During normoglycaemia, the frontal region was more activated in the low RAS group than in the high RAS group. During hypoglycaemia, the high RAS group was more activated in the pituitary gland than the low RAS group., Conclusion: Basal RAS activity influenced cerebral activity. Low RAS was associated with more pronounced cortical activation in all glycaemic conditions. High RAS was associated with pituitary activation during hypoglycaemia and post-hypoglycaemia, and this was associated with a greater growth hormone response.

In the United States, environmental regulatory agencies are required to use "best available" scientific information when making decisions on a variety of issues. However, agencies are often hindered by coarse or incomplete data, particularly as it pertains to threatened and endangered species protection. Stakeholders often agree that more resolute and integrated processes for decision-making are desirable. We demonstrate a process that uses species occurrence data for a federally endangered insect (Karner blue butterfly), a readily available habitat modeling tool, and spatially explicit information about an important Michigan commodity (tart cherries). This case study has characteristics of many protected species regulatory decisions in that species occurrence data were sparse and unequally distributed; regulatory decisions (on pesticide use) were required with potentially significant impacts on a viable agricultural industry; and stakeholder relations were diverse, misinformed, and, in some situations, unjustly contentious. Results from our process include a large-scale, empirically derived habitat suitability map for the focal species and a risk ranking of tart cherry orchards with risk based on the likelihood that pesticide applications will influence the focal protected species. Although the majority (77%) of pesticide-influence zones overlapped Karner blue butterfly habitat, risk scores associated with each orchard were low. Through our process we demonstrated that spatially explicit models can help stakeholders visualize and quantify potential protected species effects. In addition, model outputs can serve to guide field activities (e.g., species surveys and implementation of pesticide buffer zones) that help minimize future effects.