Disease risk analysis in sea turtles: A baseline study to inform conservation efforts.

The impact of a range of different threats has resulted in the listing of six out of seven sea turtle species on the IUCN Red List of endangered species. Disease risk analysis (DRA) tools are designed to provide objective, repeatable and documented assessment of the disease risks for a population and measures to reduce these risks through management options. To the best of our knowledge, DRAs have not previously been published for sea turtles, although disease is reported to contribute to sea turtle population decline. Here, a comprehensive list of health hazards is provided for all seven species of sea turtles. The possible risk these hazards pose to the health of sea turtles were assessed and "One Health" aspects of interacting with sea turtles were also investigated. The risk assessment was undertaken in collaboration with more than 30 experts in the field including veterinarians, microbiologists, social scientists, epidemiologists and stakeholders, in the form of two international workshops and one local workshop. The general finding of the DRA was the distinct lack of knowledge regarding a link between the presence of pathogens and diseases manifestation in sea turtles. A higher rate of disease in immunocompromised individuals was repeatedly reported and a possible link between immunosuppression and environmental contaminants as a result of anthropogenic influences was suggested. Society based conservation initiatives and as a result the cultural and social aspect of interacting with sea turtles appeared to need more attention and research. A risk management workshop was carried out to acquire the insights of local policy makers about management options for the risks relevant to Queensland and the options were evaluated considering their feasibility and effectiveness. The sea turtle DRA presented here, is a structured guide for future risk assessments to be used in specific scenarios such as translocation and head-starting programs.

Histological 3D reconstruction and in vivo lineage tracing of the human endometrium.

Regular menstrual shedding and repair of the endometrial functionalis is unique to humans and higher-order primates. The current consensus postulates endometrial glands to have a single-tubular architecture, where multi-potential stem cells reside in the blind-ending glandular-bases. Utilising fixed samples from patients, we have studied the three-dimensional (3D) micro-architecture of the human endometrium. We demonstrate that some non-branching, single, vertical functionalis glands originate from a complex horizontally interconnecting network of basalis glands. The existence of a multipotent endometrial epithelial stem cell capable of regenerating the entire complement of glandular lineages was demonstrated by in vivo lineage tracing, using naturally occurring somatic mitochondrial DNA mutations as clonal markers. Vertical tracking of mutated clones showed that at least one stem-cell population resides in the basalis glands. These novel findings provide insight into the efficient and scar-less regenerative potential of the human endometrium. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

A visualization method measuring the performance of biomarkers for guiding treatment decisions.

Biomarkers that predict efficacy and safety for a given drug therapy become increasingly important for treatment strategy and drug evaluation in personalized medicine. Methodology for appropriately identifying and validating such biomarkers is critically needed, although it is very challenging to develop, especially in trials of terminal diseases with survival endpoints. The marker-by-treatment predictiveness curve serves this need by visualizing the treatment effect on survival as a function of biomarker for each treatment. In this article, we propose the weighted predictiveness curve (WPC). Based on the nature of the data, it generates predictiveness curves by utilizing either parametric or nonparametric approaches. Especially for nonparametric predictiveness curves, by incorporating local assessment techniques, it requires minimum model assumptions and provides great flexibility to visualize the marker-by-treatment relationship. WPC can be used to compare biomarkers and identify the one with the highest potential impact. Equally important, by simultaneously viewing several treatment-specific predictiveness curves across the biomarker range, WPC can also guide the biomarker-based treatment regimens. Simulations representing various scenarios are employed to evaluate the performance of WPC. Application on a well-known liver cirrhosis trial sheds new light on the data and leads to discovery of novel patterns of treatment biomarker interactions.

Increased delignification by white rot fungi after pressure refining Miscanthus.

Pressure refining, a pulp making process to separate fibres of lignocellulosic materials, deposits lignin granules on the surface of the fibres that could enable increased access to lignin degrading enzymes. Three different white rot fungi were grown on pressure refined (at 6 bar and 8 bar) and milled Miscanthus. Growth after 28 days showed highest biomass losses on milled Miscanthus compared to pressure refined Miscanthus. Ceriporiopsis subvermispora caused a significantly higher proportion of lignin removal when grown on 6 bar pressure refined Miscanthus compared to growth on 8 bar pressure refined Miscanthus and milled Miscanthus. RM22b followed a similar trend but Phlebiopsis gigantea SPLog6 did not. Conversely, C. subvermispora growing on pressure refined Miscanthus revealed that the proportion of cellulose increased. These results show that two of the three white rot fungi used in this study showed higher delignification on pressure refined Miscanthus than milled Miscanthus.

Quantification of histochemical stains using whole slide imaging: development of a method and demonstration of its usefulness in laboratory quality control.

AIMS: Histochemical staining of tissue is a fundamental technique in tissue diagnosis and research, but it suffers from significant variability. Efforts to address this include laboratory quality controls and quality assurance schemes, but these rely on subjective interpretation of stain quality, are laborious and have low reproducibility. We aimed (1) to develop a method for histochemical stain quantification using whole slide imaging and image analysis and (2) to demonstrate its usefulness in measuring staining variation. METHODS: A method to quantify the individual stain components of histochemical stains on virtual slides was developed. It was evaluated for repeatability and reproducibility, then applied to control sections of an appendix to quantify H&E staining (H/E intensities and H:E ratio) between automated staining machines and to measure differences between six regional diagnostic laboratories. RESULTS: The method was validated with <0.5% variation in H:E ratio measurement when using the same scanner for a batch of slides (ie, it was repeatable) but was not highly reproducible between scanners or over time, where variation of 7% was found. Application of the method showed H:E ratios between three staining machines varied from 0.69 to 0.93, H:E ratio variation over time was observed. Interlaboratory comparison demonstrated differences in H:E ratio between regional laboratories from 0.57 to 0.89. CONCLUSIONS: A simple method using whole slide imaging can be used to quantify and compare histochemical staining. This method could be deployed in routine quality assurance and quality control. Work is needed on whole slide imaging devices to improve reproducibility.

Limited sampling strategy for the estimation of mycophenolic acid area under the curve in adult renal transplant patients treated with concomitant tacrolimus.

BACKGROUND: Significant relationships between the mycophenolic acid (MPA) area under the concentration-time curve (AUC(0-12h)) and the risks for acute rejection and side effects have been reported. We developed a practical method for estimation of MPA AUCs. Regression equations were developed using repeated cross-validation for randomly chosen subsets, characterized statistically, and verified for acceptable performance. METHODS: Twenty-one renal transplant patients receiving 0.5 or 1.0 g of mycophenolate mofetil twice daily and concomitant tacrolimus provided a total of 50 pharmacokinetic profiles. MPA concentrations were measured by a validated HPLC method in 12 plasma samples collected at predose and at 30 and 60 min; 2, 3, 4, 6, 8, 9, 10, 11, and 12 h; 1 and 2 weeks; and 3 months after transplantation. Twenty-six 1-, 2-, or 3-sample estimation models were fit (r(2) = 0.341-0.862) to a randomly selected subset of the profiles using linear regression and were used to estimate AUC(0-12h) for the profiles not included in the regression fit, comparing those estimates with the corresponding AUC(0-12h) values, calculated with the linear trapezoidal rule, including all 12 timed MPA concentrations. The 3-sample models were constrained to include no samples past 2 h. RESULTS: The model using c(0h), c(0.5h), and c(2h) was superior to all other models tested (r(2) = 0.862), minimizing prediction error for the AUC(0-12h) values not included in the fit (i.e., the cross-validation error). The regression equation for AUC estimation that gave the best performance for this model was: 7.75 + 6.49c(0h) + 0.76c(0.5h) + 2.43c(2h). When we applied this model to the full data set, 41 of the 50 (82%) estimated AUC values were within 15% of the value of AUC(0-12h) calculated using all 12 concentrations. CONCLUSIONS: This limited sampling strategy provides an effective approach for estimation of the full MPA AUC(0-12h) in renal transplant patients receiving concomitant tacrolimus therapy.