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Love what you do, you’ll never work a day in your life: Colour bias affects nest-building decisions
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Does compost derived from organic municipal solid waste affect microbial community structure and function in three different agricultural soils of Alberta?
Get PDFThe overuse of chemical fertilizers can lead to environmental issues, including water pollution due to nutrient runoff, as well as detrimental impacts on soil health such as increased soil compaction, loss of organic matter, and disruption of soil microbial communities. Application of compost to soil is a regenerative agriculture practice that may offset the decrease in soil organic matter resulting from conventional synthetic fertilizer use over time. Compost may also increase C sequestration, microbial diversity, and nutrient density leading to improved soil health. This study was designed to determine microbial community diversity across three different soil types in central Alberta after amendment with different types of compost treatments blended with biochar, wood ash, and gypsum, and compared to synthetic fertilizer. Soils were sampled after harvest from the 0-15 cm depth and analyzed for a variety of biological and physico-chemical parameters. After assessment of soil physico-chemical properties and community physiological profiling (CLPP), DNA extracts were sequenced with the ILLUMINA Muse platform using the 250-bp paired end kit (V2 500-cycle PE Chemistry, Illumina, USA) after amplification with 16S and ITS primers. Bioinformatics (ASV tables) and statistical analysis were performed in R (version 4.2.2). Results indicated that each soil exhibited distinct physico-chemical properties, including variations in soil type and soil organic matter content. Consequently, each soil harboured a unique microbial community with distinctive levels of diversity and heterogeneity. The findings indicated that coarse soil had reduced microbial activity and consequently poorer crop productivity. Short-term use of compost treatments on these low OM sandy soils did not alter the microbial community diversity significantly. Nevertheless, the changes that were observed reflected the higher functioning communities of fine textured soils indicating that compost additions have the potential to alter microbial function and agricultural productivity. Furthermore, the use of synthetic fertilizer alone had a negative impact on the heterogeneity of microorganisms in the two fine textured soils, as well as the interactions between microorganisms in all soils
Utilization of plasma circulating microRNA levels for evaluation of a predictive biomarker of response to systemic therapy in advanced renal cell carcinoma
No full textPredictive biomarkers of response to immune checkpoint-based therapies (ICBT) remain a critically unmet need in the management of advanced renal cell carcinoma (aRCC). Predicting the complex interplay of the tumour microenvironment (TME) and the antitumour immune response has proven to be challenging. MicroRNAs (miRNAs) have been increasingly studied as biomarkers, given their role in post-transcriptional regulation of gene expression and immunomodulatory properties.
Utilizing an institutional biobank of archival blood samples from March 2017 to January 2020, we evaluated the presence of immune-specific extracellular vesicle (EV) miRNAs in the plasma of patients with aRCC prior to initiation of ICBT. A control group of healthy participants with no history of malignancy or autoimmune conditions was used for comparison. Plasma samples from 40 patients and 30 healthy participants were analyzed. Clinical data was accessed from the patient chart. Levels of miRNAs were compared first between patients and healthy control participants, then amongst patients, comparing those who responded to ICBT versus those who did not respond.
We found significantly lower levels of microRNA155-3p (miR155) in responders to ICBT, when compared to non-responders. This miRNA has unique immunomodulatory properties, thus providing potential biological rationale for our findings. Our results are hypothesis-generating in nature and support further work in exploring microRNAs as potential biomarkers of response to immunotherapy
A Probabilistic Approach to Kidney Transplant Biopsy Lesion Scores
No full textIntroduction: The Banff classification system for histological diagnosis of rejection in kidney transplant biopsies uses guidelines to assess designated features – lesions, donor-specific antibody (DSA), and C4d staining. The present research evaluated whether the use of regression equations to interpret the features and current guidelines could establish the relative importance of each feature and improve histological interpretation.
Objective: A novel approach is proposed – employing regression models and leveraging lesion scores from the INTERCOMEX study (ClinicalTrials.gov #NCT01299168) to estimate the probability of molecular rejection. The aim was also to determine the hierarchy of importance of biopsy lesion scores associated with antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR). Additional aims were to evaluate whether antibody-medicated rejection could be reliably predicted from lesions alone before C4d and DSA measurements are available and to establish if including time post-transplant would add to the models. Lastly, the question of whether lesion-based equations could be used to screen for missed diagnoses in biopsies, called “no rejection” by Banff, was investigated.
Design, setting and participants: Logistic regression equations were developed using the designated features to predict antibody-mediated rejection (ABMR/mixed) and T cell-mediated rejection (TCMR/mixed) in 1,679 indication biopsies from the INTERCOMEX study (ClinicalTrials.gov NCT01299168). The equations were ‘trained’ on molecular diagnoses independent of the designated features.
Results: In regression and random forests, the important features that predicted molecular rejection were as follows: for ABMR, g > ptc, followed by cg; for TCMR, t > i. V-lesions were relatively unimportant when compared to other variables present in the models. C4d and DSA were also relatively unimportant for predicting ABMR: by AUC, the model excluding them (0.853) was nearly as good as the model including them (0.860). If the time post-transplant was included, it slightly but significantly improved all of the models. Regression analysis predicted molecular ABMR and TCMR by AUC better than Banff histological diagnoses. More importantly, in biopsies called “no rejection” by Banff guidelines, regression equations based on histology features identified histological and molecular rejection-related changes in some biopsies and improved survival predictions. Thus, regression analysis can screen for missed rejections.
Conclusions: Using lesion-based regression equations, in addition to Banff histology guidelines, the relative importance of histology features for identifying rejection have been defined, which permitted screening for potential missed diagnoses and facilitated early estimates of ABMR when C4d and DSA measurements were not available. This method offers a probabilistic approach that can accurately predict molecular rejection and quantify diagnostic uncertaint