Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

peer-reviewedIn vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro

Age appropriate reference intervals for eight kidney function and injury markers in infants, children and adolescents.

Objectives The use of kidney function and injury markers for early detection of drug-related glomerular or tubular kidney injury in infants, children and adolescents requires age-specific data on reference intervals in a pediatric healthy population. This study characterizes serum values for eight kidney function and injury markers in healthy infants, children and adolescents. Methods A single center prospective observational study was conducted between December 2018 and June 2019. Serum samples from 142 healthy infants, children and adolescents aged between 0 and ≤15 years were collected. Statistical analyses for eight markers (albumin (ALB), β2-microglobulin (B2M), β-trace protein (BTP), creatinine (SCR), cystatin C (CYSC), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), uromodulin (URO)) were performed to obtain reference intervals and associations with age, sex and weight were investigated (Pearson correlation, linear and piecewise regression). Results ALB and SCR increased with age (p<0.01), whereas B2M, BTP and KIM-1 values decreased with advancing age (p<0.05) in this healthy pediatric study population. CYSC showed dependency on sex (lower concentration in females) and decreased with age until reaching approximately 1.8 years; thereafter an increase with age was seen. NGAL and URO did not show any age-dependency. Conclusions This study provides age appropriate reference intervals for key serum kidney function and injury markers determined in healthy infants, children and adolescents. Such reference intervals facilitate the interpretation of changes in kidney function and injury markers in daily practice, and allow early detection of glomerular and tubular injury in infancy, childhood and adolescence

Estimation of GFR using β-trace protein in children

Background and objectives Sexmay affect the performance of smallmolecularweight proteins as markers of GFR because of differences in fat mass between the two sexes. The hypothesis was that the diagnostic performance of b-trace protein, a novel marker of GFR, would be significantly better in boys than in girls. Design, setting, participants, & measurements GFR, height, weight, serum creatinine, and β-trace protein were measured in 755 children and adolescents (331 girls) undergoing 99technetium diethylenetriamine penta–acetic acid renal scans from July of 1999 to July of 2006. Boys and girls were separated into formula generation cohorts (284 boys and 220 girls) and formula validation cohorts (140 boys and 111 girls). GFRestimating formulas on the basis of β-trace protein, creatinine, and height were derived using stepwise linear regression analysis of log-transformed data. The slope of the regression lines of the sex-specific eGFRswere compared. Bland–Altman analysis was used for testing agreement between 99technetium diethylenetriamine penta–acetic acid GFR and calculated GFR both with this equation in boys and girls as well as previously established Benlamri, White, and Schwartz formulas. Results In the stepwise regression analysis, β-trace protein (R2=0.73 for boys and R2=0.65 for girls) was more important than creatinine (which increased R2 to 0.81 for boys and R2 to 0.75 for girls) and height (which increased R2 to 0.88 for boys and R2 to 0.80 for girls) in the data generation groups. GFR can be calculated using the following formulas: formula present Bland–Altman analysis showed better performance in boys than in girls. The new formulas performed significantly better than the previous Benlamri, White, and Schwartz formulas with respect to bias, precision, and accuracy. Conclusions Improved and sex-specific formulas for the estimation of GFR in children on the basis of β-trace protein, serum creatinine, and height are now available

Three myths about risk thresholds for prediction models

Acknowledgments This work was developed as part of the international initiative of strengthening analytical thinking for observational studies (STRATOS). The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies (http://stratos-initiative.org/). Members of the STRATOS Topic Group ‘Evaluating diagnostic tests and prediction models’ are Gary Collins, Carl Moons, Ewout Steyerberg, Patrick Bossuyt, Petra Macaskill, David McLernon, Ben van Calster, and Andrew Vickers. Funding The study is supported by the Research Foundation-Flanders (FWO) project G0B4716N and Internal Funds KU Leuven (project C24/15/037). Laure Wynants is a post-doctoral fellow of the Research Foundation – Flanders (FWO). The funding bodies had no role in the design of the study, collection, analysis, interpretation of data, nor in writing the manuscript. Contributions LW and BVC conceived the original idea of the manuscript, to which ES, MVS and DML then contributed. DT acquired the data. LW analyzed the data, interpreted the results and wrote the first draft. All authors revised the work, approved the submitted version, and are accountable for the integrity and accuracy of the work.Peer reviewedPublisher PD

Season- and depth-dependent variability of a demersal fish assemblage in a large fjord estuary (Puget Sound, Washington)

Fjord estuaries are common along the northeast Pacific coastline, but little information is available on fish assemblage structure and its spatiotemporal variability. Here, we examined changes in diversity metrics, species biomasses, and biomass spectra (the distribution of biomass across body size classes) over three seasons (fall, winter, summer) and at multiple depths (20 to 160 m) in Puget Sound, Washington, a deep and highly urbanized fjord estuary on the U.S. west coast. Our results indicate that this fish assemblage is dominated by cartilaginous species (spotted ratfish [Hydrolagus colliei] and spiny dogfish [Squalus acanthias]) and therefore differs fundamentally from fish assemblages found in shallower estuaries in the northeast Pacific. Diversity was greatest in shallow waters (80 m) that are more common in Puget Sound and that are dominated by spotted ratf ish and seasonally (fall and summer) by spiny dogfish. Strong depth-dependent variation in the demersal fish assemblage may be a general feature of deep fjord estuaries and indicates pronounced spatial variability in the food web. Future comparisons with less impacted fjords may offer insight into whether cartilaginous species naturally dominate these systems or only do so under conditions related to human-caused ecosystem degradation. Information on species distributions is critical for marine spatial planning and for modeling energy flows in coastal food webs. The data presented here will aid these endeavors and highlight areas for future research in this important yet understudied system

The Kinetics of Cystatin C: A Marker for Dialysis Adequacy

When 90% or more of native kidney function is lost, renal replacement therapy must be initiated to sustain life. Renal transplantation is the preferred method, but availability is limited. The ideal dialysis prescription remains elusive. Small molecular weight molecules (such as urea and creatinine) have been used as markers of both kidney (native and transplant) and dialysis toxin clearance (function), but there are pitfalls in using these markers to assess total ‘renal’ dose (kidney plus dialysis). Body weight, gender and other factors also affect the concentrations of these small molecules, but not cystatin C. Furthermore, cystatin C has been shown to be a better marker for estimating kidney function than creatinine, and is associated with cardiovascular morbidity and mortality. Studies have shown that it is removed by dialysis. Therefore, we investigated the use of cystatin C, a naturally occurring endogenous protein, as a marker for estimating dialysis adequacy and renal clearance. This investigation was comprised of four studies to understand the kinetics of cystatin C in patients with advanced kidney disease with or without dialysis. We found that the amount of cystatin C reduction was influenced positively by hemodialysis blood flow rate and treatment time, and negatively by ultrafiltration rate. We further demonstrated that renal hyperfiltration significantly influenced the error of creatinine-based glomerular filtrate rate equation, but not for the cystatin C equation. Therefore, cystatin C appears to be a useful marker for the assessment of kidney function in patients with advanced kidney disease but not yet on dialysis. This was taken further in our third study where we developed an equation, which gave a better estimate of residual renal function than previously published equations in patients on dialysis but who have some remaining kidney function. Finally, we confirmed our hypothesis that cystatin C is cleared during dialysis by both diffusion and convection. It is distributed mainly in the extracellular space but equilibrates slowly between the extravascular and intravascular spaces. Furthermore, we have shown that cystatin C while cleared by dialysis is stable between dialysis treatments rather than being influenced by a single dialysis treatment. It is a marker for both dialysis and renal clearances and, thus, gives a stable index of total renal clearance. The long term goal will be to define the cystatin C threshold level that influences patient morbidity and mortality and to allow better dialysis prescriptions for patients with varying (and changing) residual renal function