Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

What Does the Boxed Warning Tell Us? Safe Practice of Using Ferumoxytol as an MRI Contrast Agent

BACKGROUND AND PURPOSE

Despite the label change and the FDA's boxed warning added to the Feraheme (ferumoxytol) label in March 2015, radiologists have shown increasing interest in using ferumoxytol as an MR imaging contrast agent as a supplement or alternative to gadolinium. The goals of this study were to provide information regarding ferumoxytol safety as an imaging agent in a single center and to assess how the Feraheme label change may affect this potential, currently off-label indication.

MATERIALS AND METHODS

This retrospective study evaluated the overall frequency of ferumoxytol-related adverse events when used for CNS MR imaging. Patients with various CNS pathologies were enrolled in institutional review board-approved imaging studies. Ferumoxytol was administered as multiple rapid bolus injections. The risk of adverse events was correlated with demographic data/medical history.

RESULTS

The safety of 671 ferumoxytol-enhanced MR studies in 331 patients was analyzed. No anaphylactic, life-threatening, or fatal (grade 4 or 5) adverse events were recorded. The overall proportion of ferumoxytol-related grade 1-3 adverse events was 10.6% (8.6% occurring within 48 hours), including hypertension (2.38%), nausea (1.64%), diarrhea (1.04%), and headache (1.04%). History of 1 or 2 allergies was associated with an increased risk of adverse events (14.61% versus 7.51% [no history]; P = .007).

CONCLUSIONS

The frequency of mild ferumoxytol-related adverse events was comparable with literature results, and no serious adverse event was recorded. Although the recommendations in the boxed warning should be followed, serious adverse events appear to be rare, and with proper precautions, ferumoxytol may be a valuable MR imaging agent.