Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

Abstract Background Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments. Results Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics. Conclusion Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli

Effect of Hospital Volume on Outcomes of Transcatheter Aortic Valve Implantation

Transcatheter aortic valve implantation (TAVI) is associated with a significant learning curve. There is paucity of data regarding the effect of hospital volume on outcomes after TAVI. This is a cross-sectional study based on Healthcare Cost and Utilization Project's Nationwide Inpatient Sample database of 2012. Subjects were identified by International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes, 35.05 (Trans-femoral/Trans-aortic Replacement of Aortic Valve) and 35.06 (Trans-apical Replacement of Aortic Valve). Annual hospital TAVI volumes were calculated using unique identification numbers and then divided into quartiles. Multivariate logistic regression models were created. The primary outcome was inhospital mortality; secondary outcome was a composite of inhospital mortality and periprocedural complications. Length of stay (LOS) and cost of hospitalization were assessed. The study included 1,481 TAVIs (weighted n = 7,405). Overall inhospital mortality rate was 5.1%, postprocedural complication rate was 43.4%, median LOS was 6 days, and median cost of hospitalization was $51,975. Inhospital mortality rates decreased with increasing hospital TAVI volume with a rate of 6.4% for lowest volume hospitals (first quartile), 5.9% (second quartile), 5.2% (third quartile), and 2.8% for the highest volume TAVI hospitals (fourth quartile). Complication rates were significantly higher in hospitals with the lowest volume quartile (48.5%) compared to hospitals in the second (44.2%), third (39.7%), and fourth (41.5%) quartiles (p <0.001). Increasing hospital volume was independently predictive of shorter LOS and lower hospitalization costs. In conclusion, higher annual hospital volumes are significantly predictive of reduced postprocedural mortality, complications, shorter LOS, and lower hospitalization costs after TAVI