Genotoxicity of synthetic amorphous silica nanoparticles in rats following short‐term exposure. Part 1: Oral route

Synthetic amorphous silica (SAS) in its nanosized form is now used in food applications although the potential risks for human health have not been evaluated. In this study, genotoxicity and oxidative DNA damage of two pyrogenic (NM‐202 and 203) and two precipitated (NM‐200 and ‐201) nanosized SAS were investigated in vivo in rats following oral exposure. Male Sprague Dawley rats were exposed to 5, 10, or 20 mg/kg b.w./day for three days by gavage. DNA strand breaks and oxidative DNA damage were investigated in seven tissues (blood, bone marrow from femur, liver, spleen, kidney, duodenum, and colon) with the alkaline and the (Fpg)‐modified comet assays, respectively. Concomitantly, chromosomal damage was investigated in bone marrow and in colon with the micronucleus assay. Additionally, malondialdehyde (MDA), a lipid peroxidation marker, was measured in plasma. When required, a histopathological examination was also conducted. The results showed neither obvious DNA strand breaks nor oxidative damage with the comet assay, irrespective of the dose and the organ investigated. Similarly, no increases in chromosome damage in bone marrow or lipid peroxidation in plasma were detected. However, although the response was not dose‐dependent, a weak increase in the percentage of micronucleated cells was observed in the colon of rats treated with the two pyrogenic SAS at the lowest dose (5 mg/kg b.w./day). Additional data are required to confirm this result, considering in particular, the role of agglomeration/aggregation of SAS NMs in their uptake by intestinal cells. Environ. Mol. Mutagen. 56:218–227, 2015. © 2014 Wiley Periodicals, Inc.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.