Background Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we aimed to engineer deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images.Methods We did a multicentre, prospective study to develop models using slit-lamp or retinal fundus images from participants in three hepatobiliary departments and two medical examination centres. Included participants were older than 18 years and had complete clinical information; participants diagnosed with acute hepatobiliary diseases were excluded. We trained seven slit-lamp models and seven fundus models (with or without hepatobiliary disease [screening model] or one specific disease type within six categories [identifying model]) using a development dataset, and we tested the models with an external test dataset. Additionally, we did a visual explanation and occlusion test. Model performances were evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and F1* score.
The global dissemination of the mobilized colistin resistance gene,
mcr-1
, threatens human health. Recent studies by our group and others have shown that the withdrawal of colistin as a feed additive dramatically reduced the prevalence of
mcr-1
. Although it is accepted that the rapid reduction in
mcr-1
prevalence may have resulted, to some extent, from the toxic effects of MCR-1, the detailed mechanism remains unclear. Here, we found that MCR-1 damaged the outer membrane (OM) permeability in
Escherichia coli
and
Klebsiella pneumonia
and that this event was associated with MCR-1-mediated cell shrinkage and death during the stationary phase. Notably, the capacity of MCR-1-expressing cells for recovery from the stationary phase under improved conditions was reduced in a time-dependent manner. We also showed that mutations in the potential lipid-A-binding pocket of MCR-1, but not in the catalytic domain, restored OM permeability and cell viability. During the stationary phase, PbgA, a sensor of periplasmic lipid-A and LpxC production that performed the first step in lipid-A synthesis, was reduced after MCR-1 expression, suggesting that MCR-1 disrupted lipid homeostasis. Consistent with this, the overexpression of LpxC completely reversed the MCR-1-induced OM permeability defect. We propose that MCR-1 causes lipid remodelling that results in an OM permeability defect, thus compromising the viability of Gram-negative bacteria. These findings extended our understanding of the effect of MCR-1 on bacterial physiology and provided a potential strategy for eliminating drug-resistant bacteria.
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