Inspired by glucose-sensitive ion channels, herein we describe a biomimetic glucose-enantiomer-driven ion gate via the introduction of the chiral pillar[6]arene-based host–guest systems into the artificial nanochannels. The chiral nanochannels show a high chiral-driven ionic gate for glucose enantiomers and can be switched “off” by d-glucose and be switched “on” by l-glucose. Remarkably, the chiral nanochannel also exhibited a good reversibility toward glucose enantiomers. Further research indicates that the switching behaviors differed due to the differences in binding strength between chiral pillar[6]arene and glucose enantiomers, which can lead to the different surface charge within nanochannel. Given these promising results, the studies of chiral-driven ion gates may not only give interesting insight for the research of biological and pathological processes caused by glucose-sensitive ion channels, but also help to understand the origin of the high stereoselectivity in life systems.

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.

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Developing Dictyostelium cells form aggregation streams that break into groups of ϳ2 ؋ 10 4 cells. The breakup and subsequent group size are regulated by a secreted multisubunit counting factor (CF). To elucidate how CF regulates group size, we isolated second-site suppressors of smlA ؊ , a transformant that forms small groups due to oversecretion of CF. smlA ؊ sslA1(CR11) cells form roughly wild-type-size groups due to an insertion in the beginning of the coding region of sslA1, one of two highly similar genes encoding a novel protein. The insertion increases levels of SslA. In wild-type cells, the sslA1(CR11) mutation forms abnormally large groups. Reducing SslA levels by antisense causes the formation of smaller groups. The sslA(CR11) mutation does not affect the extracellular accumulation of CF activity or the CF components countin and CF50, suggesting that SslA does not regulate CF secretion. However, CF represses levels of SslA. Wild-type cells starved in the presence of smlA ؊ cells, recombinant countin, or recombinant CF50 form smaller groups, whereas sslA1(CR11) cells appear to be insensitive to the presence of smlA ؊ cells, countin, or CF50, suggesting that the sslA1(CR11) insertion affects CF signal transduction. We previously found that CF reduces intracellular glucose levels. sslA(CR11) does not significantly affect glucose levels, while glucose increases SslA levels. Together, the data suggest that SslA is a novel protein involved in part of a signal transduction pathway regulating group size.