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Plasmonic nanoparticle-based biosensors often report a colorimetric signal through the aggregation or clustering of the nanoparticles (NPs), but these mechanisms typically struggle to function in complex biofluids. Here, we report a matrixinsensitive sensor array approach to detect bacteria, fungi, and viruses whose signal is based on the dissociation of the peptideaggregated NPs by thiolated polyethylene glycol (HS-PEG) polymers. We show that the HS-PEGs of differing sizes have varying capabilities to dissociate citrate-capped gold nanoparticle (AuNP) and silver nanoparticle (AgNP) assemblies. The dissociative abilities of the HS-PEGs were used in this sensor array to discriminate at the 90% confidence level the microorganisms Porphyromonas gingivalis, Fusobacterium nucleatum, and Candida albicans in water and saliva using linear discriminant analysis (LDA). We further demonstrate the versatility of the sensor array by detecting various subtypes of the viruses SARS-CoV-2 (beta, delta, and omicron) and influenza (H3N2) spiked in saliva samples using LDA. In the final demonstration, the sensor array design stratified healthy saliva samples from patient samples diagnosed with periodontitis as well as COVID-19. 

Microfluidic droplet generation typically entails an initial stabilization period on the order of minutes, exhibiting higher variation in droplet volume until the system reaches monodisperse production. The material lost during this period can be problematic when preparing droplets from limited samples such as patient biopsies. Active droplet generation strategies such as antiphase peristaltic pumping effectively reduce stabilization time but have required off-chip control hardware that reduces system accessibility. We present a fully integrated device that employs on-chip pneumatic logic to control phase-optimized peristaltic pumping. Droplet generation stabilizes in about a second, with only one or two non-uniform droplets produced initially. 

This is a correction to the original manuscript. The original manuscript had missing text describing funding sources. 

ABSTRACT The aggregation of plasmonic nanoparticles can lead to new and controllable properties useful for numerous applications. We recently showed the reversible aggregation of gold nanoparticles (AuNPs) via a small, cationic di‐arginine peptide; however, the mechanism underlying this aggregation is not yet comprehensively understood. Here, we seek insights into the intermolecular interactions of cationic peptide‐induced assembly of citrate‐capped AuNPs by empirically measuring how peptide identity impacts AuNP aggregation. We examined the nanoscale interactions between the peptides and the AuNPs via UV‐vis spectroscopy to determine the structure‐function relationship of peptide length and charge on AuNP aggregation. Careful tuning of the sequence of the di‐arginine peptide demonstrated that the mechanism of assembly is driven by a reduction in electrostatic repulsion. We show that acetylated N‐terminals and carboxylic acid C‐terminals decrease the effectiveness of the peptide in inducing AuNP aggregation. The increase in peptide size through the addition of glycine or proline units hinders aggregation and leads to less redshift. Arginine‐based peptides were also found to be more effective in assembling the AuNPs than cysteine‐based peptides of equivalent length. We also illustrate that aggregation is independent of peptide stereochemistry. Finally, we demonstrate the modulation of peptide‐AuNP behavior through changes to the pH, salt concentration, and temperature. Notably, histidine‐based and tyrosine‐based peptides could reversibly aggregate the AuNPs in response to the pH. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.