Rapid, field-deployable diagnostics are critical for detecting high-priority biothreats, yet most platforms lack sensitivity, multiplexing, or usability in resource-limited settings. We present VeriFAST, a compact vertical flow immunoassay (VFI) system integrating automated fluid handling, a nitrocellulose-based multiplex membrane, and smartphone-enabled image processing for real-time analysis. Using a gold nanoparticle sandwich format, VeriFAST simultaneously detects four Tier 1 bacterial antigens LcrV and F1 (Yersinia pestis), FtLPS (Francisella tularensis), CPS (Burkholderia pseudomallei). The system achieved limits of detection of 0.05 ng/mL for F1, 0.0125 ng/mL for CPS and 0.625 ng/mL for LcrV in serum, urine, and soil extracts, with total assay time under 30 min. Reagent stability testing showed that buffers and detection antibodies retained full function for 12 months under ambient or refrigerated storage, whereas capture antibodies began to show signal drift after 3 months at room temperature, even with desiccant protection. Matrix-specific optimization further enabled high reproducibility and low background across complex samples. By combining multiplexing, automated processing, and mobile analytics in a portable format, VeriFAST addresses critical gaps in field diagnostics and offers a scalable solution for biothreat detection in public health, military, and environmental response.

Significance:Thyroid cancer is the most common endocrine malignancy, and diagnosis is often challenging due to overlapping features between benign and malignant nodules. Fine-needle aspiration, the clinical gold standard, frequently yields indeterminate results and lacks spatial context, leading to unnecessary surgeries. Real-time margin assessment also remains limited. There is a need for accurate, label-free, spatially resolved imaging. Dynamic optical contrast imaging (DOCI), which measures autofluorescence lifetimes of endogenous fluorophores, offers a promising platform for intraoperative cancer detection. Aim:We develop and evaluate a machine learning integrated DOCI framework to classify thyroid tissue subtypes and segment cancerous regions from ex vivo hyperspectral sections, with potential for real-time surgical use. Approach:Fresh ex vivo thyroid specimens were imaged using a 23-channel DOCI acquisition. A pixel-level principal component analysis (PCA) and logistic regression classifier produced tissue probabilities, aggregated by a regional majority-vote gate to categorize specimens as normal, follicular, or papillary. Tumor-specific squeeze-and-excitation U-Net models were trained on voxel-only inputs for semantic segmentation. PCA-guided channel ablation identified a reduced spectral subset, and the pipeline was retrained using a compact 12-channel input. Results:The first two PCA components explained over 70% of spectral variance and yielded well-separated tissue clusters. The regional PCA classifier achieved 92.3% validation accuracy and 100% accuracy on the test set. Full 23-channel U-Net models delivered strong segmentation (papillary: Dice nonempty 0.829, balanced Dice 0.914; follicular: Dice nonempty 0.618, balanced Dice 0.809). Reduced-channel models preserved most performance and improved follicular segmentation (Dice nonempty 0.762), confirming spectral redundancy. Conclusions:Integrating DOCI with interpretable machine learning enables accurate, label-free differentiation and segmentation of thyroid tissues. Channel reduction demonstrates that high performance is achievable with a compact spectral subset, supporting faster, more cost-efficient DOCI systems and future real-time intraoperative deployment.

Colorimetric lateral flow immunoassays (LFIA) have revolutionized point-of-care testing (POCT) methods by providing simple, rapid, accessible, and instrumentation-free detection of infectious diseases. However, conventional LFIAs relying on gold nanospheres (GNPs) can suffer from limited sensitivity due to weak colorimetric signal intensity at the test line, restricting their diagnostic potential. To overcome this limitation, we developed a magneto-plasmonics enhanced colorimetric LFIA (mpLFIA)- a breakthrough platform that integrates a novel hybrid nanoparticle system-magnetic gold nanostars (mpGNS). By leveraging dual enhancement mechanisms - magnetic preconcentration and plasmonic amplification through the gold nanostar shell coating of the magnetic core- the mpLFIA achieves unprecedented signal intensity and detection performance. Among the tested mpGNS variants, mpGNS-3- distinguished by its longest spikes and highest branch density- emerged as the most effective colorimetric signal amplifier when evaluated using Rift Valley fever virus (RVFV) nucleoprotein as a model analyte. Our mpLFIA platform achieves an outstanding limit of detection (LOD) of 2.24 pg/mL for RVFV nucleoprotein in 1X PBS buffer, demonstrating a 1000-fold enhancement over conventional GNP-based assays. Our prototype mpLFIA platform, utilizing highly spiked mpGNS-3, exhibits great potential as a powerful bioanalytical tool, combining high sensitivity with practical portability for point-of-care applications.

Cerebrospinal fluid circulation through the glymphatic system plays a crucial role in removing metabolic waste from the central nervous system. However, the mechanism underlying the brain-wide glymphatic dynamics is not yet fully understood, in part due to the lack of glymphatic imaging technologies on deep brains. Here, we report a hybrid imaging technology that integrates three-dimensional photoacoustic tomography and ultrasound localization microscopy (3D-PAULM), enhanced by a photoacoustic dye with strong optical absorption in the second near-infrared window (NIR-II). 3D-PAULM allows for continuous, noninvasive, whole-brain imaging in mice through intact skull, providing superresolution mapping of the brain vasculature and highly sensitive tracing of the NIR-II dye in the glymphatic system. Using 3D-PAULM, we investigated the glymphatic function impaired by ischemic stroke, aging, and anesthesia. Our results provide insights into glymphatic transport under various physiological as well as pathological conditions and establish 3D-PAULM as a valuable tool for preclinical glymphatic research.

Uric acid, a vital circulating metabolite, is a key biomarker for various health conditions including gout, preeclampsia, and kidney disorders. This underscores the need for noninvasive, rapid, sensitive, and cost-effective methods for monitoring uric acid to enable early preventive interventions. This study introduces a simple and sensitive separation-free "mix-and-detect" method for the direct surface-enhanced Raman scattering (SERS) detection of uric acid in urine, using bimetallic gold-silver nanostars functionalized with sodium dodecyl sulfate (BGNS@SDS). The SDS capping layer facilitates efficient uric acid capture through multiple hydrogen bonds under alkaline conditions, as confirmed by density functional theory (DFT) analysis. Using the optimized nanostar morphology (BGNS-3@SDS), uric acid was detected in water and spiked artificial urine samples with limits of detection of 2.2 and 3 μg/mL, respectively. These detection limits are substantially lower than the clinically relevant concentration range of uric acid in urine and well below the pathological threshold (∼750 μg/mL). The platform also successfully quantified uric acid levels in urine from ten healthy volunteers without sample pretreatment, enabling differentiation between healthy individuals and those at risk. This straightforward and sensitive SERS strategy holds strong promise for rapid, point-of-care diagnostics targeting low-affinity biomarkers.

Nanoparticle-mediated photothermal therapy (PTT) is a promising strategy for cancer treatment; however, nanoparticle instability and lack of precise imaging tools for real-time temperature monitoring during therapy and nanoparticle tracking have hindered investigations in animal models. To address these critical issues, we present a theranostic platform that seamlessly integrates armored core-gold nanostar (AC-GNS)-mediated PTT with full-view photoacoustic computed tomography (PACT), enabling nanoparticle tracking and real-time imaging-guided PTT in deep tissues. The AC-GNS platform delivered exceptional photostability and thermal resilience beyond those of conventional nanoparticles while serving as a high-performance contrast agent for PACT and a photothermal transducer for PTT. Integrating AC-GNS-mediated PTT with noninvasive PACT enabled whole-body nanoparticle tracking, PTT treatment monitoring via thermal imaging, and thermal dose determination, culminating in a 100% survival rate in a murine bladder cancer model without long-term treatment-related toxicity. This theranostic platform lays the foundation for broader research applications and provides opportunities for advancing solid tumor treatment and response assessment research.

Here, we first introduce caged gold nanostars (C-GNS), a novel hybrid nanoplatform combining the exceptional plasmonic properties of nanostars with the loading capability of hollow-shell structures. We present two synthetic routes used to produce C-GNS particles and highlight the benefits of the galvanic replacement-free approach. FEM simulations explore the enhanced plasmonic properties of this novel nanoparticle morphology. Finally, in a proof-of-concept study, we successfully demonstrate in vivo hyperspectral imaging and photothermal treatment of tumors in a mouse model with the C-GNS nanoplatform.

Acoustically probing biological tissues with light or sound, photoacoustic and ultrasound imaging can provide anatomical, functional, and/or molecular information at depths far beyond the optical diffusion limit. However, most photoacoustic and ultrasound imaging systems rely on linear-array transducers with elevational focusing and are limited to two-dimensional imaging with anisotropic resolutions. Here, we present three-dimensional diffractive acoustic tomography (3D-DAT), which uses an off-the-shelf linear-array transducer with single-slit acoustic diffraction. Without jeopardizing its accessibility by general users, 3D-DAT has achieved simultaneous 3D photoacoustic and ultrasound imaging with optimal imaging performance in deep tissues, providing near-isotropic resolutions, high imaging speed, and a large field-of-view, as well as enhanced quantitative accuracy and detection sensitivity. Moreover, powered by the fast focal line volumetric reconstruction, 3D-DAT has achieved 50-fold faster reconstruction times than traditional photoacoustic imaging reconstruction. Using 3D-DAT on small animal models, we mapped the distribution of the biliverdin-binding serpin complex in glassfrogs, tracked gold nanoparticle accumulation in a mouse tumor model, imaged genetically-encoded photoswitchable tumors, and investigated polyfluoroalkyl substances exposure on developing embryos. With its enhanced imaging performance and high accessibility, 3D-DAT may find broad applications in fundamental life sciences and biomedical research. The authors demonstrate integration of a single-slit diffraction method into linear-array transducers for advancing 3D photoacoustic and ultrasound imaging in biological tissues. The method enhances spatial resolution and signal-to-noise ratio while maintaining low-cost and practicality.

Rift Valley fever (RVF) is a vector-borne, zoonotic infectious disease with a proven history of morbidity and mortality in both humans and animals. Rift Valley fever virus (RVFV) is categorized as a high-priority biothreat agent by the Centers for Disease Control and Prevention and poses a serious national threat due to its ease of dissemination and potential for social disruption. RVF often presents as a febrile disease without specific symptoms, making early-stage detection particularly challenging. As such, it is critical that rapid, sensitive, and specific diagnostics are available for the detection of RVFV. While lateral flow immunoassays (LFIs) have been developed and validated for point-of-care (POC) diagnostics, vertical flow immunoassays (VFIs) provide enhanced analytical sensitivity and are equally suitable for POC use. In this study, we developed a VFI system for the detection of RVFV, achieving a limit of detection of 0.78 ng/mL, which is a 2.5-fold increase in analytical sensitivity compared to an LFI prototype. Furthermore, minimal cross-reactivity was demonstrated when performing the assay with target analytes of other high-priority biothreats and one other common viral nucleoprotein. This high-sensitivity VFI has the potential to prove useful for the detection of RVFV and other high-priority biothreat agents at the POC.IMPORTANCEIn this study, we have developed a rapid, sensitive vertical flow immunoassay (VFI) for the detection of Rift Valley fever virus (RVFV) in spiked human serum. The prototype diagnostic described in this research was shown to be more sensitive than traditional methods, such as lateral flow dipstick tests. Moreover, the VFI is readily deployable at the point of care in resource-limited settings. The ability of the described diagnostic to accurately and rapidly detect RVFV in samples could expedite the delivery of life-saving care and thus improve patient outcomes.

Lateral flow immunoassays (LFIA) are widely recognized as cost-effective point-of-care diagnostic tools (POCT) for infectious disease diagnosis. Despite their widespread use, traditional colorimetric LFIAs, which rely on gold nanospheres (GNP), are constrained by a limited sensitivity. To overcome this challenge, we have engineered gold nanocages (GNCs) with optimized core-to-shell morphologies, achieving significant amplification of both colorimetric and photothermal LFIA readout signals. The distinctive morphology of GNCs, featuring adjustable core-to-shell gap thicknesses, enables fine-tuning of the localized surface plasmon resonance (LSPR) peak across a broad spectral range from 600 to 1200 nm. Among the GNC morphologies evaluated, the optimized GNC (GNC-4), characterized by its larger size and maximal core-to-shell gap thickness, exhibited superior color brightness and enhanced photothermal efficiency compared to other GNC morphologies and traditional GNP. The enhanced performance of GNC-4 enabled the detection of influenza A (H1N1), used as the model analyte, achieving a limit of detection (LOD) of 1.8 ng/mL via colorimetric analysis and 1.51 pg/mL using photothermal LFIA. Compared to traditional GNP-based colorimetric LFIA detection, the colorimetric sensitivity of the GNC-4-based LFIA was enhanced by 7-fold, while the photothermal detection sensitivity showed an improvement of over 8000-fold. By incorporating a portable smartphone-based photothermal LFIA platform, our dual-modal LFIA exhibits high sensitivity, practicality in detecting H1N1 in spiked saliva samples, and long-term stability over five months, making it a promising tool for infectious disease detection and a potential model for diagnosing other pathogens.

In this study, we have developed a plasmonic hybrid heterostructure integrating two elements: Two-dimensional (2D) reduced graphene oxide-gold nanostars composite (rGO-GNS), and gold nanostars (GNS) substrate. By harnessing the unique plasmonic properties of rGO in chemical enhancement and that of GNS in electromagnetic enhancement, the hybrid heterostructure offers synergistic enhancement effects that enable ultra-low sensitivity and accurate identification and analysis of trace quantities of target substances. It is noteworthy that the high-density hotspots generated by strong plasmonic coupling of rGO-GNS and GNS results in ultra-high surface-enhanced Raman spectroscopy (SERS) enhancement compared to individual substrate either GNS or rGO-GNS substrate. Moreover, the uniformity and reproducibility of the GNS@rGO-GNS substrate were studied by using thiophenol (TP) as a model analyte, which indicates that the SERS sensor exhibited superior signal reproducibility with an RSD value 5% and long-term stability with a minimal signal loss after 30 days. To demonstrate a potential application of our SERS substrate, SERS detection of the pesticide thiram in river water was realized with a limit of detection (LOD) up to 50 pM, showing the potential for new opportunities for efficient chemical and biological sensing applications.

A simple, sensitive, and cost-effective SERS substrate preparation method is introduced, leveraging plasmonics-active gold nanostars (GNS) on commercially available hydrophobic adhesive tape to create a 3D plasmonic nano-cauliflower (PNC) architecture. Using the optimized SERS-active PNC substrate (PNC-5), we achieved ultra-low detection limits of 3.3 nM for ciprofloxacin (CIP) and 1.5 nM for ampicillin (AMP) antibiotics. Our PNC platform exhibits exceptional stability for over six months and outstanding reproducibility with an RSD of less than 5

Bilirubin, a critical circulating metabolite, functions as a key biomarker for a range of health conditions, including jaundice, hepatitis, cirrhosis, and liver disorders. This highlights the need for a straightforward, rapid, sensitive, and cost-effective method for bilirubin monitoring to enable early diagnosis. In this study, we developed a simple and efficient solution-based SERS platform using highly stable gold nanostars (GNS) with tunable spike numbers (7-20) for direct bilirubin detection in urine without any sample pretreatment. Among the variants tested, GNS with the highest spike number (GNS-4) exhibited the strongest SERS enhancement, achieving a detection limit of 7.4 nM with methylene blue (Mb) as a model analyte. GNS-4 also enabled the direct detection of bilirubin spiked in artificial urine at a 20 nM detection limit. Furthermore, our "mix-and-detect" SERS platform effectively monitored bilirubin levels in urine samples from ten healthy volunteers without any pretreatment, highlighting its potential to differentiate healthy individuals from those at risk. Notably, GNS-4 maintained excellent SERS reproducibility and stability after six months of storage at room temperature. These results underscore the potential of solution-based SERS platforms for rapid, reliable, and cost-effective point-of-care diagnostics.

The lateral flow immunoassay (LFIA) has become a widely accepted point-of-care diagnostic tool (POCT) due to its simplicity, portability, cost-effectiveness, and rapid biomarker detection capabilities. However, its sensitivity in detecting target analytes has been limited by the visual signals produced by traditional gold nanoparticles. In this study, we introduce a highly sensitive near infrared (NIR) photothermal platform using gold nanostars (GNS) with a tunable plasmon resonance band spanning wavelengths from 700 to 850 nm. The GNS, particularly the GNS-3 probe with its large number of branches, exhibited exceptional light-to-heat conversion efficiency, significantly enhancing photothermal conversion. Using GNS-3 as an efficient photothermal probe, we successfully detected the high-risk pathogen Francisella Tularensis biomarker lipopolysaccharide (FtLPS) as the model analyte, achieving an outstanding limit of detection (LOD) of 3.5 pg/mL for photothermal LFIA. This photothermal LFIA enhances the detection sensitivity nearly 1000-fold compared to traditional colorimetric gold nanosphere-based LFIA. Furthermore, we demonstrate the potential of the photothermal LFIA platform for real-world applicability by detecting ultra-low levels of FtLPS spiked in blood serum samples, achieving an LOD as low as 4 pg/mL. This photothermal LFIA platform shows promise for establishing high-performance photothermal sensing in point-of-care settings and holds great potential for future advancements in rapid, on-site screening of infectious diseases.

The tunable optical properties and exceptional electromagnetic field enhancement of nanostar-based plasmonic nanoparticles make them highly promising for a wide array of biomedical applications. However, a great challenge for their widespread use is the time-sensitive nature of the various processes in the nanostar synthesis workflow, which could lead to imprecise control of their homogeneity and high batch-to-batch variability. To address these challenges, we have developed an automated synthesis system with AI capability to reproducibly synthesize large quantities of nanostar particles. This platform uses key synthesis parameters such as reagent volume and reagent addition timing to systematically evaluate how these factors determine the optical properties and SERS enhancement of gold nanostars and bimetallic nanostars. We developed and trained different machine learning (ML) models using nanoparticle characterization data to predict absorbance features and SERS enhancement from synthesis parameters. We compared the performance of five different machine learning models, including artificial neural networks, support vector regression, and several tree-based models, including random forest, extreme gradient boost, and categorical boost. A grid matrix was fed into the final trained models to create a look-up table to synthesize gold nanostars with an absorbance maximum at specific wavelengths, culminating in the reproducible synthesis of desired nanostar platforms with a peak absorbance wavelength of less than 1.2

Core-shell gold nanoparticles offer significant potential for enhancing surface-enhanced Raman spectroscopy (SERS) detection by integrating the elemental properties of both the core and shell materials. However, achieving an optimized core-shell nanoparticle system with uniformly distributed, densely packed hotspots for highly sensitive, direct in situ SERS detection of analytes remains a significant challenge. In this study, we introduce a simple, sensitive, and direct in situ SERS detection platform using multibranched magnetic core-shell gold nanostars (mGNS). This system capitalizes on the enhanced SERS signal from the branched nanostar morphology coupled with magnetic concentration effects, leading to a significantly amplified SERS response. The optimized mGNS-3, with ideal size and spike density, demonstrated the highest SERS enhancement using para-mercaptobenzoic acid (pMBA) as a model analyte. This solution-based magnetic SERS method achieved a detection limit of 1.5 nM, with the SERS signal being five times stronger than conventional SERS measurements. To showcase its practical utility, we employed the platform for the direct detection of ceftriaxone, an antibiotic, in milk without any sample preparation. The platform achieved a detection limit of 2.4 nM, which is significantly lower than the regulatory limits set by the USA and the European Union for antibiotic concentrations in milk. Overall, this magnetic SERS platform based on mGNS highlights its potential for highly sensitive antibiotic detection in point-of-care settings without the need for preprocessing.