Timely diagnosis of tuberculosis disease is critical for positive patient outcomes, yet potentially millions go undiagnosed or unreported each year. Sputum is widely used as the testing input, but limited by its complexity, heterogeneity, and sourcing problems. Finding methods to interrogate noninvasive, non-sputum clinical specimens is indispensable to improving access to tuberculosis diagnosis and care. In this work, economical plasmonic gratings were used to analyze tuberculosis biomarker lipoarabinomannan (LAM) from clinical urine samples by single molecule fluorescence assay (FLISA) and compared with gold standard sputum GeneXpert MTB/ RIF, culture, and reference ELISA testing results. In this study, twenty sputum and urine sample sets were selected retrospectively from a repository of HIV-negative patient samples collected before initiation of anti-tuberculosis therapy. GeneXpert MTB/RIF and culture testing of patient sputum confirmed the presence or absence of pulmonary tuberculosis while all patient urines were reference ELISA LAM-negative. Plasmonic gratings produced by low-cost soft lithography were bound with anti-LAM capture antibody, incubated with patient urine samples, and biotinylated detection antibody. Fluorescently labeled streptavidin revealed single molecule emission by epifluorescence microscope. Using a 1 fg/mL baseline for limit of detection, single molecule FLISA demonstrated good qualitative agreement with gold standard tests on 19 of 20 patients, including accurately predicting the gold-standard-negative patients, while one gold-standard-positive patient produced no observable LAM in urine. Single molecule FLISA by plasmonic grating demonstrated the ability to quantify tuberculosis LAM from complex urine samples of patients from a high endemic setting with negligible interference from the complex media itself. Moreover, agreement with patient diagnoses by gold standard testing suggests that single molecule FLISA could be used as a highly sensitive test to diagnose tuberculosis noninvasively.

The increasing demand for rapid, cost-effective, and reliable diagnostic tools in personalized and point-of-care medicine is driving scientists to enhance existing technology platforms and develop new methods for detecting and measuring clinically significant biomarkers. Humanity is confronted with growing risks from emerging and recurring infectious diseases, including the influenza virus, dengue virus (DENV), human immunodeficiency virus (HIV), Ebola virus, tuberculosis, cholera, and, most notably, SARS coronavirus-2 (SARS-CoV-2; COVID-19), among others. Timely diagnosis of infections and effective disease control have always been of paramount importance. Plasmonic-based biosensing holds the potential to address the threat posed by infectious diseases by enabling prompt disease monitoring. In recent years, numerous plasmonic platforms have risen to the challenge of offering on-site strategies to complement traditional diagnostic methods like polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA). Disease detection can be accomplished through the utilization of diverse plasmonic phenomena, such as propagating surface plasmon resonance (SPR), localized SPR (LSPR), surface-enhanced Raman scattering (SERS), surface-enhanced fluorescence (SEF), surface-enhanced infrared absorption spectroscopy, and plasmonic fluorescence sensors. This review focuses on diagnostic methods employing plasmonic fluorescence sensors, highlighting their pivotal role in swift disease detection with remarkable sensitivity. It underscores the necessity for continued research to expand the scope and capabilities of plasmonic fluorescence sensors in the field of diagnostics.

Background: Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention.Methods and findings: A single molecule fluorescence immunosorbent assay was used to detect Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.Conclusions: The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.

In this work, we report a facile and green reduction method to synthesize reduced graphene oxide (rGO) sheet by using epigallocatechin gallate (EGCG) and hydroiodic acid (HI) simultaneously as a reduction agents and investigate its efficiency as an electrode. Two steps reduction and modification of graphene oxide GO with epigallocatechin gallate (EGCG) as a green reductant and hydroiodic acid (HI) as strong chemical reducing agent are investigated in this work. The indication of the reduction efficiency of rGO surface was determined by scanning electron microscopy (SEM). The results showed that using both of the reductants simultaneously was effective for removing oxygen-containing functional groups in GO to obtain rGO. The rGO obtained from these two-step processes had C/O ratio of about ∼10.26 and conductivity of 2385 S/m. The results also show that the presence of EGCG in the final rGo sheet provides active sites for furthered modification of rGO such as decorating it with metal nanoparticles in a single-step chemical deposition. These features will facilitate its further use as a flexible electrode in composite materials of conductors and many other applications.

The scope of this study was to apply advances in materials science, specifically the use of organosilicate nanoparticles as a high surface area platform for passive sampling of chemicals or pre-concentration for active sensing in multiple-phase complex environmental media. We have developed a novel nanoporous organosilicate (NPO) film as an extraction phase and proof of concept for application in adsorbing hydrophobic compounds in water and sediment. We characterized the NPO film properties and provided optimization for synthesis and coatings in order to apply the technology in environmental media. NPO films in this study had a very high surface area, up to 1325 m²/g due to the high level of mesoporosity in the film. The potential application of the NPO film as a sorbent phase for sensors or passive samplers was evaluated using a model hydrophobic chemical, polychlorinated biphenyls (PCB), in water and sediment. Sorption of PCB to this porous high surface area nanoparticle platform was highly correlated with the bioavailable fraction of PCB measured using whole sediment chemistry, porewater chemistry determined by solid-phase microextraction fiber methods, and the Lumbriculus variegatus bioaccumulation bioassay. The surface-modified NPO films in this study were found to highly sorb chemicals with a log octanol-water partition coefficient (K_ow) greater than four; however, surface modification of these particles would be required for application to other chemicals.