Point-Of-Need One-Pot Multiplexed RT-LAMP Test For Detecting Three Common Respiratory Viruses In Saliva

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Abstract Respiratory viral infections pose a significant global public health challenge, partly due to the difficulty in rapidly and accurately distinguishing between viruses with similar symptoms at point of care, hindering timely appropriate treatment limiting effective infection control prevention efforts. Here, we developed multiplexed, non- invasive saliva-based, reverse transcription loop-mediated isothermal amplification (RT- LAMP) test that enables simultaneous detection three most common respiratory infections, severe acute syndrome coronavirus 2 (SARS-CoV-2), Influenza (Flu), syncytial virus (RSV), single reaction via specific probes monitored real-time by machine-learning-enabled compact analyzer. Our results demonstrate multiplexed assay can effectively detect target RNAs high accuracy. Further, testing spiked saliva samples showed strong agreement polymerase chain (RT-PCR) assay, area under curve (AUC) values 0.82, 0.93, 0.96 for RSV, Influenza, SARS-CoV-2, respectively. By enabling rapid from easily collected device presented here offers practical efficient tool improving outcomes helping prevent spread contagious diseases. Significance This research presents an innovative approach diagnostics combining one-pot molecular machine learning-based analysis simultaneously RSV samples. The battery- powered portable analyzer features novel machine-learning-assisted fluorescence reporter quantification, eliminating need traditional filter- based optical components adaptation new targets without hardware changes. demonstrates accuracy detecting co-infections samples, providing rapid, cost-effective point-of-need solution. expand access, improve patient outcomes, support more disease control, particularly resource-limited or decentralized healthcare settings.

Язык: Английский

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A Patient-Centered Approach in Sensor Science: Embracing Patient Engagement for Translational Clinical Technologies

ACS Sensors, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

With the goal of impacting patient quality life and outcomes, sensor science offers significant potential to revolutionize healthcare by providing advances in detection molecular biomarkers for personalized clinical technologies. The community has achieved technical advancements that can impact diagnostics, health monitoring, disease treatment; however, many innovations remain confined laboratory, failing bridge translational gap between research real-world applications. This perspective presents a new direction community, where development centers on needs experiences primary beneficiaries: patients. We provide guidelines resources researchers engage with patients early continuously throughout process inform specifications better align technologies needs, improving their adoption impact. also present examples implementing patient-centered approach planning engagement research. In design impactful sensors patients, must expand focus beyond embrace approach, which will likely lead opportunities collaboration evolution community.

Язык: Английский

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Fully Automated and AI-Assisted Optical Fiber Sensing System for Multiplexed and Continuous Brain Monitoring

ACS Sensors, Год журнала: 2024, Номер 9(12), С. 6605 - 6620

Опубликована: Дек. 4, 2024

Continuous and comprehensive brain monitoring is crucial for timely identification of changes or deterioration in function, enabling prompt intervention personalized treatments. However, existing systems struggle to offer continuous accurate multiple biomarkers simultaneously. This study introduces a multiplexed optical fiber sensing system simultaneous six cerebrospinal fluid (CSF) using tip-functionalized fibers computational algorithms. Optimized machine learning models are developed integrated real-time spectra analysis, allowing precise readout biomarker concentrations. The learning-assisted optic exhibits high sensitivity (0.04, 0.38, 0.67, 2.62, 0.0064, 0.33 I/I0 change per units temperature, dissolved oxygen, glucose, pH, Na+, Ca2+, respectively), reversibility, selectivity toward target with total diameter less than 2.5 mm. By metabolic ionic dynamics, this accurately identified physiology recovery ex vivo traumatic injury models. Additionally, the successfully tracked fluctuations clinical CSF samples accuracy (R2 > 0.93), demonstrating excellent reflecting disease progression real time. These findings underscore enormous potential automated intraoperative postoperative physiologies.

Язык: Английский

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Detection of Triple Respiratory Viruses in Saliva Using Multiplexed RT-LAMP on a Machine Learning-Empowered Portable Device

Опубликована: Сен. 19, 2024

Язык: Английский

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