Daily Respiratory Research Analysis
Three studies advance respiratory science across diagnostics and monitoring: a label-free SERS-plus-deep learning platform delivers 15-minute, multiplex, quantitative detection of respiratory virus co-infections with high accuracy; a solid-state esophageal pressure sensor shows good agreement with balloon catheters from bench to ICU, streamlining advanced ventilatory monitoring; and targeted NGS for pneumonia co-detects pathogens, antimicrobial resistance, and virulence genes with strong diagnos
Summary
Three studies advance respiratory science across diagnostics and monitoring: a label-free SERS-plus-deep learning platform delivers 15-minute, multiplex, quantitative detection of respiratory virus co-infections with high accuracy; a solid-state esophageal pressure sensor shows good agreement with balloon catheters from bench to ICU, streamlining advanced ventilatory monitoring; and targeted NGS for pneumonia co-detects pathogens, antimicrobial resistance, and virulence genes with strong diagnostic accuracy and severity associations.
Research Themes
- AI-enabled point-of-care multiplex diagnostics for respiratory infections
- Translational advances in esophageal pressure monitoring for mechanical ventilation
- Genomics-driven pathogen, AMR, and virulence detection with severity stratification in pneumonia
Selected Articles
1. Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms.
A label-free SERS–deep learning platform (MultiplexCR) accurately classifies and quantifies respiratory virus coinfections directly from saliva within 15 minutes. Trained on over 1.2 million spectra across 11 viruses and mixed combinations, it reached 98.6% classification accuracy with precise concentration regression and maintained performance in blind tests.
Impact: This introduces a rapid, multiplex, quantitative diagnostic that could transform point-of-care testing for respiratory infections and antimicrobial stewardship during viral seasons and pandemics.
Clinical Implications: If clinically validated, this platform could enable same-visit differentiation of single versus coinfections and quantitative viral load estimation, informing isolation decisions, antiviral choices, and outbreak control.
Key Findings
- Collected >1.2 million SERS spectra from saliva across 11 viruses, nine two-virus mixtures, and four three-virus mixtures.
- Achieved 98.6% accuracy for coinfection classification and mean absolute error of 0.028 for concentration regression.
- Blind tests confirmed consistent high accuracy and precise quantification; full workflow completed in ~15 minutes.
- Utilized silica-coated silver nanorod array substrates enabling sensitive, label-free detection.
Methodological Strengths
- Extensive spectral dataset spanning multiple viruses and mixture conditions with quantitative labels
- Independent blind testing demonstrating generalization of both classification and regression
Limitations
- Clinical validation in real patient cohorts and diverse saliva matrices is not reported
- Potential variability in SERS substrate manufacturing and matrix effects may affect robustness outside controlled settings
Future Directions: Prospective clinical validation across care settings, robustness to matrix variability, head-to-head comparisons with RT-PCR/antigen tests, and pathway to regulatory clearance for point-of-care deployment.
2. Solid-state esophageal pressure sensor for the estimation of pleural pressure: a bench and first-in-human validation study.
A novel solid-state esophageal pressure transducer demonstrated good agreement with reference pressures and balloon catheters across bench testing, healthy volunteers, and mechanically ventilated ICU patients. This technology may simplify Pes monitoring and broaden adoption for guiding PEEP, driving pressure, and patient-ventilator interaction.
Impact: By reducing calibration complexity and improving usability, this device could enable wider, more reliable esophageal pressure monitoring to personalize mechanical ventilation and reduce ventilator-induced lung injury.
Clinical Implications: Simplified Pes monitoring can facilitate bedside assessment of transpulmonary pressures, optimize PEEP/driving pressures, and improve synchrony management without the workflow burdens of balloon calibration.
Key Findings
- Bench testing over 5 days showed the solid-state sensor had acceptable bias and stability versus reference pressures.
- In 15 volunteers and 16 ventilated ICU patients, solid-state Pes measurements agreed with balloon catheter values by Bland-Altman analysis.
- Demonstrated feasibility and accuracy across spontaneous and controlled breathing conditions.
Methodological Strengths
- Bench-to-bedside validation including bench setup, healthy volunteers, and ventilated ICU patients
- Appropriate agreement analysis using mixed-effects Bland-Altman with bootstrapping
Limitations
- Modest sample size and single-prototype evaluation limit generalizability
- No assessment of downstream clinical outcomes or workflow/time savings versus balloon catheters
Future Directions: Multicenter evaluations with larger cohorts, standardization of calibration-free workflows, and trials assessing impacts on ventilator management decisions and patient outcomes.
3. Targeted Next-Generation Sequencing in Pneumonia: Applications in the Detection of Responsible Pathogens, Antimicrobial Resistance, and Virulence.
In a prospective comparison of 78 suspected pneumonia cases, targeted NGS achieved an accuracy of 0.852 against a composite standard for identifying responsible pathogens, comparable to mNGS and superior to conventional testing. tNGS simultaneously reported 81 AMR genes and 144 virulence genes, with virulence detection associated with markedly higher rates of severe pneumonia and ARDS.
Impact: tNGS offers actionable, rapid, and comprehensive pathogen/AMR/virulence profiling, enabling earlier targeted therapy and risk stratification in pneumonia while reducing reliance on culture.
Clinical Implications: Clinicians can leverage tNGS to guide initial antimicrobial choice, detect priority drug-resistant organisms, and identify virulence-associated high-risk cases (e.g., ARDS propensity), informing ICU triage and escalation.
Key Findings
- tNGS achieved 0.852 accuracy (95% CI 0.786–0.918) for responsible pathogen detection versus a composite standard, comparable to mNGS and superior to conventional tests.
- Reported 81 AMR genes and directly identified 75.8% (25/33) of priority drug-resistant pathogens.
- Detected 144 virulence genes; virulence-positive patients had higher severe pneumonia (95.0% vs 42.9%) and ARDS (55.0% vs 0%) rates.
Methodological Strengths
- Prospective head-to-head comparison with mNGS and conventional microbiology using a composite reference standard
- Simultaneous multi-dimensional outputs (pathogen, AMR, virulence) with clinical severity associations
Limitations
- Single-center study with modest sample size may limit generalizability
- Turnaround time, cost, and impact on patient outcomes were not evaluated
Future Directions: Multicenter trials assessing clinical utility, time-to-result, antimicrobial stewardship impact, and outcome benefits; expansion of panels and on-instrument analytics.