Evidence Generation for a Host-Response Biosignature of Respiratory Disease
Background: In just twenty years, three dangerous human coronaviruses—SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based d...
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MDPI AG
2025-07-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/17/7/943 |
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| author | Kelly E. Dooley Michael Morimoto Piotr Kaszuba Margaret Krasne Gigi Liu Edward Fuchs Peter Rexelius Jerry Swan Krzysztof Krawiec Kevin Hammond Stuart C. Ray Ryan Hafen Andreas Schuh Nelson L. Shasha Jumbe |
| author_facet | Kelly E. Dooley Michael Morimoto Piotr Kaszuba Margaret Krasne Gigi Liu Edward Fuchs Peter Rexelius Jerry Swan Krzysztof Krawiec Kevin Hammond Stuart C. Ray Ryan Hafen Andreas Schuh Nelson L. Shasha Jumbe |
| author_sort | Kelly E. Dooley |
| collection | DOAJ |
| description | Background: In just twenty years, three dangerous human coronaviruses—SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed. Methods: We hypothesized that the lungs act as biomechanical instruments, with infection altering tissue tension, wave propagation, and flow dynamics in ways detectable through subaudible vibroacoustic signals. In a matched case–control study, we enrolled 19 RT-PCR-confirmed COVID-19 inpatients and 16 matched controls across two Johns Hopkins hospitals. Multimodal data were collected, including passive vibroacoustic auscultation, lung ultrasound, peak expiratory flow, and laboratory markers. Machine learning models were trained to identify host-response biosignatures from anterior chest recordings. Results: 19 COVID-19 inpatients and 16 matched controls (mean BMI 32.4 kg/m<sup>2</sup>, mean age 48.6 years) were successfully enrolled to the study. The top-performing, unoptimized, vibroacoustic-only model achieved an AUC of 0.84 (95% CI: 0.67–0.92). The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94–1.0), with 100% sensitivity (95% CI: 82–100%) and 99.6% specificity (95% CI: 85–100%). Vibroacoustic data from the anterior chest alone reliably distinguished COVID-19 cases from controls. Conclusions: This proof-of-concept study demonstrates that passive, noninvasive vibroacoustic biosignatures can detect host response to viral infection in a hospitalized population and supports further testing of this modality in broader populations. These findings support the development of scalable, host-based diagnostics to enable early, agnostic detection of future pandemic threats (ClinicalTrials.gov number: NCT04556149). |
| format | Article |
| id | doaj-art-ed44a10a646b4cbbbf4b0acfc38b1983 |
| institution | Kabale University |
| issn | 1999-4915 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-ed44a10a646b4cbbbf4b0acfc38b19832025-08-20T03:32:36ZengMDPI AGViruses1999-49152025-07-0117794310.3390/v17070943Evidence Generation for a Host-Response Biosignature of Respiratory DiseaseKelly E. Dooley0Michael Morimoto1Piotr Kaszuba2Margaret Krasne3Gigi Liu4Edward Fuchs5Peter Rexelius6Jerry Swan7Krzysztof Krawiec8Kevin Hammond9Stuart C. Ray10Ryan Hafen11Andreas Schuh12Nelson L. Shasha Jumbe13Vanderbilt University Medical Center, Nashville, TN 37232, USALevel 42 AI, Inc., Mountain View, CA 94041, USAHylomorph Solutions, Ltd., Glasgow G2 4JR, UKJohns Hopkins University School of Medicine, Baltimore, MD 21205, USAJohns Hopkins University School of Medicine, Baltimore, MD 21205, USAJohns Hopkins University School of Medicine, Baltimore, MD 21205, USALevel 42 AI, Inc., Mountain View, CA 94041, USAHylomorph Solutions, Ltd., Glasgow G2 4JR, UKHylomorph Solutions, Ltd., Glasgow G2 4JR, UKHylomorph Solutions, Ltd., Glasgow G2 4JR, UKJohns Hopkins University School of Medicine, Baltimore, MD 21205, USALevel 42 AI, Inc., Mountain View, CA 94041, USALevel 42 AI, Inc., Mountain View, CA 94041, USALevel 42 AI, Inc., Mountain View, CA 94041, USABackground: In just twenty years, three dangerous human coronaviruses—SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed. Methods: We hypothesized that the lungs act as biomechanical instruments, with infection altering tissue tension, wave propagation, and flow dynamics in ways detectable through subaudible vibroacoustic signals. In a matched case–control study, we enrolled 19 RT-PCR-confirmed COVID-19 inpatients and 16 matched controls across two Johns Hopkins hospitals. Multimodal data were collected, including passive vibroacoustic auscultation, lung ultrasound, peak expiratory flow, and laboratory markers. Machine learning models were trained to identify host-response biosignatures from anterior chest recordings. Results: 19 COVID-19 inpatients and 16 matched controls (mean BMI 32.4 kg/m<sup>2</sup>, mean age 48.6 years) were successfully enrolled to the study. The top-performing, unoptimized, vibroacoustic-only model achieved an AUC of 0.84 (95% CI: 0.67–0.92). The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94–1.0), with 100% sensitivity (95% CI: 82–100%) and 99.6% specificity (95% CI: 85–100%). Vibroacoustic data from the anterior chest alone reliably distinguished COVID-19 cases from controls. Conclusions: This proof-of-concept study demonstrates that passive, noninvasive vibroacoustic biosignatures can detect host response to viral infection in a hospitalized population and supports further testing of this modality in broader populations. These findings support the development of scalable, host-based diagnostics to enable early, agnostic detection of future pandemic threats (ClinicalTrials.gov number: NCT04556149).https://www.mdpi.com/1999-4915/17/7/943host-responsevibroacoustics biosignatureinaudible vibrations and audible soundinfrasonictensegritymechanotransduction |
| spellingShingle | Kelly E. Dooley Michael Morimoto Piotr Kaszuba Margaret Krasne Gigi Liu Edward Fuchs Peter Rexelius Jerry Swan Krzysztof Krawiec Kevin Hammond Stuart C. Ray Ryan Hafen Andreas Schuh Nelson L. Shasha Jumbe Evidence Generation for a Host-Response Biosignature of Respiratory Disease Viruses host-response vibroacoustics biosignature inaudible vibrations and audible sound infrasonic tensegrity mechanotransduction |
| title | Evidence Generation for a Host-Response Biosignature of Respiratory Disease |
| title_full | Evidence Generation for a Host-Response Biosignature of Respiratory Disease |
| title_fullStr | Evidence Generation for a Host-Response Biosignature of Respiratory Disease |
| title_full_unstemmed | Evidence Generation for a Host-Response Biosignature of Respiratory Disease |
| title_short | Evidence Generation for a Host-Response Biosignature of Respiratory Disease |
| title_sort | evidence generation for a host response biosignature of respiratory disease |
| topic | host-response vibroacoustics biosignature inaudible vibrations and audible sound infrasonic tensegrity mechanotransduction |
| url | https://www.mdpi.com/1999-4915/17/7/943 |
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