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A target amplification-free CRISPR biosensor with digital microfluidic processing for microRNA profiling in AI-enhanced pediatric sepsis diagnosis

MicroRNA signatures provide an informative readout of host-response dysregulation in pediatric sepsis, but their clinical translation remains limited by low target abundance and multiplexing requirements. Here, a target amplification-free CRISPR biosensing platform was established. The sensing mechanism couples Cas13a target recognition to promoter release, template reconstruction, and transcription-driven Cas13a reactivation, enabling fluorescence amplification through internal molecular cycling without amplify…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1844· cite & verify
A target amplification-free CRISPR biosensor with digital microfluidic processing for microRNA profiling in AI-enhanced pediatric sepsis diagnosis
Granada - Hospital Doctor Olóriz 3 by Zarateman. CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en

In brief

Researchers built a diagnostic platform that detects four sepsis-associated microRNAs plus a reference control without amplifying the target molecules. A digital microfluidic chip automates the reactions. The resulting profiles were fed into a machine learning model that classifies sepsis and estimates risk in children.

The work matters because microRNA signatures reflect how a child's body responds to sepsis, yet low target abundance and multiplexing needs have slowed clinical use. The evidence is early. Results come from one sample set, and the tracking of disease progression rests on a single representative case, so real-world clinical performance is still unproven.

Main points

  1. The sensing chemistry links Cas13a target recognition to promoter release and transcription-driven reactivation, amplifying the fluorescence signal rather than the target miRNA.
  2. Four biomarkers (miR-15a, miR-21, miR-146a, miR-155) and the control snRNA U44 were detected at attomolar levels, with good specificity and minimal inter-droplet interference.
  3. In 252 clinical samples the model reached a test-set AUC of 0.973 and 92.1% overall accuracy.
  4. The model-derived risk score was associated with the Phoenix Sepsis Score.

The gain

A machine learning model trained on microRNA profiles from an automated CRISPR microfluidic platform separated sepsis from non-sepsis in 252 clinical samples with a test AUC of 0.973.

The rundown

The platform uses an electrowetting-based digital microfluidic chip that compartmentalizes parallel reactions. It automates droplet generation, reaction assembly, isothermal incubation, magnetic separation, and fluorescence readout.

Under optimized conditions, detection reached an expected mean input of 1.67 molecules per reaction. The microRNA profiles then served as input features for machine learning sepsis classification and risk assessment.

Beyond classification, the risk score was linked to the Phoenix Sepsis Score. In one representative longitudinal case it showed preliminary signs of tracking disease evolution. The authors describe the platform as potentially useful for in-vitro diagnosis of pediatric sepsis.

Sources

  1. Peer-reviewedBiosensors and Bioelectronics2026-10-05

The debate