Audio Summary April 2026

This is the April 2026 issue of clinical chemistry, volume 72, issue 4.

Quantum Machine Learning and Data Re-Uploading Evaluation on Benchmark and Laboratory Medicine

Data Sets by Thomas J. S. Durant at all. Quantum Machine Learning may offer advantages for

biomedical data analysis, but its practical performance remains uncertain. This study found that the

quantum data re-uploading algorithm performs well on low-dimensional data sets, showing potential

for early research applications and laboratory medicine. However, its effectiveness declines

with higher-dimensional data, which has more independent variables. Even after optimization,

it underperforms compared to non-linear classical machine learning models. These findings highlight

the need for further development in quantum algorithms and hardware to realize the full potential

of quantum approaches in biomedical research. Discovery of an artificial intelligence label

feedback loop, how the success of a clinically implemented artificial intelligence algorithm has

created an unforeseen challenge to algorithm retraining by Patrick L. Day at all.

Artificial intelligence or AI augmented laboratory tests can enhance quality,

efficiency, staff satisfaction, and cost effectiveness in laboratory medicine. Retraining

these models with additional data has the potential to further improve the performance of these

tests. However, there is a paucity of literature surrounding the challenges associated with real

world model retraining. This study reports an AI label feedback loop discovered during the

retraining of a clinically developed kidney stone composition test. Continuous human annotation and

robust validation strategies can help attenuate this risk.

Towards clinical integration of deep learning-based classification of urinary sediment particles from

digital microscopy images, a prospective study by Stelianos Musla at all. This study evaluated a

deep learning method for automated classification of urinary sediment particles using digital microscopy

images. An efficient net-based model was trained on annotated data from varied collection schemes

and a tool was developed for laboratory integration. The model achieved high accuracy of around 97%

in controlled tests, but lower performance in perspective evaluation. Interpretability and

calibration analyses investigated robustness and failure modes, offering insights for integrating

artificial intelligence tools into urine microscopy workflows.

Development of a novel liquid chromatography coupled to multiple reaction monitoring,

or LC-MRM assay, for the quantification of neurofilament-light chain and cerebral spinal fluid

and comparison with ultra-sensitive amino assay, a step towards standardization,

by Salome copens at all. To ensure reliable clinical implementation of the neurofilament-light chain

test, a key biomarker for neurodegeneration, standardized measurement procedures, and calibrators

is required. This study presents a validated immunoprecipitation liquid chromatography

mass spectrometry assay using an assay traceable calibrator and compares it with luma pulse and

somoa platforms to assess agreement and bias. Analysis of cerebral spinal fluid samples and pools

by the immuno and mass spectrometry assays were performed. Following the development and validation

of the new SI traceable method, there was close correlation with both available immunosays,

but biases were identified, reinforcing the need for standardization of this test's measurement.

Establishing reference values in healthy participants for the Cardiactroponin T-High Sensitivity Gen 6 assay

ref T-6 Global Reference Study by Laurie B. Daniels at all. In this article, the authors determined 99th

percentile upper reference limits or URLs for the new Cardiactroponin T-High Sensitivity Gen 6 assay

in a prospectively collected global healthy reference range cohort or ref T-6. Sex-specific 99th

percentile URLs were 18 nanograms per liter for females and 32 nanograms per liter for males.

The uniform 99th percentile URL was 27 nanograms per liter. These findings met the International

Federation for Clinical Chemistry and Laboratory Medicine criteria to be designated as a Cardiactroponin T-High

Sensitivity Assay. 89.7% of results were above the limit of detection and the coefficient of

variation was well below 10% at sex-specific and uniform 99th percentile URLs. Going forward,

the 99th percentile URLs determined in ref T-6 will be validated in a clinical performance study.

6.Color Multiplex Digital PCR assays for comprehensive screening and identification

of multiple driver mutations associated with pancreatic carcinogenesis by Chio Maeda at all.

In this article, the authors described the development of two 6-color digital PCR assays,

PlexScreen DPCR for broad mutation screening and PlexID DPCR for identifying 14-specific

K-RAS and G-NAS variants. Upon using synthetic DNA, cell lines and clinical samples, both assays

exhibited high sensitivity, limit of detection 0.02 to 0.05% and strong concordance with standard

methods. The 6-color system exhibited significantly improved a multiplexing efficiency

compared to conventional two-color platforms. These assays represent scalable, cost-effective

tools for early pancreatic cancer detection with the potential for expansion to broader genomic

applications.