Clinical Chemistry

BackgroundCirculating tumor associated cell (CTAC) detection-based multi-cancer early detection (MCED) strategies may be hindered by the rarity of CTACs among millions of peripheral blood nucleated cells (PBNCs). We developed an advanced U-Net-based encoder-decoder model for pixel-level CTAC discrimination that integrates attention-gated skip connections to preserve morphological and fluorescence details. MethodsModel suitability was explored in an initial cohort of asymptomatic individuals (n = 428) and patients with advanced solid tumors (n = 354). A case-control study assessed clinical performance in therapy-naive stage I/II cancer patients (n = 185), individuals with benign conditions (n = 129), and asymptomatic individuals (n = 111). The model was then validated across four prospective studies on distinct populations: recurrent cancer cases with low tumor burden (n = 224); patients with solid tumors in the peri-operative setting (n = 17); suspected cancer cases (n = 259); and asymptomatic individuals (n = 7,183), respectively. All studies used blinded peripheral blood specimens from which PBNCs were isolated, stained for EpCAM / Hoechst 33342, and imaged. Ground truth annotations were established via pathologist review. The U-Net pipeline encoded spatial information in the images via convolutional and pooling layers and generated pixel-wise segmentation masks to identify CTACs. In all studies, sensitivity was based on CTAC detection rate in cancer specimens and CTAC undetectability rate in specimens from healthy asymptomatic individuals or those with benign conditions ResultsIn the exploratory study, the model had 90.68% (95% CI: 87.16%, 93.50%) sensitivity and 99.53% (95% CI: 98.32%, 99.94%) specificity. In the case-control cohort, the model had 88.65% sensitivity (95% CI: 83.17%, 92.83%), 78.95% (95% CI: 71.03%, 85.53%) specificity in benign conditions, and >99.9% specificity in asymptomatic individuals. Among the four prospective studies, the model had: (a) 91.96% (95% CI: 87.60%, 95.17%) sensitivity in pretreated patients with low tumor burden; (b) 100% sensitivity in pre-surgery specimens, and 29.41% sensitivity in post-surgery specimens; (c) 96.34% PPV (95% CI: 93.22%, 98.05%) and a 32.35% NPV (95% CI: 25.58%, 39.95%) for diagnostic triaging; and, (d)11% PPV (95% CI: 31.72%, 53.24%) and 99.97% NPV (95% CI: 99.90%, 99.99%) for MCED in healthy asymptomatic individuals. ConclusionsThe attention-enhanced U-Net achieved robust, generalizable performance for CTAC-detection in case-control and prospective cohorts, supporting its clinical utility for accurate cancer detection.

One of the current workhorses of next-generation sequencing in clinical molecular diagnostics laboratories for profiling somatic mutations in tumours are amplicon-based targeted sequencing panels. Many open-source somatic variant callers are available; however, their use in clinical applications remains under explored. Therefore, we integrated outputs of six variant callers (FreeBayes, MuTect2, Pisces, Platypus, VarDict and VarScan) into a Snakemake pipeline and evaluated tumour-only data from the HD789 commercial reference standard sequenced in triplicate on three different sequencing runs using the Illumina AmpliSeq Focus panel on MiSeq and NextSeq 2000. A 1:4 dilution sample was sequenced for evaluating limits of variant detection. The called variants were analysed along depth, allele frequency, and other sequencing metrics. The variant callers were evaluated by their level of concordance and performance on known somatic variants. FreeBayes consistently called the largest number of somatic variants in each sample but also included more potential artifacts. Overall, FreeBayes, VarScan, MuTect2, and Pisces had the best performance on HD789 data.

Plasma cell-free DNA (cfDNA) is a central analyte in liquid biopsy applications spanning prenatal testing, oncology, and epigenomic profiling. To minimize contamination by high-molecular-weight genomic DNA (gDNA) released from nucleated blood cells, standard pre-analytical workflows typically mandate a double-centrifugation protocol prior to cfDNA extraction. This requirement has limited the use of many existing plasma biorepositories that were prepared using only a single low-speed centrifugation step. In this study, we evaluated whether single-spun plasma is sufficient for accurate cfDNA analysis when samples are processed under controlled conditions. Using paired single- and double-spun plasma aliquots derived from the same early-pregnancy maternal blood samples collected in EDTA tubes, we performed whole-genome DNA methylation sequencing and assessed cfDNA integrity across multiple orthogonal dimensions. These included cell-type proportion deconvolution using large and small DNA methylation reference signatures, CpG-level methylation rate estimation with explicit variance modeling, beta-binomial-corrected correlation analyses across libraries, cfDNA fragment length profiling, and genotype-based fetal fraction estimation. Across all analyses, we found no evidence that a second high-speed centrifugation step improved accuracy, reduced technical variability, or enhanced analytical fidelity. Cell-type proportion estimates and CpG-level methylation rates were statistically indistinguishable between single- and double-spun plasma, fragment length distributions were nearly identical, and fetal fraction estimates showed near-perfect concordance. Together, these results demonstrate that a single low-speed centrifugation step is sufficient for high-fidelity cfDNA methylation, fragmentomic, and genotyping analyses. Our findings support the expanded use of legacy single-spun plasma collections for liquid biopsy research and assay development and motivate a re-evaluation of rigid double-centrifugation requirements in cfDNA workflows.

BackgroundWhole-genome sequencing (WGS) has improved the diagnosis of rare genetic disorders, yet interpretation of non-coding variants that affect splicing remains challenging. In silico predictions alone are insufficient, and short-read RNA sequencing may fail to capture complex or low-abundance splicing events. Targeted amplicon-based long-read RNA sequencing (Amp-LRS) offers a cost-effective approach for functional validation of candidate splice-altering variants. MethodsWe applied Amp-LRS to five patients with neurological disorders (central nervous system, peripheral nervous system, or muscle) harbouring candidate non-coding variants predicted to alter splicing. RNA was extracted from fibroblasts or peripheral blood, and full-length transcript amplicons were sequenced using Oxford Nanopore Technologies. Nonsense-mediated decay (NMD) inhibition was performed on fibroblast cultures using cycloheximide. ResultsAmp-LRS validated all five candidate variants, including intronic and UTR variants in POLR3A, OPA1, PYROXD1, GDAP1, and SPG11. Aberrant splicing events included exon skipping, intron retention, cryptic splice site activation, and pseudoexon inclusion, often resulting in frameshifts and premature termination codons. For POLR3A and OPA1, multiple abnormal isoforms arose from single variants, highlighting the complexity of splicing disruption. Some pathogenic effects were detectable only in a minority of reads and variably enriched by NMD inhibition, consistent with being hypomorphic. The approach was successfully applied using accessible tissues and enabled multiplexed sequencing at low per-sample cost. ConclusionsAmp-LRS is a sensitive, versatile, and cost-effective method for functional assessment of non-coding splice-altering variants identified by WGS. By enabling full-length transcript analysis from accessible tissues, this approach improves interpretation of variants of uncertain significance and could enhance molecular diagnosis in rare neurological diseases.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Latent tuberculosis infection (LTBI) remains a significant barrier to global TB control and elimination efforts. The QuantiFERON-TB Gold (QFT) assay is commonly used for the diagnosis of LTBI. However, blood collected in QFT tubes is seldom utilized for molecular and genetic analysis due to the presence of heparin and a dense gel barrier that hinders efficient DNA extraction. To address this limitation, we aimed to develop a method for directly isolating high-quality DNA from blood in QFT tubes, eliminating the need for additional blood sampling and enabling their use in both diagnostic and molecular workflows. In this study, DNA was extracted from blood in EDTA and QFT tubes using a hybrid approach that combined manual lysis with three commercial kits: Thermo Scientific GeneJET, QIAamp DNA Blood Kit, and FavorPrep Blood Genomic DNA Extraction Kit. DNA concentration and purity were measured with a Multiskan SkyHigh Microplate Spectrophotometer, while integrity was assessed through agarose gel electrophoresis. Two nucleic acid amplification techniques (NAATs), ARMS-PCR and whole exome sequencing (WES) were performed to validate applicability of extracted DNA for molecular biology applications. We did not find any differences in the quantity, quality, or application of PCR or sequencing for DNA extracted from EDTA or QFT tubes. The extracted DNA from both EDTA and QFT tubes exhibited A260/280 ratios of 1.7-1.9 and concentrations ranging from 4.9 to 118.5 {micro}g/mL, indicating an adequate yield and purity. Intact genomic DNA and PCR product bands on agarose gel indicated suitability for downstream applications. Additionally, WES produced 6.47-8.71 GB of data per sample, with 42.8-57.7 M reads and GC content between 49.29% and 52.54%. Sequencing metrics were consistently strong, with Q20 values exceeding 98.6% and Q30 values above 95%. Our study presents an optimized and reproducible protocol for extracting high-quality DNA from QFT tubes, producing DNA suitable for both PCR and sequencing technologies. This protocol provides a cost-effective and practical strategy to integrate LTBI diagnosis with genomic research, particularly beneficial in resource-limited settings. This study introduces a novel analytical workflow applicable to diagnostic laboratory settings, enabling the integration of routine LTBI immunodiagnostic testing with downstream genomic analysis. The approach supports improved utilization of clinical specimens in laboratory medicine and may facilitate future biomarker and precision diagnostics research.

Nicking Loop is a PCR-free targeted library preparation method that combines conversion of linear target DNA into circular single-stranded DNA (CssDNA) library with early sample indexing in a single step. The resulting CssDNA libraries can be either directly sequenced or optionally amplified, offering maximum flexibility across sequencing applications. This study demonstrates the compatibility of Nicking Loop circular libraries with a MGIs DNBSEQ platform. Compatibility was evaluated against established linear Nicking Loop libraries sequenced on Illumina MiSeq platform. Using synthetic reference samples with defined variant allele frequencies, Nicking Loop method demonstrated matching performance across both library formats and sequencing platforms. Key quality metrics, including unique molecular identifier (UMI) distributions, error profiles and VAF detection, were all highly consistent. Both library types generated over 97% singleton UMIs, indicating uniform template sampling, and VAF measurements were strongly concordant across platforms (Spearmans {rho} = 0.939). Collectively, these findings demonstrate that Nicking Loop method is directly applicable to circular NGS platforms, such as DNBSEQ, strongly supporting its use as a platform-agnostic library preparation strategy for targeted sequencing applications.

Current clinical management of multiple myeloma (MM) relies on bone marrow (BM) biopsies for minimal residual disease (MRD) assessment. While BM biopsies are the gold standard, their invasive nature and potential to miss extramedullary or patchy disease necessitate sensitive, non-invasive liquid biopsy platforms. In this study, we evaluated the analytical performance of the CellSearch CMMC assay to determine its utility for deep-MRD monitoring. Using a standard 4 mL whole blood input, the assay achieves a WBC-normalized sensitivity of 2.45 x 10-7, supported by a limit of quantitation of 5 cells per run. Given this high analytical sensitivity, the assay provides a robust negative predictive value, rendering false-negative findings highly unlikely in populations with detectable peripheral disease. These findings characterize the CellSearch CMMC assay as a highly sensitive, analytically validated platform for non-invasive deep-MRD level longitudinal surveillance monitoring. When integrated into a clinical workflow that accounts for its specificity profile, the platform offers a patient-friendly complement to serial BM biopsies, with the potential to reduce their frequency in appropriate clinical contexts.

PurposeVentilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with high morbidity and mortality. Early identification of patients at risk for developing VAP could enable timely diagnostics and intervention. However, current clinical tools are limited in their ability to detect early physiologic signals preceding VAP onset. We aimed to build supervised machine learning models to predict short term onset of VAP. MethodsWe analyzed electronic health record data from a prospective observational cohort of ICU patients, where VAP was adjudicated using a standardized published protocol by a panel of critical care physicians. Clinical features (including vital signs, ventilator settings, laboratory values, and support devices) were extracted for each patient-ICU-day. We explored unsupervised clustering to characterize feature dynamics associated with VAP onset. We built multiple machine learning models across different prediction windows (3, 5, 7 days before VAP). We examined model performance in two external cohorts, MIMIC-IV and secondary analysis of the AMIKINHAL trial. Results were evaluated with discrimination metrics such as AUROC. ResultsThe internal cohort included 507 patients with BAL-confirmed diagnoses: 261 developed VAP and 246 did not have VAP. Visualization using clustering identified distinct physiologic states enriched for VAP-labeled days. The best-performing model achieved an AUROC of 0.866 in predicting VAP up to seven days before clinical diagnosis. Temporal model probability trajectories showed rising model confidence in the days leading up to VAP. On external validation in MIMIC-IV, the best model achieved an AUROC of 0.817 for forecasting VAP within five days. There was low feature overlap with the AMIKINHAL trial data, leading to poor model performance. Feature analysis revealed that platelet count, positive end-expiratory pressure (PEEP), ventilator duration, and inflammatory markers were key drivers of model predictions. ConclusionsMachine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.

Objective: To develop a workflow that transforms electronic health record data into machine learning-ready features for molecular endotype assignment and to evaluate whether clinician-informed feature engineering improves model performance and interpretability. Materials and Methods: We developed parallel clinician-informed and clinician-agnostic feature engineering pipelines to prepare raw EHR data from mechanically ventilated patients with respiratory failure. Molecular endotype labels derived from paired deep lung and blood profiling of subjects with acute lung injury were used to train candidate machine learning classifiers. Champion models from each pipeline were compared on predefined performance metrics. Results: Bayesian network classifiers were the top-performing models in both pipelines. The clinician-informed pipeline generated fewer features than the clinician-agnostic pipeline (645 vs 1,127) and produced a lower misclassification rate in the final Bayesian network model (0.047 vs 0.14). In an independent cohort of subjects with acute lung injury, the clinician-informed model better distinguished corticosteroid-responsive from non-responsive subgroups. Discussion: Clinical context improved feature engineering efficiency, model interpretability, and classification performance. These findings support the integration of domain expertise into machine learning workflows intended for critical care implementation. Conclusions: Clinician-informed feature engineering can simplify machine learning models while improving performance and preserving clinical relevance. AI tools developed for healthcare should incorporate subject matter expertise early in the feature engineering and analytic workflow.

Blood-based biomarkers have emerged as a promising tool for the detection and monitoring of neurodegenerative diseases such as Alzheimers disease (AD), yet broad implementation of ultrasensitive protein quantification remains constrained by reliance on specialized instrumentation and centralized laboratory infrastructure. Here we present SPLASH (Solid Phase Ligation Assay with Single wasH), an ultrasensitive proximity ligation assay platform that achieves sub-pg/mL sensitivity using only standard benchtop qPCR equipment. We developed five assays targeting Alzheimers disease biomarkers - pTau-217, A{beta}1-40, A{beta}1-42, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - with limits of detection ranging from 0.0005 to 0.119 pg/mL. Direct comparison with Simoa demonstrated high concordance (R2 = 0.95) for plasma pTau-217 quantification across AD-positive and AD-negative samples. We further established compatibility with dried plasma spot samples, enabling decentralized collection and quantitation without cold-chain storage. A multiplexed five-analyte panel was applied to 69 plasma samples, revealing heterogeneous biomarker profiles consistent with AD-associated patterns. By eliminating dependencies on proprietary instrumentation, SPLASH facilitates broad implementation of ultrasensitive protein quantification for neurodegenerative disease research and diagnostics.

Small extracellular vesicles (sEV) are membrane-enclosed nanoparticles found in body fluids that carry molecular cargo from their cells of origin. Their stability and disease-associated molecular signatures make them promising targets for the development of non-, or minimally invasive liquid biopsies, yet scalable approaches enabling single-vesicle quantification of sEV while resolving their heterogeneity remain limited. Here, we present PICO (Protein Interaction Coupling), a reference-free quantitative assay adapted for sensitive multiplex profiling of individual intact vesicles. PICO detects vesicle markers by requiring colocalization of two or more copies of the same protein or of distinct proteins (e.g., CD9 or CD9/CD63) on individual vesicles, using DNA-barcoded antibodies and digital PCR (dPCR) for quantitative readout. We demonstrate that this unique architecture of the assay provides high specificity by distinguishing EV-bound proteins from soluble counterparts, and can be adapted to target either surface-exposed or intravesicular biomarkers. PICO requires minimal sample input (1 {micro}l) and no specialized instrumentation beyond standard digital PCR. In a head-to-head comparison with nano-flow cytometry, PICO achieved a comparable limit of detection for sEV subpopulations. Profiling sEV isolates from blood for canonical markers (CD9, CD63 and CD81) and HER2 demonstrates precise, high-resolution quantification of sEV subpopulations in complex clinical samples and supports integration of scalable single-EV analysis into research and diagnostic workflows.

Bacterial and Viral infections have identical symptoms, making it difficult to diagnose based on visible symptoms. This results in the overprescription of antibiotics, even in cases of viral infections, to avoid missing bacterial infections that could progress into a more severe condition and/or sepsis. This diagnostic gap contributes to the overuse of antimicrobials. To address the issue, a deployable diagnostic test has been developed and evaluated that can help differentiate between bacterial and viral infections. The approach is centred on three crucial biological markers: CD64 expression on leukocytes to detect bacterial infections, CD169 expression on leukocytes to detect viral infections and total leukocyte count as a general health indicator of the patient. ABxSure comprises a cartridge for sample processing, a device with in-built pneumatics, and an optical reader for quantifying biomarkers from blood. The ABxSure performance was tested and compared with a commercial plate reader and flow cytometer, yielding a promising correlation of 0.89. This comprehensive test will potentially provide clinicians with valuable information for effective and timely treatment.

The 2024 dengue epidemic in Brazil-the largest arboviral emergency in the country's history-exposed critical gaps in the reliability of molecular diagnostics across its national public health laboratory network. Quality control (QC) of RT-qPCR assays performed by geographically dispersed Central Public Health Laboratories (LACENs) is essential to ensure the accuracy of epidemiological surveillance and clinical management. We conducted a multicenter QC evaluation of 3,192 complete RT-qPCR runs (19,152 datapoints) for dengue virus serotypes 1-4 (DENV1-4), Zika virus (ZIKV), and Chikungunya virus (CHIKV) across 15 LACENs over one epidemic year. An automated R-based bioinformatic pipeline applied hierarchical clustering (AGNES and DIANA), principal component analysis (PCA), linear and quadratic discriminant analysis (LDA/QDA), Shewhart and XmR control charts, process capability analysis, ANOVA, Baker's gamma permutation testing, and PVClust bootstrap clustering to positive-control cycle threshold (CT) value datasets. Median CT values for DENV4 positive controls ranged from 26.3 to 30.5 across laboratories, representing an approximately 16-fold difference in measured RNA quantity. PCA explained 54.1%-100% of total variance on PC1 across viral targets. Baker's gamma permutation tests confirmed significant concordance between AGNES and DIANA hierarchies across all six viral targets. LDA achieved 37.7% and QDA 49.1% cross-validated accuracy in laboratory-of-origin classification. PVClust bootstrap clustering identified DENV2+DENV4 (approximately unbiased probability, AU = 90) as the most analytically coherent serotype pair. ANOVA confirmed significant operator effects on ZIKV CT values (F = 8.799, df = 23), with regression coefficients for specific operators reaching beta; = +4.01 cycles-equivalent to an approximately 16-fold inferred difference in RNA quantity. Extreme outlier CT values signaled data integrity failures requiring immediate corrective action. The integrated multivariate QC framework substantially outperformed univariate Westgard-rule monitoring. Operator-specific CT deviations of up to four cycles carry direct consequences for clinical classification of borderline specimens. The automated R-based pipeline is operationally feasible in low-resource public health networks and provides a replicable model for arboviral diagnostic QC governance during epidemic emergencies.

BackgroundSepsis is a major public health challenge, and reliable biomarkers are essential for distinguishing sepsis from other conditions. Neutrophil Gelatinase-Associated Lipocalin (Neutrophil gelatinase-associated lipocalin (NGAL)) has shown promise as a diagnostic marker due to its role in the immune response. This study evaluates plasma NGAL as a diagnostic tool at the time of ICU admission. MethodsWe analysed plasma NGAL and C-reactive protein (CRP) levels in 4732 adult patients admitted to four ICUs between 2015 and 2018. All patients were retrospectively screened for Sepsis-3 criteria at ICU admission. The discriminative performance of NGAL and CRP for sepsis was assessed using receiver operating characteristic (ROC) analysis, with NGAL levels adjusted for Chronic kidney disease (CKD) and age. Patients were stratified by renal function. ResultsPlasma NGAL levels were significantly higher in septic patients (p<0.001). For the whole cohort, NGAL alone yielded an Area under the curve (AUC) of 0.67 (Confidence interval (CI) 0.66-0.69), CRP yielded an AUC of 0.72 (CI 0.71-0.73, p<0.001), and combining NGAL with CRP nominally improved discriminative performance (AUC 0.74 vs 0.72, p<0.001). Stratified analyses indicated that NGAL, together with CRP, significantly outperformed CRP alone in patients with no kidney injury and those with Acute Kidney Injury (AKI) only. In contrast, differences were not significant in patients with CKD only or CKD and AKI. ConclusionIn this large cohort, NGAL showed modest discrimination for sepsis, with a nominal improvement when combined with CRP. These findings do not indicate that NGAL meaningfully improves sepsis diagnosis in the ICU.

Whole-genome sequencing (WGS) enables comprehensive analysis of tumour genomes, but its use in formalin-fixed paraffin-embedded (FFPE) samples is limited by DNA fragmentation and low yields. Whole-genome amplification (WGA) methods such as multiple displacement amplification (MDA) can boost DNA availability but distort copy-number alteration (CNA) profiles. DNA ligation-mediated MDA (DLMDA) mitigates this bias by reconstituting fragmented templates, yet its performance in FFPE-derived DNA remains uncertain. We compared paired DLMDA pre-amplified (2h, 8h) and non-pre-amplified FFPE prostate tumour samples from 22 archival blocks (5, 15 and 20 years old). DLMDA increased DNA yield by 42- to 86-fold, with global CNA patterns largely preserved. However, DLMDA significantly reduced the number of detected CNA deletions and amplifications. These effects were independent of both block age and reaction time. CNA dropouts were randomly distributed across the genome, indicating that DLMDA does not introduce regional bias. Our results show that DLMDA enables robust DNA yield recovery and avoids false-positive CNA artefacts, but at the cost of reduced CNA sensitivity. While suitable for CNA screening pipelines through WGS, further improvements are required to minimise the false-negative risk and improve the techniques sensitivity for FFPE-based genomics.

PurposeAntibodies to Epstein-Barr virus (EBV) proteins can predict nasopharyngeal carcinoma (NPC) risk. We previously defined a prototype EBNA1 protein panel and multiplex immunoblot assay that distinguishes NPC risk several years pre-diagnosis. Assay throughput and specificity are critical to effectively implement a population-level screening program. Here, we developed a strip test assay - EBNA1 SeroStrip-HT - with an objective to increase throughput and maximize specificity. Experimental DesignEBNA1 full-length (FL) and glycine-alanine repeat deletion mutants (dGAr) were purified from insect and mammalian cells to screen serum IgA/IgG from prospective cohorts in Singapore and Shanghai, China, with known time intervals to NPC diagnosis. Twenty pre-diagnostic sera within 4 years to diagnosis were compared to 96 healthy controls using a nested case-control study design. ResultsIgA to mammalian-derived EBNA1 dGAr achieved 85.0% sensitivity and 94.8% specificity (AUC, 0.939) for NPC status. IgA to insect-derived EBNA1 dGAr showed the same sensitivity (85.0%) and similar specificity (93.8%) (AUC, 0.941). IgA to insect-derived EBNA1 FL had a higher 90% sensitivity, but lower 91.7% specificity (AUC, 0.940). Combining EBNA1 FL and dGAr results showed that subjects positive for both proteins had a 243.67 odds ratio for NPC incidence compared to double-negative scores. ConclusionThis study demonstrated the efficacy of EBNA1 SeroStrip-HT for NPC risk assessment and stratification in high- and intermediate-risk populations, yielding high accuracy and a 12-fold increased throughput over the prototype. The insect system was appropriate for large-scale production of purified EBNA1. Larger, geographically diverse cohorts are warranted to confirm these results, especially in low-incidence populations.

Sequencing the respiratory tract transcriptome has the potential to provide insights into infectious pathogens and the hosts immune response. While DNA-based sequencing is more standard in clinical laboratories due to its stability, RNA assays offer unique advantages. RNA reflects dynamic physiological changes, and for RNA viruses, viral RNA particles directly represent copies of the viral genome, enabling greater diagnostic sensitivity. However, RNAs susceptibility to degradation remains a significant challenge, particularly in RNase-rich specimens like saliva. To address this, we conducted a systematic, combinatorial evaluation of 24 distinct mNGS workflows, crossing eight nucleic acid extraction methods with three RNA-Seq library preparation protocols. Remnant saliva samples (n = 6) were pooled and spiked with MS2 phage as a control. The SARS-CoV-2 virus was spiked into half of the samples, which were extracted using the eight different extraction methods (n = 3) and compared using RNA Integrity Number equivalent (RINe) scores and RNA concentration. The extracted RNA was then processed across the three library construction methods and subjected to short-read sequencing to assess all 24 combinations head-to-head. We compared methods based on viral read recovery and found that RINe and concentration did not correlate with viral detection. The Zymo Quick-RNA Magbead kit and the Tecan Revelo RNA-Seq High-Sensitivity RNA library kit were the extraction and library-preparation kits that yielded the most SARS-CoV-2 reads, respectively. Importantly, our combinatorial analysis revealed that any small variability attributable to different nucleic acid extraction methods was heavily overshadowed by differences in quality attributable to the RNA-Seq library preparation methods. These findings challenge the reliance on conventional RNA quality metrics for clinical metagenomics and underscore the need to redefine extraction quality standards for mNGS applications. IMPORTANCEmNGS is a powerful and unbiased approach towards pathogen detection that has mostly been applied to blood and cerebrospinal fluid samples. However mNGS has recently been applied to more areas including the respiratory pathogen detection space, with potential applications in both in-patient diagnostics and public health surveillance. Saliva samples are an ideal sample type for these use cases since they can be collected non-invasively. However, saliva is also a challenging sample type due to its high RNase activity and often yields low-quality nucleic acid. This study explores the feasibility of using saliva specimens in mNGS with contrived SARS-CoV-2 samples to optimize the combination of two factors: nucleic acid extraction and RNA-seq library preparation. Exploration in this area could enhance the sensitivity of saliva-based mNGS assays, with the goal of future expansion of this specimen type in clinical diagnostics and public health surveillance. Key PointsO_LIThe choice of RNA-Seq library preparation kit has a greater impact on pathogen detection than the nucleic acid extraction method. C_LIO_LIThe combination of Zymo Quick-RNA Magbead extraction kit and TECAN Revelo RNA-Seq High Sensitivity RNA library kit recovered the highest percentage of total SARS-CoV-2 reads. C_LIO_LIRNA quantity and RINe score do not correlate with viral read capture, indicating a need for an alternative metric to assess RNA quality for downstream mNGS clinical diagnostics. C_LI

BackgroundDetecting copy number variations (CNVs) in circulating tumor DNA (ctDNA) is crucial for the companion diagnosis and resistance monitoring of various solid tumors (e.g., NSCLC, Glioblastoma). However, when tumor-derived DNA fractions are extremely low (often <1%), traditional depth-based methods frequently fail due to non-linear sequencing depth fluctuations and probe-specific capture biases inherent to targeted Next-Generation Sequencing (NGS). MethodsWe developed CESAR (CNV Estimation with Segmentation and Anchor Recalibration), a novel computational tool optimized for ultra-sensitive, tumor-only CNV detection in targeted NGS panels. CESAR utilizes Circular Binary Segmentation (CBS) to re-partition target regions based on relative capture efficiency. It then introduces a dynamic "anchor" selection algorithm that identifies a personalized set of genomic segments mirroring the non-linear coverage behavior of each target gene. By minimizing the Coefficient of Variation (CV) through iterative anchor selection, CESAR effectively recalibrates the baseline to suppress technical noise. ResultsValidation using standard DNA reference materials demonstrated that CESAR successfully identified both amplifications (e.g., MET, ERBB2, EGFR) and relative copy number deletions at ultra-low tumor fractions. Notably, CESAR achieved stable detection of focal alterations as subtle as 2.18 copies (a mere 1.09x fold change relative to the diploid baseline), while maintaining zero false positives in control regions. Evaluation across distinct clinical biofluids--36 clinical plasma samples and 41 glioma cerebrospinal fluid (CSF) samples--identified critical, previously undetected CNV events, including subtle ERBB2 gains and distinct MET deletions. Furthermore, comprehensive benchmarking revealed that CESAR consistently outperformed the widely used CNVkit, particularly in suppressing technical variance and resolving ultra-low-level copy number gains that CNVkit failed to distinguish from background noise. ConclusionsCESAR provides a highly stable and sensitive algorithmic framework for tumor-only CNV calling in liquid biopsies, facilitating precise therapeutic decision-making in precision oncology.

Rapid and accurate diagnosis of UTIs remains difficult because culture-based methods are slow and less sensitive. This study evaluates URINN, a metagenomic workflow that detects uropathogens, antibiotic resistance genes, and virulence factors directly from patient urine samples. The optimized protocol was tested on a combined set of 349 clinical urine samples. URINN demonstrated 99% accuracy across all samples and 97% sensitivity for identifying 294 pathogens, including both bacteria and fungi. It predicted antibiotic susceptibility with 91% accuracy across 2099 antibiotics. The method detected pathogens at concentrations as low as 9.3 x 103 CFU/mL and provided results within approximately four hours. Flow cytometry and DNA yield analyses helped establish thresholds to differentiate culture-positive from culture-negative samples, with genome coverage linked to the accuracy of susceptibility predictions for certain species. Virulence profiling revealed that adherence and nutritional factors are crucial for colonization and persistence. Leukocyte counts were comparable between genders, but bacterial loads were higher in females. The catheterized group had significantly higher leukocyte counts, and their urine showed increased cephalosporin resistance. This approach could enhance clinical decision-making, support personalized treatment, and improve the management of complicated UTIs, thereby contributing to better UTI care and antibiotic stewardship.