Clinical Chemistry

Overview: SeptiCyte RAPID is an FDA-cleared gene expression test that quantifies host immune response to aid in the diagnosis of sepsis. The test yields a score (the SeptiScore) ranging from 0-15, distributed across four bands (1-4) based on increased likelihood of sepsis. Each band can be characterized by average positive and negative likelihood ratios (LR+, LR- respectively) for the discrimination of sepsis versus the non-infectious systemic inflammatory response syndrome (SIRS). Methods: A retrospective analysis of prospectively collected data from a combined cohort of critically ill patients suspected of sepsis (N=889), recruited across 19 hospitals in the USA and Europe. The analysis quantified the LR+ and LR- parameters as a function of SeptiScore, for discrimination of sepsis vs. SIRS in patients admitted to ICU. Hypotheses: (1) The likelihood ratio (LR) framework provides a clinically useful interpretive approach that complements the previously used SeptiScore banding scheme; (2) Low Band 1 SeptiScores are associated with sufficiently small LR- to support the use of SeptiCyte RAPID as a rule-out test for sepsis; (3) High Band 4 SeptiScores are associated with sufficiently large LR+ to support the use of SeptiCyte RAPID as a rule-in test for sepsis; and (4) SeptiScore-derived LR+ and LR- values can be combined with estimates of pre-test probability (derived from patient characteristics and/or other diagnostic tests) to generate individualized, patient-specific post-test probabilities of sepsis. Results: The SeptiCyte RAPID test demonstrates strong diagnostic performance in distinguishing sepsis from SIRS. The likelihood ratios across different score bands provide clear clinical utility: the median LR+ was 3.26 (range 2.57-4.24) for Band 3, and 6.97 (range 4.35-15.57) for Band 4 providing evidence toward ruling in sepsis at high SeptiScores. Conversely, the median LR- was 0.16 (range 0.14-0.20) for Band 2 and 0.085 (range 0.014-0.16) for Band 1, providing evidence toward ruling out sepsis at low SeptiScores. A higher-resolution analysis of SeptiCyte RAPID performance confirmed these trends by evaluating LR+ and LR- at specific values within each band. The sepsis group was further stratified according to whether patients were classified as blood-culture positive (BC+) or blood culture negative (BC-), and the detailed LR+ and LR- analyses were repeated. A monotonic increase in likelihood ratio with increasing SeptiScore was consistently observed, independent of whether sepsis patients were culture-positive, culture-negative, or unstratified with respect to blood culture status. Conclusion: High SeptiScores have correspondingly high LR+ values, and low SeptiScores have correspondingly low LR- values, both of which may have clinical utility. High likelihood ratios for band 4 SeptiScores, which precede traditional microbiology results, may provide clinicians with early confidence of a sepsis diagnosis and microbiology diagnostic stewardship. Low likelihood ratios for band 1 SeptiScores may prompt clinicians to consider an alternate diagnosis to sepsis. Such results, obtained early in the diagnostic workup process, may lead to fewer missed diagnoses and more efficient use of hospital resources.

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.

ContextPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy often diagnosed at advanced stages due to the lack of early clinical symptoms. DNA methylation alterations arise early in PDAC tumorigenesis and may serve as promising biomarkers for blood-based cancer detection. ObjectiveTo evaluate the performance of EPISEEK, a laboratory-developed blood-based multi-cancer early detection (MCED) assay, for detecting PDAC across disease stages. DesignA retrospective cohort study included 97 patients with stage I-IV PDAC and 201 asymptomatic healthy controls. Sensitivity, specificity, area under the curve (AUC), and stage-specific performance were assessed. EPISEEK-MCED performance was also compared with CA 19-9 alone and in combination with CA 19-9. ResultsEPISEEK-MCED classified 65 of 97 PDAC cases as positive, corresponding to an observed sensitivity of 70.1% (95% CI, 60.3% - 78.3%) at 99.5% specificity. The assay demonstrated strong discrimination between PDAC cases and healthy controls, with an AUC of 0.916 (95% CI, 0.88 - 0.952). Sensitivity increased with advancing stage while remaining substantial in early-stage disease, measuring 53.6% for stage I and 65.1% for stage II PDAC, 100% for stage III and 94.7% for stage IV. Across stages, EPISEEK-MCED outperformed CA 19-9 alone, particularly in early-stage disease. Combined analysis of EPISEEK-MCED and CA 19-9 further improved detection performance, achieving sensitivity of 57.1% and 81.4% for stage I and II, respectively. ConclusionsEPISEEK-MCED demonstrated high specificity and sensitivity for PDAC detection across disease stages, including early-stage disease. Combining EPISEEK-MCED with CA19-9 further improved performance, supporting its clinical utility for PDAC detection.

BackgroundClassification of central nervous system (CNS) tumors has become increasingly complex over the past decade, raising concerns about the availability, feasibility and sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. MethodsWe 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. ResultsNanopore WGS enabled 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 standard-of-care testing, and 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. ConclusionsNanopore WGS 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. Its broad applicability supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Blood-based biomarkers are increasingly used to investigate brain health, but collecting venous blood is difficult in remote and field settings. Capillary microsampling offers a practical alternative, although the ability to delay processing and its agreement with gold-standard venous blood require validation. We evaluated Tasso+, a minimally invasive upper-arm capillary blood collection system, for measuring neurological and host-response biomarkers in plasma and serum during an exercise-based protocol. Sampling occurred before, immediately after, and approximately 24-to-36 hours after exercise; Tasso+ samples were processed with or without a 72-hour room-temperature delay. Tasso+ samples were compared with matched venous blood, and Capitainer SEP10 dried plasma spots were also evaluated, using Quanterix Simoa and Alamar Biosciences NULISAseq CNS panel. Tasso+ enabled reliable measurement of several key biomarkers, including GFAP and NfL, even after delayed processing. These findings support capillary microsampling for neurological biomarker studies where venepuncture is challenging, including field-based research and participant-led remote sampling.

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.

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.

Measles virus remains a significant global health threat, and despite the availability of an effective vaccine, measles cases continue to increase worldwide in recent years. Genomic surveillance has become an essential tool for monitoring virus circulation and investigating outbreaks. Here, we describe a wet-laboratory method for whole-genome sequencing of measles virus using a tiled amplicon approach and Illumina sequencing technology. A previously published Oxford Nanopore-based tiled primer scheme was adapted to include both circulating measles genotypes and for use on the Illumina platform. Two Illumina library preparation kits, Illumina DNA Prep (IDP) and Nextera XT (XT), were evaluated for performance. The IDP kit demonstrated more complete genomes and consistent genome coverage compared with XT. Using quantified reference genomes, the limit of detection was determined to be 10,000 genome copies for genotype B3 and D8. Sequence accuracy was evaluated using previously characterized clinical samples and showed high concordance. This method provides a reliable and sensitive approach for measles virus whole-genome sequencing using Illumina platforms and is suitable for genomic surveillance applications.

Background: Multi-drug resistant Bacterial (MDRB) Infections in the intensive care units (ICUs) substantially elevate patient mortality, prolong hospital stays, and impose heavy healthcare cost burdens. Existing predictive models for ICU-acquired MDRB infection predominantly focus on static admission-risk assessment, lacking the capacity to leverage longitudinal treatment data for dynamic risk re-stratification during the ICU stay. Meanwhile, most models suffer from poor clinical interpretability, overreliance on hard-to-collect biomarkers, or absence of deployable clinical tools, limiting real-world translation. Therefore, there is an urgent need to develop a parsimonious, interpretable tool based on routine cumulative data to guide timely intervention. This study aimed to develop a interpretable model with a web calculator to improve clinical applicability. Methods: In this study, we conducted a retrospective analysis of ICU inpatients at the First Affiliated Hospital of Dali University between January 1, 2023, and January 1, 2026. Using the create Data Partition function in R software (random seed = 42), the dataset was stratified and divided into a training group and a validation group in a 7:3 ratio. Feature selection was performed using the Boruta algorithm to validate variable rationality. A multivariable logistic regression model was constructed and visualized as a nomogram, and its performance was compared with six machine learning algorithms (Random Forest, XG Boost, Neural Network, etc.). Model validation was conducted using receiver operating characteristic curves (ROC), Decision Curve Analysis (DCA), and SHAP value interpretation. Finally, an online R Shiny calculator was developed based on the final model. Results: A total of 3,631 patients were enrolled and divided into a training group (n=2,543) and a validation group (n=1,088) using stratified random sampling. Five independent predictors were identified in the training group, which were hypertension combined with diabetes, antibiotic types, ventilator days, urinary catheter days, and PCT abnormality times. The Logistic regression model achieved an AUC of 0.772 (95%CI: 0.733-0.812) in the validation group, outperforming XG Boost (0.763) and Random Forest (0.703). The model demonstrated excellent calibration (Hosmer-Leme show {chi}{superscript 2} = 1.94, P = 0.9829) and positive net clinical benefit across threshold probabilities of 0%-40%. SHAP analysis aligned with regression-derived variable importance rankings, confirming predictor contributions. An open-access online calculator was successfully deployed (https://dongfangshao666.shinyapps.io/MDR_shiny2/), enabling real-time individualized risk stratification at the bedside. Conclusion: This study developed and validated a dynamic, interpretable multi-drug-resistant bacterial infection risk prediction model requiring only five routinely collected clinical indicators. The model balances robust predictive performance with high transparency, overcoming key limitations of prior tools. The accompanying web calculator supports dynamic risk reassessment throughout the ICU stay, facilitating precise antimicrobial stewardship, targeted infection control interventions, and optimized resource allocation, bridging the gap between statistical modeling and frontline clinical decision-making.

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.

BackgroundPeripheral blood smears (PBS) review is labor-intensive, subjective, and challenging for rare or morphologically heterogeneous cell types in hematologic malignancies. Artificial intelligence (AI) offers a scalable alternative, but broader clinical translation is constrained by annotation burden and limited interpretability. MethodsWe developed an interpretable, annotation-efficient AI framework that learns leukocyte morphology through a two-stage process: label-free representation learning to construct a morphological embedding space, followed by supervised fine-tuning for cell type and morphological attribute classification. The model was trained and evaluated on 5,952 PBS images from cancer patients at MD Anderson Cancer Center, including blast cells, and 17,092 images from public sources. Active learning strategies were assessed to improve label efficiency, and interpretability was examined using saliency and embedding visualization. An interactive web application, HemoSight, was developed to support clinical review. FindingsThe framework achieved a macro-F1 score of 0{middle dot}96 for 9-way leukocyte classification on the internal test split and 0{middle dot}83 on the held-out patient cohort. Active learning substantially reduced annotation requirements, reaching peak performance with only 13{middle dot}3% of available labels and significantly improving learning efficiency across 8 of 9 cell types. The model generalized to classifying 11 leukocyte morphological attributes with a mean F1 score of 85{middle dot}8% and revealed structured morphological landscapes. Saliency maps, embedding visualizations, and the HemoSight application enabled transparent morphological inspection of model predictions, supporting confidence in model behavior and feasibility for clinical integration. InterpretationOur framework enables scalable, annotation-efficient, and interpretable modeling of leukocyte morphology, supporting the integration of AI-assisted PBS review for hematopathology workflows. FundingSeed funding from The University of Texas MD Anderson Cancer Center. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSPeripheral blood smear review is essential for diagnosing and monitoring hematologic malignancies, but manual case review is time-consuming and variable, particularly for rare or abnormal leukocyte types. Automated hematology analyzers are widely used to flag abnormal cells; however, they provide limited morphological insight and often require frequent manual correction, especially in cancer settings where disease and treatment alter cell appearance. Previous artificial intelligence approaches for leukocyte classification have shown promise, but most rely on fully supervised learning, require extensive expert annotation, focus on a limited set of cell types, and frequently exclude diagnostically important rare cells such as blasts. Interpretability is inconsistently addressed, and few studies provide tools that allow clinicians to inspect and interpret model outputs within routine workflows. Added value of this studyThis study introduces an annotation-efficient framework trained on a large collection of peripheral blood smear images, including cancer patient samples with hematopathologist-verified rare cell types such as blasts. The framework learns leukocyte morphology from unlabeled images and adapts to multiple classification tasks with minimal expert labeling. Performance is evaluated on both internal test splits and a held-out patient cohort to provide a realistic estimate of generalization. Iterative, uncertainty-guided annotation substantially reduces labeling requirements while improving learning efficiency across most leukocyte classes. Beyond cell-type classification, the framework is extended to 11 clinically relevant morphological attributes and reveals a structured morphological landscape. These capabilities are integrated into a web application, HemoSight, enabling real-time inference and transparent morphological inspection of predictions within hematopathology workflows. Implications of all the available evidenceAdvancing artificial intelligence for hematology requires methods that reduce expert labeling demands, provide interpretable outputs, and perform reliably across clinically diverse patient samples. This study shows that learning from largely unlabeled data combined with iterative expert annotation can support scalable and flexible modeling of leukocyte morphology for classification tasks. Integrating quantitative predictions and interactive visualization supports the use of artificial intelligence as an assistive tool for diagnostic peripheral blood smear review, with potential to improve efficiency, consistency, and reviewer confidence.

Brain-derived tau (BD-tau) is an emerging blood-based biomarker for neurodegeneration, yet there are currently limited well validated BD-tau assays available for research and clinical use. To enhance access to this vital biomarker for neurological disorders including traumatic brain injury (TBI), we developed a novel blood-based immunoassay for BD-tau on the ultra-sensitive Quanterix HD-X platform using Single Molecule Array technology. Analytical validation assessed dilution linearity, specificity, precision, detection limits, and spike recovery, each recording robust metrics in agreement with international expert recommendations. The assay demonstrated robust validation metrics, achieving between-run stability of 95% when analyzing aliquots from six independent plasma and serum samples across five analytical runs. It also showed strong dilution linearity when diluted four-fold and achieved over 90% recovery when spiked with cerebrospinal fluid. Next, we evaluated the clinical utility of the assay in cohorts of individuals with traumatic brain injury (TBI), where strong performances were recorded whether using the 2-step or 3-step assay formats ({rho}= 0.94; p < 0.0001). Furthermore, plasma BD-tau distinguished samples from TBI patients based on time from injury and severity (AUC=0.93). Plasma BD-tau differentiated between favorable and unfavorable functional outcomes in the acute-severe group. Our findings underscore the significant potential of the BD-tau assay as a biomarker for TBI in the severe phase.

Background: Modern therapy for childhood and adolescent leukemia requires accurate risk classification of genomic subtype. Although short-read next-generation sequencing (NGS)- based approaches provide comprehensive clinical diagnostics in limited, highly resourced settings, they remain expensive, slow, and inaccessible to most children worldwide. Transformative approaches are needed to improve diagnostic classification for leukemia globally. Methods: We simultaneously continued to develop an analytical pipeline NASVar (Nanopore variant calling for adaptive sampling), and conducted a multicenter, type-two hybrid clinical validation study of an Oxford Nanopore Technologies (ONT) adaptive-sampling whole-genome sequencing (asWGS) assay across hospitals with varying diagnostic resources. In preparation for implementation, a global panel developed a leukemia-based standardized gene set and consensus laboratory-developed test (LDT) validation guidelines. Measures of assay effectiveness compared to both conventional and orthogonal NGS methods, where available, were simultaneously collected with data to measure the implementation outcomes of feasibility, fidelity, appropriateness, and cost. Results: All four centers successfully completed the LDT validation, with minimal adaptations required for regulatory compliance. A total of 457 specimens were sequenced (331 B-ALL, 83 AML, 43 T-ALL). For the 210 B-ALL cases with locally resolved genomic subtypes defined by DNA alterations, asWGS was 100% concordant (210/210). Cases locally defined as B-other were resolved via asWGS with disease-defining DNA alterations in 47% (49/105) of cases. An additional 41% (43/105) of locally defined B-other cases were classified by incorporation of DNA methylation, and all 16 B-ALL patient-derived xenograft controls were correct, for a total of 96% (318/331) of all B-ALL cases in the cohort resolved with single assay asWGS. For AML, 97% (56/58) of cases with locally resolved genomic subtypes were identified by automated asWGS analysis, while an additional two cases were identified after targeted manual review. At Indus Hospital in Pakistan, the B-ALL and AML diagnostic genomic subtype yield increased from 28% with local standard of care diagnostic testing, to 84% with asWGS. The cost of reagents and consumables in the United States, assuming pooled three-plexing, was $343/sample. Based on the combined hybrid validation results, all centers are independently preparing for clinical return of results. Conclusions: ONT asWGS was successfully validated as a clinical assay in four diverse hospital settings. As a single, multi-omic platform that delivers value across the continuum of high-resource to resource-limited contexts, the approach offers a disruptive solution to address the global equity gap in cancer diagnostics.

Background: Accurate early identification of sepsis remains a major clinical challenge due to its heterogeneous presentation and overlap of clinical signs with the non-infectious systemic inflammatory response syndrome (SIRS). Timely differentiation is crucial for improving patient outcomes, meeting sepsis bundle requirements and reducing inappropriate antimicrobial use. We hypothesized that clinical and laboratory data available within the first 3 hours of patient presentation could be used to identify patients with sepsis to an actionable level of accuracy, in lieu of traditional microbiology results which would not become available until at least 12-24 hours. Data from two independent studies were used to quantify the diagnostic value of demographic, vital, clinical-laboratory, and microbiological data available at three time points for distinguishing retrospectively diagnosed critically ill patients with either sepsis or non-infectious SIRS. A particular focus of this work was an assessment of the utility of SeptiCyte RAPID (Immunexpress Inc., Seattle, Washington, USA) as an aid to sepsis diagnosis, producing actionable data within 1 hour. Methods: Data from two independent study cohorts were analysed. The 510k cohort consisted of 419 adult patients in intensive care (ICU) (MARS, VENUS, and NEPTUNE trials). The Andalusian cohort consisted of 353 ICU patients from the PANGEA study. Logistic regression models, selected by a greedy search algorithm and validated by repeated cross-validation, were used to determine the contributions of different variables to diagnostic accuracy. Diagnostic performance was quantified by area under the receiver operating characteristic curve (AUC). Results: For the 510k cohort, a baseline AUC of 0.69-0.73 was observed using 5-7 vital and demographic variables assessed immediately upon presentation (time T1). The addition of clinical-laboratory variables, in particular SeptiCyte RAPID, within 1-3 hours post-presentation (time T2) increased the AUC to 0.83-0.85). Finally, the addition of microbiological data 12-24 hours post-presentation (time T3) further improved the AUC to 0.90-0.91. Similar results were obtained for the Andalusian cohort. AUC values at the three time points were as follows: At time T1, AUC = 0.67 based solely on vital signs and demographics; at time T2, AUC = 0.87 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data; at time T3, AUC = 0.93 based on vitals + demographics + SeptiCyte RAPID or other clinical laboratory data + microbiology results). For both cohorts, the most significant variables included temperature, mean arterial pressure, respiratory rate, suspected infection site; SeptiCyte RAPID, procalcitonin, confirmed bacterial infection and positive blood culture confirmation. Conclusions: Accuracy of identification of sepsis increases markedly as demographics and vital signs are supplemented with clinical-laboratory information, and ultimately with microbiological culture results. The fastest improvement occurs within the first 3 hours when laboratory data, and in particular SeptiCyte RAPID results, become available. Integrating rapid host-response testing with SeptiCyte RAPID into time-based diagnostic frameworks may enhance early sepsis recognition, improve antimicrobial stewardship, and support guideline-driven clinical decisions.

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.

Introduction: Blood neurofilament light chain (NfL) is an accessible biomarker of neuroaxonal injury across a broad range of neurological disorders, but its clinical implementation requires robust cross-platform analytical and clinical comparability. The objective of this study was to evaluate the analytical and clinical comparability of plasma NfL measurements using Simoa and Lumipulse across different neurological conditions, by assessing cross-platform agreement and the ability of both assays to distinguish neurological diseases from healthy controls. Paired CSF analyses were performed in a subset of participants to biologically anchor plasma findings to the central compartment. Methods: 383 individuals were included, comprising healthy controls and patients with neurodegenerative conditions, multiple sclerosis and stroke. Plasma NfL was measured in all participants using both Simoa and Lumipulse, with paired CSF analyses in a subset of 92 individuals The Lumipulse testing intermediate precision and between-day repeatability was assessed as by the CLSI EP15. Cross-platform agreement for plasma NfL was evaluated using correlation analyses, Passing-Bablok regression and Bland-Altman analysis. Associations between plasma/CSF NfL concentrations were assessed using Spearman's rank correlation analysis for each platform, separately. Age-adjusted cross-diagnostic differences were evaluated using permutation ANCOVA and multiple linear regression models for each platform, separately. Results: Plasma NfL measured by Simoa and Lumipulse showed strong cross-platform concordance in the whole cohort ({rho}=0.90), with similarly strong concordance observed for CSF NfL in the subset with paired samples ({rho}=0.90). Method-comparison analyses in plasma demonstrated consistent agreement between platforms, with identifiable constant and proportional bias, alongside systematically higher absolute plasma NfL values measured by Lumipulse. Within-platform analyses showed significant correlations between plasma and CSF NfL concentrations ({rho}=0.72 for Simoa; {rho}=0.78 for Lumipulse). Noteworthy, Lumipulse NfL CSF and Blood kits exhibited high precision and analytical accuracy. Across both assays, plasma NfL increased with age and was significantly elevated in patients with neurological disorders compared with healthy controls. Discussion: Simoa and Lumipulse capture a consistent biological signal in plasma across patients with neurological disorders, although their absolute NfL values differ, supporting the use of platform-specific reference ranges in clinical practice.

Nanopore sequencers have the potential to liberate DNA sequencing from centralized core facilities to distributed analytical nodes. Until now, Oxford Nanopore Technologies (ONT) has been the sole manufacturer of a portable nanopore sequencer, but analogous platforms are in production. Nanopore sequencers from Qitan Technology (QT) are widely used in China but have been unavailable outside that nation and lack independent performance testing. Enabled by early access to QTs least expensive sequencer and flow cell, the QNome-3841 and QCell-384, we tested whether they could generate accurate DNA barcodes cost-effectively. In several tests involving amplicon pools from 95 to 9,120 specimens, QT recovered valid DNA barcodes from nearly as many specimens (98%) as ONT. QT sequences had slightly lower fidelity than their ONT counterparts and QT frequently failed to resolve the correct length of G/C homopolymers. However, barcode sequences from the two platforms were nearly indistinguishable after bioinformatic treatment. QTs wash kit performed well, enabling a QCell to sequence eight amplicon pools with zero carryover between runs and minimal degradation of the flow cell. Its ultra-fast protocol allowed library preparation in a single step that could be completed in 15 minutes, but this came at the cost of lower quality data. Once widely available, QT devices will be well-suited for supporting DNA barcode analysis.

Enteric fever caused by Salmonella enterica serovars Typhi and Paratyphi A, B and C remains a major public health burden in endemic regions. Existing molecular assays frequently demonstrate limited specificity due to cross-reactivity with non-typhoidal Salmonella (NTS). In this study, we developed and validated a genomics-informed multiplex PCR assay capable of simultaneously differentiating all four typhoidal Salmonella serovars. A curated dataset of 3,239 Salmonella genomes, including S. Typhi (n=361), S. Paratyphi A (n=453), S. Paratyphi B (n=511), S. Paratyphi C (n=62), and NTS genomes (n=1,853), was used for comparative genomic analysis. Thirty published PCR targets were evaluated in silico, followed by pangenome and SNP analyses to identify discriminatory loci for mismatch amplification mutation assay (MAMA)-based primer design. Candidate primers were validated using in silico PCR, BLASTn analysis, and laboratory testing against a panel of typhoidal Salmonella, clinical NTS isolates, and non-Salmonella bacterial pathogens. In silico evaluation demonstrated substantial cross-reactivity among many published targets, whereas SNP-informed primer design targeting staG (S. Typhi), SPA0152 (S. Paratyphi A), SPAB_03490 (S. Paratyphi B), and SPC_0571 (S. Paratyphi C) achieved predicted specificities of 98-100% while retaining high analytical sensitivity (>97%) across target genomes. Combined with a pan-Salmonella invA target, the multiplex assay precisely identified all target serovars in vitro with minimal cross-reactivity. These findings demonstrate that genomics-informed SNP-based primer design enables reliable multiplex differentiation of typhoidal Salmonella serovars and provides a scalable framework for improving enteric fever diagnosis and surveillance in endemic settings. ImportanceTyphoidal Salmonella serovars remain major causes of enteric fever in endemic regions, yet molecular differentiation from non-typhoidal Salmonella (NTS) remains challenging because of extensive genomic conservation and cross-reactivity of commonly used diagnostic targets. In this study, we combined large-scale comparative genomics of 3,239 Salmonella genomes with SNP-informed primer design to develop a multiplex PCR assay capable of simultaneously differentiating all four typhoidal serovars (S. Typhi, S. Paratyphi A, B, and C) from NTS and other non-Salmonella pathogens. Unlike conventional gene-content-based assays, this approach incorporated lineage-specific SNPs and mismatch amplification strategies to improve specificity while maintaining high analytical sensitivity. In silico evaluation demonstrated high diagnostic performance across diverse global lineages, while in vitro testing confirmed accurate serovar-level discrimination with minimal cross-reactivity. These findings demonstrate the value of population-scale genomics for molecular assay development and provide a scalable framework for improving diagnosis and surveillance of enteric fever in endemic settings.

Background Thalassemia is one of the most common monogenic disorders worldwide, current screening strategies combining hematological testing with molecular assays still carry a risk of missed diagnoses and undesirable efficiency, particularly for complex structural variants and rare mutations. Methods In this prospective double-blind, multicenter cohort study of 3,842 participants (3,362 pregnant women and 480 male partners), we conducted a head-to-head comparison to systematically evaluate the incremental clinical value and detection performance of single-molecule nanopore sequencing in thalassemia (SMITH) against conventional hematological testing and next-generation sequencing (NGS). Findings The overall concordance rate between NGS and SMITH was 98.6% (3789/3842). The discrepant cases (n=53) were directly attributed to the superior detection capabilities of SMITH, which successfully identified complex structural rearrangements-including 45 -globin gene triplications and four HK alleles-that were missed by NGS. Furthermore, SMITH accurately detected four rare variants (c.134_135insT/, c.-22(C>T)/, {beta}N/{beta}c.316-290delinsAGGGCAATAATTT and {beta}3.5 kb deletion/{beta}N ) and resolved ten trans and three cis configurations within the globin gene allele. Clinically, these technical advantages translated to a 9.3% (5/54) increase in the detection rate of high-risk prenatal couples, effectively preventing one birth affected by moderate-to-severe thalassemia. Additionally, SMITH corrected a diagnostic discrepancy in one case (HK vs. -3.7), sparing the couple from an unnecessary invasive procedure. Interpretation Our findings demonstrate that SMITH provides a powerful platform for resolving globin gene rearrangements, detecting rare variants, and enabling direct haplotype phasing. By effectively eliminating diagnostic blind spots, SMITH is expected to become an optimal method for thalassemia prevention programs. Funding This study was supported by Chinese National Natural Science Foundation Projects 81760037 and 82271894.

The current "gold standard" for diagnosing and assessing treatment response is tumor biopsy; however, biopsies are not always feasible, safe or easily repeated during treatment. Utilization of peripheral blood mononuclear cells (PBMCs) as a surrogate for tumor biopsy allows for longitudinal sampling and is a safer, more readily available option. However, collection conditions, sample transfer time across multiple clinical sites, and PBMC processing conditions are external pre-analytical factors that must be understood and controlled to mitigate bias in downstream functional analyses. This study aims to systematically evaluate the pre-analytical variables affecting PBMC integrity and functional immune readouts as a prerequisite for downstream translational biomarker applications. Peripheral blood samples were collected from 80 treatment-naive patients with a diagnosis of head and neck squamous cell carcinoma. Blood was collected in cell preparation tubes (BD Vacutainer(R) CPT), potassium ethylenediaminetetraacetic acid (EDTA), or sodium heparin (SH) tubes and diluted 1:1 with sterile PBS or remained undiluted. PBMCs were processed and cryopreserved immediately or held for 8- and 24-hours before processing. PBMC viability was measured at cryopreservation and upon thawing. CD8+ T cells or natural killer (NK) cells derived from PBMCs were subjected to cytotoxicity assays using flow cytometry. CPT tubes provided lower cell viability and yield at cryopreservation and upon thaw compared to EDTA and SH tubes while dilution had no effect on viability. NK cell cytotoxicity was similar between EDTA and SH tubes irrespective of dilution. However, diluted EDTA tubes resulted in lower T cell cytotoxicity after 24-hour hold. Viability and NK and T cell cytotoxicity were equivalent between cryopreserved PBMCs that were processed immediately or processed after 8- or 24-hour hold. Here we report cryopreservation methods for reproducibility of viable cells that maintain functional immunological capacity even after significant delay in processing allowing flexibility and feasibility for collection from multiple clinical sites for deferred processing.