Clinical Chemistry

Neurologic autoantibody cell-based assay images are difficult to interpret when fluorescence is weak, focal or dominated by background signal. We developed a visual artificial intelligence overlay that transfers detected cell boundaries from the structural channel to the antibody-staining image and evaluated standalone model concordance and its association with reader classifications. The analysis used 2 763 records for model development and 499 held-out records spanning 14 antibody targets. Four readers assessed the held-out records first without assistance and then with the boundary overlay and model category available. The selected model agreed with the reader-derived three-class reference in 85.2% of records, but recall was lowest for weak-positive or equivocal records. Reader agreement increased in the assisted session, and almost all revised ratings moved towards the model category. The combined display may support consistent visual review while also creating a risk of model-directed anchoring; its role as a second-read aid requires independent validation.

Background: Pre-operative triage of adnexal masses with serum CA-125, HE4, and ultrasound (O-RADS) has limited accuracy, contributing to unnecessary surgery. We develop and validate a first of its kind, biomarker panel-free plasma proteomic classifier that distinguishes malignant from benign adnexal masses from a single blood draw, operating directly on discovery-mode mass spectrometry. Methods: In this multicenter prospective observational study, plasma from women with adnexal masses and from healthy controls at a large urban academic center (discovery and four validation cohorts) and an external academic center from another Urban-suburban area (external validation) was analyzed by data-independent-acquisition mass spectrometry (Orbitrap Astral), quantifying >1,000 proteins per sample. Cancerous (malignant, borderline adnexal mass, or metastasis to ovary) versus benign adnexal masses were classified with ADAPT-MS (Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry), which retrains an ElasticNet model for each sample based on the proteins measured. The primary outcome was discrimination (area under the receiver operating characteristic curve [AUC]). Pre-operative O-RADS scores and serum CA-125 were compared and combined with proteomics on matched subsets. Findings: Using a discovery cohort (n=1062), an ADAPT-MS classifier, validated across five independent cohorts (n=623), achieved a summary AUC of 0.835 (cohort range 0.779-0.904). Among patients with pre-operative O-RADS (n=231), proteomics (AUC 0.852) was comparable to O-RADS alone (0.863) and combining them raised discrimination to 0.922. Proteomics outperformed serum CA-125 (0.915 versus 0.777; n=125) and proteome-derived ROMA-like and two-marker surrogates. Biologically, the classification-relevant proteins are enriched for host response proteins rather than tumor cell associated protein changes, comprising ECM remodeling, complement and innate immune response pathways. This explains good detection of tumor with heterogenous pathology and also metastasis to ovary. Interpretation: A single plasma proteomic measurement interpreted using adaptive machine learning discriminates malignant from benign adnexal masses with good internal and external validation, and is complementary to ultrasound, warranting prospective evaluation as a panel-free diagnostic adjunct to imaging.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Sepsis is life-threatening, yet diagnosis remains a major global challenge because existing approaches lack speed and accuracy. Here we show that label-free imaging cytometry of 10 L whole-blood samples by Remoscope rapidly distinguishes patients with sepsis from those with non-infectious critical illness and healthy controls. In a prospective study of 29 critically ill adults, multi-physician adjudication identified sepsis (n=13), non-infectious critical illness (n=12), and indeterminate status (n=4); these patients were compared to healthy controls (n=22). A multiple instance learning (MIL) model pooled unbiased features from thousands of blood images per patient. Optimized for rule-out, the three-class model detected sepsis with 100% sensitivity and 85.3% specificity. Class activation heatmaps, unsupervised single cell embeddings, and morphometrics converged on abnormal leukocyte and erythrocyte morphology, including immature neutrophil-like cells. Label-free whole-blood imaging with MIL detects sepsis-associated hematologic signatures without stains or cold chain, establishing a foundation for rapid critical care diagnostics.

BACKGROUND: The protein 4.1a/4.1b ratio is an intrinsic molecular marker of mean red blood cell (RBC) age resulting from progressive deamidation of protein 4.1b during RBC circulation. Despite its potential for estimating mean RBC age from a single blood sample, its application has been limited by labor-intensive RBC membrane preparation, and its interindividual variability has not been characterized in a large human population. METHODS: We developed a low-volume method for direct RBC membrane isolation from whole blood using saponin permeabilization and differential centrifugation without prior RBC washing. Membrane proteins were separated by SDS-PAGE, and protein 4.1a/4.1b ratios were determined by densitometric analysis with Gaussian peak fitting. Assay conditions, sample-volume requirements, membrane protein recovery, and analytical reproducibility were evaluated. The method was subsequently applied to 114 anonymized residual samples from routine diagnostic testing selected for complete blood count parameters within the corresponding laboratory reference intervals. RESULTS: The optimized procedure used 50 uL of whole blood, 0.06% saponin, and centrifugation at 9000 g, although protein 4.1a/4.1b remained measurable from starting volumes as low as 5 uL. Mean within-subject CVs were 3.90% for electrophoretic/densitometric determination and 5.39% for membrane protein recovery; whole-procedure CVs in 3 subjects were 1.40% to 3.50%. In the population analysis, the mean protein 4.1a/4.1b ratio was 1.428 (SD 0.146), with an observed range of 1.052 to1.799 and a central 95% interval of 1.151 to 1.695. Ratios did not differ significantly between females and males (P=0.246) or among females aged 18 to 50 years, females aged >50 years, and males (P=0.151). The ratio showed modest positive correlations with chronological age in females (Spearman p=0.26, P=0.047) and males (p=0.33, P=0.015), while substantial interindividual variability remained across the age range. CONCLUSIONS: Direct isolation of RBC membranes from small volumes of whole blood enables reproducible determination of the protein 4.1a/4.1b ratio using a simplified workflow. Application of the assay to a comparatively large human population revealed substantial interindividual variation in this molecular marker of mean RBC age that was not explained by sex or chronological age alone. The method provides a practical approach for investigating biological variation in mean RBC age and its potential contribution to laboratory measurements influenced by RBC survival.

Tissue-based assays provide broad screening for neural autoantibodies but remain vulnerable to technical and reader-dependent variability. We evaluated an integrated workflow combining automated 3,3'-diaminobenzidine tissue-based assay processing, digital whole-slide imaging, and interpretable deep learning. Model development used 2,357 retrospective serum and cerebrospinal fluid images referred from more than 80 institutions and processed in a centralized laboratory, with reference labels established by expert-consensus tissue-based assay interpretation. A prospective validation cohort included 200 images from 200 unique patients (154 positive and 46 negative; 100 serum and 100 cerebrospinal fluid). The same images were evaluated by two expert and two novice readers in a fixed-order two-round study, with unaided interpretation followed by artificial-intelligence-assisted interpretation after a washout of at least two weeks. The standalone binary classifier achieved an area under the receiver operating characteristic curve of 0.922, accuracy of 86.5%, sensitivity of 85.7%, and specificity of 89.1% in prospective validation. Pooled reader accuracy was 90.4% in the unaided round and 97.4% in the artificial-intelligence-assisted round (McNemar P < 0.001), with specificity of 82.6% and 99.5%, respectively. Novice-reader accuracy was 83.3% unaided and 96.5% with artificial-intelligence assistance (P < 0.001), approaching unaided expert accuracy of 97.5%. Among tissue-based-assay-positive cases, reader accuracy for cytoplasmic/nuclear antigen-associated versus neural surface antigen-associated staining-pattern subclassification was 88.8% unaided and 94.0% with artificial-intelligence assistance (P < 0.001). These findings support further multicentre, cross-platform evaluation of artificial-intelligence-assisted neural autoantibody tissue interpretation.

Platelet-leukocyte aggregates (PLAs) are a proposed interface between coagulation and immune regulation, but their distribution across leukocyte subsets beyond monocytes and neutrophils, and their behavior in acute disorders of immune regulation, such as sepsis and severe trauma, remain largely undefined. The study of thromboimmune mechanisms in these acute conditions has relied heavily on murine sepsis models, whose species differences in platelet and leukocyte biology limit translation to human disease. Direct measurement in human whole blood is needed yet reported PLA frequencies vary widely because quantification is highly sensitive to sample handling. Here we present a robust, minimally manipulative 27-color spectral flow cytometry protocol that allows simultaneous deep immunophenotyping and classification of PLAs across >40 leukocyte subpopulations. In healthy donors, PLA frequency ranged from 1.7% on memory CD4 T cells to over 50% on monocyte subsets, with monocytes contributing 25% of all PLAs. We also found novel platelet-binding leukocyte populations, including Th9 and Th22 CD4+ T cells. Optimization identified anticoagulant choice, centrifugation conditions, dilution, and sample processing time as key determinants of reproducible measurement; PLA quantification was validated by imaging flow cytometry. In a small validation cohort of sepsis and trauma patients, the assay recapitulated established immunophenotypic features of critical illness, including neutrophilia and myeloid-derived suppressor cells (MDSC) expansion, and quantified PLAs within each subset. This protocol provides a reference framework for studying thromboinflammation and other dysregulated immune response diseases directly in human blood, and a foundation for further studies, defining the mechanisms and clinical relevance of platelet-leukocyte interactions in disease.

Introduction Timely diagnosis of kidney allograft rejection is critical for post-transplant management, yet treatment decisions may need to be made empirically while awaiting the final pathology report. We previously demonstrated that biopsy transport medium (BTM) contains molecular signals reflective of allograft rejection. Building on these findings, we developed a rapid BTM qPCR workflow using a targeted gene panel for early molecular assessment of rejection. Methods Kidney transplant biopsies assessed at Leiden University Medical Center from April 2023 to November 2024 were eligible. A predefined gene panel was quantified in BTM by qPCR, while matched formalin-fixed paraffin-embedded tissues were profiled using Tempo-Seq. Elastic Net, Random Forest, and support vector machine models were developed to classify rejection and predict continuous histological indices directly from the BTM. Results A total of 134 paired BTM samples and biopsy tissues were analyzed. The best-performing BTM gene-panel models demonstrated good discrimination for rejection, with a median AUC of 0.80, and good calibration (median slope, 0.92; median intercept, 0.08). For continuous histological indices, the best-performing BTM gene-panel models achieved R square values of 0.33 for Activity, 0.31 for TCMR/TI, and 0.27 for AMR/MVI. Corresponding biopsy gene-panel models showed similar patterns across diagnostic outcomes. Conclusions The BTM qPCR workflow captures molecular features of kidney allograft rejection and enables prediction of rejection status along the spectrum of continuous activity indices. By providing same-day molecular information while definitive biopsy interpretation is pending, this rapid approach has the potential to complement conventional histopathological diagnostics and support early clinical decision-making.

Introduction: Neoepitopes form the basis of tumor-specific immune responses. Tissue biopsy, the primary source for neoepitope detection, is limited and invasive. Therefore, we aimed to identify neoepitopes by mutanome-guided immunopeptidomics from plasma of cancer patients. Methods: Mass spectrometry (MS) data analysis of HLA ligands from plasma (n = 4) was guided by patient-specific mutanomes of cell-free DNA (cfDNA) from plasma or tumor genomic DNA (tgDNA) from tissue. Matched tumor tissue and healthy donor plasma served as controls. Neoepitopes were validated with synthetic peptides, and immunogenicity was assessed using IFN-gamma ELISpot and intracellular cytokine staining. Results: Wild-type immunopeptidomes from tissue and plasma overlapped by 58%, with 91% of plasma HLA ligands rediscovered in tissue. 13 out of 15 tumor-associated HLA ligands detected in plasma were rediscovered in the matching tissue. However, no neoepitopes in plasma were identified by immunopeptidomics guided by cfDNA mutanomes, likely reflecting the limited overlap between cfDNA and tgDNA mutanomes (15%). Using the tgDNA mutanome as a complementary reference, two neoepitopes were detected in one patient's plasma, albeit at the MS detection limit. Both neoepitopes were also discovered in tissue, along with three tissue-exclusive neoepitopes. Two tissue-exclusive neoepitopes induced antigen-specific T cell responses in healthy donor PBMCs. Conclusion: In summary, plasma immunopeptidomics enables profiling of HLA ligands from wild-type proteins, including TAAs. In principle, neoepitope detection from plasma at the peptide level is feasible, but tissue remains the gold standard for variant calling and neoepitope identification. Improved detection methods may enable minimally invasive approaches in the future.

Blood-based metabolomic profiling has been widely investigated for breast cancer (BC) detection; however, clinical implementation remains limited due to variability in sample handling, analytical reproducibility, and overfitting during statistical analysis. We established a plasma GC/MS metabolomics workflow for discriminating BC from healthy controls (HC) using conventional machine-learning algorithms. Plasma samples (n = 360; BC = 180, HC = 180) were collected prospectively under standardized preanalytical conditions before surgery and the initiation of systematic anticancer therapy and analyzed using a quantitative GC/MS platform with automated derivatization. Feature selection and model development were conducted using three machine-learning (ML) algorithms (Lasso logistic regression (LR), random forest classifier (RFC), and support vector machine (SVM)). A total of 45 metabolite candidate biomarkers were identified, and the optimal number of metabolite features for each algorithm was estimated by a recursive feature elimination (RFE)-based strategy. The best-performing models achieved area under the ROC curve values (AUC) of 0.910 (LR), 0.893 (RFC), and 0.843 (SVM). We selected prioritizing candidate biomarkers consistently expressed across the multi-algorithm pipeline. A bagging ensemble model improved stability (AUC = 0.911) and reduced false-positive predictions in the independent HC dataset. In addition, model stability with respect to false-positive predictions was assessed using an independent HC cohort (n = 15) that was collected at a separate institution. These results indicate that a plasma metabolomics workflow combined with conventional multi-algorithm ML, algorithm-specific feature selection, and independent assessment provides stable discrimination between BC and HC in a moderately sized cohort.

The WHO recently endorsed two commercial culture-free targeted next-generation sequencing (tNGS) assays for detecting drug resistant Mycobacterium tuberculosis. These proprietary tNGS platforms present a black box. We developed an in-house tNGS assay using Oxford Nanopore Technology sequencing to detect an expanded range of resistance mutations directly from clinical specimens. The assay targeted 44 genomic loci; hsp65 for Mycobacterium tuberculosis complex (MTBC) identification; 12 loci for MTBC lineage classification; and 31 drug resistance loci predicted resistance to 16 anti-TB drugs. In total, 98 amplicons were distributed across three PCR pools. Analytical sensitivity was assessed using M. tuberculosis H37Rv, and proof-of-concept performance was evaluated in 63 archived clinical specimen extracts and one real-life sputum specimen in comparison with Illumina whole genome sequencing (WGS). All amplicons were successfully amplified and sequenced from M. tuberculosis H37Rv genomic DNA. At [&ge;]100 genome copies/L, 71.4% of amplicons met [&ge;]20x; mean depth across six replicates, including all loci for rifampicin, ethambutol, pyrazinamide, fluoroquinolone and bedaquiline resistance. In 63 clinical specimens, [&ge;]90% of amplicons achieved a mean depth of [&ge;]20x in specimens with IS6110 PCR cycle threshold (Ct) values [&le;]24. With Ct value [&le;]27 lineage classification was concordant with WGS in 94.7% (18/19) of specimens and drug-susceptibility prediction showed 94.7% (18/19) agreement with both phenotypic and genotypic (WGS) testing. In a single clinical case (Ct 26.5), lineage and resistance-associated mutations were accurately identified directly from sputum. The findings provide new insights into rapid culture-independent tNGS approaches for rapid M. tuberculosis drug resistance detection with lineage assignment.

Chimeric antigen receptor (CAR) T cells are patient-derived T cells engineered to recognize specific antigens on malignant cells, representing a major breakthrough in personalized cancer immunotherapy. While CAR-T cell therapy is an established treatment for relapsed or refractory hematologic malignancies, understanding the rationale and methodology behind its development is essential for training the next generation of oncology researchers. Here, we describe a novel, hands-on small-enrollment laboratory course designed to teach upper-level undergraduate and graduate students about immunotherapies and CAR-T cell technology by paralleling a typical bench-scale development process. Students isolated T cells from porcine peripheral blood mononuclear cells (PBMCs) via immunomagnetic selection, followed by flow cytometry to assess purity, viability, and yield. Students then used pre-engineered B7H3 CAR-T cells to perform co-culture assays with U87 glioblastoma cells to evaluate tumor cell killing efficacy. Cytotoxicity was qualitatively evaluated using immunofluorescence microscopy, while IFN-gamma release was quantified using Enzyme-linked Immunosorbent Assay (ELISA). Student performance was evaluated through electronic laboratory notebooks, discussion questions, a capstone project, and student-generated data. Our results demonstrate students high academic performance across assignments and the successful execution of complex laboratory workflows. Collectively, these findings establish the feasibility and implementation of a research-integrated curriculum providing foundational exposure to immuno-oncology research tools and CAR-T cell production techniques.

Hybrid capture approaches can detect a broad range of pathogens directly from clinical samples with greater sensitivity than untargeted approaches. Clinical adoption has been limited by long turnaround times, complex workflows, and perceived high costs. We iteratively developed and validated a rapid hybrid capture workflow based on Illumina's RPIP protocol (standard workflow). Rapid-RPIP v1 reduced wet-lab time from ~11.4 to ~7.3 h, for a turnaround time (TAT) of <15 h. Rapid-RPIP v2 further reduced wet-lab time by ~0.7 h and used Illumina Turbo Sequencing shortening the sequencing time from ~5.5 to ~2.5 h thus achieving a TAT of <10 h without compromising sensitivity and yielding higher RPKMs for RNA viruses than the standard workflow. This approach could be readily adapted to a broad range of clinical and public health applications.

Abstract Background: In patients with sepsis and pre-existing hypertension, admission findings may not adequately describe the subsequent course of circulatory support and organ dysfunction. We developed and externally validated a model to reassess in-hospital mortality risk after the first 72 hours of intensive care. Methods: This retrospective study used MIMIC-IV for development and eICU-CRD for external validation. Patients had received vasoactive agents and remained in the ICU for at least 72 hours. Static variables were combined with features from six consecutive 12-hour windows. Imputation, feature selection, model fitting, and calibration were performed within training folds during five-fold cross-validation. The final XGBoost model was applied externally without refitting. Evaluation included discrimination, calibration, decision curves, and risk stratification, with an exploratory comparison of static-only and static-plus-dynamic feature sets. Results: The development and external cohorts included 1019 and 1747 patients, with 338 (33.2%) and 516 (29.5%) deaths, respectively. Internal area under the receiver operating characteristic curve (AUC) was 0.752 (95% CI 0.720-0.784). External AUC was 0.713 (95% CI 0.687-0.739), average precision was 0.536, the Brier score was 0.185, and the calibration slope was 0.744. Mortality increased across external risk tertiles from 13.7% to 47.3%. In an exploratory feature-set comparison, the static-plus-dynamic feature set showed a 0.071 higher internal AUC than the static feature set (95% CI 0.042-0.109). Conclusions: Information accumulated during the first 72 hours supported subsequent mortality risk stratification in this selected population. The model retained moderate discrimination externally, supporting further evaluation as an adjunct to clinical reassessment. Local calibration and the clinical consequences of acting on model predictions require evaluation before routine use.

Importance: Phosphorylated tau at threonine 217 (p-tau217) and brain-derived tau (BD-tau) are increasingly used as blood biomarkers of Alzheimer's disease (AD) and related neurological disorders. However, the distributions and biological correlates of elevated plasma p-tau217 and BD-tau in plasma from individuals eligible for blood donation remain unclear. Objective: To estimate the proportions of plasma p-tau217 and BD-tau exceeding exploratory study-specific thresholds, and examine associated inflammatory and transcriptomic features. Design: Cross-sectional study. Setting: Hospital blood bank. Participants: 257 de-identified donor samples were obtained between September 2025 and June 2026. Plasma samples from 30 population reference participants and 20 AD patients were used to derive study-specific thresholds. Complementary transcriptomic analyses were performed in 29 fresh whole blood samples. Main Outcomes and Measures: The primary outcome was the proportion of donor samples exceeding the study-specific ROC-derived Youden threshold. The proportion exceeding the prespecified published p-tau217 threshold was the secondary outcome. Other measures included plasma concentrations of BD-tau, tumor necrosis factor alpha (TNF-alpha), and interleukin 6 (IL-6); correlations among plasma p-tau217, BD-tau and inflammatory cytokines; and associated transcriptomic alterations. Results: Plasma p-tau217 concentrations exceeded the study-specific ROC-derived Youden threshold in 53 of 256 donor samples (21%; 95% CI, 16%-26%) and the published threshold, used for a secondary comparison, in 99 of 256 samples (39%; 95% CI, 33%-45%). Plasma BD-tau concentrations exceeded the study-specific threshold in 89 of 257 samples (35%; 95% CI, 29%-41%). Plasma p-tau217 concentrations were positively associated with plasma TNF-alpha (Kendall's tau = 0.339; P < .001) and IL-6 (Kendall's tau = 0.310; P < .001) concentrations. Exploratory transcriptomic analyses identified gene expression patterns associated with p-tau217 concentrations. Measurement using a second p-tau217 antibody showed the similar findings. Conclusions and Relevance: Plasma p-tau217 and BD-tau concentrations exceeded exploratory study-specific thresholds in a subset of blood-donor samples, and higher p-tau217 concentrations were associated with inflammatory biomarkers and exploratory transcriptomic differences. These findings raise the possibility of elevated circulating tau species in some blood donors, but their clinical significance requires confirmation. Whether elevated circulating p-tau217 in donated blood has biological consequences for transfusion recipients is unknown, but deserves investigation.

INTRODUCTION: We report the development and validation of a novel computational tool to cross-convert plasma pTau217 results from different assays to facilitate recruitment for therapeutic trials and observational studies. METHODS: Participants underwent A{beta} PET imaging and plasma pTau217 measurement on up to three assay platforms across three cohorts. We then developed conversion equations using Deming regression -- collectively named Biomarker Convert -- and evaluated robustness using independent research cohorts. Finally, we modeled the potential impact of pTau217 screening on trial enrollment. RESULTS: While absolute plasma pTau217 concentrations differed, the assays had similar predictive characteristics for A{beta} positivity (LC-MS/MS [C2N], AUC=0.952; Janssen Simoa, AUC=0.946; Lumipulse, AUC=0.929). Biomarker Convert enabled transformation of plasma pTau217 data across assays independent of A{beta} PET and clinical status. Plasma pTau217 reduced A{beta} PET scans in the model screening paradigm by >60%. DISCUSSION: Biomarker Convert equations enable harmonization of plasma pTau217 values from immunoassay and mass spectrometry platforms, supporting direct data comparison.

Non-small-cell lung cancer (NSCLC) diagnosis is challenged owing to the need for reliable, non-invasive biomarkers. Circulating cell-free microRNAs (ccf-miRNAs) are promising candidates, but their low levels and short sequences in blood complicate detection. In this study, a novel nano-hybrid fluorescent array was developed to enable rapid and sensitive identification of NSCLC-related ccf-miRNAs. The array consists of poly(T)-modified fluorescent nano-polystyrene beads for capturing target miRNAs, and fluorophore-labeled, sequence-specific locked nucleic acid (LNA) probes for their detection. This design forms a sandwich structure with the target miRNA. Nano cytometry and fluorescence microscopy assessed both the capture and specificity of detection. The assay was tested on plasma samples for miR-16-5p and U6, evaluating selectivity, sensitivity, and reproducibility. The results showed that the poly(T)-modified beads efficiently captured the target miRNAs, and the LNA probes accurately distinguished the sequences. The assay enabled direct detection of miR-16-5p and U6 from plasma without amplification, demonstrating high selectivity, sensitivity, and reproducibility. Combining enrichment with nano-polystyrene beads and sequence-specific LNA probes addressed the main challenges of ccf-miRNA detection, namely their low abundance and short sequence length. As ccf-miRNAs are linked to NSCLC progression, this method could assist in early diagnosis, disease monitoring, and assessment of treatment response. The target-independent nature of the poly(T) capture layer allows for easy calibration to new miRNAs by changing the LNA probes, rendering it suitable for multiplex detection in clinical settings. However, further preclinical and clinical studies are needed before adoption in routine practice. This nano-hybrid fluorescent array presents a rapid and reliable approach for detecting low-abundance ccf-miRNAs in plasma. Its encouraging performance and flexible design show potential for future application in NSCLC diagnosis and monitoring, subject to additional confirmation.

Quantitative measurement of fetal hemoglobin (HbF) using high-performance liquid chromatography (HPLC) is standard but expensive and widely unavailable in low-income settings. The newer Gazelle method, which employs single-use alkaline electrophoresis cartridges, provides a portable and relatively inexpensive potential alternative. In the current study, we tested 3055 venous blood samples collected from 1780 children with sickle cell anemia who were enrolled into the H-PRIME trial by both Gazelle and the Bio-Rad D-10 HPLC instrument, before and during hydroxyurea treatment. Data reinterpretation was required to quantify accurately high HbF values derived using the D-10. Mean (standard deviation) baseline HbF values by HPLC and Gazelle (N=2042) were 11.4% (SD 5.9%) versus 16.3% (SD 7.5%), respectively, while mean on-treatment HbF values (N=1013) were 18.9% (SD 9.0%) versus 25.4% (SD 11.5%), respectively. While overall agreement was good (Lins concordance 0.76, 95% CI 0.75-0.78), HbF values derived using Gazelle were modestly higher than HPLC values (Bland-Altman agreement mean +5.4%, 95%CI -2.4% to +13.2%), with the difference between measures increasing at higher HbF percentages. With strong concordance and more easily interpretable HbF values, Gazelle represents an attractive and inexpensive alternative to HPLC for HbF quantification in the low-income settings where most patients with sickle cell anemia live.

GeneCapture is developing a novel, point-of-care, rapid AST technique that, when fully automated, can accomplish AST within 2 hours directly from a raw sample, representing a significant advancement in diagnostic capabilities. For this study, a panel of uropathogen isolates was collected from clinical specimens positive for urinary tract infection from various laboratories in the southern US. A selection of these isolates were then spiked into fresh clean-catch urine samples to produce mock samples covering a variety of antibiotic responses. Testing was performed on 20 strains of Escherichia coli, the cause of over 80% of UTIs, and 6 strains each of Klebsiella pneumoniae and Proteus mirabilis, the next most common causes. Each strain was tested against the susceptible, intermediate and resistant (S-I-R) breakpoints of up to 5 appropriate antibiotics including Amoxicillin-Clavulanate, Cefpodoxime, Ciprofloxacin, Nitrofurantoin and Trimethoprim-Sulfamethoxazole. The new method uses a phenotypic assessment of bacterial growth, comparing responses from bacteria treated with antibiotics at S-I-R breakpoint concentrations to untreated controls as the metric for determining susceptibility. Exposure to the drugs for only 90 minutes gave a Categorical Agreement of 98% to gold standard culture results across the resulting 247 bacteria/antibiotic pairs. Testing on separate days of nine of these strains in triplicate gave a reproducibility of 97%. The sequential steps of this novel AST - sample processing, growth, labeling, and counting - can readily be performed on an automated cartridge that is currently under development. The presented work, which took place in a laboratory setting, demonstrates the efficacy of the assay.

Antibiotic resistance is an increasingly serious problem worldwide. Identifying the bacterial species can help guide antibiotic selection and improve patient outcomes. In sepsis, where every minute counts, rapid identification is particularly important, and new approaches are needed. In this study, deep learning models were trained to identify bacterial species from phase-contrast time-lapse images of cells growing in microfluidic traps, without fluorescent labels. We demonstrated this approach in two previous studies using laboratory strains. Here, the previous work is extended to clinical bacterial isolates, evaluating performance on isolates not seen during training. Furthermore, it was tested whether autofluorescence could aid species identification. Two validation sets were acquired in separate experiments from the training data, the first containing clinical-isolate traps with five species (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus aureus and Enterobacter cloacae), imaged every two minutes, and the second containing clinical isolates of two species (E. coli and K. pneumoniae) recovered from spiked donor blood, imaged every minute. Training with the best previous CNN method achieved unsatisfactory results. Therefore, vision transformers were explored, and a self-supervised adaptation procedure was developed for pretrained DINOv3 using a frozen teacher, followed by supervised classification using time-lapse montages. Mean macro-F1 was 93.6 {+/-} 1.3% for isolates precultured in Mueller-Hinton medium using 60 frames (118 minutes) and 87.2 {+/-} 2.7% for isolates precultured in blood using 55 frames (54 minutes) (mean {+/-} sample standard deviation across training seeds). Performance was evaluated over time by increasing the number of frames available to the model during testing, and Fourier-domain filtering approximated reduced numerical aperture. The results are promising and suggest that the aperture can be decreased while retaining good results. However, further evaluation on independent isolates, experiments and patient samples is needed to establish generalization. Together with previous findings, these results bring us closer to rapid image-based diagnostics that could transform the initial treatment of acute bacterial infections by enabling better-informed antibiotic selection.