Clinical Chemistry and Laboratory Medicine (CCLM)

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.

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.

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.

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.

Background: Epigenetic deregulation is a defining feature of leukemia, and DNA methylation and chromatin accessibility profiles encode the cell of origin, lineage and clinical behaviour of the disease. Artificial intelligence (AI) and machine learning (ML) can extract predictive patterns from these high-dimensional data, but the published models have not been systematically appraised. We systematically reviewed AI models built on epigenomic data in leukemia for diagnosis, prognosis, relapse and treatment-response prediction, and measurable residual disease (MRD) detection. Methods: This review followed PRISMA 2020 and the SWiM guideline. PubMed was searched on 7 October 2026 with three structured queries, supplemented by a broader PubMed query, citation searching and web searching. Eligible studies were peer-reviewed primary studies that applied an ML or deep-learning algorithm to DNA methylation, chromatin-accessibility or histone-modification data from human leukemia samples and reported a quantitative diagnostic, prognostic, response or MRD outcome. Risk of bias and applicability were assessed with PROBAST, informed by PROBAST+AI. Owing to heterogeneity, results were synthesized narratively and in structured tables, without meta-analysis. Results: Of 68 unique database records and 32 reports from other sources, 30 studies (2010-2026; 22 since 2020) were included: 16 in acute myeloid leukemia, 4 in acute lymphoblastic leukemia, 2 spanning acute leukemia lineages, 3 in juvenile myelomonocytic leukemia and 5 in chronic lymphocytic leukemia; 10 were pediatric. DNA methylation was the input in 28 studies and chromatin accessibility in 2; no eligible study modelled histone marks. Tree ensembles (n = 9) and support vector machines (n = 5) were the most frequent algorithms. Diagnostic classifiers reported accuracy, concordance or F1 scores of 0.87-1.00, and two classifiers were applied to nanopore sequencing data, one returning acute leukemia subtype calls within about 2 hours. Prognostic epigenomic scores were associated with survival (reported hazard ratios 2.36-10.82), but discrimination fell on transport to independent cohorts (C-index 0.677 internally vs 0.529 in one external cohort). Fifteen studies validated models on an external epigenomic cohort, one prospectively. Overall risk of bias was high in 21 studies, unclear in 8 and low in 1, driven by the analysis domain (small samples, absent calibration, possible data leakage). Calibration was reported in 3 studies and code was available for 11. Conclusions: Epigenomic AI models classify leukemia subtypes with high reported accuracy and can deliver same-day diagnoses, and methylation-based scores add prognostic information. However, the evidence is dominated by retrospective, high-risk-of-bias development studies with limited external validation and almost no prospective or impact evaluation. Prospective, multi-center validation with calibrated, transparently reported models is required before clinical adoption.

Background. Nigrostriatal dopaminergic dysfunction, a biological anchor of Parkinson's disease (PD), is routinely assessed using single-photon emission computed tomography of the dopamine transporter (DaT-SPECT). Dopa decarboxylase (DDC) in cerebrospinal fluid (CSF) is a promising biomarker of dopaminergic dysfunction, but its association with DaT-SPECT has to be demonstrated. Objectives. To investigate the association of CSF DDC with DaT-SPECT. Methods. We first analysed proteomic and DaT-SPECT data from the Parkinson's Progression Markers Initiative (PPMI), including PD (n=891), prodromal PD (n=867) and healthy controls (n=161). We then verified this association in a single-center clinical cohort (UNIPG), including PD/Dementia with Lewy Bodies (DLB) (n=78), atypical parkinsonisms (AP, n=11), other neurodegenerative diseases (OND, n=25) and controls (CTRL, n=40). Here, CSF DDC was measured by a novel fully automated quantitative immunoassay. Results. In PPMI, among 129 proteins assessed, DDC showed the strongest association with DaT-SPECT ({beta} = -0.132, FDR = 6.92 x 10-33). Across PPMI and UNIPG cohorts, DDC correlated with striatal binding ratio (SBR) ({rho} = -0.377, P < 0.001; {rho} = -0.466, P < 0.001, respectively) and accurately discriminated positive from negative scans (AUC 0.811, 95% CI 0.762-0.859; AUC 0.870, 95% CI 0.777-0.963, respectively). In PPMI prodromal cases, higher DDC predicted faster motor progression. In UNIPG, DDC distinguished PD/DLB and AP from OND and CTRL groups (AUC 0.830, 95% CI 0.764-0.896), independently of dopaminergic treatment. Conclusion. CSF DDC reflects nigrostriatal dopaminergic dysfunction, representing a potential quantitative biochemical alternative to DaT-SPECT that might be readily implemented via automated immunoassays.

Background. Persistent SARS-CoV-2 antigen is a leading hypothesis for post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID. Impaired Fc-mediated antiviral antibody function could contribute to inefficient clearance of infected cells and viral antigen despite preserved antibody levels. Therefore, we tested whether PASC is associated with altered Fc{gamma} receptor-mediated antibody function and differences in IgG1 Fc fucosylation, a major regulator of Fc effector function. Methods. Serum samples from RECOVER-Adult participants with prior SARS-CoV-2 infection (99 PASC, 139 non-PASC) were analyzed for anti-SARS-CoV-2 spike receptor-binding domain (RBD) IgG and subclasses, Fc{gamma}RIIIa- and Fc{gamma}RIIa-mediated reporter activity as surrogate measures of antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP), anti-RBD IgG1 Fc glycosylation, and circulating fucose-modifying enzymes. Results. Despite higher anti-RBD IgG and IgG1 abundance, PASC sera showed lower ADCC- and ADCP-associated Fc{gamma}R reporter activity, including after normalization for antibody abundance. Anti-RBD IgG1 showed greater fucosylation in PASC samples (30 PASC, 34 non-PASC). Serum FUCA2 was lower in PASC samples (24 PASC, 34 non-PASC), whereas FUCA1 and soluble CD16a levels did not differ between the two groups. Conclusion. PASC is associated with impaired antiviral IgG Fc function, increased IgG1 Fc fucosylation, and reduced circulating FUCA2, identifying a glycoimmune axis that may contribute to impaired Fc-dependent antigen clearance and represents a potentially targetable pathway in Long COVID.

Current biobanking guidelines require in-person blood collection and immediate processing for Alzheimer's disease (AD) biomarker assessment. We recently reported that the P100 blood collection tubes containing protease inhibitors improve protein biomarker stability over delayed centrifugation periods. However, whether this comparability extends beyond a small set of biomarkers to the broad panels enabled by high-plex platforms has not been established. Here, we performed a head-to-head comparison of 127 neurodegeneration-related biomarkers in paired P100 vs. standard ethylenediaminetetraacetic acid (EDTA) plasma samples from n = 43 community-dwelling older adult participants using the NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) proteomic technology. The NULISA assay demonstrated excellent technical reproducibility (intra- and inter-plate coefficients of variation [CVs] <10%). Biomarker levels in P100 and EDTA tubes were strongly correlated, with mean and median Spearman coefficients exceeding rho = 0.8. Six conventional AD biomarkers showed strong P100-EDTA correlations (rho > 0.8) with minimal mean differences and no significant proportional bias in Bland-Altman analysis, including plasma A42, GFAP, MAPT, NEFL, p-tau181, and p-tau231, while p-tau217 showed a P100-EDTA correlation of rho = 0.57. Two biomarker ratios (A{beta}42/A{beta}40 and p-tau217/A{beta}42) also showed minimal bias with no proportional bias detected. Receiver operating characteristic analysis using amyloid status, assessed with positron emission tomography in a subset of participants, showed comparable classification performance between the two tube types, with classification concordance up to 92% and no significant DeLong differences for most (80%) biomarkers. These findings indicate that, on the NULISA platform, P100 tubes yield analytical and classification performance comparable to conventional EDTA tubes, supporting their use for blood-based neurodegeneration biomarker research where immediate sample processing is not feasible.

Objective: The principal diagnostic test for rheumatoid arthritis (RA) relies on the detection of anti-cyclic citrullinated peptide (CCP) antibodies. The CCP test exhibits moderate sensitivity and specificity but does not adequately detect a high proportion of cases or effectively distinguish RA from related conditions such as systemic lupus erythematosus (SLE). To discover improved diagnostic biomarkers for RA, we performed a comprehensive proteomic analysis of antibody-bound antigens present in RA, SLE, and healthy control (HC) sera and identified proteins associated with transforming growth factor-beta-1 (TGF-beta-1) signaling and assessed the sensitivity and specificity of antibodies against these antigens in comparison to CCP. Methods: Total antibodies were immunoprecipitated from pooled sera of HC individuals (n=3) and patients with clinically diagnosed RA (n=3) or SLE (n=3). Antibody-bound antigens were identified using multidimensional protein identification technology (MudPIT) shotgun proteomics. Candidate antigens associated with TGF-beta-1 signaling were selected for evaluation, and corresponding serum autoantibodies were assessed for their ability to classify RA using a POC lateral-flow immunochromatographic format. Results: Proteins present in antibody immunoprecipitations included TGF-beta-1-induced protein (TGFBI), thrombospondin 1 (TSP1), serum amyloid A1 (SAA), and fibronectin (FN). POC assays using these candidates, as well as TGF-beta-1 itself, as capture antigens were used to detect corresponding serum autoantibodies in 107 subjects with clinically diagnosed RA, 42 subjects with other rheumatic diseases (non-RA), and 121 HC subjects. Receiver operating characteristic curves were constructed using RA and HC groups to assess diagnostic performance, and optimal cutpoints identified using the Youden Index (maximizing the sum of sensitivity and specificity). CCP test results were determined using previously established thresholds. Using an analysis et of 169 subjects in the HC and RA groups who had values for TGFBI, SAA, and CCP, TGFBI autoantibodies achieved a sensitivity of 82%, specificity of 98.8%, positive likelihood ratio (PLR) of 65.6, and NLR of 0.2. SAA autoantibodies achieved a sensitivity of 92.1%, specificity of 85%, PLR of 6.1, and NLR of 0.1. Both outperformed CCP (sensitivity of 68.5%, specificity of 95%, PLR of 13.7, and NLR of 0.3). Using an analysis set of 120 subjects in the RA and non-RA groups who had values for TGFBI, SAA, and CCP, TGFBI and SAA autoantibodies exhibited sensitivities and specificities of 82 and 92.1% and 74.2 and 45.2%, respectively, compared to 68.5 and 74.2% for CCP. Autoantibodies to TGF-beta-1, TSP1, and FN exhibited only modest diagnostic performance. Conclusion: RA sera contain autoantibodies targeting multiple proteins associated with the TGF-beta-1 signaling pathway that is implicated in inflammatory processes in RA. Among the candidates tested, TGFBI and SAA autoantibodies exhibited the highest diagnostic performance in a POC lateral-flow format, supporting their potential utility as biomarkers for RA diagnosis. These findings also support further investigation of the TGF-beta-1 signaling pathway as a potential therapeutic target in RA.

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.

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.

Background: Relapsing-remitting multiple sclerosis (RRMS) often progresses to secondary-progressive MS (SPMS), but diagnosis is typically retrospective, delaying potential interventions. Existing rule-based and machine learning approaches often lack prospective validation and provide no uncertainty estimates, limiting clinical adoption. Objective: To prospectively evaluate a conformal prediction (CP)-enabled random forest model for reproducing clinician-recorded disease course labels in the Swedish Multiple Sclerosis Registry (SMSReg) and compare its classifications with the Lorscheider criteria. Methods: The CP model was trained and calibrated on SMSReg-2022 (10,787 individuals, 85,461 hospital visits) and then validated on SMSReg-2025 (15,719 individuals, 45,077 hospital visits; not included in the SMSReg-2022 training/calibration/testing datasets). Performance was assessed at the hospital visit and individual levels and compared with the Lorscheider criteria. Results: The model maintained good calibration on the prospective dataset. At the per-hospital visit-level, the model accuracies were 92% (RRMS) and 95% (SPMS). At the individual level, sensitivity was 98%, specificity 89%, and F1-score 0.75. With clinician-recorded labels as reference, the model showed higher sensitivity than the Lorscheider criteria (98% vs. 71%), more transition assignments (90% vs. 57%), and higher F1-score (0.76 vs. 0.69). Conclusion: The CP-enabled model prospectively reproduced clinician-recorded disease course labels with calibrated uncertainty, supporting harmonization and decision support.

Importance: Blood biomarkers for Alzheimer disease(AD), Parkinson disease(PD), and amyotrophic lateral sclerosis(ALS) are typically developed as disease-specific single-analyte tests. Whether one shared plasma panel can support disease-selective classification remains unclear. Objective: To test whether the Olink Target 48 Neurodegeneration plasma panel, analyzed with disease-specific Elastic Net models, can discriminate AD, PD, and ALS in a heterogeneous neurological cohort. Design, Setting, and Participants: Single-center diagnostic cohort study at Keio University Hospital, Tokyo, Japan. After quality control and one consent withdrawal, 302 participants were analyzed(healthy controls n = 62; AD continuum n = 93; PD n = 39; ALS n = 27; other neurological disorders n = 81) and allocated by diagnosis-stratified sampling into a discovery training set(n = 211) and a locked hold-out test set(n = 91). Exposures: Plasma normalized protein expression from 42 assays retained after protein quality control. Plasma DOPA decarboxylase was excluded a priori from all classifiers because blood levels primarily reflect dopaminergic treatment exposure. Main Outcomes and Measures: Hold-out receiver operating characteristic area under the curve(AUC). Sensitivity and specificity at a training-set Youden threshold locked for Test. Results: Hold-out AUCs were 0.974(95% CI, 0.942-0.995) for AD, 0.926(0.820-1.000) for PD, and 0.995(0.980-1.000) for ALS. At locked Youden cutoffs, sensitivity was 0.929 (95% CI, 0.765-0.991) and specificity was 0.921 (95% CI, 0.824-0.974) for AD, 0.833 (95% CI, 0.516-0.979) and 0.975 (95% CI, 0.912-0.997) for PD, and 1.000 (95% CI, 0.631-1.000) and 0.940 (95% CI, 0.865-0.980) for ALS. Conclusions and Relevance: A single plasma neurodegeneration panel supported disease-selective classification of AD, PD, and ALS in internal validation. Multicenter external validation is needed before clinical application.

Background: The Central Brain Tumor Registry of the United States (CBTRUS) uses a histology grouping model based on the World Health Organization (WHO) to classify cancer registry records for clinically relevant statistical reporting. Methods: CBTRUS, together with its consulting neuropathologists, reviewed and aligned its histology groupings with the 2021 WHO Classification update. Obsolete histology nomenclature along with their International Classification of Diseases for Oncology 3rd edition (ICD-O-3) codes were identified, currently included histologies were reclassified as appropriate, and new codes found in 2021 WHO were incorporated. An evaluation was conducted of the frequency of histology codes affected in the reclassification process, as well as incidence and survival pre- and post- reclassification of CBTRUS Tumor Type Groupings. Results: After review of the CBTRUS grouping scheme, 205 codes were reclassified and 2 new codes were incorporated. Tumor type groups most affected were those classified as glioma. Reclassification resulted in 167,038 of 491,804 (34%) cases having a grouping reclassification or reporting change, indicating that the 2021 revision of the WHO Classification has significantly impacted the collection and reporting of primary b CNS tumors. Conclusion: This work demonstrates the need to be responsive to changes in classification and coding for statistics to reflect real-world clinical use. This will require collaboration from all parties to have the ability to reconcile clinical practices and surveillance requirements.

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.

Background: Treponema pallidum quantitative polymerase chain reaction (qPCR) positivity and cycle threshold (Ct) by rapid plasma reagin (RPR), disease stage, and other characteristics are poorly characterized in routine-care specimens, leaving it unclear when qPCR of such specimens is informative for diagnosis. Methods: This Japanese multicenter cross-sectional study explored qPCR positivity and Ct by these characteristics. Nonblood specimens collected during routine care underwent qPCR at a central laboratory for this study. Bayesian logistic and normal regressions modeled positivity and mean Ct of TP0574 and polA with stage-specific continuous automated RPR terms, patient characteristics, collection site, and facility random intercepts. Estimates were standardized over the analyzed specimens. Population-group predictions were instead averaged within each group. Results: Among 154 nonblood specimens from 145 patients (71.7% non-MSM men and women) at 14 facilities, 109 (70.8%) were qPCR positive. At RPR 1, adjusted positivity was 72.2% (95% credible interval, 60.9%--82.9%) in primary and 43.8% (16.7%--69.9%) in secondary syphilis. Positivity showed a modest downward trend with RPR in primary but increased in secondary syphilis, reflecting a stage-by-RPR interaction (secondary-to-primary ratio of odds ratios, 1.78; 1.25--2.65). Predicted positivity showed an upward trend with RPR values among MSM; adjusted positivity was relatively high for genital/urethral/urinary and anal/perianal/rectal specimens. Adjusted mean Ct increased with RPR in primary and followed an inverted-U pattern in secondary syphilis. Conclusions: In primary syphilis, nonblood qPCR positivity was high even at low RPR. When serologic findings remain inconclusive in this stage, qPCR is highly recommended for securing an early diagnosis.

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.

BackgroundPost-acute COVID-19 syndrome (PACS) can cause persistent disability involving fatigue, post-exertional malaise, neurocognitive symptoms, and autonomic manifestations. A clinically overlapping condition has been reported following COVID-19 vaccination and termed post-acute COVID-19 vaccination syndrome (PACVS), but its underlying mechanisms remain poorly understood. We investigated whether immune dysregulation, autoreactivity, antigen persistence, and viral reactivation--mechanisms implicated in PACS--are also present in PACVS. MethodsThis cross-sectional study included 28 PACS patients, 29 PACVS patients, and 16 convalescent controls (CCG). Clinical manifestations were assessed using ME/CFS-related symptom questionnaires and the Bell Score. T-cell phenotypes and T cells reactive to SARS-CoV-2 Spike, {beta}1/{beta}2-adrenergic receptor, and M3/M4 muscarinic receptor antigens were analyzed by multicolor flow cytometry. Circulating Spike protein and GPCR autoantibodies were quantified by ELISA, and EBV load by qPCR. ResultsPACS and PACVS showed substantial clinical overlap, although several symptoms were more severe in PACS. GPCR autoantibody profiles were comparable between patient groups, while {beta}2-adrenergic receptor autoantibodies were increased compared with CCG. Only three of 57 conventional immunophenotyping parameters differed between PACS and PACVS in unadjusted analyses, whereas both groups showed multiple alterations compared with CCG. Circulating Spike protein was detected in both patient groups, with the highest prevalence in PACVS. {beta}1/{beta}2- and M3/M4-reactive CD4+ T cells were increased, whereas Spike-specific CD4+ T cells were reduced compared with CCG. EBV reactivation was rare. ConclusionsPACS and PACVS exhibit substantial clinical and immunological overlap, including GPCR-directed autoreactivity and altered receptor-reactive T-cell responses. These shared immune-mediated features suggest overlapping pathophysiological mechanisms and warrant investigation of whether therapeutic strategies currently explored for PACS may also be relevant to PACVS.

Background: To determine the crude and age-adjusted point prevalence of motor neuron disease (MND) and its phenotypes (classical amyotrophic lateral sclerosis [ALS], primary lateral sclerosis [PLS] and progressive muscular atrophy [PMA]) using a comprehensive multi-source case ascertainment strategy. Methods: Cross-sectional population-based study with a prevalence date of 1 January 2025. Cases were identified through five complementary regional data sources and confirmed by clinical review against the revised El Escorial and Gold Coast criteria. Cases were classified by phenotype as ALS, PLS or PMA. The reference population was the population aged 15 years or older in Valencian Community (n=4,433,999). Crude prevalence was calculated per 100,000 with 95% Poisson confidence intervals; age-adjusted rates were obtained by direct standardisation to the 2013 European Standard Population. Results: A total of 374 confirmed MND cases were identified. The overall crude prevalence of MND was 8.43 per 100,000 (95% CI: 7.60-9.29) and the age-adjusted prevalence was 7.85 per 100,000 (95% CI: 7.05-8.65). By phenotype, ALS accounted for 325 cases (86.9%; crude prevalence 7.33 per 100,000), PLS for 26 (7.0%; 0.59 per 100,000) and PMA for 23 (6.1%; 0.52 per 100,000). Prevalence was consistent across provinces. Mean age was 65.9 years, the male-to-female ratio was 1.37:1, and 83.4% were receiving riluzole. Conclusions: This is the first comprehensive, multi-source population-based estimate of MND prevalence and its subtypes in the Valencian Community. The estimate is higher than the previous single-unit figure and consistent with recent European data, underscoring the value of multi-source ascertainment.

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.