Clinical Chemistry and Laboratory Medicine (CCLM)

Background Blood biomarkers are increasingly used to support the diagnosis and monitoring of neurodegenerative diseases. However, their interpretation is complicated by physiological determinants, including age, sex, body-mass index, and renal function, and by differences in absolute concentrations between analytical methods. We aimed to develop population-based reference equations allowing individualized interpretation of the main blood biomarkers used in neurology. Methods In this cross-sectional study, we analysed plasma samples from cognitively unimpaired participants selected from the French CONSTANCES and Three-City population-based cohorts. Generalized additive models for location, scale, and shape were used to model neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau 181 (p-tau181), amyloid-{beta}40, amyloid-{beta}42, and their ratios according to age, sex, body-mass index, and renal function. The resulting equations provided individualized expected concentrations, percentiles, and Z-scores. Previously established disease-specific concentrations were converted into Z-score. Cross-calibration equations were developed for NfL measurements across analytical methods and sample matrices. Findings The final reference populations comprised 5123 participants for amyloid biomarkers and p-tau181 and 5122 for NfL and GFAP; median age was 52.3 years and half were women. Between ages 40 and 80 years, expected NfL and GFAP concentrations increased by an average of 2.6% and 2.2% per year, respectively. Renal function, body-mass index, and sex had additional biomarker-specific effects. Application of the equations to clinical cohorts preserved distinct disease-associated profiles: NfL Z-scores were increased across disorders characterised by neuroaxonal injury, whereas p-tau181 and GFAP showed its greatest increase in Alzheimer disease. NfL cross-calibration equations showed excellent agreement between methods and matrices, with intraclass correlation coefficients greater than 0.90. Interpretation This population-based multibiomarker framework enables blood biomarker concentrations to be interpreted relative to individuals with similar physiological characteristics. Publicly available equations, reference curves, and standardized Z-scores could improve individualized interpretation and comparability across biomarkers, laboratories, and clinical populations.

Chronic Wasting Disease (CWD) is a fatal transmissible spongiform encephalopathy (TSE) that is confined to cervids (deer, moose, elk, and reindeer) but shares key properties with human neurodegenerative conditions such as Alzheimers, Parkinsons, Huntingtons disease and frontal-temporal dementia. CWD and other TSEs are caused by the misfolded prion protein (PrP). Although the identification of diagnostic and prognostic biomarkers at all stages of disease progression is becoming exceedingly critical as CWD continues to increase in prevalence, accurate antemortem testing techniques are extremely limited. This study made use of cervidized transgenic mice (mice carrying the cervid PrP) that recapitulate CWD in various disease stages and investigated the utility of neurological biomarkers and neurobehavioral manifestations for CWD detection. Neurofilament light chain (NFL), glial fibrillary acidic protein (GFAP), and total Tau (t-Tau) were assessed under the hypothesis that combined biomarker signatures might more reliably reflect CWD-related neurodegeneration and disease progression. Analyses at 90, 132, 174, and 230 days post-CWD inoculation show distinct biomarker elevation, with all three biomarkers significantly elevated in the CWD animals by 132 days post-inoculation. To our knowledge, this is the first demonstration that these three plasma biomarkers are useful not only for detecting CWD, but also for identifying it at early antemortem stages of disease. Novel phenotypes were also revealed by comprehensive phenotypic profiling, including rigid tail elevation, increased grip strength, and impaired coordination, to lend further support to plasma biomarker data indicating neurologic impairment associated with brain pathology. Ultimately, the goal is to improve antemortem, non-invasive CWD detection methods to enable earlier detection and assist with disease management.

BackgroundSensitive biomarkers that objectively stage Huntington disease (HD) are needed to improve participant stratification and facilitate the enrichment of clinical trials with biologically and clinically homogeneous populations. The HDClarity study, an international longitudinal biofluid collection initiative for HD, provides a unique resource for large-scale proteomic profiling of matched CSF and serum samples spanning the disease spectrum. Here, we leveraged baseline proteomic data from HDClarity to characterize protein signatures associated with HD stage and clinical severity, compare measurements across analytical platforms and biofluid compartments, and identify candidate multi-protein panels for disease staging. MethodsBaseline proteomic data generated using Olink Explore ([~]3,000 proteins) and SomaScan v4.1 ([~]7,000 proteins) were analyzed in matched CSF and serum samples from 315 HD gene-expansion carriers and 92 non-HD controls. A total of 2,119 proteins overlapped between Olink and SomaScan, enabling assessment of cross-platform concordance, while CSF-serum relationships were evaluated using all available protein measurements within each assay. Covariate-adjusted linear regression models were used to assess disease stage-associated differences in protein abundance, while partial correlation analyses evaluated relationships between protein abundance, clinical severity in HD gene-expansion carriers, and estimated years to disease onset in premanifest participants. A nested machine-learning pipeline incorporating univariate feature ranking, penalized regression-based feature selection, and repeated cross- validation was used to derive compact multi-protein classifiers for HD staging. ResultsCross-platform and CSF-serum correlations were highly protein-dependent, with some analytes showing strong concordance and others exhibiting weak or inverse relationships. These findings highlight substantial heterogeneity in biomarker behaviour across analytical platforms and biofluids. Adjusted models identified both known HD-associated markers (NEFL, GFAP, CHI3L1) and less well-characterized proteins in CSF and serum whose baseline abundance differed across HD-Integrated Staging System (HD-ISS) and clinical stages. Partial correlation analyses revealed additional candidate biomarkers associated with clinical severity and estimated time to disease onset. Machine-learning models derived compact CSF and serum protein panels that accurately classified participants across HD-ISS stages 0 and 1, as well as the transition from premanifest to early manifest disease. ConclusionsThis study provides the first large-scale orthogonal comparison of matched CSF and serum proteomes in HDClarity, establishing robust baseline proteomic signatures across the HD continuum. Our findings demonstrate the importance of considering both analytical platform and biofluid when interpreting protein biomarkers and identify compact protein panels with potential utility for objective disease staging, patient stratification, and clinical trial enrichment in HD. Trial RegistrationNot applicable. One Sentence SummaryCaron et al. analyzed matched baseline CSF and serum proteomic data from the HDClarity study generated using two orthogonal proteomic platforms, identifying reproducible multi-protein panels capable of staging and stratifying Huntington disease.

Myasthenia gravis (MG) is an autoimmune disease caused by pathogenic autoantibodies against proteins at the neuromuscular junction (NMJ). The diagnosis and clinical management of MG patients largely relies on the detection of antigen-specific autoantibodies targeting acetylcholine receptor (AChR) or muscle-specific kinase (MuSK). Yet a subset of patients remains seronegative for known MG autoantibodies, highlighting a critical need for alternative approaches to identify pathogenic NMJ antibodies. We established a new human in vitro model of the NMJ based on primary human muscle cells that recapitulates key features of the NMJ: differentiation to myotubes, expression of key NMJ proteins and formation of postsynaptic AChR clusters in response to agrin stimulation. The model allows new insights into myogenesis and genetic muscle diseases, and the new muscle cell-based assay (CBA) detected autoantibodies in sera from patients with AChR- and MuSK-positive MG with 96.43% sensitivity and 100% specificity, while healthy control sera showed no reactivity. Incubation with patient sera significantly reduced AChR clustering compared to controls, demonstrating functional pathogenic effects. Thus, we established a physiologically relevant human NMJ model that enables detection and functional characterization of neuromuscular autoantibodies. This novel approach addresses a key limitation of current antigen-specific diagnostics and provides a method for improved detection and characterization of MG antibodies, independent of antigen specificity. One Sentence SummaryWe established a postsynaptic human in vitro neuromuscular junction model to assess binding and pathogenicity of MG autoantibodies. Key messagesO_ST_ABSWhat is already known on this topic?C_ST_ABSCurrent diagnosis of myasthenia gravis (MG) relies largely on the detection of antigen-specific autoantibodies against AChR and MuSK, leaving a clinically relevant subset of patients seronegative. What are the new findings?We established a physiologically relevant human in vitro neuromuscular junction model based on primary human muscle cells and developed a novel muscle cell-based assay (CBA) for the detection of neuromuscular autoantibodies. How might this impact on clinical practice or future developments?The CBA detected autoantibodies in patients with AChR- or MuSK-positive MG with high sensitivity and specificity and demonstrated their functional pathogenic effects on AChR clustering. This antigen-independent approach may improve the detection and functional characterization of MG autoantibodies, particularly in patients who are seronegative in current diagnostic assays. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743478v1_ufig1.gif" ALT="Figure 1000"> View larger version (38K): org.highwire.dtl.DTLVardef@18ed154org.highwire.dtl.DTLVardef@151036corg.highwire.dtl.DTLVardef@1b7ab34org.highwire.dtl.DTLVardef@1490fe9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Blood culture-negative infective endocarditis (BCNIE) poses a significant diagnostic challenge. This study evaluated a multimodal diagnostic algorithm combining serological and molecular methods at the Argentine National Reference Laboratory. Methods: A prospective analysis was conducted on 53 consecutive patients with suspected BCNIE referred between January 2019 and December 2024. The diagnostic workflow included indirect immunofluorescence for Bartonella spp. and Coxiella burnetii, species-specific PCR for Bartonella spp. and Tropheryma whipplei, and broad-range 16S rRNA PCR with Sanger sequencing on available blood and valvular tissue specimens. Results: An etiological diagnosis was established in 17 of 53 patients (32.1%). Bartonella spp. was the predominant pathogen (47.1%; 8/17), followed by T. whipplei (35.3%; 6/17) and Streptococcus spp. (17.6%; 3/17). All Bartonella cases were initially detected via serology, with molecular confirmation achieved exclusively through valvular tissue analysis. Conclusions: Implementing a standardized multimodal diagnostic algorithm significantly enhances etiological yields in BCNIE. The findings emphasize the complementary value of frontline serology and targeted molecular testing, highlighting that simultaneous submission of serum, blood, and valvular tissue is essential for optimal diagnosis.

Background: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease with no known cure. Disease progression in people living with ALS is heterogeneous, hindering personalized treatment development. The current gold standard for measuring disease progression in ALS, the ALS Functional Rating Scale - Revised (ALSFRS-R), is widely used but based on subjective measurements. Blood-based neurofilament light (NfL) has been studied as a diagnostic and prognostic biomarker but less information exists on its utility as a disease progression biomarker. Methods: We present results from blood draws of 300 participants in the FDA-funded Clinic-Based Multi-Site ALS Natural History and Biofluid study of the ALS Natural History Consortium (NHC). Plasma NfL levels were measured and analyzed against different disease progression metrics based on the ALSFRS-R. Results: NfL levels were found to be correlated with the ALSFRS-R average rate of change (r=-0.53, 95% CI -0.62 to -0.42). This association differed at a cutoff value of 61 pg/mL, with stronger correlations below this cutoff (r=-0.51 vs r=-0.18). Survival differed stratifying by this cutoff value, with participants under the cutoff having higher survival probabilities. The predictive value of NfL when predicting time to death was higher compared with the first ALSFRS-R across different event horizons. A model including both was better when predicting events up to 2 years after diagnosis. Conclusions: These results highlight the utility of NfL as a disease progression biomarker in ALS alongside ALSFRS-R based disease progression metrics. The cutoff value can aid in clinical trial stratification, pragmatic trial planning, and clinical care.

Huntington's disease is caused by a CAG repeat tract expansion in the huntingtin gene, resulting in production of pathogenic N-terminal huntingtin protein fragments associated with disease pathology. Despite their central role, detection of these fragments has relied on a limited antibody repertoire with reproducibility concerns. Here, we describe the generation and characterization of recombinant rabbit monoclonal antibodies targeting two reciprocal neoepitopes flanking the huntingtin exon 1/exon 2 junction corresponding to amino acids P90 and K91. The P90 antibodies (clones 1B12, 11G2) demonstrate fragment-length-selective recognition of the C-terminal HTTexon1 P90 neoepitope with no detectable binding to full length huntingtin. A side-by-side comparison of the widely used monoclonal antibody MW8 from two different sources revealed measurable lot-to-lot drift in its fragment selectivity, whereas the recombinant P90 antibodies, expressed from a defined, sequenced clone, maintained consistent specificity, addressing this source-dependent variability. Whereas P90-positive fragments can arise through alternative splicing of the HTT1a transcript, generation of the reciprocal K91 N-terminal HTTexon2 neoepitope would require site-specific proteolytic cleavage, a mechanism that has not yet been directly tested for lack of a suitable reagent. The K91 antibody (clone 7G10) binds the N-terminal K91 neoepitope with high affinity and specificity over full length huntingtin and provides, for the first time, a tool capable of directly interrogating whether such cleavage occurs. Neoepitope specificity of these antibodies was orthogonally confirmed by protease digestion (Lys-N and Arg-C) coupled with intact mass spectrometry. As an additional outcome of the immunization and selection strategy, we discovered human-mouse cross-reactive antibodies (clones 27F5, 31C10) targeting the proline-rich domain of huntingtin that will facilitate mouse-human translational studies. All antibodies are recombinant, ensuring long-term reproducibility, and are being made available, along with their sequences, to the research community.

Background: Plasma phosphorylated tau at threonine 217 (p-Tau217) is a leading blood-based biomarker for Alzheimer's disease (AD). Robust analytical characterization on high-throughput platforms is essential for research use and clinical translation. Objective: To evaluate the analytical performance of an automated plasma p-Tau217 immunoassay and characterize its discrimination of PET-defined amyloid status. Methods: We performed analytical validation of the Access Research Use Only (RUO) plasma p-Tau217 immunoassay on the Beckman Coulter DxI 9000 Access Immunoassay Analyzer and evaluated biomarker discrimination of PET-defined amyloid pathology in a subset of the Bio-Hermes-001 cohort spanning the symptomatic cognitive continuum (mild cognitive impairment or mild AD dementia; cognitively unimpaired participants excluded; n = 449). Analytical precision, sensitivity, linearity, specificity, interference, and sample stability were assessed per Clinical and Laboratory Standards Institute guidelines. Discrimination of PET-defined amyloid status was evaluated using receiver operating characteristic curve and indeterminate zone analyses. Results: The assay demonstrated high precision (within-laboratory CV </=7.1%), excellent sensitivity (limit of detection 0.018-0.021 pg/mL), linearity across the analytical measuring range (R-squared > 0.99), strong epitope specificity (</=1.0% cross-reactivity with other tau phosphoisoforms), and minimal interference from over 60 endogenous and exogenous substances. In 449 research participants plasma p-Tau217 showed strong discrimination between amyloid-positive and amyloid-negative groups (AUC 0.881; 95% CI 0.846-0.915). Application of indeterminate zones systematically improved classification metrics at the cost of fewer definitive classifications. Conclusions: These findings support the Access p-Tau217 (RUO) assay as a robust, high-throughput assay for plasma biomarker-based discrimination of PET-defined amyloid pathology in AD applications.

Pre-infusion cerebrospinal fluid (CSF) proteomics may enable risk stratification for immune effector cell-associated neurotoxicity syndrome (ICANS) after chimeric antigen receptor T-cell therapy, but disease-specific baseline variation may influence biomarker interpretation. We compared pre-infusion CSF proteomic profiles from 28 patients with diffuse large B-cell lymphoma (DLBCL) and 9 with multiple myeloma (MM). Although principal component analysis showed substantial overlap, orthoPLS-DA identified significant disease-associated discrimination supported by permutation testing. Proteins contributing to this separation were enriched for plasma cell-related, extracellular, and metabolic signatures. ICANS occurred in 7 of 28 DLBCL patients but in none of the 9 MM patients. MM cases aligned with the ICANS-negative group in binary analysis while remaining distinct from both DLBCL subgroups in three-group analysis. These findings indicate that pre-infusion CSF proteomics captures disease-specific molecular structure that should be considered when developing and interpreting biomarkers of CAR-T-associated neurotoxicity.

Background: The Boston Criteria v2.0 represent the gold standard for diagnosing Cerebral Amyloid Angiopathy (CAA), but their application is currently precluded in mixed small vessel disease (SVD), where deep and lobar hemorrhages coexist. The aims of this study are: (i) to determine which cerebrospinal fluid (CSF) biomarker (A{beta}42, A{beta}40, A{beta}42/40 ratio) is the best candidate to support the CAA diagnosis; (ii) to define a data-driven cut-off, and (iii) to explore if a biomarker-integrated classification significantly improves the phenotypical concordance with the suspected predominant SVD (CAA vs. arteriosclerosis). Methods: We analyzed data from a retrospective multicenter cohort of patients with suspected CAA, defined as probable CAA (Boston criteria v2.0) but allowing deep hemorrhagic lesions, and with available CSF biomarkers. We visually quantified MRI-visible SVD markers (e.g., cerebral microbleeds [CMB], cortical superficial siderosis [cSS], lacunes) and their association with MRI-visible SVD features. We employed a Gaussian Mixture Model (GMM) to identify a data-driven threshold for amyloid positivity (A+). Then, we compared the prevalence of MRI-visible manifestations of SVD between subgroups applying different frameworks, namely the current MRI-based classification (probable CAA vs. mixed SVD) and a CSF biomarker-integrated classification (A+ vs. A-). Results: We enrolled 121 patients (age: 72 [66-77] years; 60% probable CAA, 40% mixed SVD with suspected CAA). The CSF A{beta}42/40 ratio showed a bimodal distribution and consistent associations with all CAA-specific radiological features. The CSF biomarker-integrated reclassification, particularly using the GMM cut-off, significantly improved the distinction between subgroups regarding CAA- and arteriosclerosis-related MRI features (e.g., cSS presence: probable CAA vs. mixed SVD: aOR=2.84 [95%CI 1.27-6.39], p=0.011; A+ vs. A-: aOR=12.68 [95%CI 4.31-37.32], p<0.001; deep lacunes presence: probable CAA vs. mixed SVD: aOR=0.20 [95%CI 0.08-0.50], p<0.001; A+ vs. A-: aOR=0.04 [95%CI 0.01-0.11], p<0.001). Notably, patients classified as A+ never demonstrated more than four deep CMBs. Discussion: A CSF biomarker-integrated classification may improve the classification of CAA compared with the current MRI-based framework. These findings are cohort-specific and would benefit from further validation, especially with a neuropathological reference. Still, these results support a future transition toward an integrated biological-radiological framework, which may refine in vivo CAA diagnosis, particularly in mixed SVD.

Abstract Background: The stem cell factor receptor or c-Kit is a type III receptor tyrosine kinase, activated by its ligand Stem cell factor (SCF). Up on activation, c-kit induces signaling pathways that regulates blood cell proliferation, survival, differentiation, and migration. Several studies reported that c-Kit/SCF signaling, contributes to the development and progression of acute myeloid leukemia (AML) in patients. However, the downstream proteins regulated by c-kit activation and their clinical significance in AML remain poorly explored. Methods: Human Acute megakaryoblastic leukemia (Mo7e) cells, were-stimulated with SCF and global protein expression were profiled using two-dimensional gel electrophoresis coupled with MALDI-TOF and LC-MS/MS. Differentially expressed proteins were functionally characterized and validated using patient data from the TCGA-LAML and matched normal data from GTEx, GEO datasets, and quantitative RT-PCR. Their diagnostic and prognostic significance was assessed using ROC, Cox regression, LASSO, Kaplan Meier survival analyses, and a prognostic nomogram model. Results: Proteomic profiling identified 14 differentially expressed proteins in SCF-stimulated Mo7e cells, which are predicted to involved in cytoskeletal organization, protein folding, metabolism, vesicular trafficking, and translational regulation. Transcriptomic analysis of the TCGA-LAML cohort revealed significant dysregulation of CFL1, CCT8, HSP90B1, MDH2, EIF5A, GSN, and TPI1. Integrated ROC, Cox regression, and LASSO analyses identified CFL1, CCT8, and GSN as the most robust prognostic biomarkers associated with poor overall survival in LAML patients. Their expression patterns were validated in independent GEO datasets and by qRT-PCR in SCF stimulated Mo7e cells. Finally, a three-gene nomogram model was developed and validated to predict the overall survival probability of AML patients at 1-, 3-, and 5-year time points. Conclusions: This study identifies CFL1, CCT8, and GSN as key downstream effectors of c-Kit signaling as prognostic biomarkers for AML. These findings provide mechanistic insights into c-Kit-driven leukemogenesis and establish a clinically relevant three-gene signature for AML risk stratification and potential therapeutic targeting.

Objective: Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods: In a retrospective case-control study at two San Francisco health systems, an academic referral center (UCSF) and a safety-net hospital (ZSFG) we identified 74 confirmed EPP cases using combined diagnostic coding, biochemical criteria, and specialty chart review. Symptom-enriched controls were sampled at a 40:1 ratio. Longitudinal diagnoses, laboratory results, medications, procedures, and encounters preceding the outcome date were modeled with a gradient-boosting classifier (CatBoost) and a state-space sequence model (MAMBA). The best model was deployed across the UCSF population and externally validated at ZSFG without retraining. Results: On the UCSF held-out test set (n=1,865; 43 cases), MAMBA outperformed CatBoost (AUC ROC 0.91 vs 0.89; average precision 0.42 vs 0.27; precision 65% vs 20%), flagging cases a median of 229 days before documented diagnosis. Deployed across 297,967 symptom-compatible patients, it identified 310 high-risk individuals, implying a prevalence approaching genetic estimates. External validation at ZSFG showed attenuated performance (AUC ROC 0.72; average precision 0.10) while preserving early detection (median 264 days). Discussion: A sequence model integrating temporal EHR signals detected EPP months before clinical recognition, corroborating genetic evidence of substantial underdiagnosis. Cross-site attenuation reflects population and documentation differences and underscores the need for local recalibration. Conclusion: Longitudinal EHR-based machine learning can shorten EPP diagnostic delay and prioritize patients for confirmatory testing, supporting proactive rare-disease case finding.

Background: Late-onset neutropenia (LON) is an infrequently reported, unpredictable side effect of anti-CD20 therapy, with incidence varying by agent, diagnosis, and screening protocol. Objective: The primary objective of this cross-sectional, retrospective study was to estimate the proportion of patients who developed LON over 13 months (April 2023-April 2024). Methods: Consecutive adult patients diagnosed with central nervous system (CNS) autoimmunity who received at least one rituximab(RTX) or ocrelizumab(OCR) infusion between January 2016 and March 2024 were included; patients who switched to another immunotherapy, had no post-treatment blood draw, or had unverifiable infusion records were excluded. LON events were assessed using all post-treatment CBCD blood draws during this period. Results: A total of 171 patients were enrolled: 141 received rituximab and 30 received ocrelizumab. A total of 319 post-treatment blood tests were performed. Sixteen patients (16/171) had neutropenia (9.4%, 95% CI 5.8-14.7): 12 on rituximab (8.5%) and 4 on ocrelizumab (13.3%; p=0.487). LON occurred at a median of 158 days (130-188) since the last infusion. All patients were asymptomatic, mostly had Grade 1 neutropenia (15/16, 93.8%). BMI (22.2 vs. 27.5 kg/m2, p=0.001) and prior natalizumab exposure (37.5% vs. 14.2%, p=0.023) were significantly different between neutropenic and non-neutropenic patients. Conclusion: The proportion of patients with LON in this cohort was higher than most previously reported, with all cases asymptomatic. Lower BMI and prior natalizumab exposure emerged as potential risk factors warranting further investigation. Larger, prospective studies with standardized surveillance are needed to establish the true frequency and risk factors.

The emergence of bispecific antibodies (BsAbs) targeting T cells (CD3+) and tumor plasma B cells (BCMA+) has provided a new therapeutic option for patients with relapsed/refractory multiple myeloma cancer. However, responses to CD3xBCMA BsAb therapy remain heterogeneous, and treatment is associated with frequent immune-related adverse events. Although baseline immune characteristics have been associated with clinical outcomes, little is known about the early immune dynamics induced by this therapy. Here, we investigated whether longitudinal clinical monitoring and high-dimensional profiling of blood circulating T cells could identify early biomarkers of response or toxicity during treatment. Our results indicate that all treated patients exhibit an early depletion of circulating T cells associated with T-cell activation within the first two weeks. Integration of clinical and immunological parameters using Factorial Analysis of Mixed Data (FAMD) identified immune features associated with treatment outcome. Responders had lower plasma soluble BCMA concentrations, fewer bone lesions, higher circulating lymphocyte counts at baseline. During the first days of treatment, responders exhibited a more pronounced increase in plasma CXCL10 levels, associated with a greater decrease in T lymphocyte counts. Overall, our findings suggest that integrating clinical and immune parameters measured during the first days of treatment may enable early patient stratification and support the development of a predictive score to identify patients with multiple myeloma who are most likely to benefit from CD3xBCMA BsAb therapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/743749v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1cb079org.highwire.dtl.DTLVardef@1860106org.highwire.dtl.DTLVardef@ad36d3org.highwire.dtl.DTLVardef@1ea5c1e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntegrated clinical and blood T-cell immune profiling using FAMD enables patient stratification following CD3xBCMA BsAb therapy. C_LIO_LIT-cell immune activation occurs predominantly within the first two weeks of therapy. C_LIO_LIFirst-week clinical and immune parameters identify patients most likely to benefit from therapy. C_LIO_LIHigh CXCL10 levels, a profound early decline in circulating T cells, low sBCMA levels, and fewer bone lesions are candidate predictive markers of treatment response. C_LI

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.

INTRODUCTION: Novel capillary-blood collection methods have not yet been evaluated for a wide range of central nervous system (CNS) and neurodegenerative disease-related proteins. Biomarkers of Alzheimer's disease (AD) and related disorders (ADRD) collected from devices like the Tasso+, a minimally invasive upper-arm capillary blood collection device, must be compared to traditional venipuncture to assess for validity. METHODS: Participants underwent blood collection via traditional venipuncture and Tasso+ in a clinical research setting. The Nucleic Acid Linked Immuno-Sandwich Assay (NULISA) CNS panel was used for biomarker quantification in venous and Tasso-derived plasma. RESULTS: Eighty-three participants (age mean{+/-}SD 76.8{+/-}8.2 years, 79.5% cognitively unimpaired) completed blood collection. Little to no correlation was found between venous and Tasso+ plasma for p-tau217, but the correlation was improved by using a brain-derived (BD)-p-tau217/BD-p-tau181 ratio. Extremely strong correlations were found for neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Among the 131 biomarkers measured, 51 (38.9%) had a Pearson R [&ge;] 0.90; 27 (20.6%) had values between 0.70-0.90; 26 (19.9%) had values between 0.30-0.70; and 27 (20.6%) had values [&le;] 0.30. DISCUSSION: The Tasso+ accurately measures NfL and GFAP, but caution is warranted when measuring other AD/ADRD biomarkers, as agreement with venous plasma appears to be protein or ratio dependent. These results highlight that important biomarker-specific differences must be considered when translating capillary blood collection approaches. They also further support foundations for development of these methods, highlighting both the opportunities and remaining challenges for translating the promise of blood-based biomarkers beyond AD/ADRD specialty clinics and research settings.

INTRODUCTION: Biomarkers for monitoring disease activity and treatment response in peripheral neuropathies remain limited. Big tau, a high-molecular-weight isoform of tau, is predominantly expressed in the peripheral nervous system (PNS). We investigated serum levels of big tau, brain-derived tau (BD-tau), and neurofilament light chain (NfL) in peripheral neuropathies, multiple sclerosis (MS), Alzheimer disease (AD), and healthy controls (HC). METHODS: Ultra-sensitive blood-based assays run on an HD-X Single Molecule Array analyser (Quanterix) were used to measure big tau and BD-tau in serum from patients with Guillain-Barr&eacute syndrome (GBS, n=81), Miller Fisher syndrome (MFS, n=20), Charcot-Marie-Tooth disease (CMT, n=102), chronic inflammatory demyelinating polyneuropathy (CIDP, n=43), MS (n=159), AD (n=20), and HC (n=41). NfL was measured in patients with neuropathies using an SR-X Single Molecule Array analyser (Quanterix). RESULTS: Serum big tau levels were higher in GBS than in AD (11.4 vs 2.4 pg/mL, p<0.0001) and MS (11.4 vs 9.0 pg/mL, p=0.01), and similar to CIDP and CMT. Contrarily, serum BD-tau levels in GBS were higher than in CIDP (3.0 vs 2.3 pg/mL, p=0.006) and MS (3.0 vs 1.7 pg/mL, p<0.0001), but similar to CMT, and lower than in AD (3.0 vs 9.8 pg/mL, p<0.0001). Serum NfL levels were higher in GBS than in CIDP (32.5 vs 13.0 pg/mL, p=0.0002), CMT (32.5 vs 12.3 pg/mL, p<0.0001), and HC (32.5 vs 7.6 pg/mL, p<0.0001). Compared with GBS, MFS patients showed higher BD-tau (12.7 vs 3.0 pg/mL, p=0.003), lower big tau (5.4 vs 11.4 pg/mL, p=0.002), and higher NfL levels, although the latter did not reach statistical significance (118.3 vs 32.5 pg/mL, p=0.16). The NfL/big tau ratio was significantly higher in MFS than in GBS, CIDP, and CMT. In GBS, BD-tau correlated with early clinical severity (MRC at 1 week; I-RODS at 4 weeks; maximum GBS-DS and GBS-DS at 4 weeks), whereas neither tau biomarker showed long-term clinical correlations. Higher BD-tau and big tau levels were associated with the need for mechanical ventilation (BD-tau: 8.6 vs 2.9 pg/mL, p=0.019; big tau: 19.7 vs 10.7 pg/mL, p=0.007), while higher BD-tau levels were associated with mortality (10.9 vs 2.9 pg/mL, p=0.003). CONCLUSIONS: Higher big tau levels in peripheral neuropathies than in CNS diseases support its role as a PNS-specific biomarker. In MFS, increased serum BD-tau, reduced big tau, and an elevated NfL/big tau ratio suggest CNS involvement with relative preservation of the PNS.

Background. Urine-based testing offers a promising non-sputum approach for diagnosing paediatric tuberculosis. However, the currently available lipoarabinomannan (LAM) assay shows limited sensitivity in children and is primarily indicated for those living with HIV. Co-detection of LAM with Mycobacterium tuberculosis (Mtb) proteins in urine could provide complementary pathogen-derived biomarkers that improve diagnostic performance. Methods. We developed an ultrasensitive multiplex electrochemiluminescence (ECL) immunoassay to measure Ag85B, CFP-10, ESAT-6, MPT32, and MPT64 in urine. We determined the analytical limits of detection and evaluated the diagnostic performance of individual proteins and LAM using urine samples from children with Confirmed, Unconfirmed, and Unlikely pulmonary tuberculosis enrolled across five high-burden countries (The Gambia, India, Peru, South Africa, and Uganda). Performance was assessed overall, by HIV and nutritional status, and across biomarker combinations. Findings. Urine samples from 630 children were analysed (median age was 4 years [IQR 2-8]; 44% female, 15% living with HIV, 19% underweight, 24% with Confirmed tuberculosis). The ECL assay achieved femtomolar limits of detection (1.5 to 4.0 fM). The sensitivity and specificity of individual Mtb proteins were 12-33% and 98-100%, respectively. Ag85B had the highest sensitivity (33%, 95% CI 26-41) for Confirmed tuberculosis and was similar to LAM. A four-antigen signature (Ag85B, MPT64, MPT32, LAM) was 50% sensitive (95% CI 42-58) and 94% specific (95% CI 90-96), and was significantly more sensitive than LAM alone, in particular among those without HIV. An additional sixteen (10%) of children with Unconfirmed TB had at least one Mtb protein or LAM detected. Interpretation. Multiple Mtb proteins are detectable in paediatric urine with high specificity, and multi-antigen signatures can augment sensitivity versus LAM alone. These findings demonstrate the potential of multi-antigen urine detection for childhood TB and define analytical targets for the development of future point-of-care diagnostics. Funding. National Institutes of Health.

Objectives: Capturing adequate blood volume for blood cultures is critical for accurate detection of bloodstream infections. Pediatric volume targets vary by age and weight, whereas adult targets are standardized. The BD BACTEC FXI Culture System (FXI) contains an integrated calibrated load cell capable of automatically reporting blood volume measurements for each vial loaded onto the system. This study evaluated the accuracy of the FXI's blood volume measurements in simulated pediatric and adult patients. Methods: Mock pediatric and adult blood draws were performed, using bagged whole blood, to replicate real-world collection protocols. Syringe-collected blood volumes ranged from 2.0 to 15.0 mL for pediatric patients, depending on mock patient weight, and were fixed at 40.0 mL for adults. Samples were inoculated into BD BACTEC Peds Plus/F, Plus Aerobic/F, and Lytic/10 Anaerobic/F Culture Vials, with a target volume of 2.0 to 10.0 mL per bottle. Reference blood volumes were determined gravimetrically using manually obtained pre- and post-inoculation weights with a blood-specific gravity of 1.055 g/mL and were compared to the automatically measured, gravimetric-based blood volumes reported by the BACTEC FXI Culture System. Results: Automated volume estimates were accurate to a mean error of -0.03 mL per bottle (SD, 0.40 mL; n=168; 95% CI, -0.09 mL, 0.03 mL) and -0.08 mL (SD, 0.79 mL; n=72; 95% CI, -0.26 mL, 0.10 mL) when assessing total volume collected per patient. Conclusions: Our findings demonstrate that the automated system can quantify blood volumes in BACTEC culture vials and support blood volume monitoring for pediatric and adult collections. The gravimetric approach is also amenable to full automation for efficient and accurate blood volume determination.

The 2026 Bundibugyo ebolavirus (BDBV) outbreak underscores the need for rapidly deployable molecular diagnostics. We developed and analytically validated reverse-transcription quantitative PCR assays detecting BDBV, Zaire ebolavirus, and Sudan ebolavirus. The platform includes a BDBV singleplex assay, a duplex assay with a human internal control, a four-target multiplex assay for ebolavirus differentiation, and a probe-free SYBR Green assay. We adapted the assays to a portable qPCR instrument, reducing runtime from 65 to 35 minutes, and validated lyophilized reagents to reduce cold-chain requirements. All TaqMan formats achieved a 95% limit of detection of 5 copies per reaction across instruments and reagent types; the SYBR Green assay achieved 50 copies per reaction. The assays detected viral RNA in contrived clinical samples without cross-reactivity among ebolavirus species tested. We shared the protocols in real time through Ampliphi (https://www.ampliphi.bio), a new open-access platform for rapidly disseminating diagnostic assays, and through protocol.io.