Clinical Chemistry

Background. Detection of cerebral amyloid pathology currently requires amyloid PET imaging ($5,000-$8,000) or cerebrospinal fluid analysis via lumbar puncture, procedures that are inaccessible for population-level screening. The FDA-cleared Lumipulse G pTau217/Abeta1-42 plasma ratio test (May 2025) represents the first approved blood-based alternative; however, single-ratio approaches cannot distinguish Alzheimer's disease (AD) from non-AD neurodegeneration or provide multi-dimensional disease characterization. Methods. We developed Virtual Spectral Decomposition (VSD), a framework that decomposes plasma biomarker profiles into biologically interpretable diagnostic channels. Four plasma biomarkers - phosphorylated tau-217 (pTau217), amyloid-beta42/40 ratio, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - were measured in 1,139 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Each biomarker was mapped to a VSD channel representing a distinct pathophysiological axis: tau/amyloid phosphorylation, amyloid clearance, neurodegeneration, and astrocytic activation. Channel weights were calibrated via logistic regression, and performance was evaluated against amyloid PET (UC Berkeley) using 10x5-fold repeated cross-validation. Results. VSD 4-channel fusion achieved AUC = 0.900 (+/-0.018), exceeding pTau217 alone (0.888+/-0.022). Optimal sensitivity was 89.7% with 78.1% specificity (NPV = 90.8%). The NfL channel received a negative weight (beta = -1.1), functioning as a disease-exclusion signal: elevated neurodegeneration without amyloid-tau coupling actively reduces the AD probability, distinguishing AD from non-AD neurodegeneration. Complementary CSF proteomics analysis (7,008 proteins, 533 participants) identified 17 amyloid-specific proteins (0.24% of the proteome), revealing a 49:1 tau-to-amyloid asymmetry that explains why blood-based tau markers outperform amyloid markers. Conclusions. Blood-based VSD provides an interpretable, multi-channel framework for amyloid detection that incorporates explicit disease-exclusion logic unavailable to single-biomarker approaches. The architecture extends to multi-disease screening, where the same blood specimen could be routed through disease-specific modules for AD, Parkinson's disease, and cancer.

Stroke-associated pneumonia (SAP) is a common, severe complication in acute ischemic stroke (AIS) patients receiving bridging therapy (intravenous thrombolysis + mechanical thrombectomy), worsening prognosis and increasing mortality. Current SAP prediction models rely heavily on subjective clinical factors, limiting accuracy. This study developed an interpretable machine learning (ML) model combining inflammatory biomarkers to stratify SAP risk in AIS patients undergoing bridging therapy. We retrospectively enrolled AIS patients who received bridging therapy, collected baseline clinical data and inflammatory biomarkers, and constructed ML models (including XGBoost, random forest) with SHAP analysis for interpretability. The model integrating inflammatory biomarkers achieved excellent predictive performance (AUC=0.XX, 95%CI: XX-XX), outperforming traditional clinical models. SHAP analysis identified key biomarkers driving SAP risk, enhancing model transparency. This interpretable ML model provides an objective, accurate tool for SAP risk stratification in AIS patients, helping clinicians identify high-risk individuals early and implement targeted interventions to improve outcomes.

Saccadic eye movements are established biomarkers in neuroscience and clinical neurology, where video-oculography (VOG) remains the gold standard. However, VOG's high cost, bulky equipment, and poor portability restrict its clinical utility. Electrooculography (EOG) offers a promising alternative by detecting cornea-retinal potential changes during eye movements. To enable quantitative saccadic analysis using EOG as a VOG alternative, this study develops and validates a mathematical transformation model converting EOG data into VOG-equivalent values. A prospective observational study was conducted on 4 healthy adults without neurological or sleep disorders. Horizontal saccades were recorded simultaneously using EOG and VOG during controlled gaze shifts. EOG peak saccadic velocity was derived from voltage change rate, whereas VOG was calculated from angular displacement over time. A derivation dataset of fixed horizontal saccades ({+/-}20{degrees}) formulated the transformation model, achieving a strong correlation coefficient (r = 0.95 rightward, r = 0.93 leftward, p < 0.0001). Multiple filter settings were evaluated, and 0.3 Hz high-pass and 35 Hz low-pass filtering were identified as optimal. The fixed horizontal saccades derived model was applied to a validation dataset of random horizontal saccades, confirming robustness across saccades without significant differences from VOG measurements. These findings establish EOG's feasibility for quantitative analysis of horizontal saccades and provide a validated transformation model. By systematically optimizing filtering parameters, this approach enables EOG as a cost-effective VOG alternative while maintaining high-precision measurement accuracy.

INTRODUCTION: Alzheimer's disease (AD) and frontotemporal dementia (FTD) have considerable clinical and pathological overlap. While plasma proteomics has advanced in AD, deep comparative analyses with FTD-particularly in diverse, biomarker-confirmed Asian cohorts-remain limited. METHODS: Plasma from 101 individuals with known pTau217 status was profiled using Olink Explore-HT. Differential expression-pathway enrichment, penalized regression-GLMNET, single-cell transcriptomic integration, associations with cognitive measures and, cross-platform validation were performed. RESULTS: Among 5,400-proteins, 1,168 were differentially expressed in AD and 370 in FTD (FDR<0.05). Distinct and overlapping proteomic signatures were identified in AD and FTD, reflecting gliosis, synaptic dysfunction, immune activation, and metabolic pathways. Prioritized proteins correlated with cognitive performance and plasma phosphorylated tau, A{beta}42, and neurofilament light chain, linking circulating proteins to disease severity. Cross platform validation revealed strong concordance with large independent datasets. CONCLUSION: Comprehensive plasma proteomics in Asian cohort supports scalable framework for blood-based biologically informed targets for precision diagnosis and therapeutic stratification.

Rationale: Sepsis is a life-threatening syndrome causing significant morbidity and mortality especially in the aging population. Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition of clonal expansion of hematopoietic stem cells harboring somatic mutations associated with increased incidence of chronic illness and all-cause mortality. Objective: Evaluate the association of pre-illness CHIP with mortality and morbidity in patients admitted to the ICU with sepsis. Methods: We performed a retrospective study using a de-identified electronic health record linked with a DNA biorepository. We identified adult patients with sepsis who had DNA collected prior to ICU admission. We tested the association between CHIP status, determined from whole-genome sequencing, and ICU mortality, organ support-free days, and long-term survival adjusting for age, sex, race and Sequential Organ Failure Assessment (SOFA) score on ICU admission. Measurements and Main Results: Pre-illness CHIP was associated with increased sepsis mortality (OR = 1.54, 95% CI 1.13 to 2.07, P = 0.005) and fewer days alive and free of organ support (-1.7 days, 95% CI -3.2 to -0.2, P = 0.028) after adjusting for age, sex, race, and SOFA score. In sepsis survivors, CHIP was also associated with increased long-term mortality after discharge (HR 1.40, 95% CI 1.01 to 1.93, P = 0.041). Conclusions: Pre-illness CHIP was independently associated with increased mortality and morbidity in critically-ill adults with sepsis. These findings suggest that CHIP is a risk factor for sepsis severity. Elucidating the mechanism underlying this association could uncover new therapeutic interventions for sepsis.

Background: Hereditary transthyretin amyloidosis (hATTR) manifests as cardiomyopathy and/or polyneuropathy. The V142I variant predominantly causes cardiac disease in African Americans, though neurological involvement may be underrecognized. We characterized neuropathy documentation and treatment patterns in a predominantly V142I cohort. Methods: Retrospective review of 54 hATTR patients at a major academic medical center. Neuropathy was classified as: objective (abnormal EMG), possible polyneuropathy (documented symptoms suggestive of polyneuropathy), symptoms only (neuropathic symptoms without specialist evaluation), or unclear. Treatment with stabilizers (tafamidis, acoramidis, diflunisal) and gene silencers (patisiran, vutrisiran, eplontersen) was assessed. Results: Of 54 patients (88.9% African American, 85.2% V142I), 51 (94.4%) had confirmed cardiac involvement. Among cardiac patients, 40/42 eligible (95.2%) received stabilizers. Overall, 16 patients (29.6%) received gene silencers, with 13 (24.1%) receiving both a stabilizer and gene silencer concurrently. Possible neuropathy (objective, possible polyneuropathy, or symptoms) was documented in 30 patients (55.6%). Gene silencer use was highest among those with objective neuropathy (8/17, 47.1%) versus symptoms only (1/10, 10.0%). All three patients without confirmed cardiac disease received gene silencers. Conclusions: In this V142I-predominant cohort with 94.4% cardiac involvement, stabilizer use was high (95.2%) among eligible patients. Over half had possible neuropathy based on clinical documentation, though EMG completion was limited (57.4%). Gene silencer use was associated with objective neuropathy documentation and non-cardiac phenotype. These findings support systematic neurological assessment in hATTR, even when cardiac disease predominates.

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with a heterogeneous clinical presentation, complicating early diagnosis and therapeutic monitoring. To identify disease-specific biomarkers, we performed an unbiased cerebrospinal fluid (CSF) proteomic analysis in 87 ALS patients, 89 healthy controls, and 61 neurological controls using mass spectrometry. Across all quantified proteins, 399 were significantly dysregulated in ALS, including established neurodegeneration (NEFL, NEFM, UCHL1) and neuroinflammatory (CHIT1, CHI3L1, CHI3L2) markers. Correlation and pathway analyses uncovered dysregulation of immune, synaptic, and metabolic processes, with aberrant complement activation emerging as a hallmark. Complement proteins increased progressively with declining ALS Functional Rating Scale-Revised and longer disease duration, whereas early-stage markers (CLSTN3, CHAD, RELN) indicated pre symptomatic neuronal and synaptic disruptions. Machine learning identified a minimal five protein CSF panel (MB, ITLN1, YWHAG, FCGR3A, PGAM1) that accurately distinguished ALS patients from healthy controls, capturing disease-specific pathophysiology beyond general neurodegeneration. Our findings define a robust ALS-specific CSF proteomic signature, reveal prognostic protein candidates across disease stages, and provide a framework for diagnostic biomarker development, enabling earlier intervention and monitoring.

Introduction: Herpes simplex virus type 1 (HSV-1) is highly prevalent worldwide, making accurate serological testing essential for both clinical diagnosis and epidemiological surveillance. Automated chemiluminescent immunoassays (CLIAs) offer operational advantages over enzyme-linked immunosorbent assays (ELISAs); however, their diagnostic performance relative to Western blot (WB) confirmation in high-prevalence settings remains insufficiently characterized. Hypothesis/Gap Statement: The comparative diagnostic accuracy of CLIA- and ELISA-based assays for HSV-1 IgG detection, when benchmarked against a WB reference standard in endemic populations, remains unclear. Aim: This study aimed to evaluate HSV-1 IgG seroprevalence and diagnostic performance of one CLIA and two ELISA platforms using Western blot as the reference method. Methodology: Four hundred archived serum samples from adult male craft and manual workers in Qatar were tested using the Mindray CL-900i CLIA, HerpeSelect ELISA, NovaLisa ELISA, and Euroimmun Western blot. Seroprevalence, diagnostic accuracy, and interassay agreement were assessed using WB as the reference standard, with equivocal and indeterminate results excluded from analysis. Results: HSV-1 IgG seroprevalence estimates were comparable across assays: HerpeSelect 72.5%, Mindray 70.5%, NovaLisa 66.3%, and Western blot 66.5%, with no statistically significant differences (all p > 0.05). The Mindray CLIA demonstrated the highest diagnostic performance (sensitivity 95.7%, specificity 88.9%, accuracy 93.4%) and strong agreement with Western blot ({kappa} = 0.85). HerpeSelect showed substantial agreement ({kappa} = 0.81), while NovaLisa exhibited lower specificity. Conclusion: CLIA- and ELISA-based assays produced comparable HSV-1 seroprevalence estimates in this high-prevalence population; however, diagnostic accuracy varied across platforms. The CLIA platform demonstrated the strongest agreement with Western blot, supporting its use in high-throughput settings, while confirmatory testing remains important to minimize misclassification.

Background: Sezary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n=26) and PCAECTCL (n=13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Chi-square or Fisher's exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. Conversational AI agents, AI-HOPE, were used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics.

Streptococcus pneumoniae is the leading cause of empyema and pneumonia in children, and monitoring of effectiveness of polyvalent pneumococcal vaccines has been essential for controlling invasive pneumococcal disease (IPD) in children and elderly adults. Conventional serotyping of pneumococci has relied on Quellung reaction following laboratory culture, however more recently whole genome sequencing (WGS) has been implemented in many reference laboratories to enhance traditional typing. Pleural fluid samples from cases with empyema are often culture negative, limiting the utility of WGS and requiring polymerase chain reaction (PCR) or 16S rRNA sequencing to detect S. pneumoniae. These molecular methods have limited sensitivity and capacity to characterise pneumococcus in clinical samples, especially in specimens with a low pathogen abundance. This study applied capture-based enrichment (tNGS) to identify and characterise S. pneumoniae directly from pleural fluid samples. A total of 51 pleural fluid samples were subjected to tNGS with a custom probe panel, for 39 known positive fluids collected from IPD cases between 2018-2025 in New South Wales, Australia. tNGS results were benchmarked against molecular-based serotyping. Our tNGS achieved 100% sensitivity and specificity in detecting S. pneumoniae. Serotyping results were concordant with PCR and 95% (37/39) of S. pneumoniae PCR positive pleural fluid cases could be serotyped using tNGS. Standard molecular methods however could only determine serotype in 56% (22/39) of samples. This tNGS enabled 39% improvement in ability to directly identify and serotype IPD-associated serotypes of S. pneumoniae in difficult-to-culture pleural fluids can significantly enhance laboratory surveillance of IPD as well as our understanding of vaccine effectiveness.

Circulating tumor cells (CTCs), and especially CTC-clusters, are linked to poor prognosis and may reveal mechanisms of metastasis and treatment resistance. Therefore, developing unbiased methods for the functional characterization of CTCs in liquid biopsies is an urgent need. Here, we present an evaluation of multiplex imaging mass cytometry (IMC) to analyze CTCs in mice with human xenograft tumors. In a single-step process, IMC uses metal-labeled antibodies to simultaneously detect a large number of proteins/modifications within minimally manipulated small volumes of blood from the tail vein or heart. We used breast cancer cell lines and a patient-derived xenograft (PDX) to assess antibodies for cross-species interpretation. Along with manual verification, HALO-AI-based cell segmentation was used to identify CTCs and quantify markers. Despite some limitations regarding human-specificity, this technology can be used to investigate the effect of genetic and pharmacological interventions on the properties of single and cluster CTCs in tumor-bearing mice.

Methods: DigiCog is a single-center cross-sectional study conducted within the Luxembourgish Predi-COVID cohort (NCT04380987). Participants aged 25-65 years, with and without persistent COVID-19 symptoms, are invited to participate. Cognitive assessments are performed during face-to-face sessions by trained nurses and neuropsychologists using both the VMTech device and standardized neuropsychological tests. Additional data on PCC symptom status, CR, sociodemographic characteristics, fatigue, and psychological factors are also collected. Agreement between digital and standard cognitive assessments will be evaluated using Cohen's kappa coefficient, with sensitivity, specificity, and receiver operating characteristic analyses as secondary measures. Cognitive performance will be compared between participants with and without PCC, and associations with CR proxies will be explored.

BackgroundIn the absence of structured donor registries, social media platforms have become a dominant mechanism for blood donor recruitment in many low-resource settings. However, the implications of this shift for transfusion timeliness and system reliability remain unclear. ObjectiveTo evaluate the impact of social media-sourced donors on transfusion delay, donor reliability, and hemovigilance-related outcomes compared with conventional donor pathways. MethodsThis prospective analytical study included 400 transfusion episodes across tertiary hospitals in Bangladesh. Donor sources were categorized as social media (SM) or conventional (CON). The primary outcome was delay-to-transfusion. Secondary outcomes included donor-related irregularities, documentation completeness, near-miss events, and acute transfusion reactions. Multivariable logistic regression identified predictors of delay [&ge;]4 hours. ResultsSocial media-sourced donors were associated with significantly longer transfusion delays (5.98 vs 2.97 hours; p<0.001). Delay [&ge;]4 hours occurred in 83.6% of SM cases versus 17.6% of CON cases (OR 23.78). Donor-related irregularities were observed in 85% of SM episodes and absent in CON donors. Safety outcomes did not differ significantly between groups. Social media donor sourcing remained the strongest independent predictor of delay (adjusted OR 18.09). ConclusionUnregulated social media-based donor recruitment introduces substantial delays and undermines system reliability without improving access. Integration of digital tools into regulated donor systems is essential to strengthen transfusion timeliness and hemovigilance in resource-limited settings.

Background: Kidney transplantation is the preferred treatment strategy for end-stage kidney disease. Deceased donor kidneys usually undergo cold storage until kidney transplantation, leading to cold ischemia injury that may contribute to poor graft outcomes. However, the molecular characterization of potential mechanisms of cold ischemia injury remains incomplete. Results: To bridge this knowledge gap, we leveraged the 10x Visium spatial transcriptomic technology to perform full transcriptome profiling of murine kidneys subject to varying durations of cold ischemia typical in a deceased donor kidney transplant setting. We developed a computational workflow to identify and compare spatiotemporal transcriptomic changes that accompany the injury pathophysiology in a tissue compartment-specific manner. We identified proportional enrichment of oxidative phosphorylation (OXPHOS) genes with increasing duration of cold ischemia injury within the oxygen-lean inner medulla region, suggestive of atypical metabolic presentation. This was distinct in cold ischemia injury tissue compared to warm ischemia-reperfusion kidney injury tissue. Spatiotemporal trends were validated by qPCR and immunofluorescence in a larger cohort of mice. We provide an interactive online browser at https://jef.works/CellCarto-ColdIschemia/ to facilitate exploration of our results by the broader scientific and clinical community. Conclusions: Altogether, our spatiotemporal transcriptomic analysis identified coordinated molecular changes within metabolic pathways such as OXPHOS deep within the cold ischemic kidney, highlighting the need for increased attention to the inner medulla and potential opportunities for new insights beyond those available from superficial biopsy-focused tissue examinations.

Objectives: Among surgically resected non-small cell lung cancer (NSCLC) patients with similar stage and histopathological characteristics, there is variability in patient outcomes which highlights urgency of identifying biomarkers to predict recurrence. The goal of this study was to systematically develop a pre-surgical CT-based habitat-based radiomics classifier to predict recurrence-of-risk in NSCLC. Methods: This study included 293 NSCLC patients with surgically resected stage IA-IIIA disease that were randomly divided into a training (n = 195) and test cohorts (n = 98). From pre-surgical CT images, tumor habitats were generated using two-level unsupervised clustering and then radiomic features were calculated from the intratumoral region and habitat-defined subregions. Using ridge-regularized logistic regression, separate classifiers were developed to predict 3-year recurrence using intratumoral radiomics, habitat-based radiomics, and a combined model (intratumoral and habitat) which was generated using a stacked learning framework. For each classifier, probability of recurrence was calculated for each patient then numerous statistical and machine learning approaches were utilized to stratify patients for recurrence-free survival. Results: The combined radiomics classifier yielded a superior AUC (0.82) compared to the intratumoral (AUC = 0.75) and habitat radiomics (AUC = 0.81) models. When the classifiers were used to stratify high- versus low-risk patients utilizing a cut-point identified by decision tree analysis, high-risk patients were yielded the largest risk estimate (HR = 8.43; 95% CI 2.47 - 28.81) compared to the habitat (HR = 5.41; 95% CI 2.08 - 14.09) and intratumoral radiomics (HR = 3.54; 95% CI 1.45 - 8.66) models. SHAP analyses indicated that habitat-derived information contributed most strongly to recurrence prediction. Conclusions: This study revealed that habitat-based radiomics provided superior statistical performance than intratumoral radiomics for predicting recurrence in NSCLC.

Background: Pharmacogenomic testing (PGx) can optimise drug efficacy and minimise toxicity, but the extent of prescriber adherence to PGx recommendations remains unclear. We aimed to quantify clinician adherence to international genotype-guided prescribing recommendations in a cohort of paediatric oncology patients. Methods: We reviewed files of children enrolled in the MARVEL-PIC (NCT05667766) randomised control trial, who had PGx recommendations available. Patients were included if 12 weeks had passed since their PGx report was released to clinicians. Prescribing events were identified for actionable PGx recommendations, and classified as "explicitly followed", "inadvertently followed", or "not followed". Adherence was assessed by patient, drug, and recommendation. Results: 2,063 PGx recommendations were available for 216 patients. 64 (3.1%) recommendations were actionable for 44 patients and 10 drugs within the 12-week study period. Recommendations were explicitly followed in 57/288 (19.8%) of prescribing events, inadvertently followed in 145 (50.3%), and not followed in 86 (29.9%). Mercaptopurine demonstrated the highest rate of explicit adherence (87.5%). No significant associations were observed between adherence and age group, cancer type, drug type, or strength of recommendation. Conclusion: Adherence to pharmacogenomic recommendations was very low, highlighting the need to understand barriers to PGx implementation, and consideration of clinical decision supports to facilitate adherence.

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Background. Adult diffuse glioma is a representative class of primary brain tumors for which accurate MRI-based tumor segmentation is indispensable for treatment planning. Conventional automated segmentation methods have relied primarily on image information and spatial prompts, and auxiliary clinical information that is routinely acquired in clinical practice has not been sufficiently exploited as an input. Objective. Building on a dual-prompt-driven Segment Anything Model (SAM) extension framework that fuses visual and language reference prompts, we propose a method that integrates patient demographics, unsupervised molecular cluster variables derived from TCGA high-throughput profiling, and histopathological parameters as learnable prompt embeddings, and we evaluate its effect on the accuracy of lower-grade glioma (LGG) MRI segmentation. Methods. An auxiliary prompt encoder converts clinical metadata into high-dimensional embeddings that are fused with the prompt representations of Segment Anything Model (SAM) ViT-B through a cross-attention fusion mechanism. The TCGA-LGG MRI Segmentation dataset (Kaggle release by Buda et al.; n = 110 patients; WHO grade II-III) was split at the patient level (train/val/test = 71/17/22) using three different random seeds, and the three slices with the largest tumor area were extracted from each patient. To avoid pseudo-replication arising from multiple slices per patient and repeated measurements across seeds, our primary analysis aggregated Dice and 95th-percentile Hausdorff distance (HD95) to the patient x seed unit (n = 66); secondary analyses at the unique-patient level (n = 22) and at the per-slice level (n = 198) are also reported. Pairwise comparisons used paired t-tests with Bonferroni correction (k = 3) and Wilcoxon signed-rank tests, and a permutation test (K = 30) served as an auxiliary check of effective use of the auxiliary information. Results. At the patient x seed level (n = 66), Proposed (full clinical) achieved a Dice gain of +0.287 over the zero-shot SAM ViT-B baseline (paired-t p = 4.2 x 10^-15, Cohen's d_z = +1.25, Bonferroni-corrected p << 0.001; Wilcoxon p = 2.0 x 10^-10), and HD95 improved from 218.2 to 64.6. Because zero-shot SAM is not designed for domain-specific medical segmentation, the large absolute HD95 gap largely reflects the expected domain gap rather than a competitive baseline. The additional contribution of the full clinical configuration over the demographics-only configuration was Dice = +0.023 (paired-t p = 0.057, Bonferroni-corrected p = 0.172), which did not reach statistical significance at the patient level and is reported as a directional trend. The permutation test (K = 30, seed 2025) yielded real-metadata Dice = 0.819 versus a shuffled-metadata mean of 0.773, giving an empirical p = 0.032 = 1/(K + 1), which is at the resolution limit of this test and should therefore be interpreted as preliminary evidence. Conclusions. Integrating auxiliary clinical information as multimodal prompts produced a large improvement over the zero-shot SAM baseline on this LGG cohort. More importantly, a robustness analysis showed that Proposed (full clinical) outperformed the trained Base (no auxiliary information) under all tested spatial-prompt conditions, including perfect centroid (+0.014), and that the advantage was most pronounced in the prompt-free regime (+0.231, p = 0.039), where the base model collapsed but the proposed model maintained meaningful segmentation by leveraging clinical metadata alone. The additional contribution of molecular and histopathological information beyond demographics was not statistically resolved at the patient level (+0.023, n.s.). Establishing clinical utility will require external validation on larger multi-center cohorts and direct comparisons with established segmentation methods. Keywords: brain tumor segmentation; Segment Anything Model (SAM); vision-language prompt-driven segmentation; auxiliary clinical prompts; multimodal learning; TCGA-LGG; deep learning

BackgroundDental caries and periodontal disease represent the most prevalent global oral health conditions, collectively affecting several billion people. The diagnostic interpretation of dental radiographs, a cornerstone of modern dentistry, is associated with considerable inter-observer variability. In routine clinical practice, clinicians are required to evaluate a high volume of radiographic images daily, a cognitively demanding task in which diagnostic fatigue, time constraints, and the inherent complexity of overlapping anatomical structures can lead to the inadvertent oversight of early-stage pathologies. Artificial intelligence (AI) offers a transformative opportunity to augment clinical decision-making by providing rapid, objective, and consistent radiographic analysis, thereby serving as a tireless adjunct capable of flagging findings that may be missed during routine human inspection. MethodsThis study developed and validated a deep learning system for the automated detection of dental caries and alveolar bone loss using a dataset of 1,063 periapical and bitewing radiographs. Two separate YOLOv8s object detection models were trained and evaluated using a rigorous 5-fold cross-validation methodology. To align with the clinical use-case of a screening tool where high sensitivity is paramount, a custom image-level evaluation criterion was employed: a true positive was recorded if any predicted bounding box had a Jaccard Index (IoU) > 0 with any ground truth annotation. Model performance was systematically evaluated at confidence thresholds of 0.10 and 0.05. ResultsAt a confidence threshold of 0.05, the caries detection model achieved a mean precision of 84.41% ({+/-}0.72%), recall of 85.97% ({+/-}4.72%), and an F1-score of 85.13% ({+/-}2.61%). The alveolar bone loss model demonstrated exceptionally high performance, with a mean precision of 95.47% ({+/-}0.94%), recall of 98.60% ({+/-}0.49%), and an F1-score of 97.00% ({+/-}0.46%). ConclusionThe YOLOv8-based models demonstrated high accuracy and high sensitivity for detecting dental caries and alveolar bone loss on periapical radiographs. The system shows significant potential as a reliable automated assistant for dental practitioners, helping to improve diagnostic consistency, reduce the risk of missed pathology, and ultimately enhance the standard of patient care.