Biosensors and Bioelectronics

BackgroundThere is a need for synthetic peptide-based serologic assays that exploit avidity to replace whole antigens while enabling low-cost diagnostics in resource-limited settings. ObjectiveTo evaluate the diagnostic accuracy of a polymeric peptide-based ELISA leveraging avidity to enhance signal. MethodA 15-member SARS-CoV-2 peptide library corresponding to multiple epitope clusters and proteins was screened by indirect ELISA using pooled sera from RT-PCR-confirmed COVID-19 patients to identify peptides with possible diagnostic utility. The identified lead candidate, S559, possessed terminal cysteine-substitution to allow disulfide polymerization, and the resulting avidity gain was evaluated by comparing the apparent dissociation constant (KDapp) before and after depolymerization with N-acetylcysteine. The performance of an optimized ELISA using S559 was evaluated on 1,222 prospectively collected COVID-19 serum samples and 218 biobanked pre-COVID control serum samples. ResultsPolymeric S559 with a KDapp of 29.26 nM-1was demonstrated to have a 218% avidity gain relative to the completely depolymerized form. At pre-defined thresholds, the optimized S559 ELISA has a sensitivity and specificity of 83.39% (95%CI: 81.18% and 85.43%) and 96.79% (95%CI: 93.50% and 98.70%), respectively. At post hoc thresholds determined by Youden index, sensitivity and specificity reached 95.01 (95% CI: 93.63% - 96.16%) and 100.00% (95% CI: 98.32% - 100.00%), respectively. ConclusionHomomultivalent epitope presentation using polymeric S559 allows a highly specific immunoassay using human sera that may have important value in detecting antibodies, whether for diagnosing infection, confirming vaccination status or conducting surveillance.

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identified secretory granules as the dominant long-wavelength fluorescence source and showed that normal pituitary had higher granule content than PitNETs. For the first time, we made use of the pituitary NIRAF during surgery and assessed its performance for pituitary/adenoma separation in vivo for 27 surgeries and showed near-perfect separability between pituitary and non-pituitary measurement sites with ROC-AUC of 0.98. The obtained results clearly demonstrate that the suggested method, based on the solid microscopic background, has the potential for clinical translation and paves the way for enhanced gland preservation during resection.

Clear cell renal cell carcinoma (ccRCC) is the leading cause of kidney cancer-related death, but how the tumor microenvironment shapes patient survival is not completely understood. Here, we describe the characterization of ccRCC tumor ecosystems from 498 patients using imaging mass cytometry with a focus on tumor, myeloid, and T cell landscapes. Data from more than 3 million single cells is analyzed using machine-learning to identify key ecosystem features that outperform basic clinical data for predicting patient survival. We define three survival ecotypes of ccRCC: Poor ecotypes, correlate with the worst survival, have high levels of ICAM1 and CD44 expression in tumor cells and are enriched in M2-like macrophages and interactions of exhausted CD8+ T cells with macrophages. Favorable ecotypes are characterized by high levels of VHL on tumor cells and of HLADR on myeloid cells and contain Th1-like CD4+ T cells. Medium ecotypes have the highest endothelial cell density and various immune-to-tumor interactions. Multi-omic characterization of these ecotypes using targeted genomic sequencing and metabolic imaging reveals distinct genomic and metabolic features, including BAP1 mutations in Poor and VHL monodriver/wild-type status in Favorable patients. We show that deep learning allows ecotype prediction directly from standard pathology H&E images. We validate the ecotypes and their associated molecular characteristics with orthogonal omics data across five clinical cohorts and more than 2,500 patients. These analyses highlight an overall survival benefit for Medium patients treated with immunotherapy. In summary, our study distills the survival-relevant information encoded in the ccRCC tumor microenvironment into prognostic survival ecotypes, which may inform clinical decision making in the future.

Immune effector cell-associated neurotoxicity syndrome (ICANS) is a common and life-threatening complication of chimeric antigen receptor (CAR) T-cell therapy, with early detection being critical for timely intervention and improved outcomes. Cytokines such as interleukin-6 (IL-6) are key mediators of the inflammatory cascade underlying ICANS pathogenesis, but prospective clinical evidence for their predictive value is limited. Here we quantify IL-6 levels in a prospective cohort of 40 CAR-T patients (270 serum samples), using a simple in-house microfluidic bead immunoassay. IL-6 levels measured by our assay were significantly associated with ICANS onset. Specifically, each [~]3.4-fold increase in IL-6 levels was linked to a 74% increase in the odds of developing ICANS the following day, independent of other clinical variables. Overall, we show the prognostic value of IL-6 for next-day ICANS, demonstrate the potential of frequent cytokine measurement to guide CAR-T patient management, and develop a simple experimental method to perform such monitoring.

Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for more farsighted resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods. Here, we used long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. The base modification data produced by PacBio SMRT sequencing was analyzed by an in-house pipeline utilizing position weight matrices and UMAP visualizations. The approach was validated by a synthetic community with known bacterial composition. Our analysis revealed several previously unreported ARGs and their hosts with varying risk levels defined by their potential as emerging public health threats. For instance, Arcobacter, as one of the prevalent taxa in influent wastewater, was shown to carry a latent beta-lactamase gene with high predicted mobility potential. Of the other emerging beta-lactamases, we provided a real-life example of ongoing pdif module-mediated genetic reshuffling of the blaMCA gene occurring at least within Acinetobacter hosts in our samples. Additionally, we identified Simplicispira, Phycisphaerae, and environmental groups of the Bacteroidales order as the carriers of established, clinically important ARGs. These findings support the intermediate host roles of strictly environmental bacteria for the further dissemination of mobilized ARGs, highlighting the importance of exploring the uncultivated, or non-pathogenic, carriers of ARGs for the early detection of newly arising ARGs and mobility mechanisms.

Serological surveillance is fundamental to infectious disease research and informed public-health decision making. Immunoassays used in the study of pathogen-specific immunity have historically relied on the collection of venous blood. While critical for many public-health applications, this sample collection method is invasive and resource intensive. The costs and logistical barriers associated with venous blood collection are exacerbated in resource-limited regions, and the shift to less invasive sampling methods would increase sample availability for pathogen surveillance and study of pathogen-specific immunity. To this end, we have developed and optimized a self-collected, saliva-based immunoassay capable of quantifying pathogen-specific antibody binding in saliva samples. Using samples collected from geographically and epidemiologically diverse regions of the world, we compared antigen-specific IgG levels in paired plasma and saliva samples. We observed that levels of IgG against multiple pathogens of public health concern - including SARS-CoV-2 and dengue virus (DENV) - were highly correlated in plasma and swab-collected saliva. In addition, the decay of maternally derived antibodies in saliva samples collected from infants was readily observed using this immunoassay, demonstrating the assay's sensitivity and potential for use in measuring antibody kinetics. We posit that this assay represents a climate stable, non-invasive tool that can aid in the surveillance and study of pathogen-specific immunity across a broad range of public-health indications.

BackgroundDengue virus (DENV) appears to manipulate several cellular metabolic pathways to permit its replication and immune evasion in the host. Here, we employed high-resolution mass spectrometry (HR-MS) to investigate the serum metabolomic landscape of clinical DENV infection. MethodsSerum specimens from primary dengue (n=11), secondary dengue (n=9) samples, and healthy controls (n=10) were used for untargeted and targeted metabolomic quantification on a Waters Xevo G2-XS QTof Mass Spectrometer. The binding potential of selected ligands against DENV NS1, NS3, and NS5 was evaluated. Crystal structures were retrieved from Protein Data Bank and prepared using the Schrodingers protein preparation wizard. Based on findings from untargeted metabolomics, we validated certain bioactive lipid metabolites using commercial enzyme immunoassays. ResultsSerum metabolomic profiling revealed multiple distinct patterns for primary and secondary dengue versus controls. A consistent peak was observed at 2.06 mins across all samples. Certain bioactive lipid metabolites, such as, lysophospholipids, phosphatidylcholines, phosphatidylserines, and phosphatidylinositols, were detected alongside carnitine fragments, ceramides, diacylglycerols (DAGs), and bile acid conjugates in dengue. Molecular docking showed that DAG consistently exhibited strong binding to all the DENV proteins. Notably, LPC 22:6 showed a selectively strong affinity for NS5. Enzyme validation showed that in the secondary dengue cohort, LPC was significantly elevated than primary and healthy controls (p<0.05). ConclusionsOur investigations of the metabolomic landscaping, unveiled certain characteristic anabolic shift revealing metabolic vulnerabilities in clinical DENV infection, warranting investigations for use as potential biomarkers of inflammation in disease diagnosis and prognosis. Author summaryDengue is a mosquito-borne tropical viral infection that can range in severity from asymptomatic to life-threatening manifestations. Dengue virus (DENV) hijacks cellular machinery to sustain its survival in the host. Using high-resolution mass spectrometry (HR-MS), we studied the serum metabolomic imprints of dengue infection. The binding ability of selected metabolomic ligands against DENV NS1, NS3, and NS5 was studied. We found several distinct retention patterns for the dengue cases, with a consistent peak at 2.06 min across all samples. Further, several bioactive lipid metabolites were detected in the dengue infected cohort. Our molecular docking studies showed that diacylglycerol, a lipid metabolite exhibited strong binding with all the DENV proteins. We concluded that certain unique lipid metabolomic imprints exist in clinical DENV infection. The identified metabolomic signatures reveal significant potential for metabolomics to elucidate host-virus interactions, contributing to the advancement of antiviral and symptomatic treatments, along with prognostic or diagnostic biomarkers of dengue disease.

IntroductionThe precise determination of diagnostic cut-off points is essential for the development of multimarker panels in oncology. In previous work on pulmonary nodules, it was observed that the standard two-parameter logistic fit could be insufficient for biomarkers with asymmetric distributions. Furthermore, the calculation of empirical cut-off points based on graphical visualization presented limitations in precision and reproducibility. ObjectiveThis study presents a methodological advancement in the data analysis phase (Stage 1), introducing new Python algorithms for the direct analytical calculation of empirical intersections and robust mathematical modeling using Dual Annealing with both two-parameter and four-parameter logistic functions. This improved methodology feeds into the ThresholdXpert 1.0 software tool for combinatorial optimization of biomarker panels (Stage 2), and is applied here to the diagnostic challenge of hepatocellular carcinoma (HCC). MethodsThe methodology was first validated by re-analyzing a dataset of patients with pulmonary nodules (N=895). It was subsequently applied to an HCC dataset derived from the cohort of Jang et al. (208 HCC, 193 cirrhosis, 401 total), randomly divided into a training set (280) and an independent test set (121). Scripts were developed to compare the previous two-parameter logistic fit with the new two- and four-parameter logistic models. Finally, ThresholdXpert 1.0 was used for multimarker panel optimization. ResultsThe integration of empirical calculation, logistic modeling, and combinatorial optimization through ThresholdXpert 1.0 provides a robust and coherent framework for the development of multimarker diagnostic panels. The four-parameter logistic model provided additional validation without substantially modifying cut-off values for most biomarkers, confirming the stability of the approach while offering greater flexibility for complex distributions. When applied to hepatocellular carcinoma, the framework identified a molecular panel composed of AFP, PIVKA-II, OPN, and DKK-1 with sensitivity of 0.77 and specificity of 0.72, and an optimized panel incorporating inverse MELD that achieved the best overall balance (sensitivity 0.73, specificity 0.75) in independent external validation. These results demonstrate the potential of this approach as a generalizable tool for the optimized design of binary diagnostic systems in oncology. ConclusionThe integration of complementary mathematical modeling enhances the capability of ThresholdXpert 1.0 to identify robust diagnostic panels, as in some cases a single biomarker may outperform biomarker combinations, and vice versa. This approach enabled the integration of molecular biomarkers and clinical variables under a unified mathematical framework. Contactroberto117343@gmail.com

Abdominal aortic aneurysms (AAA) affect more than 1% of adults over 50 and carry significant mortality risk. Current surveillance relies on intermittent imaging (ultrasound or MRI) at 6-24 month intervals, which may miss rapid growth acceleration between visits. We investigate the feasibility of continuous aneurysm diameter tracking using photoplethysmography (PPG) signals. Using a one-dimensional hemodynamic model that simulates pulse wave propagation from the heart to the digital artery, we demonstrate that while single-observation diameter estimation is fundamentally limited by noise and confounding variables, aggregating thousands of observations over one or more days may achieve sub-millimeter precision. Specifically, the lower bound error analysis shows diameter uncertainty decreases to 0.7 mm with 1,600 measurements under baseline noise conditions. We validate this approach through 12- month tracking simulations of eight virtual patients with constant and accelerating growth rates, achieving root-mean-square tracking errors of [~]0.3 mm. Furthermore, we demonstrate that patient-specific model calibration from clinical measurements, despite yielding imperfect parameter estimates, still enables accurate diameter tracking (median RMSE = 0.49 mm across 50 virtual patients). These results suggest that wearable PPG monitoring could complement traditional imaging for aneurysm surveillance, potentially enabling earlier detection of growth acceleration and more timely clinical intervention. Data and Code AvailabilityAll data produced in the present study and code for generating said data are available upon reasonable request to the authors. Institutional Review Board (IRB)This research does not require IRB approval since it is not "human subjects research" as it does not include activities that involve interaction with individuals or access to identifiable private information.

Syphilis remains a major public health concern. However, current serologic assays are limited in their ability to distinguish active from previously treated disease. We applied tandem mass tag-based quantitative proteomics to plasma from 10 adults with active syphilis and 10 age- and gender-matched non-diseased controls. We identified 54 differentially regulated proteins (36 upregulated, 18 downregulated). Those proteins map to immune and inflammatory responses, acute-phase signaling, coagulation and vascular pathways, and cellular stress processes. Three sets of between 2-5 proteins achieved >99% discrimination between cases and controls. Our exploratory findings support proteomics as a potential tool to develop novel syphilis diagnostics.

Multi-cancer early detection (MCED) tests can detect several cancer types and stages. We previously developed a methylation and protein (MP V1) MCED classifier. In this study, we present a refined MP V2 classifier, developed by evaluating model architectures that improved performance in prospectively enrolled case-control cohorts under standard testing conditions. The newly developed MP V2 classifier was trained to be more generalizable and achieve increased early-stage sensitivity at a target specificity of [&ge;]97.0%. MP V1 and MP V2 classifier performances were compared using a previously described test set, and MP V2 performance was also evaluated in a new independent clinical validation set. Compared to MP V1, the MP V2 classifier demonstrated a 7.3% increase in overall sensitivity, with sensitivity increases of 7.6%, 9.2%, and 8.3% for stages I, II, and stages I/II, respectively, in the intended use (breast and prostate cancers excluded) test set. In an independent validation intended use set, the MP V2 classifier showed an overall sensitivity of 55.6%, with sensitivities of 26.8%, 42.9%, and 34.8% for stages I, II, and stages I/II, respectively. In a case-control setting, the MP V2 classifier offered improved sensitivity for early-stage cancers at a lower specificity target.

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and time-effective detection methods. In this paper, this paper introduces a novel approach to the diagnosis of CAD based on a Convolutional Neural Network (CNN) with a temporal attention mechanism. The model will be developed on an architecture that will automatically extract and emphasize critical features from sequential medical imaging data from coronary angiograms, allowing subtle signs of CAD to be easily spotted, which could not have been detected by convention. The temporal attention mechanism strengthens the ability of a model to focus on relevant temporal patterns, thus improving sensitivity and robustness in detecting CAD for various stages of the disease. Experimental validation on a large and diverse dataset demonstrates the efficacy of the proposed method, with significant improvements in both detection accuracy and processing time compared to traditional CNN architectures. The results of this study propose a scalable solution system for the diagnosis of CAD. This proposed system can be integrated into clinical workflows to assist healthcare professionals. Ultimately, this research contributes to the field of AI-driven healthcare solutions and has the potential to reduce the global burden of CAD through early automated detection.

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration with data-driven machine learning approaches for growth prediction. The proposed arterial G&R model incorporates key mechanisms influencing aneurysm progression, including elastin degradation and stress-mediated collagen production. A modified elastin degradation formulation was introduced to generate realistic aneurysm geometries exhibiting clinically relevant features such as asymmetry and tortuosity. By systematically varying parameters governing elastin damage and collagen production, 200 distinct G&R simulations were performed to produce a diverse set of AAA geometries. The dataset was further expanded using kriging-based spatial interpolation to construct a large in silico cohort. The synthetic dataset, combined with longitudinal imaging data from 25 patients, was used to train and validate four machine learning models: Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). A two-step training strategy was adopted to predict maximum aneurysm diameter and growth rate based on prior geometric characteristics. The LSTM model achieved the highest performance for maximum diameter prediction (R{superscript 2} = 0.92), while the RNN demonstrated strong overall performance (R{superscript 2} = 0.90 for maximum diameter and 0.89 for growth rate). The DBN and GRU models also showed competitive predictive capability. Overall, this study demonstrates that integrating physics-based G&R simulations with machine learning enables accurate prediction of AAA growth and maximum diameter. The proposed framework provides a scalable strategy for augmenting limited clinical datasets and offers a promising tool to support personalized risk assessment and treatment planning.

Mycobacterium tuberculosis (MTB) disease is a major global health threat with most tuberculosis (TB) cases occurring in low-and middle-income countries (LMIC) with limited healthcare infrastructure. Near-point-of-care testing which can be deployed at peripheral clinical settings is needed to start treatment earlier and thereby improve treatment outcomes. Here we report the development and preliminary characterization of an MTB detection assay that utilizes tongue swab or sputum specimens for The DASH(R) Rapid PCR System which employs cartridge-based automated sequence specific capture sample prep combined with dual target qPCR multicopy MTB insertion sequences IS6110 and IS1081 amplification and detection. MTB is resistant to conventional bacterial lysis techniques; therefore, we evaluated two pre-cartridge lysing techniques, mechanical lysis and sonication, and selected sonication for all subsequent studies. The DASH MTB assay demonstrated a limit of detection of 2.5 MTB cells/swab with no detection of 10 non-tuberculosis Mycobacterium strains. Clinical testing of 100 (49 positive and 51 negative) de-identified blinded sputa from South African symptomatic clinic attendees yielded an overall test sensitivity of 96% (100% for smear positive samples and 88% for smear negative samples) and specificity of 88% when compared to sputum culture. In a separate study of 110 tongue swab specimens (70 positive and 40 negative) from South African symptomatic clinic attendees, the sensitivity was 93% and the specificity was 100%. We further demonstrated that the test is compatible with peripheral LMIC settings via external battery operation and cartridge stability at 45{degrees}C for up to one year. ImportanceTuberculosis (TB) is the single most deadly infectious disease with 1.23 million deaths in 2024. Near-point-of-care testing which can be deployed at peripheral settings that lack laboratory infrastructure to deliver prompt and accurate diagnosis is needed to start treatment earlier and thereby improve treatment outcomes. In this study, we have developed an automated test to detect Mycobacterium tuberculosis (MTB), the cause of TB, from sputum and tongue swab specimens. Its high sensitivity and specificity, rapid time to result, and compatibility with environments that lack air conditioning and consistent electricity make this assay suitable for diverse clinical settings.

Micro- and nanoplastics (MNPs) are increasingly detected in human tissues, yet their causal contribution to cardiovascular disease remains poorly understood. Here we show that oral exposure to polyethylene (PE) and polyvinyl chloride (PVC) -- the most abundant polymers found in human atheromas -- accelerates atherosclerosis in ApoE-/-mice through distinct, polymer-specific molecular mechanisms. While both polymers increased plaque burden and reduced contractile smooth muscle cell (SMC) markers, single-cell transcriptomic profiling revealed divergent phenotypic trajectories. PE exposure drives SMCs toward a chondromyocyte-like cell (CMC) state, characterized by upregulated osteogenic signaling and markedly increased vascular calcification. Conversely, PVC exposure promotes a fibromyocyte-like program associated with altered collagen metabolism and accelerated cell migration without enhancing calcification. These distinct SMC programs are reflected in the transcriptional signatures of symptomatic human carotid plaques, suggesting clinical relevance for polymer-specific vascular remodeling. Our findings establish a causal link between common environmental plastics and accelerated atherosclerosis, demonstrating that MNP-induced vascular risk is mediated by divergent SMC fate decisions. These results provide a mechanistic framework for assessing the cardiovascular impact of global plastic pollution and identifying potential therapeutic targets to mitigate MNP-associated vascular toxicity.

This research presents multimodal deep learning for structural heart disease prediction. We evaluated multiple deep learning architectures, including TCN, Simple CNN, ResNet1d18, Light transformer and Hybrid model. The models were examined across the three seeds to ensure robustness, and bootstrap confidence interval is used to measure performance differences. TCN consistently outperforms other competing architectures, achieving statistically significant improvements with stable performance across runs. Similarly in predictive analysis, TCN has efficient computation and stable training compared to all competing architectures. Our results show that TCN emphasizes fairness evaluation when developing deep learning models for healthcare applications.

Frequent blood testing is a routine but burdensome reality for many children, particularly those with chronic, rare, or medically complex conditions. Repeated clinic, hospital, and laboratory visits can disrupt family life, increase stress for children and caregivers, and limit access to timely monitoring and research participation. Despite advances in pediatric care, blood collection has remained largely tethered to in-person clinical settings. This study validates a new model: safe, effective, parent-administered pediatric blood collection performed at-home. We evaluated the RedDrop ONE capillary blood collection device in a real-world, parent-administered home setting to determine whether non-clinical caregivers can reliably collect clinically meaningful blood samples from children without venipuncture, specialized training, or in-clinic support. Conducted under Institutional Review Board (IRB) oversight, this observational usability study enrolled 50 children aged 3-17 years across a geographically diverse U.S.-based pediatric population, including healthy and medically fragile children with chronic autoimmune and rare diseases. All study activities, including enrollment, consent, instruction, collection, and sample return, were completed remotely, reflecting real-world adoption conditions rather than controlled clinical environments. Parents successfully collected blood samples from their children at home with high consistency, low perceived pain, and strong overall acceptance. Across collections, blood and serum volumes were sufficient and reproducible, and laboratory analysis confirmed strong analytical concordance between samples collected from two different anatomical sites, arm and leg. Parents reported high confidence using the device, short collection times, and a high likelihood of completing collections on the first attempt. Importantly, both parents and children rated the overall experience as better than expected, and parents consistently reported that the RedDrop ONE experience was superior to traditional finger-prick and needle-based venous blood draws. Parents reported minimal child discomfort and greater flexibility by avoiding in-clinic phlebotomy visits. These benefits are especially meaningful for families managing chronic or rare pediatric conditions that require repeated blood monitoring. By enabling blood collection at-home, this model reduces travel burden, scheduling constraints, and procedural anxiety while maintaining analytical reliability. This study also demonstrated that parent-administered pediatric blood collection can support real-world clinical workflows beyond research. All samples were successfully shipped overnight at ambient temperature and processed by a CLIA-certified laboratory, supporting feasibility for remote pediatric patient monitoring and decentralized clinical trials. While lipid testing served as the representative clinical use case, the volumes and consistency achieved exceeded volume thresholds commonly required for advanced downstream applications, including proteomics, metabolomics, transcriptomics, and genomic analyses. Taken together, these findings validate parent-administered pediatric blood collection as a practical, scalable alternative to in-clinic phlebotomy for many use cases. By shifting blood collection from the clinic to the home, this approach has the potential to reduce reliance on in-person phlebotomy, integrate seamlessly into routine pediatric care, and expand access to monitoring and research for families who face geographic, logistical, or medical barriers. For health systems, researchers, and parents alike, this study supports a future in which clinically meaningful pediatric blood collection is no longer limited by healthcare facility location but instead centered on the child and family.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecular heterogeneity and inconsistent responses to gemcitabine-based therapy. Although KRAS mutations are nearly ubiquitous, the broader RTK-RAS and MAPK signaling networks, and their association with therapeutic response, remain insufficiently characterized. We performed an integrative clinical-genomic study of 184 PDAC tumors, stratified by age at diagnosis and gemcitabine exposure, systematically evaluating somatic alterations within curated RTK-RAS/MAPK gene panels. Conversational artificial intelligence agents (AI-HOPE-RTK-RAS and AI-HOPE-MAPK) were deployed to dynamically construct cohorts and conduct pathway-level analyses, with results subsequently confirmed using conventional statistical approaches. Among late-onset PDAC cases, ERBB2 and RET mutations were significantly enriched in gemcitabine-treated tumors. In early-onset disease, CACNA2D family alterations were more common in untreated tumors, whereas FLNB and TP53 mutations were observed at higher frequencies in treated cases. Notably, late-onset patients who did not receive gemcitabine and lacked RTK-RAS or MAPK pathway alterations demonstrated significantly improved overall survival. These findings identify age- and treatment-specific signaling dependencies extending beyond canonical KRAS alterations and reinforce a precision oncology framework in PDAC. Conversational AI enabled rapid, multidimensional integration of clinical and genomic data, facilitating the identification of clinically meaningful pathway architectures.

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

Pulmonary hypertension (PH) is a progressive condition characterized by increased pulmonary arterial pressure. Endothelial cell dysfunction is one important characteristic of PH. Recently, capillary endothelial cells, including aerocytes (aCaps) and general capillary cell (gCaps), have been detected in developing lungs but their role and the regulatory mechanisms underlying PH remain poorly understood. The goal of this study was to identify changes in Caps and their effects on hypertensive pulmonary circulation. We set up a Capillary Alveoli Micro-physiological System (CAMS) incorporated with hPSCs(human pluripotent stem cells)-aCaps to show loss of Cap connection under dynamically cultured hypoxic condition. We employed single-cell RNA sequencing (scRNA-seq) and immunofluorescence to demonstrate impaired gCaps differentiation with increased expression of cell membrane receptor CD93 in PH patients and a Sugen 5416/hypoxia (SuHx) rat model. Conditional Knockdown or Lentiviral overexpression of CD93 alleviated the pathology observed in SuHx mice. We also revealed that CD93 overexpression upregulated SMAD2/3 to repress Apelin (APLN) expression by CHIP assay. Finally, supplementation with an APLNR agonist in the PH rat model promoted gCaps-to-aCaps differentiation and improved haemodynamic indices. Overall, our results highlight the potential for promoting capillary cell differentiation with G protein biased APLNR agonist as a therapeutic strategy for pulmonary vascular disease.