搜索结果：Translational pediatrics

Neuroblastoma is a childhood tumor in which MYC oncogenes are commonly activated to drive tumor progression. Survival for children with high-risk neuroblastoma remains poor despite treatment that incorporates high-dose chemotherapy, stem cell support, surgery, radiation therapy and immunotherapy. More effective and less toxic treatments are sought and one approach under clinical development involves re-purposing the anti-protozoan drug difluoromethylornithine (DFMO; Eflornithine) as a neuroblastoma therapeutic. DFMO is an irreversible inhibitor of ornithine decarboxylase (Odc), a MYC target gene, bona fide oncogene, and the rate-limiting enzyme in polyamine synthesis. DFMO is approved for the treatment of Trypanosoma brucei gambiense encephalitis ("African sleeping sickness") since polyamines are essential for the proliferation of these protozoa. However, polyamines are also critical for mammalian cell proliferation and the finding that MYC coordinately regulates all aspects of polyamine metabolism suggests polyamines may be required to support cancer promotion by MYC. Pre-emptive blockade of polyamine synthesis is sufficient to block tumor initiation in an otherwise fully penetrant transgenic mouse model of neuroblastoma driven by MYCN, underscoring the necessity of polyamines in this process. Moreover, polyamine depletion regimens exert potent anti-tumor activity in pre-clinical models of established neuroblastoma as well, in combination with numerous chemotherapeutic agents and even in tumors with unfavorable genetic features such as MYCN, ALK or TP53 mutation. This has led to the testing of DFMO in clinical trials for children with neuroblastoma. Current trial designs include testing lower dose DFMO alone (2,000 mg/m(2)/day) starting at the completion of standard therapy, or higher doses combined with chemotherapy (up to 9,000 mg/m(2)/day) for patients with relapsed disease that has progressed. In this review we will discuss important considerations for the future design of DFMO-based clinical trials for neuroblastoma, focusing on the need to better define the principal mechanisms of anti-tumor activity for polyamine depletion regimens. Putative DFMO activities that are both cancer cell intrinsic (targeting the principal oncogenic driver, MYC) and cancer cell extrinsic (altering the tumor microenvironment to support anti-tumor immunity) will be discussed. Understanding the mechanisms of DFMO activity are critical in determining how it might be best leveraged in upcoming clinical trials. This mechanistic approach also provides a platform by which iterative pre-clinical testing using translational tumor models may complement our clinical approaches.

Changes in medicine domestically and globally are transforming primary care in the United States. Many have suggested that primary care is in crisis or at least at a crossroads in the United States. The Annals of Internal Medicine recently devoted much of one issue to this topic.1 Primary care for children and adolescents, however, was not addressed specifically. This article focuses on pediatrics and identifies potential roles and new models for primary care pediatrics. The Institute of Medicine has defined primary care as “the provision of integrated, accessible health care services by clinicians who are accountable for addressing a large majority of personal health care needs, developing a sustained partnership with patients, and practicing in the context of family and community.”2 Starfield3 has defined 4 attributes of primary care including first-contact care, longitudinality, comprehensiveness, and coordination. September 11, 2001, the anthrax scare, and emerging threats such as severe acute respiratory syndrome (SARS) have brought a new focus on the importance of individual-level contacts in addressing population-level threats. Before these world events, however, primary care pediatrics was already grappling with its identity and responding to significant changes in medical systems, science, and family needs. The pace and scope of these changes are such that primary care pediatricians of the future will not be performing the same role as today.Historically, American medicine has tended to be reactive rather than proactive in defining its roles in society. However, dynamic change demands collective reflection; it is time to be proactive in assessing the needs of patients, exploring potential roles as health care providers, and developing the mechanisms to redefine the primary care pediatrician of the future.Projecting future trends requires reflection on the history of the profession of preventive pediatrics. In the 1800s, few physicians in the United States routinely treated children; mothers were responsible for treating pediatric illnesses at home. Influential physicians sought to address the needs of this underserved population of children. Some credit social feminism and women physicians such as Elizabeth Blackwell, who established infant hospitals in the 1850s, and S. Josephine Baker, who on a wide scale implemented maternal education on health issues by nurses and established a network of infant welfare stations that included physical examinations and anticipatory guidance.4 Others recognize Nathan Straus, who in the late 1890s initiated a free-milk program and child-weighing stations seeking to combat mortality from diarrheal disease and monitor child health.5 Primary care pediatrics in the United States originated with a strong emphasis on prevention, public health, and advocacy. The advent of vaccines and toxoids in the 1930s and antimicrobials in the 1940s and 1950s expanded the role of primary care. Since then, the history and physical examination, a variety of other screening tests, and numerous immunizations have been added to the repertoire of the primary care pediatrician.In the 1970s, psychosocial issues were termed the “new morbidity.”6 Attention to these psychosocial issues affecting child and adolescent health have further expanded the role of pediatricians in prevention through screening and anticipatory guidance on behavioral issues. The American Academy of Pediatrics published “The Pediatrician and the ‘New Morbidity’”7 in 1993 and “The New Morbidity Revisited: A Renewed Commitment to the Psychosocial Aspects of Pediatric Care”8 in 2001. Although this new morbidity is not particularly new, the breadth of pediatric primary care has broadened, with the provider increasingly called on to address an array of social problems during the 15-minute office visit. As the range of preventive roles has widened, pediatricians have become increasingly grounded in public health efforts and population-level goals, even as individual-level contacts have remained the central focus of health care delivery.Evolution in the role of the primary care pediatrician has occurred in concert with the changing organization and financing of health services. Although current changes seem especially tumultuous and difficult to negotiate, it has been almost 2 decades since Green9 noted that organizational and funding changes, despite their regressive character, offered an opportunity to promote a “new prevention.” That opportunity continues to exist.As with a historical perspective, understanding the role of primary care pediatricians in other countries can offer a broader view of pediatric primary care in the United States. In fact, the United States is among a minority of countries that utilize highly trained pediatricians as providers of first-contact care and well-child care.4 In Canada and Britain, primary care of children is largely provided by general practitioners and public health units, with pediatrician involvement concentrated in more complicated cases and hospital-based care. In many nonindustrialized countries, first-contact care typically is provided by public health workers, with pediatricians providing specialized Western medical care in combination with traditional medical modalities.The United States is unique also in integrating well care and acute care into the primary care system (although such integration is far more complete for affluent Americans than for the poor). The concept of the medical home led by a pediatrician that provides primary, secondary, and tertiary preventive care has been endorsed by the American Academy of Pediatrics.10,11 In many other countries, preventive services are offered by public health workers in a setting separate from those dedicated to illness care. The growth of pediatric nurse practitioners and other primary care providers may begin a trend in which US primary care pediatrics will become a “specialty” similar to international models.Internationally, many view the United States as already having a specialty orientation with underdevelopment of a primary care health system.12,13 Although many look abroad when discussing models of health care financing and the appropriate balance between private and public sector involvement, Starfield reflects that “little of the debate centers on the value of the systems as reflected by indicators of health that are amenable by medical care.”13 Starfield compared 10 Western industrialized nations on their extent of primary health service, health indicators (eg, infant mortality, life expectancy, and death rates), and satisfaction in relation to overall costs of the health system. Primary health services correlated with better health and public satisfaction in 9 of the 10 countries. In the cross-national comparison, the United States ranked low in the extent of primary health service, health indicators, and public satisfaction. Within the US, states with more primary care physicians had better health indicators.14 International, national, and individual patient studies have shown that continuity of relationships and primary care improve health.In “Crossing the Quality Chasm: A New Health System for the 21st Century,” the Institute of Medicine states that “the American health care delivery system is in need of fundamental change…the frustration levels of both patients and clinicians have probably never been higher.”15 Whether this is a sober or alarmist assessment is debatable, but it highlights the need to create a vision for the future of primary care pediatrics. Such reflection begins with acknowledgment of a few clear trends. One is the rise of nonpediatrician providers. Currently, general pediatricians provide the bulk of primary health care to children. Data from the National Ambulatory Medical Care Survey show that >60% of office visits to primary care physicians are to general pediatricians, with remaining visits to family practitioners, general practitioners, or other providers,16 and this percentage has increased in the last 20 years.17 A growing number of pediatric nurse practitioners and other nonphysician providers has begun working alongside primary care pediatricians. There are ∼10000 pediatric nurse practitioners in the United States,18 with the majority involved in the delivery of primary care services. More than 60% of the 6700 members of the National Association of Pediatric Nurse Associates and Practitioners work in urban areas with populations of >100000, increasing access for many poor, underserved families.19 Telephone pediatrics once provided by primary care pediatricians is increasingly provided by nurse call systems.Many managed care plans initially were reluctant to use midlevel professionals such as pediatric nurse providers or child health associates (pediatric physician assistants) to provide pediatric primary care services because of a concern that these practitioners would be perceived as providing inferior care.20 However, it has been shown that they can meet the majority of children’s primary care needs as currently conceived, and it is likely that there will be an increasing reliance on midlevel providers.21–24 Pediatricians Pizzo and Lovejoy have asserted that “. . . routine well child care and some aspects of acute care management in otherwise healthy children should be increasingly delivered by the nurse clinician and family practitioner who are working in collaboration with the pediatric generalist.”25A second trend is the increasing bureaucratization of health care. It was hoped that managed care would promote the status of the primary care provider by allowing them to control the coordination and referral of patients. Instead, the role of the “gatekeeper” has become mired in paperwork and bureaucracy and viewed negatively by patients. The rise of managed care and capitation has challenged the productivity and financial management of outpatient pediatrics. Malpractice insurance costs have risen dramatically. Increasing numbers of primary care pediatricians are dissatisfied,26 and some are opting out of managed care or out of medicine altogether because of increased stress and financial concerns. Some have predicted a tiering of providers with the rich using non-managed care providers and those unable to afford it staying in managed care.27A third trend on the inpatient side is the growth of hospitalist systems in pediatrics. In many locales, hospitalists have replaced primary care pediatricians on the general inpatient ward. In 1996 the term “hospitalist” was coined to describe physicians primarily dedicated to care of hospitalized patients.28 Eight years later the movement has grown dramatically. Although it has been argued that the cost29 and efficiency of inpatient care can be improved with full-time hospitalists without harmful effects on quality or patient satisfaction,28 there are challenges to continuity of care, the doctor-patient relationship, financial reimbursement,30,31 and communication among multiple providers, institutions, and systems.A fourth trend related to the hospitalist trend is growing specialization.32 The numbers of primary care pediatricians who regularly attend deliveries and are involved in the care of critically ill newborns are declining. With the rise of neonatology as a specialty, this role has diminished. Some pediatric specialists have been performing primary care services or have defined specialty areas that were previously in the domain of primary care (eg, neonatology, adolescent medicine, and developmental-behavioral pediatrics). Faced with a growing list of primary care needs (eg, new morbidities), some primary care pediatricians perceive less time to handle complicated medical issues and to interface with subspecialists. Growing financial pressures have sometimes pitted generalists and specialists against each other despite a shared goal of quality child health.Challenges to primary care pediatrics are many. However, amid challenges and crises there are opportunities. What are the core values of the primary care pediatrician, and are they salient today? What is the niche for the primary care pediatrician of the future? Some present and future niches may include the following.A central tenet of primary care is the long-term, trusting relationship between patient and provider. Americans want this continuity. In a survey of patients, 94% valued having a primary care physician who knew all their medical problems; most wanted initial care for common problems to come from their primary physician.33 Another study found that 92% of US patients valued continuity with a doctor, compared with 71% of patients from the United Kingdom.34 Unfortunately, there is much evidence that the quality of primary care relationships has eroded over the years.35,36Considerable research demonstrates that characteristics of primary care are linked to health outcomes. Patient trust in the physician and physician knowledge of the patient increase the likelihood of patient adherence to a physician’s advice and are correlated with satisfaction and improved health status.37 A continuous relationship with a primary care health provider has been linked to a wide range of positive outcomes: higher trust between patient and physician,34 improved chronic disease management,38 fewer hospitalizations and emergency department visits,39,40 healthy behaviors,41 and fewer lawsuits.42–44 In pediatrics, continuity has been associated with timely immunization,45 parent report of higher quality of care,46 and better care coordination.47Primary care pediatricians of the future will continue to carry the banner of continuity, although the and of may With the rise of hospitalist systems, in continuity during will be a Primary care providers will need to new mechanisms to communication among care members and communication for systems and for the integrating and for integration of health services in the in including the of the are having a on understanding of disease and the of the medical system. 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Large language models (LLMs) and vision-augmented LLMs (VLMs) have significantly advanced medical informatics, diagnostics, and decision support. However, these models exhibit systematic biases, particularly age bias, compromising their reliability and equity. This is evident in their poorer performance on pediatric-focused text and visual question-answering tasks. This bias reflects a broader imbalance in medical research, where pediatric studies receive less funding and representation despite the significant disease burden in children. To address these issues, a new comprehensive multi-modal pediatric question-answering benchmark, PediatricsMQA, has been introduced. It consists of 3,417 text-based multiple-choice questions (MCQs) covering 131 pediatric topics across seven developmental stages (prenatal to adolescent) and 2,067 vision-based MCQs using 634 pediatric images from 67 imaging modalities and 256 anatomical regions. The dataset was developed using a hybrid manual-automatic pipeline, incorporating peer-reviewed pediatric literature, validated question banks, existing benchmarks, and existing QA resources. Evaluating state-of-the-art open models, we find dramatic performan

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatric

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Identifying key pathological features in brain MRIs is crucial for the long-term survival of glioma patients. However, manual segmentation is time-consuming, requiring expert intervention and is susceptible to human error. Therefore, significant research has been devoted to developing machine learning methods that can accurately segment tumors in 3D multimodal brain MRI scans. Despite their progress, state-of-the-art models are often limited by the data they are trained on, raising concerns about their reliability when applied to diverse populations that may introduce distribution shifts. Such shifts can stem from lower quality MRI technology (e.g., in sub-Saharan Africa) or variations in patient demographics (e.g., children). The BraTS-2024 challenge provides a platform to address these issues. This study presents our methodology for segmenting tumors in the BraTS-2024 SSA and Pediatric Tumors tasks using MedNeXt, comprehensive model ensembling, and thorough postprocessing. Our approach demonstrated strong performance on the unseen validation set, achieving an average Dice Similarity Coefficient (DSC) of 0.896 on the BraTS-2024 SSA dataset and an average DSC of 0.830 on the BraTS

Artificial intelligence systems that record voice and video during pediatric emergencies are emerging as human-computer interaction (HCI) technologies with direct implications for clinical work, promising improvements in documentation, team performance, and post-event debriefing. Yet the perspectives of those most affected, including clinicians, parents, and child patients, remain largely absent from the design and governance of these technologies. This position paper argues that this has direct consequences for the legitimacy and effectiveness of these systems. We examine four areas where these missing perspectives prove consequential (consent, emotional impact, surveillance dynamics, and participatory governance) and propose four positions for reorienting AI recording in pediatric emergency care toward stakeholder-centered HCI inquiry.

Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interventions is crucial for effective decision support. To address the challenges of high complexity and data scarcity in pediatric Extracorporeal Membrane Oxygenation (ECMO), we frame clinical decision-making as learning to act from trajectories, i.e., imitation learning that learns action models from observational data, with a key feature that actions are not directly observed. We consider TabPFN, a recent transformer-based approach for tabular data, and traditional baselines including XGBoost and Multi-Layer Perceptrons(MLPs) on real-world pediatric ECMO data to learn the action models. We find that the TabPFN-based approach consistently outperforms these classical baselines, supporting its use as a strong clinician-behavior baseline for pediatric ECMO decision support.

Computer modeling of the cardiovascular system has potential to revolutionize personalized medical care. This is especially promising for congenital heart defects, such as ventricular septal defect (VSD), a hole between the two ventricles of the heart. However, relatively few studies have built computer models for VSD, nor have they considered how natural adaptation to the cardiovascular system with age might interact with the presence of a small, medium, or large size VSD. Here, we combine a lumped parameter model of the cardiovascular system with two key modeling components: a size-dependent resistance dictating shunt flow between the two ventricles and age-dependent scaling relationships for the systemic and pulmonary circulations. Our results provide insight into changes in hemodynamic conditions with various VSD sizes. We investigate the combined effects of VSD size, vascular parameters, and age, showing distinct differences with these three factors. This study lays the necessary foundation for studying VSD and towards building digital shadows and digital twins for managing VSD in pediatrics.

Accurate diagnosis of pediatric brain tumors, starting with histopathology, presents unique challenges for deep learning, including severe data scarcity, class imbalance, and fine-grained morphologic overlap across diagnostically distinct subtypes. While pathology foundation models have advanced patch-level representation learning, their effective adaptation to weakly supervised pediatric brain tumor classification under limited data remains underexplored. In this work, we introduce an expert-guided contrastive fine-tuning framework for pediatric brain tumor diagnosis from whole-slide images (WSI). Our approach integrates contrastive learning into slide-level multiple instance learning (MIL) to explicitly regularize the geometry of slide-level representations during downstream fine-tuning. We propose both a general supervised contrastive setting and an expert-guided variant that incorporates clinically informed hard negatives targeting diagnostically confusable subtypes. Through comprehensive experiments on pediatric brain tumor WSI classification under realistic low-sample and class-imbalanced conditions, we demonstrate that contrastive fine-tuning yields measurable improvements i

Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over maximal predictive accuracy. Methods: A supervised machine learning model was trained using population-level pediatric data including age, income-to-poverty ratio, race/ethnicity, gender, and medical history. Model performance was assessed using receiver operating characteristic (ROC) analysis and calibration curves. Explainability was achieved using SHapley Additive exPlanations (SHAP) to provide global and individual-level interpretation of predictions. Results: The model achieved modest discrimination (AUC = 0.61) with conservative calibration

Children with rare genetic diseases often exhibit distinctive facial phenotypes, yet developing computer vision systems for early diagnosis remains challenging due to extreme data scarcity, privacy constraints, and limited data sharing in pediatric settings. These challenges not only hinder automated diagnosis but also restrict the availability of visual resources for clinical genetic counseling. While prior work has shown that synthetic data can augment real datasets and preserve phenotype-level semantics, it remains unclear whether synthetic data alone is sufficient for learning in ultra-low-resource pediatric settings. In this work, we study the synthetic-only regime for pediatric rare disease recognition. Under a controlled experimental setup, models are trained exclusively on phenotype-aware synthetic facial images at increasing scales. We find that synthetic-only training achieves performance comparable to real-data-only baselines at sufficient scale across multiple backbones, suggesting that high-fidelity synthetic data can approximate clinically meaningful distributions. These findings together further enable the use of synthetic pediatric facial images as privacy-preservin

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI

Pediatric liver tumors are one of the most common solid tumors in pediatrics, with differentiation of benign or malignant status and pathological classification critical for clinical treatment. While pathological examination is the gold standard, the invasive biopsy has notable limitations: the highly vascular pediatric liver and fragile tumor tissue raise complication risks such as bleeding; additionally, young children with poor compliance require anesthesia for biopsy, increasing medical costs or psychological trauma. Although many efforts have been made to utilize AI in clinical settings, most researchers have overlooked its importance in pediatric liver tumors. To establish a non-invasive examination procedure, we developed a multi-stage deep learning (DL) framework for automated pediatric liver tumor diagnosis using multi-phase contrast-enhanced CT. Two retrospective and prospective cohorts were enrolled. We established a novel PKCP-MixUp data augmentation method to address data scarcity and class imbalance. We also trained a tumor detection model to extract ROIs, and then set a two-stage diagnosis pipeline with three backbones with ROI-masked images. Our tumor detection mode

Automated pediatric electrocardiogram (ECG) interpretation remains challenging because developmental differences in heart rate, intervals, and waveforms limit the transferability of models trained mainly on adult data, while expert-labeled pediatric ECG cohorts are scarce. We propose PEACE (Pediatric-Adult ECG Alignment via Cross-modal Enhancement), an adult-to-pediatric ECG transfer framework pretrained on MIMIC-IV ECGs and adapted to pediatric targets. PEACE integrates label-specific bidirectional contrastive learning (LSBC) to align ECG representations with diagnostic semantics and curriculum adaptive fusion (CAF) to stabilize optimization under limited pediatric supervision. Label-conditioned short text descriptors provide auxiliary semantic supervision during training, whereas inference requires ECG signals only. On ZZU-pECG, PEACE achieves macro-average AUCs of 59.39%, 81.74%, and 91.56% under zero-shot, 50-shot, and full fine-tuning settings, respectively, outperforming ECG-only, multimodal, and generic domain adaptation baselines including DANN and MMD. On PTB-XL, it reaches 96.90% macro-average AUC after full fine-tuning over nine harmonized labels with nonzero mapped inci

Translational research is expanding and has become a focus of National Research funding agencies, touted as the primary avenue to improve health care practice. The use of human tissues for research on disease etiology is a pillar of translational research, particularly with innovations in research technologies to investigate the building blocks of disease. In pediatrics, translational research using human tissues has been hindered by the many practical and ethical considerations associated with tissue procurement from children and also by a limited population base for study, by the increasing complexities in conducting clinical research, and by a lack of dedicated child-health research funding. Given these obstacles, pediatric translational research can be enhanced by developing strategic and efficient biobanks that will provide scientists with quality tissue specimens to render accurate and reproducible research results. Indeed, tissue sampling and biobanking within pediatric academic settings has potential to impact child health by promoting bidirectional interaction between clinicians and scientists, helping to maximize research productivity, and providing a competitive edge for attracting and maintaining high-quality personnel. The authors of this review outline key issues and practical solutions to optimize pediatric tissue sampling and biobanking for translational research, activities that will ultimately reduce the burden of childhood disease.

Photoacoustic (PA) imaging systems based on clinical linear ultrasound arrays have become increasingly popular in translational PA research. Such systems can be more easily integrated in a clinical workflow due to the simultaneous access to ultrasonic imaging and their familiarity of use to clinicians. In contrast to more complex setups, hand held linear probes can be applied to a large variety of clinical use cases. However, most translational work with such scanners is based on proprietary development and as such not accessible to the community. In this contribution, we present a custom-built, hybrid, multispectral, real-time photoacoustic and ultrasonic imaging system with a linear array probe that is controlled by software developed within the Medical Imaging Interaction Toolkit (MITK) a highly customizable and extendable open-source software platform. Our software offers direct control of both the laser and the ultrasonic system and may serve as a starting point for various translational research projects and developments. To demonstrate the applicability of the platform, we used it to implement a new method for blood oxygenation estimation in the presence of non-rigid inter-f

Demyelinating disorders of the central nervous system may have multiple causes, the most common are infections, autoimmune responses, genetic or vascular etiology. Demyelination lesions are characterized by areas were the myelin sheath of the nerve fibers are broken or destroyed. Among autoimmune disorders, Multiple Sclerosis (MS) is the most well-known Among these disorders, Multiple Sclerosis (MS) is the most well-known and aggressive form. Acute Disseminated Encephalomyelitis (ADEM) is another type of demyelinating disease, typically with a better prognosis. Magnetic Resonance Imaging (MRI) is widely used for diagnosing and monitoring disease progression by detecting lesions. While both adults and children can be affected, there is a significant lack of publicly available datasets for pediatric cases and demyelinating disorders beyond MS. This study introduces, for the first time, a publicly available pediatric dataset for demyelinating lesion segmentation. The dataset comprises MRI scans from 13 pediatric patients diagnosed with demyelinating disorders, including 3 with ADEM. In addition to lesion segmentation masks, the dataset includes extensive patient metadata, such as diag

Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease that leads to increased pulmonary pressures, vascular remodeling, and eventual right ventricular (RV) failure. Pediatric PAH remains understudied due to limited data and the lack of targeted diagnostic and therapeutic strategies. In this study, we developed and calibrated multi-scale, patient-specific cardiovascular models for four pediatric PAH patients using longitudinal MRI and catheterization data collected approximately two years apart. Using the CRIMSON simulation framework, we coupled three-dimensional fluid-structure interaction (FSI) models of the pulmonary arteries with zero-dimensional (0D) lumped-parameter heart and Windkessel models to simulate patient hemodynamics. An automated Python-based optimizer was developed to calibrate boundary conditions by minimizing discrepancies between simulated and clinical metrics, reducing calibration time from weeks to days. Model-derived metrics such as arterial stiffness, pulse wave velocity, resistance, and compliance were found to align with clinical indicators of disease severity and progression. Our findings demonstrate that computational modeling can