The Bristol-Myers Squibb Children's Hospital

BK polyomavirus (BKPyV) is a common opportunistic infection in kidney transplant recipients, typically reactivating in the context of immunosuppression. Although asymptomatic in immunocompetent individuals, reactivation in transplant recipients can cause BKPyV-associated nephropathy (BKPyVAN), a leading cause of graft dysfunction and loss. BKPyV viremia affects approximately 10%-15% of transplant recipients, and once BKPyVAN is established, the risk of graft failure can exceed 50%. The current standard of care involves immunosuppression reduction to restore virus-specific immunity, but this approach increases the risk of acute and chronic rejection. Intravenous immunoglobulin (IVIg), which contains antiviral and immunomodulatory antibodies, has been proposed as an adjunctive therapy, although evidence supporting its efficacy remains limited to small observational studies. The BEAT-BK trial is a multicenter, adaptive, randomized controlled trial comparing standard immunosuppression reduction with or without IVIg in kidney and simultaneous pancreas-kidney transplant recipients with BKPyV viremia or biopsy-confirmed BKPyVAN. The trial will enroll participants aged 2 y and older, randomizing them 1:1 to receive standard care alone or in combination with a total IVIg dose of 2 g/kg for 8 wk. An adaptive Bayesian design will permit interim analyses with the potential for early trial success or futility. The primary endpoint is a composite ordinal outcome at 12 wk, incorporating death, graft loss, estimated glomerular filtration rate decline, acute rejection, viral load, and degree of immunosuppression reduction. Secondary outcomes include viral clearance, development of BKPyVAN and donor-specific antibodies, infections, malignancy, quality of life, and cost-effectiveness. This trial will provide the first high-quality evidence on the role of IVIg in BKPyV management and inform clinical decision-making in posttransplant care.

This short communication describes a spontaneous hemangiosarcoma within the abdomen, which appeared to have originated from the prostate gland area with infiltration, and that metastasized to the lungs in a 46-week-old male Sprague-Dawley rat. At necropsy, a 90 mm × 60 mm × 50 mm dark cystic neoplastic mass was present in the abdomen and the lung lobes had multiple dark cystic neoplastic structures. The abdominal mass was necrotic with a few peripheral areas of plump oval cells and frond-like structures lined by spindle-shaped cells. The lung neoplastic cysts were filled with blood and there were foci of plump oval cells within and peripheral to the cysts, as well as multiple small clusters throughout the lung parenchyma. Neoplastic cells of the abdominal mass and lung cysts stained positively for von Willebrand Factor by immunohistochemistry, indicating a vascular endothelial cell origin and consistent with a diagnosis of hemangiosarcoma. The lung hemangiosarcomas were considered metastases from the abdominal hemangiosarcoma based on their similar cellular morphology and multiple foci throughout the lung lobes.

Millions of children worldwide are experiencing prolonged symptoms after SARS-CoV-2 infection, yet social risk factors for developing long COVID are largely unknown. As child health is influenced by the environment in which they live and interact, adverse social determinants of health (SDOH) may contribute to the development of pediatric long COVID. To identify whether adverse SDOH are associated with increased odds of long COVID in school-aged children and adolescents in the US. This cross-sectional analysis of a multicenter, longitudinal, meta-cohort study encompassed 52 sites (health care and community settings) across the US. School-aged children (6-11 years; n = 903) and adolescents (12-17 years; n = 3681) with SARS-CoV-2 infection history were included. Those with an unknown date of first infection, history of multisystem inflammatory syndrome in children, or symptom surveys with less than 50% of questions completed were excluded. Participants were recruited via health care systems, long COVID clinics, fliers, websites, social media campaigns, radio, health fairs, community-based organizations, community health workers, and existing research cohorts from March 2022 to August 2024, and surveys were completed by caregivers between March 2022 and August 2024. Twenty-four individual social determinant of health factors were grouped into 5 Healthy People 2030 domains: economic stability, social and community context, caregiver education access and quality, neighborhood and built environment, and health care access and quality. Latent classes were created within each domain and used in regression models. Presence of long COVID using caregiver-reported, symptom-based, age-specific research indices. The mean (SD) age among 4584 individuals included in this study was 14 (3) years, and 2330 (51%) of participants were male. The number of latent classes varied by domain; the reference group was the class with the least adversity. In unadjusted analyses, most classes in each domain were associated with higher odds of long COVID. After adjusting for many factors, including age group, sex, timing of infection, referral source, and other social determinant of health domains, economic instability characterized by difficulty covering expenses, poverty, receipt of government assistance, and food insecurity were associated with an increased risk of having long COVID (class 2 adjusted odds ratio [aOR], 1.57; 95% CI, 1.18-2.09; class 4 aOR, 2.39; 95% CI, 1.73-3.30); economic instability without food insecurity (class 3) was not (aOR, 0.93; 95% CI, 0.70-1.23). Poorer social and community context (eg, high levels of discrimination and low social support) was also associated with long COVID (aOR, 2.17; 95% CI, 1.77-2.66). Sensitivity analyses stratified by age group and adjusted for race and ethnicity did not alter or attenuate these results. In this study, economic instability that included food insecurity and poor social and community context were associated with greater odds of pediatric long COVID. Those with food security, despite experiencing other economic challenges, did not have greater odds of long COVID. Further study is needed to determine if addressing SDOH factors can decrease the rate of pediatric long COVID.

The mutagenic potential of N-nitrosamines has heightened regulatory scrutiny and prompted numerous drug recalls due to their formation as impurities during pharmaceutical manufacturing. However, the simultaneous and rapid analysis of multiple N-nitrosamines remains challenging. In this study, we show a rapid (<1 min) and robust multiple reaction monitoring (MRM) approach for the trace-level quantitation of N-nitrosamines across a wide concentration range (10 ng/mg to 500 μg/mg) in diverse pharmaceutical matrices. Lithium cation (Li+) adduct formation during ionization is utilized to enable diagnostic fragmentation in tandem mass spectrometry (MS/MS), yielding a limit of detection of 5 ng/mg with high quantitative accuracy and precision. This method is also applied to trace analysis of N-nitrosated drug impurities within their parent pharmaceutical matrices. With minimal sample preparation, rapid analysis times, and broad applicability to complex pharmaceutical formulations, this MRM method offers a practical and powerful tool for the identification and quantitation of N-nitrosamines and its potential adoption in pharmaceutical quality control and industrial settings.

Single-molecule fluorescence microscopy (SMFM) can reveal important biological insights. However, uncovering rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches. To facilitate systematic and efficient discovery from SMFM time traces, we introduce META-SiM, a transformer-based foundation model pretrained on diverse SMFM analysis tasks. META-SiM rivals best-in-class algorithms on a broad range of tasks including trace classification, segmentation, idealization and stepwise photobleaching analysis. Additionally, the model produces embeddings that encapsulate detailed information about each trace, which the web-based META-SiM Projector ( https://www.simol-projector.org ) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison and sharing. Combining this Projector with the objective metric of local Shannon entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. Applying META-SiM to an existing single-molecule Förster resonance energy transfer dataset, we discover a previously undetected intermediate state in pre-mRNA splicing. META-SiM removes bottlenecks, improves objectivity and both systematizes and accelerates biological discovery in single-molecule data.

Photomechanical materials are comprised of organic molecules (e.g. azobenzenes) that convert light into motion. Thin films composed of supramolecular polymers comprising hydrogen-bonded polyvinylpyridine and hydroxyazobenzene exhibit noteworthy photomechanical behavior under laser irradiation. Our prior investigations of such films, featuring derivatives of hydroxyazobenzene, revealed marked differences in the photomechanical response. To better understand the noted differences, at the molecular level, we sought to prepare and analyze photomechanical cocrystal model systems using the same azobenzene molecules (4-hydroxyazobenzene, 4-hydroxy-4'-nitroazobenzene and 4-Hydroxy-4'-dimethylaminoazobenzene) and bipyridyl derivatives (bipyridylethane and bipyridylethylene). In this presentation, we will outline the synthesis and characterization of these cocrystals and draw a correlation between the crystal packing and photomechanical response.

For older unfit patients receiving venetoclax-based induction, data on the significance of interim bone marrow biopies (BMBx) findings on clinical outcomes is lacking. We retrospectively evaluated interim BMBx results performed on Cycle 1 days 21–28 of venetoclax-based therapy in 69 adults with myeloid malignancies to determine whether blast clearance was associated with overall survival (OS) and overall response rate (ORR). Median age was 75 years (range 69–78). Results demonstrated blast reduction (BR, <5% blasts) in 71%. Venetoclax was held to allow count recovery in 86% of these patients. Achieving interim BMBx BR was significantly associated with OS (p = 0.0033) and ORR (Cohen’s kappa 0.39). Patients whose venetoclax was held experienced low rates of infection and reduced cytopenias. These findings support the importance of cycle 1 BMBx assessment during venetoclax-based therapies, specifically in predicting which patients will achieve optimal outcomes and in mitigating toxicity.

ICH Q12 asserts that science- and risk-based approaches are applicable to stability studies supporting Chemistry, Manufacturing and Controls (CMC) post-approval changes (PAC) to enable more timely implementation; however, no guidance or specific examples are provided to demonstrate how prior knowledge of the product can inform the risk assessment for the proposed change(s). Ten diverse case studies are presented in this manuscript to demonstrate how science- and risk-based stability strategies were used to support drug substance and product CMC PAC and lifecycle management activities. The accumulated stability knowledge held by original manufacturers of marketed products is substantial, and different elements of this knowledge base were used to assess the risks and impact of the proposed changes for confident change management. This paper provides ways to leverage science- and risk-based stability strategies as part of the post-approval change-management risk-mitigation strategy, which may enable a reduced stability data commitment and/or a reduced reporting category for change implementation.

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

To effectively address the unprecedented acceleration of climate change, cities across the United States are leading efforts to reduce greenhouse gas emissions. Coherent, aggressive, and lasting mitigation policies in controlling carbon emissions are beginning to be adopted to help strengthen climate resilience across different sectors. However, evaluating the effectiveness of current climate legislation requires careful monitoring of emissions through measurable and verifiable means to inform policy decisions. As a part of this effort, we developed a new method to spatially allocate aircraft-based mass balance carbon dioxide (CO2) emissions. In this work, we conducted 7 aircraft flights, performed downwind of New York City (NYC) to quantify CO2 emissions during the nongrowing seasons between 2018 and 2020. We used an ensemble of emission inventories and transport models to calculate the fraction of enhancements (Φ) produced by sources within the policy-relevant boundaries of the 5 NYC boroughs and then applied that to the bulk emissions calculated using the mass balance approach. We derived a campaign-averaged source-apportioned mass balance CO2 emission rate of (57 ± 24) (1σ) kmol/s for NYC. We evaluated the performance of this approach against other top-down methods for NYC including inventory scaling and inverse modeling, with our mean emissions estimate resulting in a 6.5% difference from the average emission rate reported by the 2 complementary approaches. By combining mass balance and transport model approaches, we improve upon traditional mass balance experiment methods to enable quantification of emissions in complex emission environments. We conducted an assessment using an ensemble of emission inventories and transport models to determine the sources of variability in the final calculated emission rates. Our findings indicate that the choice of inventory accounted for 2.0% of the variability in the emission estimates and that the atmospheric transport model contributed 3.9% at the campaign level. Additionally, on average, at the daily scale, the transport model contributed 7.6% and the inventory accounted for 14.1%. The daily flight-to-flight variability contributed a significant portion, at 42.1%. This approach provides a solution to the difficulty of interpreting aircraft-based mass balance results in complex emission environments.