Bioconjugates for improved delivery of oligonucleotide therapeutics to the central nervous system

Advanced Drug Delivery Reviews · 2026-01-15 · 1 citations

Oligonucleotide therapeutics, including antisense oligonucleotides (ASOs) and small interfering RNAs (siRNAs), have gained increasing attention as a novel modality for gene-targeted interventions for central nervous system (CNS) disorders, particularly in the context of rare and inherited neurological conditions. By correcting pathogenic abnormalities in gene splicing or expression, oligonucleotide therapeutics offer a combination of extreme specificity and disease-modifying or even curative effects. However, achieving robust delivery to the CNS after systemic administration remains a significant challenge due to the presence of the blood-brain barrier and the intrinsic physicochemical limitations of oligonucleotide therapeutics, such as their large molecular size, high charge, and susceptibility to enzymatic degradation. Peptide-, antibody-, and lipid-based conjugates have emerged as versatile strategies for CNS oligonucleotide delivery, offering distinct advantages in molecular recognition, tunability, biocompatibility, and structural uniformity. Here, we review emerging design principles for engineering peptide, antibody, and lipid conjugates to enhance binding affinity, target selectivity, pharmacokinetics, and pharmacodynamics of oligonucleotide therapeutics for CNS applications. We also discuss how engineered delivery platforms have the potential to improve therapeutic efficacy across a spectrum of neurological disorders, from rare hereditary syndromes to highly prevalent neurodegenerative diseases.

A stepped wedge cluster randomized implementation trial to increase outpatient management of low-risk pulmonary embolism from the emergency department – the MEDIC ALERT PE study

Implementation Science Communications · 2025-04-02 · 1 citations

BACKGROUND: Home-based care for patients diagnosed in emergency departments (EDs) with low-risk pulmonary embolism (PE) is an evidence-based, guideline-recommended practice that is not widely adopted in the US. Few studies demonstrate how this care pathway can be implemented effectively or test whether implementation strategies can address known barriers. Further, prior studies have lacked diversity in population and health system type and did not integrate theory-informed implementation frameworks. Although essential for establishing the evidence base for safe home management of low-risk acute PE, these studies have thus fallen short of guiding broad dissemination and equitable implementation. To bridge this gap, we are conducting a pragmatic multi-site implementation trial, guided by implementation science theory and frameworks, across twelve diverse hospital settings to assess the effectiveness of new care pathways for patients with low-risk PE presenting to EDs. METHODS/DESIGN: The study uses a cluster-randomized stepped wedge trial design to investigate a set of implementation strategies to support establishing low-risk PE pathways in 12 EDs. Clusters of three hospitals were randomly assigned to one of four start dates, staggered over a 12-month period. During an initial three-month pre-implementation period, we will work with site champions to identify key site personnel and understand site barriers and facilitators. We will then tailor the care pathway to local needs and capabilities. During the six-month active implementation period, we will provide coaching to help sites implement a multi-component intervention informed by behavioral economics intended to address multi-level (site, provider, patient) barriers and integrate the new care pathway for discharging low-risk PE patients. Sites are then followed for a minimum of 12 months post-implementation. Our primary aim is to assess the change in discharge rates of patients with acute PE pre- and post-implementation. Secondary and exploratory aims will assess change in patient safety outcomes along with other key implementation outcomes guided by the RE-AIM framework. DISCUSSION: This study expands upon prior effectiveness research to tailor, implement, and robustly evaluate a multi-component implementation intervention for diverse health systems aiming to increase guideline-based outpatient management of low-risk PE. Broad-scale implementation in the US could avert up to 100,000 hospitalizations annually. TRIAL REGISTRATION: Clinicaltrials.gov (NCT06312332), registered on March 13, 2024.

Qualitative experience implementing an emergency department-based outpatient low-risk pulmonary embolism management pathway

Vascular Medicine · 2025-04-30

Physician Perspectives on Diagnostic Uncertainty in Radiographic Imaging Reports for Pulmonary Embolism: A Qualitative Study

Annals of Emergency Medicine · 2025-12-01 · 1 citations

Comparative Radiotracing Quantifies Brain Cellular Uptake and Catabolism of Bispecific Antibodies Targeting Transferrin Receptor and CD98hc

ACS Chemical Neuroscience · 2025-03-12 · 11 citations

Bispecific antibodies (bAbs) that engage cerebrovascular targets, induce transport across the blood-brain barrier (BBB), and redistribute to secondary targets within the brain parenchyma have the potential to transform the diagnosis and treatment of a wide range of central nervous system disorders. Full understanding of the pharmacokinetics (PK) of these agents, including their potential for delivering cargo into brain parenchymal cells, is a key priority for the development of numerous potential therapeutic applications. To date, the brain PK of bAbs that target transferrin receptor (TfR-1) and CD98 heavy chain (CD98hc) has been characterized using techniques incapable of distinguishing between CNS clearance of intact protein from uptake and catabolism by brain parenchymal cells. Herein, we address this knowledge gap via a comparative radiotracing strategy using two radioisotopes with distinct residualizing properties, iodine-125 (I-125) and zirconium-89 (Zr-89). We first identify reaction conditions for tetravalent chelator modification and Zr-89 radiolabeling that do not adversely affect in vitro or in vivo function. We then use comparative radiotracing to define the PK of TfR-1 and CD98hc targeted bAbs without a parenchymal target, generating quantitative evidence of TfR-1-mediated cellular uptake and catabolism that implicates these processes in previously reported differences in the brain retention of IgGs shuttled across the BBB via these two pathways. Finally, we perform comparative radiotracing on a TfR-1 bAb with an internalizing neuronal target (TrkB), demonstrating rapid divergence of Zr-89 and I-125 PK curves, with a > 30-fold difference in brain content of the two radioisotopes. Together, these results establish comparative radiotracing as a valuable technique for identifying internalizing cellular targets within the brain parenchyma and quantifying the extent and timing of bAb uptake and catabolism following target engagement.

Factors in Initial Anticoagulation Choice in Hospitalized Patients With Pulmonary Embolism

JAMA Network Open · 2025-01-03 · 13 citations

Importance: Despite guideline recommendations to use low-molecular-weight heparins (LMWHs) or direct oral anticoagulants in the treatment of most patients with acute pulmonary embolism (PE), US-based studies have found increasing use of unfractionated heparin (UFH) in hospitalized patients. Objective: To identify barriers and facilitators of guideline-concordant anticoagulation in patients hospitalized with acute PE. Design, Setting, and Participants: This qualitative study conducted semistructured interviews from February 1 to June 3, 2024, that were recorded, transcribed, and analyzed in an iterative process using reflexive thematic analysis. Interview participants were physicians in emergency medicine, hospital medicine (hospitalist), interventional cardiology, and interventional radiology. Participants were recruited using maximum variation sampling targeting UFH-dominant vs LMWH-dominant approaches in hospitalized patients with acute PE. We triangulated results with a group of interventional cardiologists and radiologists (interventionalists). Main Outcomes and Measures: Common themes and factors associated with anticoagulant selection for hospitalized patients with acute PE. Reflexive thematic analysis was used to identify these themes and factors. Results: Of the 46 interviewees (median [IQR] age, 43 [36-50] years; 33 who identified as men [71.7%]), 25 (54.3%) were emergency physicians, 17 (37.0%) were hospitalists, and 4 (8.7%) were interventionalists. Each interview lasted a median (IQR) of 29 (25-32) minutes. Prominent themes associated with anticoagulant selection included agnosticism regarding choice of anticoagulant, the inertia of learned practice, and therapeutic momentum after anticoagulation initiation. Institutional culture and support were factors associated with choice of the dominant anticoagulation strategy. Additionally, factors associated with UFH use were fear of decompensation and misperceptions regarding the pharmacology of anticoagulants and catheter-directed treatments. Conclusions and Relevance: In this qualitative study, physicians across a spectrum of specialties and geographical settings reported common barriers and facilitators to the use of guideline-concordant anticoagulation in patients hospitalized with acute PE, particularly agnosticism regarding choice of anticoagulant, inertia of learned practice, therapeutic momentum after anticoagulation initiation, and institutional culture and support. Future implementation efforts may consider targeting these domains.

Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA

ArXiv.org · 2025-03-03 · 1 citations

Medical imaging plays a pivotal role in modern healthcare, with computed tomography pulmonary angiography (CTPA) being a critical tool for diagnosing pulmonary embolism and other thoracic conditions. However, the complexity of interpreting CTPA scans and generating accurate radiology reports remains a significant challenge. This paper introduces Abn-BLIP (Abnormality-aligned Bootstrapping Language-Image Pretraining), an advanced diagnosis model designed to align abnormal findings to generate the accuracy and comprehensiveness of radiology reports. By leveraging learnable queries and cross-modal attention mechanisms, our model demonstrates superior performance in detecting abnormalities, reducing missed findings, and generating structured reports compared to existing methods. Our experiments show that Abn-BLIP outperforms state-of-the-art medical vision-language models and 3D report generation methods in both accuracy and clinical relevance. These results highlight the potential of integrating multimodal learning strategies for improving radiology reporting. The source code is available at https://github.com/zzs95/abn-blip.

Performance of an Electronic Health Record–Based Automated Pulmonary Embolism Severity Index Score Calculator: Cohort Study in the Emergency Department

JMIR Medical Informatics · 2025-01-20 · 2 citations

Background: Studies suggest that less than 4% of patients with pulmonary embolisms (PEs) are managed in the outpatient setting. Strong evidence and multiple guidelines support the use of the Pulmonary Embolism Severity Index (PESI) for the identification of acute PE patients appropriate for outpatient management. However, calculating the PESI score can be inconvenient in a busy emergency department (ED). To facilitate integration into ED workflow, we created a 2023 Epic-compatible clinical decision support tool that automatically calculates the PESI score in real-time with patients' electronic health data (ePESI [Electronic Pulmonary Embolism Severity Index]). Objective: The primary objectives of this study were to determine the overall accuracy of ePESI and its ability to correctly distinguish high- and low-risk PESI scores within the Epic 2023 software. The secondary objective was to identify variables that impact ePESI accuracy. Methods: We collected ePESI scores on 500 consecutive patients at least 18 years old who underwent a computerized tomography-pulmonary embolism scan in the ED of our tertiary, academic health center between January 3 and February 15, 2023. We compared ePESI results to a PESI score calculated by 2 independent, medically-trained abstractors blinded to the ePESI and each other's results. ePESI accuracy was calculated with binomial test. The odds ratio (OR) was calculated using logistic regression. Results: Of the 500 patients, a total of 203 (40.6%) and 297 (59.4%) patients had low- and high-risk PESI scores, respectively. The ePESI exactly matched the calculated PESI in 394 out of 500 cases, with an accuracy of 78.8% (95% CI 74.9%-82.3%), and correctly identified low- versus high-risk in 477 out of 500 (95.4%) cases. The accuracy of the ePESI was higher for low-risk scores (OR 2.96, P<.001) and lower when patients were without prior encounters in the health system (OR 0.42, P=.008). Conclusions: In this single-center study, the ePESI was highly accurate in discriminating between low- and high-risk scores. The clinical decision support should facilitate real-time identification of patients who may be candidates for outpatient PE management.

Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data

Journal of Thoracic Imaging · 2025-04-09 · 3 citations

PURPOSE: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE survival. MATERIALS AND METHODS: In total, 918 patients (median age 64 y, range 13 to 99 y, 48% male) with 3978 CTPAs were identified via retrospective review across 3 institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and clinical variables were then incorporated into independent DL models to predict survival outcomes. Cross-modal fusion CoxPH models were used to develop multimodal models from combinations of DL models and calculated PESI scores. Five multimodal models were developed as follows: (1) using CTPA imaging features only, (2) using clinical variables only, (3) using both CTPA and clinical variables, (4) using CTPA and PESI score, and (5) using CTPA, clinical variables, and PESI score. Performance was evaluated using the concordance index (c-index). Kaplan-Meier analysis was performed to stratify patients into high-risk and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction. RESULTS: For both data sets, the multimodal models incorporating CTPA features, clinical variables, and PESI score achieved higher c-indices than PESI alone. Following the stratification of patients into high-risk and low-risk groups by models, survival outcomes differed significantly (both P <0.001). A strong correlation was found between high-risk grouping and RV dysfunction. CONCLUSIONS: Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.

Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for pulmonary embolism diagnosis and report generation from CTPA

Medical Image Analysis · 2025-08-30 · 2 citations

Medical imaging plays a pivotal role in modern healthcare, with computed tomography pulmonary angiography (CTPA) being a critical tool for diagnosing pulmonary embolism and other thoracic conditions. However, the complexity of interpreting CTPA scans and generating accurate radiology reports remains a significant challenge. This paper introduces Abn-BLIP (Abnormality-aligned Bootstrapping Language-Image Pretraining), an advanced diagnosis model designed to align abnormal findings to generate the accuracy and comprehensiveness of radiology reports. By leveraging learnable queries and cross-modal attention mechanisms, our model demonstrates superior performance in detecting abnormalities, reducing missed findings, and generating structured reports compared to existing methods. Our experiments show that Abn-BLIP outperforms state-of-the-art medical vision-language models and 3D report generation methods in both accuracy and clinical relevance. These results highlight the potential of integrating multimodal learning strategies for improving radiology reporting. The source code is available at https://github.com/zzs95/abn-blip . • We integrate multi-label abnormality recognition to enhance diagnostic accuracy in CTPA report generation, optimizing hierarchical analysis, particularly in the pulmonary artery region. • The abnormality-aligned contrastive learning framework enables fine-grained disease-level alignment, reducing redundant normal descriptions and improving the representation of rare and complex diseases. • Abn-QFormer’s visual querying mechanism dynamically refines information retrieval, mimicking a clinician’s perspective by adaptively aggregating multi-scale image-text features. • Guided by medical diagnostic principles, the framework models hierarchical relationships between anatomical regions and abnormalities, ensuring comprehensive and clinically meaningful CTPA reports.