Publications

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes of a hypertrophic phenotype. This study sought to (1) evaluate the performance of artificial-intelligence (AI) derived measurements incorporated into the established multiparametric echocardiographic scoring system to detect CA; (2) develop and validate an AI-based deep-learning model for video-based detection of CA on echocardiography.

METHODS: The study population comprised 5776 patients (CA, 2756; controls, 3020). The training data set included patients from the UK National Amyloidosis Center and Taiwan MacKay Memorial Hospital (CA, 2241; controls, 2130). External test data sets were obtained from the US Duke University Health System (CA, 334; LVH controls, 668) and Japan National Cerebral and Cardiovascular Center (CA, 181; LVH controls, 222).

RESULTS: The multiparametric echocardiographic score computed using AI-derived measurements achieved an accuracy of 79.5% (sensitivity, 75.4%; specificity, 81.5%) in the United States cohort and 79.7% (sensitivity, 81.6%; specificity, 78.1%) in the Japan cohort. The deep-learning model demonstrated accuracies of 96.2% (sensitivity, 96.8%; specificity, 95.7%) and 95.8% (sensitivity, 97.3%; specificity, 94.3%) in the internal validation and internal test sets, respectively. External validation of the deep-learning model showed accuracies of 87.5% (sensitivity, 86.6%; specificity, 87.9%) in the United States and 88.4% (sensitivity, 92.3%; specificity, 85.3%) in the Japanese cohort. Subgroup analysis demonstrated that the deep-learning model showed robust discrimination of CA from other hypertrophic phenocopies: CA versus hypertension (area under the curve [AUC], 0.92 [95% CI, 0.91-0.94]), CA versus hypertrophic cardiomyopathy (AUC, 0.91 [95% CI, 0.87-0.94]), CA versus aortic stenosis (AUC, 0.93 [95% CI, 0.90-0.95]), CA versus chronic kidney disease (AUC, 0.93 [95% CI, 0.91-0.95]). The deep-learning model was able to classify a greater proportion of patients compared with the AI-derived multiparametric echocardiographic score and achieved superior diagnostic accuracy (AUC, 0.93 [95% CI, 0.91-0.95] versus AUC, 0.88 [95% CI, 0.85-0.90]; P<0.001).

CONCLUSIONS: Both the multiparametric echocardiographic score computed from AI-derived measurements and the fully automated deep-learning model can accurately identify patients with CA in globally diverse cohorts, with the deep-learning model providing superior performance.

BACKGROUND: The benefits of physical activity (PA) for both physical and mental health, including major depressive disorder (MDD), are well established. Mobile devices, such as smartphones, offer a scalable way to monitor PA and its relationship with depressive symptoms in daily life.

OBJECTIVE: This study aimed to investigate the association between passive smartphone-recorded step counts and current depressive symptoms in individuals with and without a lifetime diagnosis of MDD, using a naturalistic bring-your-own-device approach.

METHODS: We used the Remote Monitoring Application in Psychiatry (ReMAP) to collect passive step count data from participants' personal smartphones. The sample included 181 individuals with a lifetime MDD diagnosis, assessed via the structured clinical interview for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition; DSM-IV), and 195 healthy controls (HCs). Current depressive symptoms were assessed using the Beck Depression Inventory. PA was operationalized as daily and weekly step counts, passively recorded via smartphone sensors. Hierarchical models were applied to examine the association between PA and depression severity.

RESULTS: Patients with MDD exhibited significantly lower daily step counts (mean 3454, SD 2683) compared to HCs (mean 4699, SD 3175; P<.001) and showed reduced diurnal variability (β=-0.36; P=.003). Higher daily step counts were associated with lower Beck Depression Inventory scores across the full sample (β=-0.06, 95% CI -0.09 to -0.02; P=.002), with similar trends in both MDD and HC groups. Weekly step counts also significantly predicted lower concurrent depressive symptoms (β=-0.29, 95% CI -0.43 to -0.14; P<.001), while patients with MDD displayed less variability in weekly activity levels than HCs (β=-0.75; P=.001).

CONCLUSIONS: These findings underscore the potential of mobile devices to be used as effective tools for monitoring PA in patients with MDD, supporting more customized and adaptive approaches to prevention and treatment. They also emphasize the importance of incorporating PA into the clinical management of depression.

OBJECTIVES: Randomized controlled trials (RCTs) of the Collaborative Care Model demonstrate strong evidence for effectively managing depression in a stepped-care approach across diverse patient populations. Despite alignment with the American Society of Clinical Oncology guidelines, which recommend a stepped-care approach for managing depression and anxiety in cancer patients, implementation of collaborative care in cancer centers remains limited and sparse real-world data exist. The Supportive Oncology Collaborative, a program integrating behavioral health and palliative care, was developed at an NCI-designated academic cancer center. This study aims to evaluate depression outcomes within this collaborative care program.

METHODS: A retrospective analysis was conducted on patients with at least 2 Patient Health Questionnaire-9 (PHQ-9) scores recorded within a 12-month period between January 2022 and December 2023 at 1 regional campus. Depression response, defined as a 50% reduction in PHQ-9 scores, was assessed at 12 and 24 weeks. Response rates were compared to those reported in RCTs of collaborative care.

RESULTS: Mean PHQ-9 scores were 17.3 at baseline (n = 47), 11.1 at 12 weeks (n = 43), and 10.1 at 24 weeks (n = 22). Depression response rates were 34.9% at 12 weeks (n = 43) and 54.5% at 24 weeks (n = 22).

SIGNIFICANCE OF RESULTS: We observed depression response rates comparable to those reported in RCTs of collaborative care in individuals with cancer. However, the high proportion of missing data highlights the difficulty of tracking outcomes in real-world clinical settings and the need for further evaluation and strategies to improve data completeness.

BACKGROUND: Dual antiplatelet therapy (DAPT) is critical for safe flow diversion (FD), yet unlike the extensive coronary literature, FD remains less common and lacks strong evidence to guide DAPT choice. Clopidogrel's variable responsiveness has pushed clinicians towards platelet function testing and more potent agents. In 2023, our institution adopted universal prasugrel-based DAPT, and this study compares outcomes across clopidogrel, ticagrelor, and prasugrel.

METHODS: We present a retrospective review of all intracranial aneurysms treated with the Pipeline Embolization Device (PED) between July 2021 and July 2024 using a prospectively maintained database. Primary outcomes were thromboembolic and hemorrhagic complications, occlusion rates, and functional outcomes (modified Rankin Scale (mRS)). Secondary analyses were conducted based on surface modification.

RESULTS: A total of 243 FD procedures were performed in 229 patients (mean age 55.2 years; 84.3% women) treating 265 aneurysms. DAPT regimens included ticagrelor (38.7%), clopidogrel (31.7%), and prasugrel (29.6%). At median 12-month follow-up, 97.8% of patients achieved favorable functional outcomes (mRS ≤2), with no differences between regimens. No significant differences in aneurysm occlusion (complete/near-complete in 86.4%) were found between the DAPT regimens. Thromboembolic (4.1%) and hemorrhagic (4.9%) complications did not differ significantly; notably, all intracranial hemorrhages occurred in ticagrelor-treated patients. Retreatment rates were significantly higher in non-surface-modified versus surface-modified PEDs (8.3% vs 0.9%, P=0.01).

CONCLUSIONS: Prasugrel showed comparable safety and occlusion outcomes relative to clopidogrel and ticagrelor. Our findings underscore a critical gap in the evidence base and highlight the urgent need for multicenter registries and prospective trials to establish standardized, data-driven DAPT protocols for intracranial FD.

Maternal immunity is modulated during pregnancy at the placental interface to prevent alloreactivity with the developing fetus. Importantly, however, maternal immunoglobulin G (IgG) freely crosses the placenta, and the presence of pre-existing alloreactive antibodies can lead to injury of fetal tissues and/or cells. Because maternal IgG continues to circulate up to 6 months after birth, these antibodies can also continue to affect the newborn, causing a variety of disease conditions including haemolytic disease of the newborn, neonatal alloimmune thrombocytopenia, neonatal lupus, neonatal Graves' disease, gestational alloimmune liver disease and others. Ig therapy, most typically in the form of intravenous Ig, is indicated in these disorders, as pooled IgG molecules can interfere with the circulating maternal IgG, lessening the interactions with the fetal or neonatal binding targets. Ig is an increasingly used therapy in this population; however, most fetal and neonatal providers do not receive comprehensive training in its development or use. Here, we review the formulation, mechanisms of action, therapeutic indications and administration of intravenous Ig in the context of fetal and neonatal medicine.

INTRODUCTION: Cumulative evidence suggests that proactive therapeutic drug monitoring (TDM) of anti-tumor necrosis factor (anti-TNF) therapy is associated with favorable outcomes in inflammatory bowel disease (IBD). However, there is limited information regarding the role of proactive TDM for de-escalating anti-TNF therapy in IBD.

AREAS COVERED: This narrative review will provide an overview of the role of proactive TDM for anti-TNF therapy de-escalation in IBD. A literature search was performed using PubMed between 2005 and June 2025.

EXPERT OPINION: Cumulative evidence suggests that proactive TDM plays a key role in guiding anti-TNF therapy de-escalation, as in patients with supra-therapeutic drug concentrations and discontinuation of the IMM in case of combination therapy with infliximab, as in patients with high drug concentrations prior to IMM withdrawal. Moreover, preliminary data suggest that proactive TDM may also help guiding anti-TNF therapy withdrawal, as in patients with sustained remission and undetectable or low drug concentrations at the time of drug discontinuation. Finally, proactive TDM is also important for surveillance of patients after treatment de-escalation to prevent low drug concentrations and immunogenicity. However, there are still some knowledge gaps including the ideal drug concentration before treatment de-escalation and if drug clearance rather than concentration can better guide anti-TNF therapy de-escalation.

BACKGROUND: Gestational diabetes mellitus (GDM) increases offspring obesity risk, but whether this occurs via changes in human milk composition, including alterations in human milk oligosaccharides (HMOs), is unknown.

OBJECTIVES: This study aimed to identify differences in HMO concentrations in mothers with and without GDM and test whether GDM-associated HMOs are associated with infant growth, body composition, and fecal microbiome characteristics over the first 6-mo of life.

METHODS: Human milk was collected at 1-mo postpartum from 337 females (49 with GDM) who fed their infants breastmilk exclusively. HMOs were quantified by high-performance liquid chromatography and multivariate regression models were used to test differences in HMO concentrations by GDM status (false discovery rate adjustment for multiple testing set at q < 0.05). HMOs associated with GDM were then tested for associations with infant growth, body composition, and 1 and 6-mo infant fecal microbial abundances measured by metagenomic whole-genome sequencing.

RESULTS: Participants with GDM had ∼1 SD higher milk 6'sialyllactose (6'SL) {[β (95% confidence interval): 0.58 (0.20, 0.96)] and lacto-N-fucopentaose III (LNFP III) III [95% CI: 0.55 (0.16, 0.94)]} compared with those without GDM and 6'SL concentration was also positively associated with weight and length gain. Although infants of mothers with GDM had lower 1-mo fecal α-diversity and altered abundances of 6 of 56 microbial species detected compared with those without GDM, microbial features were not associated with the concentration of either 6'SL or LNFP III and evidence for mediation of GDM-growth and GDM-microbiome by HMOs was not found.

CONCLUSIONS: Mothers with a GDM diagnosis had higher milk concentrations of LNFP III and 6'SL, and 6'SL was in turn associated with increased infant growth rate, but neither HMO was associated with differential infant gut microbial abundances. The results suggest that the link between 6'SL and faster infant growth, if causal, occurs via mechanisms independent of the infant gut microbiome. This study was registered at clinicaltrials.gov as NCT03301753.

PURPOSE: Experts have called for systematic implementation of cognitive behavioral therapy (CBT)-based interventions for cancer pain; however, implementation of CBT for cancer pain has been extremely limited.

DESIGN: We explored nurses' perceptions of implementing a brief, evidence-based pain CBT protocol using a mixed-methods analysis.

METHODS: Registered nurses working within ambulatory oncology/infusion clinics were recruited. Survey items and interviews focused on familiarity and current use of pain self-management strategies and attitudes toward the CBT protocol. We used univariate descriptive statistics to analyze the survey and rapid qualitative analysis for interview data.

RESULTS: Ninety-nine nurses participated in the survey (46% response rate) and 13 nurses participated in the follow-up interviews. Most survey respondents (between 51% and 84%) were moderately/very familiar with nearly all pain self-management strategies mentioned. The three strategies that nurses described as using to a moderate/great extent were advising patients about lifestyle changes (54%), problem-solving (43%), and coping with difficult pain-related emotions (39%).

CONCLUSION: Respondents generally viewed an oncology nurse-led model of pain CBT as acceptable and appropriate. Future studies will need to provide support for a nurse/nurses dedicated to pain CBT and should evaluate the clinical effectiveness of that model while also testing practical strategies for training/supervision.

CLINICAL IMPLICATIONS: Oncology nurses are generally supportive of implementing a brief, evidence-based CBT protocol for cancer pain management. Despite some uncertainty about feasibility, positive attitudes highlight the potential for enhancing cancer pain care through nurse-led behavioral interventions.

Nature employs post-translational modifications (PTMs) to induce proximity between proteins by engendering new interactions. Furthermore, we find that protein ligands are invariably proximal to a lysine. Inspired by these two observations, we developed group-transfer chimeras (GRCs) that append a moiety-of-interest to the lysine side chain. GRCs employ a protein's ligand and a handle with a transferase-type reactivity to modify the proximal lysine. Contemporary lysine-targeting group-transfer handles were incompatible with GRCs due to their hydrolytic instability, large size, high reactivity, and synthetic incompatibility with diverse ligands. Accordingly, we developed an N-(sulfonyl)-N-(trifluoroethyl)-ethanamide (SuFA) handle that is stable, small, and exhibits tunable reactivity and synthetic compatibility with diverse ligands and proteins. Using GRCs that group-transfer binders of tags (e.g., HaloTag, FKBP) onto proteins overexpressed in cancer cells, we displayed these binders on the surface of the cancer cell. With a universal T cell engager (UniTE) that binds to the displayed ligands and T cells, these GRCs induced proximity between cancer cells and cytotoxic T cells, leading to the latter's activation. We envision the GRC platform to find utility in basic research and biomedicine.