Publications by Year: 2026

BACKGROUND: Reduced mismatch negativity (MMN) is a widely replicated schizophrenia biomarker. Time-frequency analyses suggest that deficient phase synchrony and/or power of electroencephalography (EEG) event-related oscillations, especially theta, contribute to MMN deficits in schizophrenia. Whether theta oscillations assessed from a passive auditory oddball paradigm are abnormal in clinical high-risk for psychosis (CHR-P) individuals and whether these oscillations predict CHR-P clinical outcomes remain unclear. These questions were addressed using data from NAPLS2 (North American Prodrome Longitudinal Study 2).

METHODS: EEG was recorded from 77 CHR-P individuals who converted to psychosis (CHR-Cs), 238 CHR-P nonconverters (CHR-NCs) who completed a 24-month follow-up, and 241 healthy control (HC) individuals. Theta oscillations elicited by standard and deviant tones were calculated. Theta (4-6 Hz) intertrial phase coherence (ITC) and total power were compared between groups and evaluated as predictors of time to psychosis conversion in the full CHR-P sample. Furthermore, analyses of covariance were used to assess whether theta deficits persisted while covarying for MMN.

RESULTS: The CHR-C group, relative to the HC and CHR-NC groups, had reduced theta ITC for standards and deviants (ps < .029) and reduced total power for standards and deviants relative to the HC group (p = .021). Reduced theta ITC for standards and deviants predicted earlier psychosis conversion. The previously reported deficit in MMN amplitude in the CHR-C group was no longer significant after accounting for theta ITC or total power averaged across stimuli (ps > .187), but this was not true for theta ITC or total power deviant-standard difference scores (ps < .039).

CONCLUSIONS: These results implicate abnormalities in microcircuit generators of theta oscillations in CHR-P individuals at highest risk.

PURPOSE: To investigate the effect of high-iodine concentration contrast material for dual-energy CT angiography (DECTA) on arterial visualizations by comparing it with medium-iodine concentration.

METHOD: This prospective, single-blind, randomized controlled trial included 100 consecutive participants undergoing DECTA from November 2023 to February 2025. The participants were randomly assigned into two protocols: receiving high-iodine concentration contrast material of 370 mgI/mL (Group A, n = 51) and receiving medium-iodine concentration of 300 mgI/mL (Group B, n = 49). The axial, coronal, and volume-rendered (VR) images were reconstructed at 40 keV in both groups. Two radiologists reviewed three image types and assessed the arterial visualizations using a five-point scale. The primary outcome was the score for the iliolumbar artery on the VR images, whereas secondary outcomes were the scores for all others. The Wilcoxon rank sum test was conducted to compare the outcomes between the two groups.

RESULTS: No statistical significance in terms of the score for the iliolumbar artery on the VR images was found between the two groups; however, the median score was higher in Group A than in Group B (3.5 vs. 3.0; P = .05). The scores for secondary outcomes in Group A were equal to or greater than that in Group B, and significant differences were observed, especially in the small arteries, including the bronchial, internal thoracic, intercostal, left gastric, and inferior phrenic arteries (P < .05).

CONCLUSION: In small arteries, the protocol with high-iodine concentration contrast material exhibited better arterial visualizations compared with medium-iodine concentration in DECTA at 40 keV.

BACKGROUND: Anhedonia-a core symptom of Major Depressive Disorder-is associated with poorer clinical outcomes, lower quality of life, and disrupted reward processing. However, putative relationships among self-reported anhedonia, well-being and anhedonic phenotypes (specifically, reward learning) remain largely unexplored. The main goal of the current study was to fill this gap in a large online adult community sample (N = 478).

METHODS: To evaluate how different approaches to assessing anhedonia may capture these relationships, we administered the Probabilistic Reward Task (PRT) to probe reward learning and two clinical scales: the Snaith-Hamilton Pleasure Scale (SHAPS) and the Dimensional Anhedonia Rating Scale (DARS). In a first step, the SHAPS was administered to identify anhedonic vs. non-anhedonic individuals, who then performed an online PRT.

RESULTS: Both scales were significantly associated with lower self-reported quality of life (QoL), as measured by the Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (QLESQ-SF), with the SHAPS showing a stronger relationship to both QoL and reward learning. Follow-up computational modeling indicated the anhedonic group showed a significantly higher level of uncertainty while completing the PRT compared to the non-anhedonic group. Moreover, trial-by-trial analyses revealed group differences in PRT response patterns, such that anhedonic individuals were less likely to incorrectly indicate that they had seen the more frequently rewarded "rich" stimulus on trials that actually presented the less frequently rewarded "lean" stimulus.

CONCLUSIONS: Findings highlight the utility of combining subjective and behavioral measures to better understand the impact of anhedonia on daily functioning and reinforcement learning processes.

Propionic acidemia is an inborn error of metabolism involving an enzymatic defect of propionyl-CoA carboxylase that results in the build-up of toxic metabolites which can induce metabolic decompensation. Secondary mitochondrial dysfunction in propionic acidemia has been commonly recognized; however, its clinical presentation and management are not well represented in literature. Here, we present a case of profound hyperglycemia and lactic acidosis without hyperammonemia in a propionic acidemia patient, where medical management incorporated mitochondrial dysfunction via a brief reduction in glucose infusion rate. We review the literature on propionic acidemia and mitochondrial dysfunction in an effort to provide a tangible clinical case where considerations of mitochondrial dysfunction were made to guide further decision making in taking care of this patient population.

Multidrug resistance (MDR) of the pathogen Acinetobacter baumannii is a major challenge to global healthcare due to the limited treatment options. The emergence of MDR bacteria necessitates innovative therapeutic approaches, especially given the associated economic burden and the rapid spread of infections. Conventional treatments such as antibiotics and vaccines face significant obstacles. Antimicrobial peptides (AMPs) such as LL37 have potential as an alternative treatment due to their broad-spectrum activity and ability to target specific bacterial structures such as the outer membrane protein A (OmpA). The efficacy of AMPs can be enhanced by using nanobodies (Nbs) that bind to bacterial OmpA, guiding LL37 precisely to its target. In this study, A. baumannii OmpA (AbOmpA)-specific Nbs (NbO7 and NbO13) were efficiently isolated through magnetic-activated cell sorting-based screening of a yeast surface display library, eliminating the need for specialized equipment. Nbs exhibited specific, dose-dependent binding to the target. Conjugation of Nbs with LL37 effectively inhibited the growth of MDR A. baumannii. This approach leverages the natural antimicrobial properties of AMPs and enhances their specificity and effectiveness by targeting bacterial cell surface proteins. LL37-conjugated AbOmpA-Nbs present a promising therapeutic strategy against MDR A. baumannii and other resistant pathogens.IMPORTANCEMultidrug-resistant (MDR) Acinetobacter baumannii poses a major global health threat due to its resistance to nearly all available antibiotics and its persistence in hospital settings. This challenge underscores the urgent need for new therapeutic approaches beyond conventional drugs. In this study, we developed an innovative strategy that combines the human antimicrobial peptide LL37 with nanobodies (Nbs) targeting the outer membrane protein A (OmpA), a key virulence and survival factor of A. baumannii. OmpA-specific Nbs were efficiently isolated from a fully synthetic library using a simple, low-cost selection approach without animal immunization. When conjugated with LL37, these Nbs bound specifically to OmpA and strongly inhibited MDR A. baumannii growth in vitro. Our findings introduce a simple yet powerful platform for generating targeted Nb-peptide conjugates, offering strong potential for adaptation against other antibiotic-resistant pathogens and contributing to the development of next-generation biologics to overcome antibiotic limitations.

Clinical trials are contingent upon enrollment of participants who are willing to volunteer their time and effort. Many persons living with HIV (PLWH) are long-term survivors aging into midlife and thus, are at risk for medical co-morbidities such as cardiovascular disease (CVD). Many PLWH have a long-standing history of research participation, including therapeutic trials, which have informed the use of highly effective antiretroviral treatments. As increasing numbers of PLWH age, research participation remains essential to generate findings to inform clinical care and treatment guidelines for aging-associated diseases specific to this population. Our study shares findings from survey data that explored reasons for clinical research participation, including participation in a CVD risk study, among midlife PLWH. Four themes emerged from content analysis of the participant responses: health and wellness, research engagement, altruism, and personal benefit. Findings may inform strategies to enhance recruitment and retention strategies for research studies among midlife PLWH.

STUDY OBJECTIVES: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Chicago, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature.

METHODS: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography (PSG) at a tertiary care sleep lab. Each PSG recording was manually scored and these annotations served as ground truth labels for training and evaluation of our models. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed.

RESULTS: After ensembling, the model attains a 3-class (wake, non rapid eye movement [NREM] sleep, rapid eye movement [REM] sleep) balanced accuracy of 84.02%, F1 score of 84.23%, Cohen's κ of 72.89%, and a Matthews correlation coefficient (MCC) score of 73.00%; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30%, F1 score of 74.10%, Cohen's κ of 61.51%, and MCC score of 61.95%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11%, F1 score of 66.15%, Cohen's κ of 53.23%, MCC score of 54.38%.

CONCLUSIONS: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent hepatic pathology worldwide, with significant potential for progression to cirrhosis and ultimately end-stage liver disease. Accordingly, a wide range of preclinical models have been developed to better understand the disease mechanisms and progression as well as to accelerate drug discovery. These include in vitro, ex vivo, and in vivo models, which offer unique advantages yet differ in terms of disease driver, species used, and biological complexity-ranging from benchtop cellular systems to whole organs and organisms. In this review, we provide a comprehensive overview of the technologies currently used for the study of MASLD, with a focus on how standardization of disease progression across models may aid therapeutic development.