Biology subjects

Nguyen, Q. A.

Publications and source records attributed to Nguyen, Q. A..

3 recordsLinked to original sources

bioRxiv2026

Development and cross-cohort evaluation of a multimodal deep-learning model for ER, PR and HER2 status classification in breast cancer

Background. Estrogen receptor (ER), progesterone receptor (PR) and HER2 status guide systemic treatment in breast cancer. Multimodal models are rarely benchmarked against key biomarker molecular baselines or evaluated for cross-cohort calibration. We assessed incremental value, dependence on label-proximal HER2 transcripts, and transport of discrimination and calibration. Methods. A model combining whole-slide images, transcriptomes, age and stage was developed in TCGA-BRCA (1051 patients; ER/PR/HER2 analysis sets: 1001/998/1006) and evaluated in CPTAC-BRCA (120 patients; 118/113/95). CPTAC had been observed during earlier development and included 13 HER2-positive cases. Analyses included paired model comparisons, transcript removal, alternative loss functions and recalibration. Internal discrimination used pooled out-of-fold predictions; external discrimination used fold-mean estimates, with ensemble calibration reported separately. Internal calibration and macro-AUC uncertainty were interpreted as exploratory. Results. Internal AUCs were 0.943 for ER, 0.901 for PR, and 0.860 for HER2; external AUCs were 0.952, 0.937, and 0.918, respectively. External macro-AUC was 0.936 for the multimodal model and 0.957 for logistic regression using ESR1, PGR, and ERBB2 (difference -0.021). Removal of the extended 29-transcript 17q12-q21 region changed HER2 AUC by -0.035 [-0.058 to -0.013] (95% CI) internally and by -0.042 [-0.089 to -0.007] externally relative to the baseline multimodal model. For external 15-model ensemble predictions, recalibrated HER2 expected calibration error (ECE) was 0.236, exceeding the ECE reference ceiling of 0.125, while calibration-in-the-large was -1.657 [-2.373 to -1.023] (profile-likelihood 95% CI), indicating systematic overprediction. ER and PR also showed overprediction. In paired fold-mean comparisons, HER2 ECE and Brier score favoured the three-transcript model (ESR1, PGR, and ERBB2). Conclusions. A multimodal deep-learning model achieved high discrimination across cohorts but did not demonstrate clear incremental value over a parsimonious three-transcript baseline, while cross-cohort calibration remained a substantial challenge. These findings highlight the importance of evaluating incremental value, calibration, and clinical utility alongside discrimination when assessing AI-based prediction models for medical use.

bioinformatics↗

bioRxiv2026

Hippocampal microcircuits constrain the generation of epileptiform activity

Epilepsy is largely characterized using macroscale measures of neural activity, such as electroencephalography, which are unable to resolve the underlying cellular-level substrates of pathological activity. Although there is a growing understanding that hippocampal microcircuits, comprised of distinct CA1 principal cells (PCs), inhibitory neurons, and input/output relationships, route information through parallel hippocampal pathways, the relevance of this microscale organization in epilepsy is poorly understood. To address this gap, we focally knocked out the {beta}3 subunit of the GABAA receptor from CA1 PCs, which strongly impaired parvalbumin-mediated inhibition to deep PCs, but not superficial PCs - potentially promoting a microcircuit-selective epilepsy manifestation. Indeed, we observed robust interictal epileptiform discharges (IEDs) at the site of focal knockout with features consistent with a potential CA2-to-deep CA1 PC generation mechanism. In line with prior observations that CA2 inputs potently drive deep PCs under physiological conditions, IEDs could be reliably evoked by optogenetic stimulation of CA2 in {beta}3 focal knockout mice. Resolving cellular activity across the CA1 PC network during IEDs, in vivo 2-photon calcium imaging demonstrated stronger activation of deep versus superficial PCs. Altogether, these data demonstrate that macroscopic electrophysiological patterns such as IEDs can have underlying microcircuit constraints, which has important implications for designing therapies that target the underlying microcircuit generators of epileptiform activity.

neuroscience↗

bioRxiv2020

Bidirectional pharmacological perturbations of the noradrenergic system differentially affect tactile detection

The brain neuromodulatory systems heavily influence behavioral and cognitive processes. Previous work has shown that norepinephrine (NE), a classic neuromodulator mainly derived from the locus coeruleus (LC), enhances neuronal responses to sensory stimuli. However, the role of the LC-NE system in modulating perceptual task performance is not well understood. In addition, systemic perturbation of NE signaling has often been proposed to specifically target the LC in functional studies, yet the assumption that localized (specific) and systemic (nonspecific) perturbations of LC-NE have the same behavioral impact remains largely untested. In this study, we trained mice to perform a head-fixed, quantitative tactile detection task, and administered an 2 adrenergic receptor agonist or antagonist to pharmacologically down- or up-regulate LC-NE activity, respectively. We addressed the outstanding question of how bidirectional perturbations of LC-NE activity affect tactile detection, and tested whether localized and systemic drug treatments exert the same behavioral effects. We found that both localized and systemic suppression of LC-NE impaired tactile detection by reducing motivation. Surprisingly, while locally activating LC-NE enabled mice to perform in a near-optimal regime, systemic activation impaired behavior by promoting impulsivity. Our results demonstrate that localized silencing and activation of LC-NE differentially affect tactile detection, and that localized and systemic NE activation induce distinct behavioral changes.

neuroscience↗