Biology subjects

Tran, B. M.

Publications and source records attributed to Tran, B. M..

5 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↗

bioRxiv2025

UC Irvine Brain Initiative Cell Atlas Network (BICAN) Brain Procurement Program for the Center for Multiomic Human Brain Cell Atlas Project

High-quality neurotypical postmortem human brain tissue is essential but difficult to obtain for constructing comprehensive human brain cell atlases. Here we describe the establishment of UC Irvines Brain Procurement Program, a coordinated initiative to collect and process neurotypical donor brains for multiomic mapping studies within the NIH BRAIN Initiative Cell Atlas Network (BICAN) consortium. Through partnerships with the Orange County Coroners Office, the UC Irvine Willed Body Program, UCI Medical Center, and the Childrens Hospital of Orange County, we have developed standardized workflows encompassing donor identification, postmortem brain recovery and processing, region-of-interest dissection, neurotypical donor selection, and data management. Our experience demonstrates the feasibility of a community-based, multi-institutional procurement framework while highlighting challenges in recruiting neurotypical donors and ensuring demographic representation reflective of Southern California. We further identify opportunities to strengthen outreach and donation pathways. This program provides a scalable model for advancing population-reflective, high-quality human brain cell atlas efforts. HighlightsO_LIEstablish a multi-site pipeline for procuring high-quality neurotypical human brains. C_LIO_LIDemonstrate feasibility of donor collection across childhood to adulthood. C_LIO_LIIdentify barriers and propose strategies to improve broad donor recruitment. C_LI

neuroscience↗

bioRxiv2024

One-day phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG using single-cell imaging and a deep neural network

Drug-resistant tuberculosis (TB) kills approximately 200,000 people every year. A contributing factor is the slow turnaround time associated with anti-tuberculosis drug susceptibility diagnostics. The prevailing gold standard for phenotypic drug susceptibility testing (pDST) takes at least two weeks. In this study, we used Mycobacterium tuberculosis variant bovis BCG (M. bovis BCG) and Mycobacterium smegmatis as models for tuberculous and nontuberculous pathogens. The bacteria were loaded into a microfluidic chip, trapping them in microchambers, and allowing simultaneous tracking of single-cell growth with and without antibiotic exposure. A deep neural network image-segmentation algorithm was employed to quantify the growth rate over time and determine how the strains responded to the drugs compared to the untreated reference. We determined that the response time of the susceptible strains to isoniazid (INH), ethambutol (EMB), and linezolid (LZD) at MIC was within 3 hours and 1.5 hours for M. bovis BCG and M. smegmatis, respectively. Resistant strains of M. smegmatis were identifiable within 3 hours, suggesting that growth-based pDST can be conducted in less than 12 hours for slow-growing M. bovis BCG. The results obtained for M. bovis BCG are most likely comparable to what we expect for M. tuberculosis as these strains share 99.96% genetic identity.

microbiology↗

bioRxiv2022

Human Liver Organoids as a Patient-derived Model for HBV Infection and Cellular Response

Background & AimsCurrent HBV in vitro model systems suffer from many physiological limitations that restrict understanding of complex viral-host interactions and thus prohibit prediction of disease in vivo. We developed and assessed adult stem cell (AdSC) derived liver organoids as a novel model system for characterisation of the HBV lifecycle, the cellular response to infection and demonstrate their utility in assessing antiviral and immunomodulator response. This model system has the potential to be used in predicting individual HBV responses to antivirals and viral reactivation in the setting of immunosuppressive agents. MethodsDuctal stem cells were isolated from healthy tissue acquired from liver resections or biopsy (n=12). Wnt3a & RSPO-1 containing medium was used to stimulate ductal stem cell expansion into organoids which were subsequently differentiated into hepatocyte-like cells. Mature hepatocyte metabolic markers (albumin, CYP3A4) and HBV entry receptor (Na-taurocholate co-transporting polypeptide, NTCP) expression were evaluated throughout differentiation using qRT-PCR and confocal microscopy. We assessed the organoids culture conditions required for HBV infection and HBV life cycle using HepAD38 (genotype D) and plasma derived HBV (genotype B & C). HBV infection was confirmed using immunofluorescence staining (HBcAg), qRT-PCR (RNA, cccDNA, extracellular DNA) and ELISA (HBsAg and HBeAg). We also assessed drug responsiveness using antivirals and an immunosuppressive agent, and cellular responses (interferon-stimulated genes) using interferon- and viral mimic (PolyI:C). ResultsFollowing differentiation, organoids underwent structural remodelling and changes in cellular polarity, accompanied with an increase in albumin, CYP3A4 and NTCP mRNA expression. Optimal HBV infection was achieved in well-differentiated organoids using spinoculation of at least 200 copies/cell of AD38 derived HBV. Infected organoids demonstrate time and donor dependent increase in HBV RNA, cccDNA, extracellular DNA, HBe and HBsAg consistent with viral replication and antigen secretion. Using these markers we assessed drug-responsiveness to the HBV entry inhibitor, Myrcludex B and the JAK inhibitor, Baricitinib. Despite having a very robust interferon stimulated gene response to interferon- and PolyI:C stimulation, HBV infection in liver organoids did not reveal innate immune activation. ConclusionsAdSC derived liver organoids support the full life cycle of HBV with significant donor dependent variation in viral replication and cellular responses. These features can be utilised for development of personalised drug testing platform for antivirals. Lay SummaryHuman liver organoid culture provides a personalised assessment of HBV infection, replication and responsiveness to antiviral therapy. This model system has a robust innate immune response and could be used to assess novel immune-modulating curative therapy.

microbiology↗

bioRxiv2022

Assessment of pre-clinical liver models based on their ability to predict the liver-tropism of AAV vectors

The liver is a prime target for in vivo gene therapies using recombinant adeno-associated viral vectors (rAAV). Multiple clinical trials have been undertaken for this target in the past 15 years, however we are still to see market approval of the first liver-targeted AAV-based gene therapy. Inefficient expression of the therapeutic transgene, vector-induced liver toxicity and capsid, and/or transgene-mediated immune responses reported at high vector doses are the main challenges to date. One of the contributing factors to the insufficient clinical outcomes, despite highly encouraging preclinical data, is the lack of robust, biologically- and clinically-predictive preclinical models. To this end, this study reports findings of a functional evaluation of six AAV vectors in twelve preclinical models of the human liver, with the aim to uncover which model is the most relevant for the selection of AAV capsid variant for safe and efficient transgene delivery to primary human hepatocytes. The results, generated by studies in models ranging from immortalized cells, iPSC-derived and primary hepatocytes, and primary human hepatic organoids to in vivo models, increased our understanding of the strengths and weaknesses of each system. This should allow the development of novel gene therapies targeting the human liver.

bioengineering↗