Biology subjects

Pham, H.

Publications and source records attributed to Pham, H..

At least 19 recordsLinked to original sources

bioRxiv2025

slideimp: Efficient Imputation for DNA Methylation Data

MotivationThere is a growing need for efficient imputation methods for high-dimensional DNA methylation (DNAm) datasets. Existing microarray imputation approaches, such as k-nearest neighbors (K-NN) or principal component analysis (PCA)-based methods, provide high accuracy but can be computationally intensive, while methods for whole-genome data are not designed for large cohorts. We developed slideimp, an R package that implements sliding window, groupable, parallelized K-NN and optimized PCA imputation to address these limitations. ResultsBenchmarks on microarray DNAm datasets demonstrate that slideimp achieves up to 150x faster runtime and 10x-100x lower memory usage while maintaining comparable or superior accuracy over existing methods and implementations. For K-NN and PCA imputation, slideimp supports grouped imputation which enhances imputation efficiency and accuracy. In a whole-genome dataset, sliding window K-NN imputation substantially increased the correlation of the Horvath 2013 clock with chronological age from 0.131 to 0.477. Additional features include targeted imputation of CpG subsets for K-NN and estimation of imputation accuracy via repeated cross-validation. The efficient and flexible DNAm imputation methods implemented by slideimp can easily be applied to other high-dimensional data types. Availability and ImplementationThe code to fully reproduce all analyses presented in this paper is available on GitHub at https://github.com/hhp94/slideimp_paper. The R package slideimp is also available on GitHub at https://github.com/hhp94/slideimp. Supplemental figures are available online.

bioinformatics↗

bioRxiv2025

Cation-Cation Photosensitization for Protein Ligation and Intracellular Catalysis

We report here a photosensitized strategy for protein labelling in which N-substituted pyridinium salts are activated using a 2,4-diaryl-N-methyl quinolinium scaffold. Structure-reactivity relationships were performed to optimize the sensitizer structure, and ultimately generated a system that gives protein labelling in minutes at micromolar reagent concentrations. Mechanistic studies suggest a photo-induced electron transfer-based sensitization mechanism. The mildness of this system enabled us to assay sensitization both on individual biomolecules and in complex proteomes and demonstrated excellent compatibility with lysate- and the live-cell-based systems. Imaging of photolabelled HeLa cells were performed and revealed that catalysis occurs in multiple cellular compartments. Chemical proteomics performed at the lysate level resulted in the enrichment of 319 proteins with a 93% selectivity to Tryptophan residues. Live cell labelling resulted in 101 enriched proteins, primarily from the nucleus.

biochemistry↗

bioRxiv2025

Erucamide regulates retinal neurovascular crosstalk

Neurovasculoglial crosstalk is critical in establishing and maintaining a functional neurovascular unit. Breakdown in the unit is central to many neurodegenerative disorders of the CNS of which the retina is a component. A growing literature indicated that primary fatty acid amides (PFAMs) can regulate this crosstalk between vasculature and neuronal tissues. In this study we describe a central role for erucamide, a 22:1 mono-unsaturated omega-9 fatty acid amide, in degenerating retinal tissues. Using high-resolution global mass spectrometry-based metabolomics, we cataloged metabolites in murine models of retinal degeneration and show that while PFAMs, in general, are highly dysregulated, erucamide is the one most significantly diminished during photoreceptor atrophy. Using rodent models of retinal degeneration and novel organosilane-modified porous silicon nanoparticles (pSiNPs) for the in vivo delivery of erucamide, we demonstrate that erucamide activates CD11b+ myeloid cells, leading to the upregulation of angiogenic and neurotrophic cytokines that stabilize retinal degeneration. We identified TMEM19 as a novel binding protein for erucamide that is crucial for human iPSC-derived macrophage precursor cells activation and subsequent neurotrophic and angiogenic factor production. These findings reveal a previously unknown PFAM pathway that is modulated during retinal degenerative diseases, demonstrating that erucamide or functional analogues and their action through TMEM19 may be useful as a therapeutic alternative to neuroprotective and stem cell-based approaches for the treatment of retinal degenerative diseases.

neuroscience↗

bioRxiv2025

Targeting ZNRF3 and RNF43 to Restore Regeneration and Reverse Metabolic Dysfunction-Associated Steatotic Liver Disease

Liver regeneration and hepatocyte metabolic identity are disrupted in metabolic dysfunction-associated steatotic liver disease (MASLD) and its advanced form, metabolic dysfunction-associated steatohepatitis (MASH), yet the mechanisms of restore liver regeneration and reprogram metabolism for disease reversal remains poorly understood. Here, we show that {beta}-Catenin activity progressively declines in hepatocytes during MASH in both humans and mice, coinciding with impaired regeneration and defective lipid clearance. Targeted deletion of the endogenous WNT pathway inhibitors ZNRF3 and RNF43 in hepatocytes after MASH onset reactivates {beta}-Catenin signaling, leading to robust regression of steatosis, inflammation, and fibrosis, and restoring regenerative capacity across multiple fatty liver disease models. Mechanistically, this therapeutic effect is driven by {beta}-Catenin-dependent induction of the alternative bile acid synthesis pathway without disrupting systemic lipid homeostasis. Importantly, both short-and long-term deletion of ZNRF3/RNF43 restores liver function without triggering tumorigenesis or hepatotoxicity, indicating a safe therapeutic window. These findings reveal that physiological activation of WNT/{beta}-catenin signaling via ZNRF3 and RNF43 offers a viable regenerative and metabolic strategy for reversing fatty liver disease.

cell biology↗

bioRxiv2024

CRISPR knockout of winter-biased SUT4 alters phenology, biomass accrual, and fertility of field-grown hybrid poplar

Climate uncertainty is intensifying the need for greater plasticity in carbohydrate reserve utilization to support winter survival and spring growth in woody perennials. In poplar, the single-copy SUT4, which encodes a tonoplast-localized sucrose transporter, and the SUT5/SUT6 genome duplicates, which encode plasma membrane-localized transporters, are expressed year-round, with SUT4 showing highest expression during cool seasons. Given its role in vacuolar sucrose efflux and winter-predominant expression, SUT4 may play a key role in modulating seasonal carbohydrate dynamics. While SUT4-knockdown and knockout effects have been studied under greenhouse conditions, their impact under field conditions remains unexplored. Here, we report a field-based study comparing CRISPR knockout mutants of winter-expressed SUT4 and SUT5/SUT6 in Populus tremula x alba. We show that sut4, but not sut56, mutants exhibited earlier autumn leaf senescence, delayed spring bud flush, reduced stem growth, and altered sugar partitioning in winter xylem and bark relative to controls. After two years in the field, all genotypes flowered before leaf flush in early spring; however, sut4 mutants produced sterile ovules despite developing normal-looking catkins. Metabolic profiling revealed disrupted sucrose and raffinose dynamics in elongating sut4 catkins, accompanied by transcriptomic signatures of elevated stress and downregulation of proanthocyanidin biosynthesis and circadian clock genes. These findings highlight the critical role of SUT4 in coordinating sugar allocation, stress responses, and seasonal development in poplar. Significance statementThis field study demonstrates that loss of SUT4, the most highly expressed sucrose transporter during cool seasons, disrupts phenology, growth, and fertility in poplar. Altered sugar and raffinose dynamics and transcriptomic signatures of stress and circadian clock gene dysregulation in the mutants underscore SUT4s role in coordinating sugar allocation and seasonal developmental transitions under natural environmental conditions.

plant biology↗

bioRxiv2023

A Complex Containing the Na+/K+ ATPase Regulates Epileptogenesis in Drosophilamelanogaster

Epileptogenesis, the process through which the brain becomes seizure-prone, is not well understood. Previous work identified a novel gene in Drosophila, julius seizure (jus), that when mutated or developmentally knocked down, leads to epilepsy in adult Drosophila, providing a useful model for dissecting epileptogenesis. Here we report that when a GFP-tagged version of Jus was used as bait in a co-immunoprecipitation (co-IP) assay, a complex of 23 associated proteins was identified that included ATPalpha and Nervana 3, two subunits of the Na+/K+ ATPase. RNAi-mediated knockdown of ATPalpha, Nervana 3, or any of 8 additional complex proteins enhanced seizure susceptibility. The critical period of Jus expression in epileptogenesis was further defined, occurring between pupal stages P4-7; remarkably, the jus seizure phenotype could be rescued by increasing neural activity of jus-expressing neurons during these mid-pupal stages, suggesting that altered neural activity in these neurons may contribute to the seizure phenotype. Our data support a model that, in wild type flies, a protein complex containing Na+/K+ ATPase and Jus prevents epileptogenesis, possibly by regulating neural activity.

neuroscience↗

bioRxiv2023

Differences in Oligomerization of the SARS-CoV-2 Envelope Protein, Poliovirus VP4, and HIV Vpu

Viroporins constitute a class of viral membrane proteins with diverse roles in the viral life cycle. They can self-assemble and form pores within the bilayer that transport substrates, such as ions and genetic material, that are critical to the viral infection cycle. However, there is little known about the oligomeric state of most viroporins. Here, we use native mass spectrometry (MS) in detergent micelles to uncover the patterns of oligomerization of the full-length SARS-CoV-2 envelope (E) protein, poliovirus VP4, and HIV Vpu. Our data suggest that the E protein is a specific dimer, VP4 is exclusively monomeric, and Vpu assembles into a polydisperse mixture of oligomers under these conditions. Overall, these results revealed the diversity in the oligomerization of viroporins, which has implications for mechanisms of their biological functions as well as their potential as therapeutic targets.

biophysics↗

bioRxiv2023

An essential role for miR-15/16 in Treg suppression and restriction of proliferation

The miR-15/16 family is a highly expressed group of tumor suppressor miRNAs that target a large network of genes in T cells to restrict their cell cycle, memory formation and survival. Upon T cell activation, miR-15/16 are downregulated, allowing rapid expansion of differentiated effector T cells to mediate a sustained immune response. Here, using conditional deletion of miR-15/16 in immunosuppressive regulatory T cells (Tregs) that express FOXP3, we identify new functions of the miR-15/16 family in T cell immunity. miR-15/16 are indispensable to maintain peripheral tolerance by securing efficient suppression by a limited number of Tregs. miR-15/16-deficiency alters Treg expression of critical functional proteins including FOXP3, IL2R/CD25, CTLA4, PD-1 and IL7R/CD127, and results in accumulation of functionally impaired FOXP3loCD25loCD127hi Tregs. Excessive proliferation in the absence of miR-15/16 inhibition of cell cycle programs shifts Treg diversity and produces an effector Treg phenotype characterized by low expression of TCF1, CD25 and CD62L, and high expression of CD44. These Tregs fail to control immune activation of CD4+ effector T cells, leading to spontaneous multi-organ inflammation and increased allergic airway inflammation in a mouse model of asthma. Together, our results demonstrate that miR-15/16 expression in Tregs is essential to maintain immune tolerance. HighlightsO_LITreg-specific miR-15/16 expression is essential to prevent systemic tissue inflammation C_LIO_LImiR-15/16 restrict Treg proliferation and regulate expression of the key functional Treg molecules FOXP3, IL2R, CTLA4, PD-1 and IL7R C_LIO_LImiR-15/16 limit formation of effector Tregs and is necessary for high suppressive capacity C_LI

molecular biology↗

bioRxiv2022

Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Ass-coding region of App

Model organisms mimicking the pathogenesis of human diseases are useful for identifying pathogenic mechanisms and testing therapeutic efficacy of compounds targeting them. Models of Alzheimers disease and related dementias aim to reproduce the brain pathology associated with these neurodegenerative disorders. Transgenic models, which involve random insertion of disease-causing genes under the control of artificial promoters, are efficient means of doing so. There are confounding factors associated with transgenic approaches, however, including target gene overexpression, dysregulation of endogenous gene expression at transgenes integration sites, and limitations in mimicking loss-of-function mechanisms. Furthermore, the choice of species is important, and there are anatomical, physiological, and cognitive reasons for favoring the rat over the mouse, which has been the standard for models of neurodegeneration and dementia. We report an initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in Long-Evans rats with humanizing mutations in the A{beta}-coding region of App, which encodes amyloid precursor protein (Apph/h rats), using the IntelliCage, an automated operant social home cage system, at 6-8 weeks of age, then again at 4-5 months of age. These rats were previously generated as control organisms for studies on neurodegeneration involving other knock-in rat models from our lab. Apph/h rats of either sex can acquire place learning and reversal tasks. They can also acquire a diagonal sequencing task by 6-8 weeks of age, but not a more advanced serial reversal task involving alternating diagonals, even by 4-5 months of age.

neuroscience↗

bioRxiv2021

FRET and LRET Biosensors for Cell-based Imaging and Screening of Rac1 Activation

Rac1 is a key regulator of several cell signaling pathways and dysregulated Rac1 activation has been implicated in cancer. Genetically encoded Forster resonance energy transfer (FRET) biosensors with enhanced dynamic range enabled live cell fluorescence imaging of Rac1 activity and a cell lysate-based assay of Rac1 inhibition in 96-well plates. We prepared HEK293 cell lines that stably expressed polypeptides with a general domain sequence (N- to C-terminus) of 1) FRET acceptor; 2) Rac/Cdc42 binding domain of human p21 protein kinase A (residues 68-150); 3) a linker domain; 4) FRET donor; and 5) full-length Rac1. Activated Rac1 binds to the protein kinase A domain, bringing donors and acceptors close together to increase FRET. We evaluated the effects on FRET signal dynamic range of alpha helical linkers comprised of alternating repeats of roughly four glutamate and four arginine or lysine residues. So-called ER/K linkers had limited effects on conventional FRET biosensors that incorporated the fluorescent protein (FP) pairs mCerulean/YPet, or mTFP1(cp227)/mVenus(cp229). Fluorometric measurements of cells that co-expresssed biosensors with positive (TIAM1) or negative (RhoGDI) Rac1 regulators revealed significant dynamic range enhancement in only one FP construct (mCerulean/YPet with 20 nm ER/K linker) relative to an analogous structure that incorporated an unstructured linker. We transfected this construct into a cell line that stably expressed a rapamycin-inducible c-Src analog (RapR-Src) and observed activation of Rac1 at protruding edges following rapamycin stimulation. In cells that expressed lanthanide-based FRET (LRET biosensors) that incorporated a luminescent terbium complex donor and GFP fluorescent acceptor, time-gated luminescence (TGL) measurements showed substantial gains in dynamic range that increased with linker length (up to 1200%). We robustly detected small molecule Rac1 inhibition following lysis of LRET biosensor-expressing cells grown directly in 96-well plates. The results herein highlight the potential of FRET and LRET biosensors with ER/K linkers for cell-based imaging and screening of protein activities.

biochemistry↗

bioRxiv2021

Hexavalent Sperm-Binding IgG Antibody Released from Self-Dissolving Vaginal Film Enables Potent, On-Demand Non-Hormonal Female Contraception

Non-hormonal products for on-demand contraception are a global health technology gap, motivating us to pursue the use of sperm-binding monoclonal antibodies as a strategy to enable safe, effective, desirable, on-demand contraception. Here, using cGMP-compliant Nicotiana-expression system, we produce an ultra-potent sperm-binding IgG antibody possessing 6 Fab arms per molecule that bind a well-established contraceptive antigen target, CD52g. We term this hexavalent antibody "Fab-IgG-Fab" (FIF) to reflect its molecular orientation. The Nicotiana-produced FIF exhibits at least 10-fold greater sperm agglutination potency and kinetics than the parent IgG, while preserving Fc-mediated trapping of individual spermatozoa in mucus. We formulate the Nicotiana-produced FIF into a polyvinyl alcohol-based water-soluble contraceptive film, and evaluate its potency in reducing progressively motile sperm in the sheep vagina. Two minutes after vaginal instillation of human semen, no progressively motile sperm are recovered from the vaginas of sheep receiving FIF-Film. In contrast, high numbers of progressively motile sperm are recovered from sheep receiving a placebo film control. Our work supports the potential of highly multivalent contraceptive antibodies to provide safe, effective, on-demand non-hormonal contraception.

bioengineering↗

bioRxiv2020

YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data

AO_SCPLOWBSTRACTC_SCPLOWLarge scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout its growth state. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts yield of multiple crops and concurrently considers the interaction between multiple crops yield. We propose a new model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. Numerical results demonstrate that our proposed method accurately predicts yield from one to four months before the harvest, and is competitive to other state-of-the-art approaches.

bioinformatics↗

bioRxiv2020

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

AO_SCPLOWBSTRACTC_SCPLOWThe success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting accurate and consistent data is a bottleneck. Due to limited time and labor, accurately phenotyping crops to record color, head count, height, weight, etc. is severely limited. However, this information, combined with other genetic and environmental factors, is vital for developing new superior crop species that help feed the worlds growing population. Recent advances in machine learning, in particular deep learning, have shown promise in mitigating this bottleneck. In this paper, we propose a novel deep learning method for counting on-ear corn kernels in-field to aid in the gathering of real-time data and, ultimately, to improve decision making to maximize yield. We name this approach DeepCorn, and show that this framework is robust under various conditions and can accurately and efficiently count corn kernels. We also adopt a semi-supervised learning approach to further improve the performance of our proposed method. Our experimental results demonstrate the superiority and effectiveness of our proposed method compared to other state-of-the-art methods.

bioinformatics↗

bioRxiv2020

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

AO_SCPLOWBSTRACTC_SCPLOWThe future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. However, due to the sheer size of a breeding program and current resource limitations, the ability to collect precise data on individual plants is not possible. In particular, efficient phenotyping of crops to record its color, shape, chemical properties, disease susceptibility, etc. is severely limited due to labor requirements and, oftentimes, expert domain knowledge. In this paper, we propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. The proposed method adopts a truncated VGG-16 network as a backbone feature extractor and merges multiple feature maps with different scales to make the network robust against scale variation. Our extensive computational experiments suggest that our proposed method can successfully count corn stands and out-perform other state-of-the-art methods. It is the goal of our work to be used by the larger agricultural community as a way to enable high-throughput phenotyping without the use of extensive time and labor requirements.

bioinformatics↗

bioRxiv2020

A Look-ahead Monte Carlo Simulation Method for Improving Parental Selection in Trait Introgression

Multiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches.

bioinformatics↗

bioRxiv2019

High-performance chemical and light-inducible recombinases in mammalian cells and mice

Site-specific DNA recombinases are some of the most powerful genome engineering tools in biology. Chemical and light-inducible recombinases, in particular, enable spatiotemporal control of gene expression. However, the availability of inducible recombinases is scarce due to the challenge of engineering high performance systems with low basal activity and sufficient dynamic range. This limitation constrains the sophistication of genetic circuits and animal models that can be created. To expand the number of available inducible recombinases, here we present a library of >20 orthogonal split recombinases that can be inducibly dimerized and activated by various small molecules, light, and temperature in mammalian cells and mice.\n\nFurthermore, we have engineered inducible split Cre systems with better performance than existing inducible Cre systems. Using our orthogonal inducible recombinases, we created a \"genetic switchboard\" that can independently regulate the expression of 3 different cytokines in the same cell. To demonstrate novel capability with our split recombinases, we created a tripartite inducible Flp and a 4-Input AND gate. We have performed extensive quantitative characterization of the inducible recombinases for benchmarking their performances, including computation of distinguishability of outputs in terms of signal-to-noise ratio (SNR). To facilitate sharing of this set of reagents, we have deposited our library to Addgene. This library thus significantly expands capabilities for precise and multiplexed mammalian gene expression control.

synthetic biology↗

bioRxiv2019

Serum Alpha-2-Macroglobulin as an intrinsic radioprotective factor in patients undergoing thoracic radiation therapy

ObjectiveTo investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis. Additionally, we establish multifactorial predictive models for pneumonitis and esophagitis.\n\nMaterials/MethodsBaseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearmans correlation (Rs) test was used to correlate A2M levels, smoking status and dosimetric variables with toxicities. Esophagitis and pneumonitis prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1000 bootstrapped datasets. Models were built using 2/3 of the data for training and 1/3 for validation.\n\nResultsThere were 36 (14.0%) patients with grade [≥]2 pneumonitis and 61 (23.6%) with grade [≥]2 esophagitis. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant correlation between baseline A2M levels and esophagitis (Rs=-0.18/p=0.003) and between A2M and smoking status (former or current) (Rs=0.13/p=0.04) but not between A2M and pneumonitis. On univariate analysis, significant parameters for grade [≥]2 esophagitis included number of fractions (Rs=0.47/p<0.0001), treatment days (Rs=0.44/p<0.0001), chemotherapy use (Rs=0.40/p<0.0001), dose per fraction (Rs=-0.34/p<0.0001), total dose (Rs=0.29/p<0.0001), age (Rs=-0.22/p=0.0003), and several dosimetric variables in esophagus with Rs>0.5 (p<0.0001). For pneumonitis, significant clinical parameters were treatment days (Rs=0.24/p=0.0001), chemotherapy use (Rs=0.22/p=0.0004), number of fractions (Rs=0.21/p=0.0007), dose per fraction (Rs=-0.18/p=0.0035), and total dose (Rs=0.15/p=0.013). The most significant dosimetric variable in lung and heart was D70 (Rs=0.28/p<0.0001) and max dose (Rs=0.27/p<0.0001), respectively. LASSO bootstrap logistic regression models on the validation data resulted in the area under the receiver operating characteristic curve of 0.84 and 0.75 for esophagitis and pneumonitis, respectively.\n\nConclusionOur findings show an association of higher A2M values with lower risk of radiation esophagitis and smoking status. Multivariate predictive models also confirmed a role of heart dose in the risk of pneumonitis.

cancer biology↗

bioRxiv2018

Knowledge, Attitudes and Practices of Hepatitis B Prevention and Immunization of Pregnant Women and Mothers in Viet Nam

Background and AimVietnams high burden of liver cancer is largely attributable to the high prevalence of chronic hepatitis B virus infection (HBV). Infection at birth due to mother-to-child (MTC) transmission is the most common cause of chronic HBV in Vietnam and increases the risk of liver cancer later in life. This study was undertaken to examine the knowledge, attitudes, and practices of pregnant women and mothers in Vietnam concerning HBV prevention and immunization.\n\nMethodsA cross-sectional study was conducted in Quang Ninh and Hoa Binh provinces in 2017. A pre-designed questionnaire was administered to women when they received care at primary and tertiary maternal health clinics. Correct responses were summarized as knowledge scores. Data was analyzed using a multivariable regression model across participant demographics.\n\nResultsAmong the 404 women surveyed, 57.6% were pregnant and 42.4% were postpartum. Despite 73.5% of participants reporting having received information about HBV during their pregnancy, gaps in knowledge and misconceptions are evident. Overall, only 10.6% provided correct answers to all questions regarding HBV transmission routes and prevention measures. Around half of the participants incorrectly believed that HBV is transmitted through sneezing, contaminated water or sharing foods with chronic HBV patients. Although 96.4% of participants believed that HBV vaccination is necessary for infants, only 69.1% were willing to have their own child vaccinated within 24 hours. More than a third of participants expressed concern about having casual contacts or sharing foods with chronic HBV patients. In multivariable analysis, having received information about HBV during their pregnancy were consistently associated with better knowledge score for transmission, prevention and immunization. However, knowledge of women who received information about HBV during their pregnancy was still suboptimal.\n\nConclusionsThe results highlight the need to prioritize educating pregnant women and mothers in future public health campaigns in order to increase knowledge, reduce misperception, and improve HBV vaccine coverage in Vietnam.

epidemiology↗