Biology subjects

Lin, Y.-W.

Publications and source records attributed to Lin, Y.-W..

6 recordsLinked to original sources

bioRxiv2026

Nano- and microplastics in pediatric tonsil tissue: bioaccumulation, distribution, and immunomodulatory effects in human lymphoid aggregate organoids

Nano- and microplastics (NMPs), by-products of the fragmentation and degradation of plastic products, are ubiquitous environmental contaminants, yet their burden in pediatric immune tissues and functional consequences for developing immunity remain unknown. Here we report the first comprehensive characterization of NMPs in surgically excised pediatric tonsils (n = 30) using pyrolysis gas chromatography- mass spectrometry (Py-GC/MS), Nile Red fluorescence microscopy, and optical photothermal infrared (O-PTIR) spectroscopy. NMPs were detected in all specimens, with polystyrene, polyethylene, polyethylene terephthalate, and acrylonitrile butadiene styrene present in >90% of samples. To bridge clinical exposure data with mechanistic insight, we formulated a cryo-milled multi-polymer mixture reflecting the patient-derived polymer profile and challenged human lymphoid aggregate culture (HLAC) tonsil organoids at environmentally relevant concentrations. Multiplexed cytokine profiling of culture supernatants revealed a robust early inflammatory response at day 3, with significant upregulation of IL-6 (p = 0.011) and MIP-1{beta}/CCL4 (p = 0.011), followed by convergence toward control levels by day 14. Functional cytokine modules spanning immune, metabolic, structural, and growth factor pathways showed coordinated deviation from controls at day 3 post-exposure with subsequent normalization. Fluorescence-guided depth profiling demonstrated time-dependent penetration of 100 nm particles into organoid aggregates (70% tissue depth at day 3 versus 95% at day 14), and transmission electron microscopy revealed intracellular polyethylene within lymphocyte lysosomes. These findings establish pediatric tonsils as a sentinel tissue for NMP bioaccumulation and demonstrate that environmentally relevant polymer mixtures elicit transient but significant immunomodulatory responses in human lymphoid tissue, with implications for mucosal and systemic immune health in children. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/728317v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@19395f4org.highwire.dtl.DTLVardef@5a0380org.highwire.dtl.DTLVardef@19c0741org.highwire.dtl.DTLVardef@a052c5_HPS_FORMAT_FIGEXP M_FIG Structure: Translational pipeline from clinical tissue characterization to patient-informed preclinical modeling of nano-microplastic (NMP) exposure in pediatric lymphoid tissue. Pediatric tonsil tissue collected from clinically indicated tonsillectomies underwent tissue digestion for NMP characterization to identify NMP type and size distributions. In parallel, tonsil tissue was used to generate human lymphoid aggregate culture (HLAC) organoids that recapitulate the cellular complexity of the native tissue. These patient-derived organoids were then exposed to environmentally relevant compositions and concentrations of NMPs over time-course experiments, with longitudinal assessment of immunomodulatory responses including cytokine profiling and functional readouts. This bedside-to-bench approach establishes a physiologically relevant human system for investigating NMP-immune interactions, bridging clinical tissue analysis with mechanistic preclinical modeling to inform understanding of pediatric environmental exposures and their potential health impacts. C_FIG

immunology↗

bioRxiv2025

Single-cell transcriptomic dynamics exposes hidden survival trajectory under antibiotic treatment

Heterogeneity plays a major role in bacterial resistance to antibiotic treatment while the mechanism at single-cell level is largely unknown. Here, we employed a robust integration of bulk and bacterial single-cell RNA-seq (scRNA-seq) to uncover how individual cells of Acinetobacter baumannii reorganise the heterogenous transcriptome in response to antibiotic. Using polymyxin as a representative, bulk RNA-seq showed canonical envelope- and efflux-centred responses but obscured underlying heterogeneity. Single-cell profiling resolved these averages into discrete subpopulations whose abundances shifted with concentration and time. Specifically, in early time an envelope-stress programme predominated survival at the low concentration, whereas an outer-membrane repair/efflux programme dominated survival at the high concentration. These patterns revealed structured and time-resolved heterogeneity, highlighting an ingenious bacterial stress responsive strategy. Through trajectory inference, we further revealed a concentration-dependent bifurcation of cell fates: low-concentration treated cells detoured through a transient stress state and rejoined growth, whereas high-concentration treated survivors diverted into a slow-growing, tolerant branch. Perturbing marker genes from these programmes altered fitness eventually and rapidly increased permeability, depolarisation and reactive-oxygen burden, linking state to survival capability. Collectively, we map a dynamic, concentration-structured landscape of antibiotic responses at the single-cell level, revealing diverse survival trajectories that are obscured in conventional population-averaged analyses. Our developed single-cell based framework revealed tolerant bacterial cells with rewiring of the transcriptional landscape, causing emergence of antibiotic resistance. Importantly, these findings urge precision antimicrobial therapy in patients to minimise emergence of antibiotic resistance.

microbiology↗

bioRxiv2024

Cross-institutional HER2 assessment via a computer-aided system using federated learning and stain composition augmentation

The rapid advancement of precision medicine and personalized healthcare has heightened the demand for accurate diagnostic tests. These tests are crucial for administering novel treatments like targeted therapy. To ensure the widespread availability of accurate diagnostics with consistent standards, the integration of computer-aided systems has become essential. Specifically, computer-aided systems that assess biomarker expression have thrusted through the widespread application of deep learning for medical imaging. However, the generalizability of deep learning models has usually diminished significantly when being confronted with data collected from different sources, especially for histological imaging in digital pathology. It has therefore been challenging to effectively develop and employ a computer-aided system across multiple medical institutions. In this study, a biomarker computer-aided framework was proposed to overcome such challenges. This framework incorporated a new approach to augment the composition of histological staining, which enhanced the performance of federated learning models. A HER2 assessment system was developed following the proposed framework, and it was evaluated on a clinical dataset from National Taiwan University Hospital and a public dataset coordinated by the University of Warwick. This assessment system showed an accuracy exceeding 90% for both institutions, whose generalizability outperformed a baseline system developed solely through the clinical dataset by 30%. Compared to previous works where data across different institutions were mixed during model training, the HER2 assessment system achieved a similar performance while it was developed with guaranteed patient privacy via federated learning.

pathology↗

bioRxiv2022

Allosteric inhibitors of Zika virus NS2B-NS3 protease targeting protease in super-open conformation

Zika virus (ZIKV) is a member of the Flaviviridae family and is considered a major health threat causing cases of microcephaly in newborns and Guillain-Barre syndrome in adults. Here we targeted a transient deep and hydrophobic pocket of the super-open conformation of ZIKV NS2B-NS3 protease to overcome the limitations of the orthosteric inhibitors. After virtual docking screening of approximately 7 million compounds against the novel allosteric site we selected the top seven candidates and tested them in an enzymatic assay. Six out of seven top candidates selected by the docking screen inhibited ZIKV NS2B-NS3 protease proteolytic activity at low micromolar concentrations, as well as suppressing viral replication. These six compounds, targeting the selected protease pocket conserved in ZIKV as well as several other Flaviviruses, have opened an opportunity for a new kind of drug candidate that might be useful to treat several flaviviral infections.

pharmacology and toxicology↗

bioRxiv2020

Bacteriophage uptake by Eukaryotic cell layers represents a major sink for phages during therapy

For over 100 years, bacteriophages have been known as viruses that infect bacteria. Yet it is becoming increasingly apparent that bacteriophages, or phages for short, have tropisms outside their bacterial hosts. During phage therapy, high doses of phages are directly administered and disseminated throughout the body, facilitating broad interactions with eukaryotic cells. Using live cell imaging across a range of cell lines we demonstrate that cell type plays a major role in phage internalisation and that smaller phages (< 100 nm) are internalised at higher rates. Uptake rates were validated under physiological shear stress conditions using a microfluidic device that mimics the shear stress to which endothelial cells are exposed to in the human body. Phages were found to rapidly adhere to eukaryotic cell layers, with adherent phages being subsequently internalised by macropinocytosis and functional phages accumulating and stably persisting intracellularly. Finally, we incorporate these results into an established pharmacokinetic model demonstrating the potential impact of phage accumulation by these cell layers, which represents a major sink for circulating phages in the body. Understanding these interactions will have important implications on innate immune responses, phage pharmacokinetics, and the efficacy of phage therapy.

microbiology↗

bioRxiv2018

Analysis and Prediction of Unplanned Intensive Care Unit Readmission using Recurrent Neural Networks with Long Short-Term Memory

BackgroundUnplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians.\n\nMethods and FindingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model.\n\nConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

bioinformatics↗