Biology subjects

Kim, M. Y.

Publications and source records attributed to Kim, M. Y..

9 recordsLinked to original sources

bioRxiv2026

A Systematic Review and Independent Benchmarking of Automated Nerve Morphometry Methods

Objective: To systematically review automated nerve morphometry tools and independently benchmark their performance on independent optic nerve datasets. Design: Systematic review and comparative benchmarking study. Controls: Benchmarking was performed using paraphenylenediamine-stained mouse (n = 85) and rat (n = 44) optic nerve images with manually annotated axon counts as ground truth. Methods: Published studies describing automated or semi-automated neural tissue morphometry tools were identified through systematic searches of PubMed, Embase, and Scopus through January 2026 following PRISMA guidelines. Data extraction covered 70 fields across tool capabilities, imaging modality, species, automation level, and validation approach. Eighteen eligible tools (8 deep learning [DL], 10 classical computer vision [CV]) were benchmarked on both mouse and rat independent datasets. Main Outcome Measures: Performance was assessed by mean absolute percentage error (MAPE), Pearson correlation, and median predicted-to-ground-truth ratio. Tools were ranked per image and compared using Friedman tests with Nemenyi post-hoc analysis. Results: Seventy-one studies met inclusion criteria, spanning from 1999 to 2026. Deep learning methods represented 38% (27/71) of studies, increasing from 0% before 2017 to over 55% of publications after 2020. Axon counting was the most common output (73%, 52/71), while only 35% (25/71) reported g-ratio. Among benchmarked tools, Marina (CV, 2010) achieved the lowest average MAPE (32.9%). The top five tools (MAPE ranging from 32.9 to 44.8%) included both CV and DL methods and were statistically indistinguishable by Friedman-Nemenyi analysis (p > 0.05). Performance varied substantially across datasets: AxonJ (CV) achieved the second best MAPE on rat images (27.7%) but the worst on mouse images (438.6%). Conclusions: No single tool demonstrated consistently superior performance across both datasets. Classical and deep learning approaches achieved comparable accuracy for axon counting. Tool selection should be guided by target species, tissue preparation protocol, and desired morphometric outputs. This systematic review and independent benchmarking study provide an evidence base for tool selection in optic nerve research.

bioengineering↗

bioRxiv2026

Visual Function Correlates More Strongly with Glial Coverage than Axon Count Across Multiple Mouse Strains

ObjectiveTo determine whether non-axon optic nerve morphometric features correlate with clinical visual function as strongly as the traditional axon count gold standard. DesignCross-sectional histological analysis with longitudinal clinical correlation. SubjectsEighteen mice from three strains: C57BL/6J (n=6), BXD51 (n=6), and DBA/2J (n=6). MethodsLeft eye (OS) optic nerves from mice euthanized at 12 months of age were resin-embedded and stained with p-phenylenediamine. Bright-field cross-sectional images were segmented using an AxonDeepSeg-based workflow to generate axon, myelin, whole nerve, and glial coverage masks for morphometric quantification. Seven morphometrics were extracted: axon count (nAx), axon density (AxDen), glial coverage area ratio (GliaR), mean solidity (Sol), mean axon diameter (AxDiam), mean myelin area (MyArea), and mean axon-myelin area (AxMyArea). Morphometrics were correlated with longitudinal clinical data collected at 1, 3, 6, 9, and 12 months, including visual acuity (VA), contrast threshold, intraocular pressure (IOP), and pattern electroretinography P50 and N95 amplitudes (PERG P50 and N95). Main Outcome MeasuresPearson correlation coefficients were used to assess associations between morphometric features and clinical measures, and Fisher z-transformed meta-analytic correlations were used to aggregate these associations across ages. ResultsVA and contrast threshold demonstrated strong correlations with GliaR that matched or exceeded nAx. Meta-analysis across ages revealed GliaR correlated with VA (r = -0.84, p = 4.49 x 10-21) and contrast threshold (r = 0.86, p=7.55 x 10-23), comparable to nAx correlations with VA (r = 0.80, p=8.13x10-17) and contrast threshold (r = -0.80, p= 1.74x10-16). Structure-function relationships shifted with age: at 6 months, GliaR had the strongest correlation with contrast threshold (r = 0.96), while at 12 months, AxDiam became the dominant correlate of both VA (r = 0.77) and contrast threshhold (r = -0.74). IOP, PERG P50, and PERG N95 exhibited weak correlations with all morphometrics (|r| < 0.27). ConclusionsNon-axon morphometrics, particularly glial coverage area ratio, correlate with visual function as strongly as traditional axon count. Automated optic nerve assessment should incorporate glial and other non-axon features. Further, stage-aware biomarker selection may better capture structure-function relationships in glaucoma.

neuroscience↗

bioRxiv2026

Anti-Buoyancy Trap for Long-Term Quantitative Density Profiling of Adipocyte Spheroids

3T3-L1 adipocyte spheroids are widely used three-dimensional adipose models that undergo pronounced physical property changes during adipogenic maturation, including progressive buoyancy driven by lipid accumulation. We quantitatively profiled the time-resolved evolution of bulk spheroid density over 60 days, revealing a 6.7% reduction from 1.022 to 0.954 g/cm3 associated with intracellular lipid accumulation. This buoyancy-driven displacement hinders standardized interrogation of spheroid physical properties during long-term culture. To enable longitudinal and reproducible analysis of density remodeling, we introduced an anti-buoyancy spheroid trap (AS-Trap) that mechanically stabilizes spheroids during extended culture. Morphometric analysis revealed that lipid droplet areas in mature spheroids (790.68{+/-}513.84 m{superscript 2}) closely approximated in vivo adipocytes (846.34{+/-}257.28 m{superscript 2}) and were substantially larger than those in 2D cultures, providing structural context for the observed density reduction. Notably, the terminal spheroid density converged toward reported values for native white adipose tissue, underscoring the physiological relevance of the material state achieved during maturation. Together, this work establishes density as a quantitative physical phenotype of adipogenic maturation and enables standardized long-term interrogation of adipose spheroids as living materials.

bioengineering↗

bioRxiv2026

Synergistic Electroceutical-Glucocorticoid Intervention Mitigates Dexamethasone-Induced Muscle Atrophy in Aging Skeletal Muscle

Dexamethasone (DEX), a synthetic glucocorticoid widely prescribed for allergic and inflammatory diseases, is known to induce adverse effects, particularly skeletal muscle atrophy. DEX-induced atrophy exacerbates sarcopenia and has a more pronounced impact on aged skeletal muscle than on young skeletal muscle. To address this unmet clinical need, we introduce an electroceutical approach that counteracts DEX-induced muscle atrophy and enhances functional recovery in aging muscle. When applied to both young and aged human-derived skeletal muscle cells (skMCs) exhibiting DEX-induced atrophy, electroceutical treatment promoted recovery of myotube diameter and upregulated hypertrophy-related gene expression. Furthermore, in a preclinical study, young and aged mice treated with DEX to induce muscle atrophy exhibited significant muscle recovery following electroceutical treatment. This effect was evident from the restored cross-sectional area (CSA) of type IIA muscle fibers and the upregulation of hypertrophy-related genes. This study highlights electroceuticals as a pioneering non-pharmacological strategy complementary to glucocorticoid therapies, potentially transforming clinical outcomes and quality of life, particularly for older populations vulnerable to muscle wasting.

bioengineering↗

bioRxiv2026

Comparison of Deep Learning Tools for Optic Nerve Axon Quantification Finds Limited Generalizability on Independent Validation

PurposeMachine learning approaches for automated quantification of optic nerve histology have emerged as potential tools for objective assessment of axonal injury in experimental glaucoma models. However, the generalizability of these models to independent datasets remains unclear. Guided by a scoping review of the literature, this study performed independent validation testing of publicly available models on a novel rat optic nerve dataset to assess their generalizability. MethodsWe conducted a scoping review following PRISMA-ScR guidelines. PubMed, EMBASE, Scopus, and Cochrane CENTRAL were searched from 2000 through 2025. Two reviewers independently screened records and extracted data on model characteristics and performance metrics. Additionally, we performed independent validation of three models (AxoNet, AxonDeepSeg, AxoNet 2.0) on a novel rat optic nerve dataset comprising 57 images with 9,514 manually annotated axons. Because AxonDeep is not publicly available, we instead evaluated AxonDeepSeg, a separate publicly available deep learning-based tool that, while not previously applied to optic nerve tissue, is widely used for nerve fiber segmentation. ResultsFrom 2,036 records, four manuscripts describing three deep learning models met inclusion criteria. Published correlation coefficients between model predictions and reference counts ranged from 0.959 to 0.99. On independent validation, performance was reduced: AxoNet 2.0 achieved the highest correlation (r = 0.89), followed by AxonDeepSeg (r = 0.86) and AxoNet (r = 0.79). Segmentation quality metrics revealed high precision (>0.94) but low recall (0.18 to 0.27), with Dice coefficients of 0.29 to 0.40, substantially below published benchmarks of 0.81. ConclusionsDeep learning models for optic nerve histology demonstrate strong within-study performance but show meaningful performance decrements when applied to independent datasets. The observed generalizability gap (correlations 0.07 to 0.182 points below published values) demonstrates the need for standardized validation datasets and multi-center testing before widespread adoption of these tools.

bioengineering↗

bioRxiv2024

TGF-β expressed by M2 macrophages promotes wound healing by inhibiting TSG-6 expression by mesenchymal stem cells

Wound healing involves the collaboration of multiple cells, including macrophages and fibroblasts, and requires the coordination of cytokines, growth factors, and matrix proteins to regulate the repair response. In this study, we investigated how M2 macrophages regulate expression of the anti-fibrotic and anti-inflammatory regulator tumor necrosis factor- (TNF-)-stimulated gene 6 (TSG-6) secreted by adipose tissue-derived stem cells (ASCs) during wound healing. Interleukin (IL)-4/IL-13, which is used to differentiate macrophage M2 phenotypes, increases TSG-6 in ASCs; however, M2 macrophages significantly decrease TSG-6 in ASCs. Transforming growth factor (TGF)-{beta} expression was increased, and TNF- expression was decreased in M2 macrophages. TGF-{beta} inhibited IL-4/IL-13-induced ASC TSG-6 expression. In addition, TSG-6 suppressed TGF-{beta}-triggered wound closure and fibrogenic responses in LX-2 cells. Collectively, TSG-6 inhibited wound healing, but M2 macrophage-expressed TGF-{beta} prevented TSG-6 production from ASCs, which ultimately helped wound healing. Our results indicate that the balance of TNF- and TGF-{beta} levels during wound healing regulates TSG-6 production from ASCs, which may ultimately modulate the healing process. Our study findings could contribute to novel therapeutic strategies that manipulate the delicate balance between TNF- and TGF-{beta} to enhance wound repair and mitigate fibrosis.

cell biology↗

bioRxiv2023

Distinct transcriptional profiles of maternal and fetal placental macrophages at term are associated with gravidity

Maternal intervillous monocytes (MIMs) and fetal Hofbauer cells (HBCs) are myeloid-derived immune cells at the maternal-fetal interface. Little is known regarding the molecular phenotypes and roles of these distinct monocyte/macrophage populations. Here, we used RNA sequencing to investigate the transcriptional profiles of MIMs and HBCs in six normal term pregnancies. Our analyses revealed distinct transcriptomes of MIMs and HBCs. Genes involved in differentiation and cell organization pathways were more highly expressed in MIMs vs. HBCs. In contrast, HBCs had higher expression of genes involved in inflammatory responses and cell surface receptor signaling. Maternal gravidity influenced monocyte programming, as expression of pro-inflammatory molecules was significantly higher in MIMs from multigravidas compared to primigravidas. In HBCs, multigravidas displayed enrichment of gene pathways involved in cell-cell signaling and differentiation. In summary, our results demonstrated that MIMs and HBCs have highly divergent transcriptional signatures, reflecting their distinct origins, locations, functions, and roles in inflammatory responses. Our data further suggested that maternal gravidity influences the gene signatures of MIMs and HBCs, potentially modulating the interplay between tolerance and trained immunity. The phenomenon of reproductive immune memory may play a novel role in the differential susceptibility of primigravidas to pregnancy complications.

immunology↗

bioRxiv2017

The FACT complex is required for DNA demethylation at heterochromatin during reproduction in Arabidopsis

The DEMETER (DME) DNA glycosylase catalyzes genome-wide DNA demethylation and is required for endosperm genomic imprinting and embryo viability. Targets of DME-mediated DNA demethylation reside in small, euchromatic, AT-rich transposons and at the boundaries of large transposons, but how DME interacts with these diverse chromatin states is unknown. The STRUCTURE SPECIFIC RECOGNITION PROTEIN 1 (SSRP1), subunit of the chromatin remodeler FAcilitates Chromatin Transactions (FACT), was previously shown to be involved in the DME-dependent regulation of genomic imprinting in Arabidopsis endosperm. Therefore, to investigate the interaction between DME and chromatin, we focused on the activity of the two FACT subunits, SSRP1 and SUPPRESSOR of TY16 (SPT16), during reproduction in Arabidopsis. We find that FACT co-localizes with nuclear DME in vivo, and that DME has two classes of target sites, the first being euchromatic and accessible to DME, but the second, representing over half of DME targets, requiring the action of FACT for DME-mediated DNA demethylation genome-wide. Our results show that the FACT-dependent DME targets are GC-rich heterochromatin domains with high nucleosome occupancy enriched with H3K9me2 and H3K27me1. Further, we demonstrate that heterochromatin-associated linker histone H1 specifically mediates the requirement for FACT at a subset of DME-target loci. Overall, our results demonstrate that FACT is required for DME targeting by facilitating its access to heterochromatin.

plant biology↗

bioRxiv2017

Graphene Oxide-Quenching-Based Fluorescence In Situ Hybridization (G-FISH) To Detect RNA In Tissue: Simple And Fast Tissue RNA Diagnostics

FISH-based RNA detection in paraffin-embedded tissue can be challenging, with complicated procedures producing uncertain results and poor image quality. Here, we developed a robust RNA detection method based on graphene oxide (GO) quenching and recovery of fluorescence in situ hybridization (G-FISH) in formalin-fixed paraffin-embedded (FFPE) tissues. Using G-FISH technique, the long noncoding BC1 RNA, {beta}-actin mRNA, miR-124a and miR-21 could be detected in the cytoplasm of a mouse brain, primary hippocampal neurons, and glioblastoma multiforme tumor tissues, respectively. G-FISH showed the increased BC1 RNA level in individual hippocampal neurons of Alzheimers disease brain. The fluorescence recovered by G-FISH correlated highly with the amount of miR-21, as measured by real time RT-PCR. We propose G-FISH as a simple, fast, inexpensive, and sensitive method for RNA detection, with very low background, which could be applied to a variety of researches or diagnostic purposes.

molecular biology↗