Seung-Nam Kim

This study aimed to identify circulating exosomal microRNA (miRNA) candidates showing a control-like expression pattern following ST36 acupuncture treatment in an LPS-induced neuroinflammation model, using empirical Bayes reanalysis of previously published microarray data. Two independent Affymetrix GeneChip miRNA 4.0 microarray datasets from C57BL/6 male mice were reanalysed using the limma empirical Bayes framework with Benjamini-Hochberg false discovery rate (FDR) correction. After detection-call filtering, 286 commonly expressed probes were identified. Control-like pattern candidates were defined by three criteria: FDR < 0.05 for the LPS effect, FDR > 0.05 for the LPS+ST36 vs CON comparison, and logFC direction opposite to the LPS effect. Target gene prediction and pathway enrichment were subsequently performed. Eight miRNA candidates were identified: five upregulated by LPS and showing a control-like pattern following ST36 (let-7d-5p, miR-103-3p, miR-107-3p, miR-451a, miR-7047-5p) and three downregulated by LPS and showing a control-like pattern following ST36 (miR-5128, miR-16-2-3p, miR-101a-5p). None overlapped with candidates from the original fold-change-based analysis. Functional enrichment of 1,596 predicted target genes revealed significant enrichment in MAPK, PI3K-Akt, and Wnt signaling pathways, with Gene Ontology analysis highlighting convergence on neuronal and synaptic processes. Limma-based reanalysis reveals a distinct set of ST36-associated exosomal miRNAs overlooked by threshold-based approaches, providing exploratory candidate molecular indicators for further investigation of acupuncture's peripheral effects in neuroinflammation.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by impairments in social communication, restricted and repetitive behaviors, sensory dysregulation, and frequent psychiatric comorbidities. Increasing attention has been directed toward acupuncture as a complementary neuro-modulatory intervention; however, its underlying molecular mechanisms remain incompletely understood. This review synthesizes evidence from preclinical, clinical, and molecular studies published between 2015 and 2025 to examine how acupuncture influences neurobiological pathways relevant to ASD. Current evidence indicates that acupuncture modulates multiple neurotransmitter systems, including glutamatergic, GABAergic, dopaminergic, serotonergic, and noradrenergic signaling, while also influencing neurotrophin-mediated plasticity, neuroinflammatory responses, and synaptic function. Studies conducted directly in ASD models demonstrate regulation of excitatory/inhibitory balance, monoaminergic signaling, neurotrophin pathways, and ASD-associated behavioral outcomes, whereas evidence from related neuropsychiatric conditions provides complementary mechanistic support for these pathways. Collectively, the findings suggest that acupuncture may act through coordinated modulation of interconnected neurotransmitter and neuroplasticity networks rather than a single molecular target. However, direct mechanistic evidence in ASD-specific models and clinical populations remains limited, and considerable heterogeneity exists among acupuncture protocols and outcome measures. Future studies integrating standardized stimulation paradigms with molecular, electrophysiological, neuroimaging, and behavioral assessments will be essential to validate the proposed mechanisms and clarify the translational potential of acupuncture in ASD.

Pain is a major clinical outcome in chronic inflammatory disease, yet molecular correlates of within-person pain change remain unclear and must be distinguished from inflammatory change. We used longitudinal whole-blood multi-omics data from the RA-MAP/TACERA early rheumatoid arthritis cohort to prioritize microRNA (miRNA) candidates whose 6-month changes co-varied with change in visual analogue scale pain (ΔVAS). Among 151 participants and 1,881 miRNA features, we applied a stability-oriented delta-omics workflow combining subsampling elastic-net selection frequency, bootstrap directional filtering, and post-selection rank-residual partial correlation adjusted for ΔCRP and ΔESR. This identified 29 directionally stable miRNA candidates; after ΔCRP/ΔESR adjustment, 16 retained sign-consistent support at post-selection BH-FDR < 0.05 within these 29 candidates. Matched miRNA–mRNA delta analysis found no individual miRNA–mRNA pair surviving pair-level BH-FDR correction. At the primary nominal pair-selection threshold of p < 0.05, 123 inverse pairs involving 117 target genes yielded exploratory Gene Ontology Biological Process enrichment involving synaptic and neuronal organization; however, no GO term remained BH-significant when the pair-selection threshold was tightened to p < 0.01. The exploratory plasma chronic neck pain comparison yielded 22 of 39 sign-consistent assay matches (56.4%; exact 95% CI, 39.6%–72.2%), which was not statistically distinguishable from the 50% chance expectation, and no match survived BH-FDR correction. These findings outline a stability-oriented, inflammation-adjusted approach for prioritizing blood miRNA candidates linked to pain change. The 16-miRNA subset should be interpreted as a hypothesis-generating candidate pool rather than a validated biomarker panel and provides a focused basis for future longitudinal validation and functional studies.

Cross-study inconsistencies in autism spectrum disorder (ASD) blood microRNA biomarker studies suggest that methodological heterogeneity may substantially limit reproducibility. We conducted an exploratory meta-analysis of publicly available ASD blood miRNA datasets from the Gene Expression Omnibus, applying rigorous inclusion criteria and standardized analytical protocols. Three datasets were included (GSE89596, GSE67979, GSE222046) comprising 614 miRNAs across 90 participants (45 ASD, 45 controls). Random-effects meta-analysis was performed using Hedges’ g effect sizes, with comprehensive heterogeneity assessment and leave-one-dataset-out cross-validation. No miRNAs survived multiple testing correction (Benjamini-Hochberg FDR < 0.05), though seven candidate signals showed consistent evidence with unadjusted p < 0.01 and large effect sizes. These candidates demonstrated near-zero between-study heterogeneity and consistent directionality across validation analyses. Potential age-related and platform-related differences were observed, with near-zero correlation between adult and pediatric effect sizes (Kendall’s τ = -0.022); however, these two sources of variability were fully confounded in the available data and could not be separated. Some miRNAs exhibited extreme between-study variability (I² > 80%), indicating substantial methodological differences. Cross-validation revealed that excluding the single adult dataset reduced sign consistency from 89.9% to 68.9%. Our findings suggest that age-related and methodological factors, including technical platform differences, may contribute to limited reproducibility in ASD blood miRNA research, and that blood-derived signals should be interpreted as potentially reflecting peripheral physiological states rather than central disease mechanisms. A supplementary cross-tissue analysis using post-mortem prefrontal cortex data (GSE59286; n = 45) provided direct empirical support for this interpretation: the majority of blood candidate miRNAs showed no corresponding expression in brain tissue, with only hsa-miR-29c-5p demonstrating directional concordance across both tissues. These findings suggest that age stratification, platform harmonization, and cross-tissue validation should be considered essential prerequisites for reliable ASD miRNA biomarker discovery, rather than optional refinements.

Background: Chronic pain involves complex neuroplastic changes and neuroinflammatory processes that may be reflected in circulating microRNA profiles. Acupuncture analgesia operates through multiple neurobiological mechanisms including modulation of neurotransmitter release and synaptic plasticity. High-dimensional small-sample (HDSS) datasets pose significant challenges for biomarker discovery in clinical omics studies, where the number of features vastly exceeds sample size. Existing approaches often lack robust validation frameworks, leading to overfitted models with poor generalizability. We developed and validated a comprehensive framework for HDSS biomarker discovery, demonstrated on circulating microRNA data from a chronic neck pain acupuncture trial. Our objective was to establish a simulation-validated pipeline that addresses preprocessing instability, model selection uncertainty, and statistical significance assessment in extreme HDSS scenarios. Methods: In this exploratory high-dimensional small-sample study, we analyzed plasma microRNA profiles from 6 participants in a chronic neck pain acupuncture trial, generating 9 paired measurements of microRNA fold-changes and pain reduction (ΔVAS) across baseline, 4-week, and 8-week time points. Our framework integrated: (1) robust preprocessing with Winsorization, arcsinh transformation, and median/MAD scaling; (2) nested leave-one-out cross-validation with LASSO/Elastic Net regularization; (3) permutation testing (10,000 iterations); and (4) comprehensive HDSS factorial simulation (19,440 runs across varying sample sizes, sparsity, signal-to-noise ratios, and preprocessing strategies). Results: The observed model achieved MAE=10.58, RMSE=13.70, and Spearman ρ=− 0.217, with permutation p< 0.001. Simulation benchmarking revealed our correlation coefficient ranked at the 91st percentile compared to null-like HDSS scenarios. Three microRNAs (miR-3681-3p, miR-4743-5p, miR-6822-5p) emerged as a dual-function panel, consistently selected across cross-validation folds and associated with both pain prediction and acupuncture response. Pathway enrichment analysis revealed significant associations with PI3K-Akt/mTOR, TGF-β signaling, synaptic plasticity, and neuroinflammatory pathways. Conclusion: We present a rigorously validated framework for HDSS biomarker discovery that demonstrates modest but statistically significant predictive signal in extremely challenging conditions. This methodology is broadly applicable to other HDSS omics problems where traditional validation approaches fail.

Introduction: Early detection of Parkinson’s disease (PD) remains challenging due to the lack of reliable blood-based biomarkers. While microRNAs (miRNAs) show promise as circulating biomarkers, translating preclinical discoveries to clinically applicable panels requires rigorous validation across platforms and populations. Methods: We performed temporal miRNA profiling in an acute MPTP mouse model (day 0 vs. day 5, n = 4 per group) using limma differential expression analysis with FDR correction. To address high-dimensional small-sample challenges, we employed global permutation testing and stability selection with elastic net regularization over 2,000 iterations. A compact miRNA panel was derived and validated in three independent human cohorts: GSE16658 (PBMC, n = 32), GSE269776 (serum exosomes 2021, n = 76), and GSE269775 (serum exosomes 2020, n = 100). Performance was assessed using ROC analysis with permutation-based p -values. Results: Seventeen miRNAs showed significant time-dependent changes in MPTP-treated mice (FDR p = 0.060), GSE269776 AUC = 0.791 ( p < 0.001), and GSE269775 AUC = 0.725 ( p < 0.001). Discussion: The signature showed platform-agnostic stability, performing comparably in PBMC and serum exosomes despite biological and technical differences. A 6-miRNA signature derived from acute MPTP response translates effectively to human blood samples, demonstrating reproducible PD discrimination across multiple platforms. The compact panel size and cross-platform compatibility support its potential for clinical biomarker development. By integrating AI-enhanced feature selection and permutation-based validation, this study provides a reproducible framework for biomarker discovery and a foundation for future early detection and precision medicine in Parkinson’s disease.

Background: Acupuncture effectively reduces chronic neck pain and plasma Substance P (SP) levels, but upstream molecular mechanisms remain unknown. Objectives: We aimed to identify circulating microRNAs (miRNAs) associated with acupuncture-induced SP reduction and explore potential neuroplasticity mechanisms. Methods: We performed longitudinal plasma miRNA profiling (Affymetrix miRNA 4.0 Array, ~4600 miRNAs) in chronic neck pain patients: Acupuncture group ( n = 3; 0, 4, 8 weeks) and Control group ( n = 3; 0, 4 weeks). Linear Mixed-Effects Models (LMMs) tested associations between each miRNA and SP dynamics (miRNA × Time × Group interaction). Statistical significance was validated using permutation testing (2000 iterations). Results: Screening identified 53 miRNAs significantly associated with SP, validated by permutation testing ( p < 0.001). Fourteen high-confidence miRNAs showed significant three-way interactions, indicating treatment-specific SP relationships. The most significant was miR-1302-6 ( p = 7.65 × 10 ⁻⁶ ), followed by miR-181b-2. These miRNAs displayed diverse temporal patterns: some (miR-196b, miR-6788) increased during treatment, while others (let-7d, miR-1302-6) decreased parallel to SP. Functional enrichment revealed striking convergence on neuroplasticity pathways: axon guidance ( p = 2.61 × 10 ⁻⁶ ), MAPK signaling ( p = 4.18 × 10 ⁻⁵ ), neuron projection development ( p = 7.52 × 10 ⁻¹⁰ ), and synaptic structures ( p = 9.52 × 10 ⁻¹² ). Conclusions: This exploratory study provides first molecular evidence for an acupuncture-miRNA-SP axis in chronic pain. The enrichment of neuroplasticity pathways suggests acupuncture may induce structural remodeling of nociceptive circuits rather than simply suppressing inflammation, offering novel mechanistic insights and potential biomarkers for personalized acupuncture therapy. The trial was registered with the Korean Clinical Trial Registry (KCT0005363).

Depression is a prevalent psychological disorder frequently associated with neuroinflammation, and microRNAs (miRNAs) have been implicated in its pathogenesis. Acupuncture, widely used in East Asia, has shown efficacy in various neuropsychiatric conditions; however, its miRNA‐related mechanisms remain unclear. Using an LPS‐induced depression model, we examined acupuncture's effects on depression‐like behaviors and circulating exosomal miRNAs in serum. Microarray data revealed multiple miRNAs significantly altered by LPS or acupuncture, and subsequent bioinformatics analyses (TargetScan, KEGG, and Gene Ontology) identified pathways related to neuroinflammation, synaptic signaling, and circadian regulation. Constructing a miRNA–target gene network further suggested that acupuncture might modulate miRNA expression to alleviate depressive symptoms. These findings not only support the therapeutic potential of acupuncture but also propose candidate exosomal miRNAs as novel biomarkers or diagnostic tools for the LPS‐induced depression model. Overall, this study provides insights into the anti‐inflammatory actions of acupuncture in depression through miRNA‐mediated gene regulation.