Biology subjects

Li, Q.

Publications and source records attributed to Li, Q..

At least 145 records · Page 8Linked to original sources

bioRxiv2026

3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network

Spatially resolved transcriptomics (SRT) is a promising new technology that enables simultaneous analysis of gene expression and spatial information for biomedical research. However, the existing statistical and deep learning algorithms used for analyzing SRT data rely solely on two-dimensional (2D) spatial coordinates, which limits their ability to accurately identify spatial domains, spatially variable genes, cell-to-cell communications, and developmental trajectories in a three-dimensional (3D) spatial manner. To address these limitations, we introduced Spa3D, which utilized the anti-leakage Fourier transform and graph convolutional neural network model to reconstruct 3D-based spatial structures from multiple 2D SRT slices. We demonstrate that Spa3D is appliable to analyze data from various SRT technology platforms and outperforms state-of-art methods by: (I) improving spatial domain identification through 3D reconstruction, (II) elucidating cell-cell communication landscape in the 3D cellular organization, (III) modeling of organ-level tempo-spatial development patterns in a 3D fashion, and (IV) annotating 3D spatial trajectory that are not captured by 2D spatial coordinates. Key pointsO_LIMost existing spatial omics analysis methods rely on 2D data, limiting their ability to capture full spatial and developmental tissue complexity C_LIO_LISpa3D incorporates physical z-axis distances, enabling accurate 3D modeling even when adjacent slices vary in tissue structure and composition C_LIO_LISpa3D reconstructs true 3D spatial structures from 2D SRT slices using graph convolutional networks and anti-leakage Fourier transforms C_LIO_LISpa3D enhances spatial domain detection, revealing detailed cell-cell communication and organ-level development patterns across multiple spatial omics platforms C_LIO_LISpa3D enables novel biological discoveries by revealing spatial features and trajectories not detectable using traditional 2D transcriptomic analysis approaches C_LI

bioinformatics↗

bioRxiv2026

Quantitative Full-length transcriptome analysis by nanopore sequencing with Error-Aware UMI mapping

Comprehensive transcriptome profiling is essential for understanding RNA diversity and regulation, yet accurate identification and quantification of full-length transcript isoforms remain challenging with short-read sequencing technologies. Nanopore sequencing enables direct sequencing of long cDNA molecules and thus offers a powerful solution for full-length transcriptome analysis, but its application to quantitative transcriptomics is limited by PCR amplification bias and the difficulty of unique molecular identifier (UMI) recognition under high sequencing error rates. Here, we developed UMImap, a dedicated pipeline for robust UMI identification, error correction, and deduplication in nanopore data. By integrating transcript-aware UMI correction with long-read isoform assembly, UMImap substantially improves UMI recognition accuracy compared with existing methods and effectively mitigates PCR-induced duplication bias. Using this framework, we identified tens of thousands of full-length transcript isoforms, including a large fraction of previously unannotated isoforms that are significantly longer than reference annotations. Quantitative analyses demonstrate the reliability of UMImap for transcript-level quantification. Functional and pathway enrichment analyses of highly expressed novel isoforms revealed coherent and biologically meaningful patterns, including strong enrichment in RNA processing, splicing, and translation pathways. Our results establish UMImap as an effective solution for UMI-based quantification in nanopore full-length transcriptome sequencing and highlight the potential of long-read sequencing to simultaneously achieve accurate isoform discovery and expression analysis in complex transcriptomes.

bioinformatics↗

bioRxiv2026

Isthmin-1 is a Key Regulator of Induced Pluripotent Stem Cell-Derived Cardiomyocytes Maturation through Activation of p53 Signaling

AimsHuman induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) exhibit an immature structural and metabolic phenotype that limits their utility for cardiac disease modeling and regenerative therapy. Although multiple extrinsic strategies have been proposed to enhance iPSC-CM maturation, intrinsic molecular regulators governing this process remain incompletely defined. This study aimed to identify and characterize a novel molecular factor that promotes cardiomyocyte maturation. Methods and ResultsCross-species transcriptomic analyses comparing adult versus fetal hearts and hypertrophic cardiomyopathy versus healthy myocardium identified Isthmin-1 (ISM1) as a conserved maturation-associated gene. In human iPSC-CMs, ISM1 overexpression enhanced sarcomere organization, mitochondrial oxidative phosphorylation, ATP production, and calcium-handling properties, whereas ISM1 knockdown impaired these maturation features. RNA sequencing revealed global transcriptional reprogramming toward an adult-like state, characterized by suppression of cell-cycle-related gene programs and activation of oxidative metabolic pathways. KEGG pathway enrichment, GSVA, and GSEA consistently identified p53 signaling as the most significantly activated pathway in ISM1-overexpressing iPSC-CMs. Mechanistically, ISM1 directly interacted with p53, enhanced its protein stability, promoted nuclear localization, and increased transcription of p53 downstream targets involved in metabolic remodeling and cell-cycle exit. Pharmacological inhibition of p53 abolished ISM1-induced structural and metabolic maturation, demonstrating that ISM1 promotes cardiomyocyte maturation in a p53-dependent manner. ConclusionsISM1 is a previously unrecognized molecular driver of cardiomyocyte maturation that promotes structural, metabolic, and functional maturation of iPSC-CMs through activation of p53-dependent transcriptional programs. These findings provide new mechanistic insight into cardiomyocyte maturation and identify ISM1 as a promising target for improving the maturity of stem cell-derived cardiomyocytes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/698535v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@15daca3org.highwire.dtl.DTLVardef@f1396dorg.highwire.dtl.DTLVardef@f13400org.highwire.dtl.DTLVardef@18d0b41_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

Friendship modulates physical effort and neural dynamics during group coordination in shared social networks

Human social interactions are embedded within social networks, yet how existing interpersonal relationships influence the behavioural and neural dynamics of collective action remains unclear. We combined social network mapping with three-person EEG-hyperscanning to investigate how friendship asymmetry within shared social networks modulates physical effort allocation during real-time coordination. Triads consisting of two close friends and one non-friend performed a novel joint force task. Non-friends consistently produced greater and more vigorous force relative to friends, particularly those embedded in smaller classroom social networks. At the neural level, non-friends also showed stronger alpha- and beta-band event-related desynchronization, inter-subject correlation, and group phase synchrony, indicating greater engagement of action-related and attentional processes. These findings suggest that non-friends invest more physical effort and attention when interacting in socially asymmetric group settings, demonstrating how real-world relational context within social networks is dynamically embodied in the neural and behavioural mechanisms supporting human group coordination.

neuroscience↗

bioRxiv2025

Sampling Function-Related Metastable States of Proteins With DASH

Rational discovery of function-specific protein modulators as well as activity-enhanced engineering proteins underscore the need to identify function-related metastable states (FMSs) of proteins. However, current experimental and computational methods struggle to generate these states directly from their native state (NS), likely because the NS [->] FMS transition is non-spontaneous. To address this challenge, we introduce Deep learning guided Adaptive sampling with seed Selection and Hopping (DASH), integrating both deep learning and physical functions to guide molecular dynamics (MD) simulation towards FMS. DASH successfully sampled NS [->] FMS transitions in 18 cases across two tasks: protein activation and cryptic allosteric site opening. DASH combined with secondary-structure collective variables is further able to sample folding of disordered regions. Compared to existing methods, DASH demonstrates superior performance while requiring comparable simulation time. Crucially, we applied DASH to sample new conformations of proteins, by which revealing new folding states, previously unknown allosteric sites, and potential activators. These predictions have been further verified in wet-lab experiments and crystal structures determinations. Collectively, our framework provides a robust strategy for function-specific pharmacy research and could accelerate future drug discovery efforts.

bioinformatics↗

bioRxiv2025

Spacing effect improves generalization in biological and artificial systems

Generalization is a fundamental criterion for evaluating learning effectiveness, a domain where biological intelligence excels yet artificial intelligence continues to face challenges. In biological learning and memory, the well-documented spacing effect shows that appropriately spaced intervals between learning trials can significantly improve behavioral performance. While multiple theories have been proposed to explain its underlying mechanisms, one compelling hypothesis is that spaced training promotes integration of input and innate variations, thereby enhancing generalization to novel but related scenarios. Here we examine this hypothesis by introducing a bio-inspired spacing effect into artificial neural networks, integrating input and innate variations across spaced intervals at the neuronal, synaptic, and network levels. These spaced ensemble strategies yield significant performance gains across various benchmark datasets and network architectures. Biological experiments on Drosophila further validate the complementary effect of appropriate variations and spaced intervals in improving generalization, which together reveal a convergent computational principle shared by biological learning and machine learning.

neuroscience↗

bioRxiv2025

The plant circadian clock exerts stronger control over the diel proteome than the transcriptome

AbstractThe plant circadian clock is a genetic circuit composed of multiple mutually-regulating transcription factors that together synchronize internal biological rhythms to the [~]24-hour period of planetary rotation. While it has been known for over a decade that nearly 40% of the transcriptome in the model plant Arabidopsis oscillates with a circadian rhythm, it is yet unclear to what extent this translates to the proteome. Here, through parallel quantitative proteome and transcriptome time-course profiling of Arabidopsis wild-type plants and a panel of clock deficient plant lines, we show that specific clock genes exercise extensive control over diel proteome rhythmicity, and to a much greater extent than they do the transcriptome. This control results in a clock-dependent synchronization of rhythmic proteins along a bimodal phase distribution that is lost in circadian clock deficient plants. This suggests pervasive post-translational control of gene expression by specific elements of the circadian system, notably the morning expressed LHY/CCA1 module. Our findings imply that the circadian clock exercises much greater control of gene expression through proteostasis mechanisms than previously recognised, necessitating a recalibration of our current understanding of clock proteins as primarily transcriptional regulators.

plant biology↗

bioRxiv2025

Engineering chimeric DNA polymerases for DNA movable type storage

DNA-based information storage offers a promising alternative to conventional media due to its high density, long-term stability, and low energy requirements. However, its application remains hindered by synthesis costs, limited sequence length and poor scalability. DNA polymerase is a critical enzymatic tool in the DNA storage systems by enabling high-fidelity data writing and targeted sequence amplification. In this study, we engineered chimeric DNA polymerases by fusing the high-fidelity 9{degrees}N DNA polymerase with double-stranded DNA binding proteins derived from thermophilic archaea. These fusions significantly enhanced processivity, thermal stability, and salt tolerance by stabilizing enzyme-template interactions, mimicking sliding clamps while preserving catalytic efficiency. Leveraging these properties, we demonstrated precise file retrieval from a mixed oligonucleotide pool using orthogonal barcode primers. Compared with wild-type 9{degrees}N, the chimeric polymerases, particularly PLS, exhibited reduced substitution error rates and improved read accuracy. We then applied these enzymes to a DNA movable type storage system, where prefabricated DNA modules were assembled into encoding blocks. Using engineered polymerases, these blocks were recombined to enable flexible data rewriting without de novo DNA synthesis. Moreover, a multi-enzyme assembly strategy enabled the construction of kilobase-scale DNA sequences encoding a classical Chinese poem, achieving complete data recovery. All assembled fragments remained stable in E. coli over 100 generations, exhibiting the potential for in vivo storage. Collectively, our findings demonstrated the role of engineered DNA polymerases for DNA-based information storage. Moreover, this system reduced synthesis demands, supported scalable rewriting, and ensured long-term preservation, offering a practical route to sustainable, high-fidelity DNA data storage.

synthetic biology↗

bioRxiv2025

Computer-assisted dissection of the DNA mismatch recognition mechanism of the MutS protein

By sensing local conformational strains in DNA, TaqMutS utilizes a push-pull mechanism via the synergy of residues S57 and D472 to modulate {pi}-stacking interactions between the key recognition residue F39 and DNA, thereby achieving specific mismatch-site recognition. Meanwhile, residues K61 and R473 fine-tune non-specific binding of TaqMutS through electrostatic synergy.

synthetic biology↗

bioRxiv2025

Decoding Neoantigen-encoding Tumor-Specific Transcripts Unveils a Shared Target Reservoir for Immunotherapy in Hepatocellular Carcinoma

Background and AimsPrimary liver cancer, predominantly hepatocellular carcinoma (HCC), has limited therapeutic options. While mutation-derived neoantigen vaccine holds promise, its success is hindered by low antigen availability. This study explores transcriptome-derived neoantigens (neoantigen-encoding tumor-specific transcripts, neoTSTs) in HCC, characterizing their features, generation mechanisms, and therapeutic potential. Approach and ResultsWe analyzed RNA-seq data from 1,013 liver cancer patients and constructed a multi-layered reference dataset. Using a customized pipeline, we identified an average of 60 neoTSTs per patient, significantly surpassing mutation-derived neoantigens (neoMuts). NeoTSTs exhibited higher population frequencies, with 73.1% providing multiple epitopes, and were validated through mass spectrometry and HLA transgenic mouse models. Mechanistically, neoTSTs were generated via retained introns, transposable element activation, HNF4A-regulated alternative promoters, and de novo transmembrane domain (TMD) generation. Single-cell analysis revealed neoTSTs cover >75% of tumor cells and identified antigen-presenting cancer-associated fibroblasts (apCAFs) that enriched in immunotherapy responders and amplified CD4 T-cell responses via MHC-II presentation. In murine HCC models, neoTST vaccination outperformed neoMuts, inducing dual MHC-I/II activation and significant tumor growth inhibition. ConclusionsNeoTSTs represent a superior neoantigen source in HCC, compensating for the limitations of mutation-derived targets. Their abundance, sharedness, and dual MHC pathway activation highlight their potential for personalized immunotherapy, particularly in low-TMB tumors. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/692877v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1bedfedorg.highwire.dtl.DTLVardef@5f6c95org.highwire.dtl.DTLVardef@d1deb5org.highwire.dtl.DTLVardef@743e85_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

ERAD Activity Distinguishes the Functional Heterogeneity of Hematopoietic Stem Cells

Hematopoietic stem cells (HSCs) rely on precisely controlled proteostasis to sustain lifelong self-renewal, yet whether intrinsic proteostatic characteristics can prospectively define functional diversity within the HSC pool remains unknown. In this study, we developed a dual-fluorescence reporter system to visualize endoplasmic reticulum-associated degradation (ERAD) activity in vivo and discovered that ERAD activity can prospectively predicts the long-term reconstitution potential of HSCs. Remarkably, ERAD states were maintained through self-renewal, revealing an inheritable "proteostatic memory" within the HSC pool. Transcriptomic and functional analyses identified SOCS2 as a key regulator of this process: SOCS2 inhibits JAK2 signaling to sustain ERAD activity, whereas hyperactive JAK2 interacts with the ERAD E3 ligase Hrd1 and suppresses VCP-mediated misfolded substrate. These findings define a SOCS2-JAK2-ERAD axis that connects cytokine signaling to intrinsic proteostasis remodeling, establishing ERAD activity as a predictive and heritable marker of HSC function and providing a conceptual framework for understanding hematopoietic stem cell heterogeneity.

cell biology↗

bioRxiv2025

scE2TM improves single-cell embedding interpretability and reveals cellular perturbation signatures

Single-cell RNA sequencing technologies have revolutionized our understanding of cellular heterogeneity, yet computational methods often struggle to balance performance with biological interpretability. Embedded topic models have been widely used for interpretable single-cell embedding learning. However, these models suffer from the potential problem of interpretation collapse, where topics semantically collapse towards each other, resulting in redundant topics and incomplete capture of biological variation. Furthermore, the rise of single-cell foundation models creates opportunities to harness external biological knowledge for guiding model embeddings. Here, we present scE2TM, an external knowledge-guided embedded topic model that provides a high-quality cell embedding and interpretation for scRNA-seq analysis. Through embedding clustering regularization method, each topic is constrained to be the center of a separately aggregated gene cluster, enabling it to capture unique biological information. Across 20 scRNA-seq datasets, scE2TM achieves superior clustering performance compared with seven state-of-the-art methods. A comprehensive interpretability benchmark further shows that scE2TM-learned topics exhibit higher diversity and stronger consistency with underlying biological pathways. Modeling interferon-stimulated PBMCs, scE2TM simulates topic perturbations that drive control cells toward stimulated-like transcriptional states, faithfully mirroring experimental interferon responses. In melanoma, scE2TM identifies malignant-specific topics and extrapolates them to unseen patient data, revealing gene programs associated with patient survival.

bioinformatics↗

bioRxiv2025

Full-length structure of CPR-containing self-sufficient cytochrome P450

Cytochrome P450 (P450s) are heme-thiolate monooxygenases that exploit electrons sourced from pyridine nucleotide to reduce the oxygen and heme iron to catalyze hydroxylation of inactivated C-H bond1-4. Self-sufficient P450s that contain the substrate-binding heme-domain and the electron-donating NADPH:cytochrome P450 reductase (CPR) domain in the same polypeptide chain are highly effective and have been engineered to catalyze various challenging reactions. The high efficacy attributes to the effective electron transfer rate, but how the electrons travel among the redox centers remains elusive owing to the lack of structural information of the full-length protein. Here, we report the structure of a homologue of the most extensively studied P450BM3 from Shimazuella soli (SoP450) resolved by single particle cryo-electron microscopy (cryo-EM) and X-ray crystallography. SoP450 primarily exists as a homodimer formed via the intertwined CPR-domains. The spatial alignment of the heme-domain that is linked via an extensive loop was also determined. Notably, a class of structure that lacks one heme-domain was identified from the cryo-EM analyses, indicating that the heme-domain is mobile. We suspected that the heme-domain could move to reach the CPR-domain for electron acquisition and built a model of putative catalytic state to reveal how the electrons are relayed from NADPH to heme. These results are of fundamental importance to understand the catalytic reaction of CPR-containing self-sufficient P450s, which shall provide critical information to the engineering and applications of these enzymes.

synthetic biology↗

bioRxiv2025

DECODING BRAIN DYNAMICS VIA CROSS-NETWORK NON-LINEAR TRAJECTORIES

Infant brain networks mature rapidly and nonlinearly, yet cross-network comparisons of developmental trajectories remain rare. We introduce cross-network non-linear trajectory characterization (CNTC), which fits cubic polynomials to age-related change in three spatial measures--network-averaged spatial similarity (NASS), network strength, and network size--derived from resting-state fMRI. Using 137 scans from 74 neurotypical infants spanning 0-6 months of age, we estimated networks via group ICA, modeled age trajectories within each network, and then contrasted coefficients across networks. Across seven canonical systems, trajectories showed robust age effects. Cross-network differences were dominated by linear terms, with selective quadratic effects and few cubic effects. Visual and cerebellar systems exhibited the steepest NASS slopes and pronounced increases in strength and size, whereas motor/attention changes were more gradual. CNTC provides a compact, coefficient-level summary of inter-network maturation and a basis for benchmarking atypical development in future work.

neuroscience↗

bioRxiv2025

Mapping the Developmental Trajectory of Functional Brain Networks in Early Infancy: Insights into Typical Maturation

Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes. Clinical RelevanceThis study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.

neuroscience↗

bioRxiv2025

B-cell follicles of lymphatic tissues act as a critical microanatomical niche for sustained viral replication, virus-host interaction and damage during chronic SIV infection

Most people living with HIV-1 (HIV) are diagnosed during the chronic stage of infection, at which point viral setpoint has been established and a substantial immunopathologic damage in the secondary lymphatic tissues (LTs) has been induced. To better characterize virus-host interaction and immunopathologic damage in LTs, we investigated viral spatial distribution patterns and the corresponding LT structural alterations in SIV chronically infected rhesus macaques. Using RNAscope in-situ hybridization (RNAscope) and a combination of CODEX with RNAscope (Comb-CODEX-RNAscope), we demonstrated that in chronic infection, SIV viral RNA (vRNA) is predominantly resides within B-cell follicles with minimal presence in T-cell zones, and there is significant B-cell follicular damage, encompassing follicular hyperplasia, fibrosis, and disintegration of the germinal center. Our results reveal that the B-cell follicles act as a critical microanatomical niche for sustained viral replication, virus-host interaction and tissue damage during SIV chronic infection.

immunology↗

bioRxiv2025

Emergence of Individualized Functional Topography in the Neonatal Brain

The early postnatal period represents a critical phase for establishing the brains fundamental architecture. However, how individualized functional topography emerges and shapes long-term cognition remains unknown. Using multimodal neuroimaging data from 419 neonates, we map personalized functional topographies in the first weeks of life and reveal their association with 18-month neurocognitive outcomes. Unique and stable functional topographies are already present at birth, with interindividual variability organized along a conserved sensorimotor-to-association hierarchy. These individualized maps encode early brain maturity and robustly predict cognitive, language, and motor outcomes at 18-month of age, with predictive features concentrated at network boundaries. This functional refinement is structurally anchored by concurrent maturation in cortical myelination and sulcal depth. Preterm birth results in distinctive topographic alterations characterized by accelerated maturation of association networks. Our work establishes a foundation for precision models of early brain development and elucidates its significance for long-term neurocognitive outcomes.

neuroscience↗

bioRxiv2025

Immunogenicity and Protective Efficacy of Toxoplasma gondii SRS67 and SRS20A Proteins in Mice

Toxoplasma gondii, a medically significant zoonotic protozoan, relies on surface SAG-family antigens for host adhesion and immune evasion. SRS67 and SRS20A, two such antigens, are hypothesized to be highly immunogenic. Here, we comprehensively evaluated their immunological properties and protective efficacy in mice. Recombinant SRS67 and SRS20A, produced via prokaryotic expression, induced high-titer, specific polyclonal antisera in BALB/c mice and rabbits. Western blotting confirmed these antisera recognized native RH strain tachyzoite antigens, validating strong immunogenicity. qPCR showed SRS67 transcription was higher in PRU (chronic) than RH (acute) strains, while SRS20A expression was comparable. Immunofluorescence, using GAP45 as a membrane marker, localized both proteins as puncta around RH tachyzoite membranes. In vitro phenotypic assays indicated that the corresponding polyclonal antibodies significantly inhibited the invasion of host cells by T. gondii and subsequent intracellular proliferation. Challenge studies demonstrated protective efficacy: in acute RH infection, immunized mice (single or combined antigens) showed 60% higher survival, with bivalent groups delaying mortality by 1-2 days. In chronic PRU infection, all immunized groups had >85% fewer brain cysts, and bivalents better attenuated neuropathology (e.g., meningeal thickening). Mechanistically, SRS67 upregulated Th1 cytokines (TNF-, IL-18) in acute infection, while both proteins downregulated IFN-{gamma} to limit immune-mediated damage; GSH upregulation indicated antioxidative involvement. In chronic infection, co-immunization synergistically enhanced SOD activity to counter oxidative stress, with IFN-{gamma} downregulation maintaining immune homeostasis. These findings support SRS67 and SRS20A as promising T. gondii vaccine candidates, acting via coordinated Th1 immunity and antioxidative pathways.

zoology↗