Biology subjects

Federico, A.

Publications and source records attributed to Federico, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrating Semantic Retrieval, LLM-based Refinement, and Structured Expert Curation for Scalable AOP Gene Mapping

Toxicogenomics can support regulatory toxicology, but its use is limited by the difficulty of translating molecular responses into mechanistic, decision-relevant interpretations. Adverse Outcome Pathways (AOPs) provide a framework for this translation, yet omics applications require scalable mapping of Key Events (KEs) to molecular features. Here, we present an AI-assisted, multi-step workflow for KE-to-gene mapping that uses embedding-based semantic retrieval to identify candidate ontology/pathway terms, large language model-assisted refinement to filter these candidates, and double-independent expert group curation with rule-based consolidation to finalize mappings and derive confidence scores. Compared with earlier NLP-based approaches, the workflow improves KE-to-ontology/pathway mapping performance and generates candidate annotations that better align with expert judgment while substantially reducing the need for manual augmentation. Explicit gene and protein mentions in KE titles were additionally grounded to improve specificity, and each curated mapping was assigned curator reason codes to support transparent, traceable, and confidence-aware reuse. Applied across AOP-Wiki, the workflow produced a comprehensive KE-to-gene set resource covering 1,254 KEs across 523 AOPs and linking 15,833 human genes. Utility is demonstrated through CTD-based AOP fingerprinting of curated reference chemical groups, highlighting expanded coverage and confidence-informed interpretation of chemical-associated gene signatures in an AOP context. The workflow and resulting resource provide a practical bridge between toxicogenomics and AOP-based mechanistic interpretation and support routine updating and future extension to additional omics layers within OECD Omics2AOP.

bioinformatics↗

bioRxiv2026

Lung-targeted cytokine-coding RNA-lipoplexes induce T and NK cell-mediated anti-tumor immune response

Lung is a major site of metastases for many primary cancers associated with poor outcomes. A central challenge in cancer immunotherapy is overcoming tumor immune evasion, which limits effective antitumor responses. Here, we investigated whether combinatorial mRNA-encoded cytokine therapy can overcome tumor immune evasion by coordinately engaging innate and adaptive immunity, using murine models of pulmonary metastases. We employed intravenously administered cationic nucleoside-modified mRNA-lipoplexes (RNA-LPX) for targeted delivery of mRNA-encoded cytokines to the lung. The cytokine mix containing interferon-, half-life extended interleukin (IL)-7, and a half-life extended IL-2 variant with reduced CD25-binding modulated the tumor immune microenvironment resulting in a potent and broad anti-tumor response and prolonged survival with good tolerability at the conditions tested. Using cell depletion experiments, we demonstrated that both T and natural killer (NK) cells are crucial mediators of the observed anti-tumor efficacy of the cytokine RNA mix, which induced activation and effector function of NK and T cells, coupled with reduced regulatory T cells (Treg) numbers and Treg activation in the lung. Importantly, antitumor efficacy was maintained in models of impaired antigen presentation, including loss of an immunodominant tumor antigen and MHC class I deficiency, where NK cells served as the primary effectors. The cytokine RNA mix induced immune cell activation in the primary human lung tumor culture, suggesting potential for translational application. Collectively, these findings demonstrate that combinatorial cytokine therapy can drive both antigen-dependent and antigen-independent tumor control for the treatment of lung metastases.

immunology↗

bioRxiv2026

Sensitivity profiling reveals consistent drug responses across preclinical neuroblastoma models

Despite intensive treatment, overall survival for high-risk and relapse neuroblastoma patients remains below 50%. Even though comprehensive molecular profiling enables treatment stratification, druggable alterations have been identified for only a subset of patients. In vitro drug screening offers a complementary approach. Here, we compare the translational potential of three preclinical drug screening methods: ex vivo short-term, in vitro patient-derived organoid and in vivo patient-derived xenograft (PDX) drug testing. In total, 55 screens were performed from 38 neuroblastoma samples and five pediatric non-malignant samples, testing 77-224 drugs per screen. Ex vivo short-term drug screens achieved higher success rates than organoid screens (65% versus 23%) and shorter turnaround times (14 days versus 3-12 months). Matched samples showed consistent drug sensitivities across sample origin (patient versus PDX-derived; mean r = 0.84) and method (ex vivo short-term versus organoids; mean r = 0.87), demonstrating that ex vivo short-term screens recapitulate drug sensitivities found in long-term organoid models. In parallel, as part of the ITCC-P4 consortium, ten compounds were tested in vivo in eight PDX models, with samples matching the ex vivo screens. For seven out of ten clinically available compounds, ex vivo drug responses were comparable with in vivo responses in matched PDX models. These results demonstrate that, while organoids and PDX models remain essential for drug discovery, ex vivo short-term drug screening provides a rapid alternative for functional precision oncology in neuroblastoma.

cell biology↗

bioRxiv2026

The ITCC-P4 PDX platform of pediatric cancers for preclinical testing

Cancer is the leading cause of disease-related deaths among children in high-income countries. Tumor heterogeneity and lack of mechanism-of-action-based therapeutic options are key challenges to overcome in order to improve pediatric cancer patients survival. Here, we report the EU-IMI-2 funded public-private partnership "ITCC-Pediatric Preclinical Proof-of-Concept Platform" (ITCC-P4), which has built a large repertoire of patient-derived xenograft (PDX) models, representing all major solid pediatric cancer types, for in vivo drug testing. Three-hundred-fifty-three PDX models from diagnostic and relapsed pediatric cancers have been established and molecularly characterized, together with matched germline/tumor samples. As proof-of-concept, we present in vivo drug screening data in neuroblastoma and rhabdomyosarcoma models. PDX data, accessible at http://r2platform.com/itcc-p4, allow the selection of models based on oncogenic drivers and/or potential biomarkers for preclinical testing. Operated by a non-profit entity (www.itccp4.com), this sustainable platform aids academic and industrial researchers in developing and prioritizing innovative therapies for pediatric cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/703023v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@195ba30org.highwire.dtl.DTLVardef@f2c2d9org.highwire.dtl.DTLVardef@1d63f4dorg.highwire.dtl.DTLVardef@d60027_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

OpTiles: An R Package for Adaptive Tiling and Methylation Variability Profiling

SummaryOpTiles is an R package that dynamically defines tiling windows based on the distribution of sequenced CpGs, addressing the limitations of traditional fixed-tiling approaches in targeted methylation datasets. By integrating CpG density with intra-region methylation variability, it provides a reliability metric and extended functionality for annotating, prioritizing, and interpreting complex methylation data. AvailabilityOpTiles is implemented in R and source code is freely available at https://github.com/fhaive/OpTiles. Data are available on Zenodo at https://doi.org/10.5281/zenodo.16961293 Contactdario.greco@tuni.fi

bioinformatics↗

bioRxiv2025

Curated and harmonised transcriptomics datasets of interstitial lung diseases

This study provides manually curated and homogenised transcriptomics data of interstitial lung disease (ILD) patients retrieved from the NCBI Gene Expression Omnibus and European Nucleotide Archive repositories. The compendium includes 30 transcriptomics datasets generated with DNA microarrays and RNA sequencing technologies for a total of 1,371 samples. All the datasets underwent metadata curation and harmonisation, data quality check, and preprocessing with standardised procedures. Furthermore, a robust data model was developed to standardise phenotypic data, thereby enhancing comparability across heterogeneous datasets. Gene expression data and lists of differentially expressed genes computed between ILD and healthy samples are provided. Among the ILDs included in this study, idiopathic pulmonary fibrosis (IPF) is the most represented worldwide. Co-expression networks of IPF and healthy samples were inferred, which are also included in this study. This work significantly improves the Findability, Accessibility, Interoperability, and Reusability (FAIR) of publicly available transcriptomics data of ILDs, providing a platform to implement and validate integrated systems biology and pharmacology approaches for novel interstitial lung disease diagnostics and therapeutics.

bioinformatics↗

bioRxiv2025

MUUMI: an R package for statistical and network-based meta-analysis for MUlti-omics data Integration

Disentangling physiopathological mechanisms of biological systems through high-level integration of omics data has become a standard procedure in life sciences. However, platform heterogeneity, batch effects, and the lack of unified methods for single- and multi-omics analyses represent relevant drawbacks that hinder the extrapolation of a meaningful biological interpretation. Statistical meta-analysis is widely used in order to integrate several omics datasets of the same type, leading to the extrapolation of robust molecular signatures within the investigated system. Conversely, statistical meta-analysis does not allow the simultaneous investigation of different molecular layers, and, therefore, the integration of multi-modal data deriving from multi-omics experiments. Although in the last few years a number of valid tools designed for multi-omics data integration have emerged, they have never been combined with statistical meta-analysis tools in a unique analytical solution in order to support meaningful biological interpretation. Network science is at the forefront of systems biology, where the inference of molecular interactomes allowed the investigation of perturbed biological systems, by shedding light on the disrupted relationships that keep the homeostasis of complex systems. Here, we present MUUMI, an R package that unifies network-based data integration and statistical meta-analysis within a single analytical framework. MUUMI allows the identification of robust molecular signatures through multiple meta-analytic methods, inference and analysis of molecular interactomes and the integration of multiple omics layers through similarity network fusion. We demonstrate the functionalities of MUUMI by presenting two case studies in which we analysed 1) 17 transcriptomic datasets on idiopathic pulmonary fibrosis (IPF) from both microarray and RNA-Seq platforms and 2) multi-omics data of THP-1 macrophages exposed to different polarising stimuli. In both examples, MUUMI revealed biologically coherent signatures, underscoring its value in elucidating complex biological processes. Availability and implementationMUUMI is freely available at https://github.com/fhaive/muumi.

bioinformatics↗

bioRxiv2024

Germline and somatic genetic effects on gene expression and outcome in patients with Multiple Myeloma

Multiple Myeloma (MM) is a hematological malignancy associated with poor prognosis and relapses. MM has a strong inherited component, and while the mutational and transcriptomic landscapes of MM have been characterized extensively, the molecular consequences of germline genetic variation in MM have not been previously explored. Here, we have leveraged matched genotype and expression data from 607 patients to map the germline and somatic regulatory variation in MM. We find that non-coding variation shapes the transcriptional landscape of MM, contributing to overall survival. These regulatory variants alter the binding of transcription factors with known importance in MM, such as IRF4, providing insights into the underlying mechanisms. These data contribute to the increasing understanding of the genetic, molecular, and cellular forces shaping MM risk and outcomes.

genomics↗

bioRxiv2024

Toxicogenomic Assessment of In Vitro Macrophages Exposed to Profibrotic Challenge.

Immune signalling is a crucial component in the progression of fibrosis. However, approaches for the safety assessment of potentially profibrotic substances, providing information on mechanistic immune responses, are underdeveloped. This study utilises a comprehensive analysis of RNA sequencing data from macrophages exposed in vitro to multiple sublethal concentrations of the profibrotic agent bleomycin, over multiple timepoints. Using a toxicogenomic framework, we performed dose-dependent analysis to filter genes truly altered by bleomycin exposure from noise and identified a subset of immune genes with a sustained dose-dependent and differential expression response to profibrotic challenge. We performed an immunoassay and revealed cytokines and proteinases responding to bleomycin exposure that closely correlate to transcriptomic alterations, underscoring the integration between transcriptional immune response and external immune signalling activity. This study not only increases our understanding of the immunological mechanisms of fibrosis, but also offers an innovative framework for the toxicological evaluation of substances with potential fibrogenic effects on macrophage signalling. Our work brings a new immunotoxicogenomic direction for hazard assessment of fibrotic compounds, through implementation of a time and resource efficient in vitro methodology.

pharmacology and toxicology↗

bioRxiv2024

Methylation and Transcriptomic Profiling Reveals Short Term and Long Term Regulatory Responses in Polarized Macrophages

Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Moreover, epigenetic alterations play an important role in the pathophysiology of macrophages, including phenomena related to the trained innate immunity, which allow faster and more efficient inflammatory responses upon subsequent encounters with the same pathogen. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, unravelling the long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure suggesting a potential mechanism for rapid adaptation to various stimuli. These conserved epigenetic modifications might represent a fundamental state that allows for flexible responses to various environmental cues. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study unveils a novel set of genes critically linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes. Highlights- Environmental signals trigger gene changes in macrophages, leaving a long-lasting epigenetic reprogramming - Epigenetic changes and metabolic shifts in polarized macrophages suggest training mechanisms - Common gene set epigenetically altered across different cues, suggest common adaptation to various stimuli Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/599278v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1006cacorg.highwire.dtl.DTLVardef@deb49eorg.highwire.dtl.DTLVardef@12428e9org.highwire.dtl.DTLVardef@f9e1c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering is used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes and Alzheimer disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

bioinformatics↗

bioRxiv2024

A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes

Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasises how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, we developed a network-based approach to characterise toxicological responses in the context of a biological system, inferring biological system specific networks. We directly linked molecular alterations to the adverse outcome pathway (AOP) framework, establishing connections with toxicologically relevant phenotypic events. We applied this framework on a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrated how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve our understanding of toxicological mechanisms from a systems biology perspective, guiding the hazard assessment of nanomaterials and other advanced materials.

pharmacology and toxicology↗

bioRxiv2024

Beyond Basic Characterization and Omics: Immunomodulatory Roles of Platelet-Derived Extracellular Vesicles Unveiled by Functional Testing

Renowned for their role in hemostasis and thrombosis, platelets are also increasingly recognized for their contribution in innate immunity, immunothrombosis and inflammatory diseases. Platelets express a wide range of receptors, which allows them to reach a variety of activation endpoints and grants them immunomodulatory functions. Activated platelets release extracellular vesicles (PEVs), whose formation and molecular cargo has been shown to depend on receptor-mediated activation and environmental cues. This study compares the immunomodulatory profiles of PEVs generated via activation of platelets by different receptors, glycoprotein VI, C-type lectin-like receptor 2, and combining all thrombin-collagen receptors. Functional assays in vivo in zebrafish and in vitro in human macrophages respectively highlighted distinct homing and secretory responses triggered by the PEVs. In contrast, omics analyses of protein and miRNA cargo combined with physicochemical particle characterization found only subtle differences between the PEV types, which were insufficient to explain their different functional immunomodulatory profiles. Constitutively released PEVs, formed in the absence of an exogenous activator, displayed a disparate activation profile from the receptor induced PEVs. Our findings underscore that PEVs are tunable through receptor-mediated activation. To truly comprehend their role(s) in mediating platelet functions among immune cells, conducting functional assays is imperative. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/570750v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@75a9dborg.highwire.dtl.DTLVardef@e2787corg.highwire.dtl.DTLVardef@543525org.highwire.dtl.DTLVardef@6d1362_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2022

Molecular annotation of AOPs guides the development of the next generation mechanistic chemical safety assessment and new approach methods

Mechanistic toxicology has emerged as a powerful framework to inform on the safety of chemicals and guide the development of new safe-by-design compounds. Although toxicogenomics provides support towards mechanistic evaluation of chemical exposures, the implementation of toxicogenomics-based evidence in the regulatory setting is still hindered by uncertainties related to the analysis and interpretation of such data. Adverse Outcome Pathways (AOPs) are multi-scale models that link chemical exposures to adverse outcomes through causal cascades of key events (KEs). The use of mechanistic evidence through the AOP framework is actively promoted for the development of new approach methods (NAMs) and to reduce animal experimentation. However, in order to unleash the full potential of AOPs and build confidence into toxicogenomics, robust and unified associations between KEs and patterns of molecular alteration need to be established. Here, we hypothesised that systematic curation of molecular events associated with KEs would enable the modelling of AOPs through gene-level data, creating the much-needed link between toxicogenomics and the systemic mechanisms depicted by the AOPs. This, in turn, introduces novel ways of benefitting from the AOP concept, including predictive models, read-across, and targeted assays, while also reducing the need for multiple testing strategies. Hence, we developed a multi-step strategy to annotate the AOPs relevant to human health risk assessment. We show that our framework successfully highlights relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, we defined and experimentally validated a panel of robust AOP-derived in vitro biomarkers for pulmonary fibrosis.

pharmacology and toxicology↗

bioRxiv2021

Structure Learning for Hierarchical Regulatory Networks

Network analysis offers a powerful technique to model the relationships between genes within biological regulatory networks. Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE - Structure Learning for Hierarchical Networks - a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple networks from high-dimensional data. We show through simulations SHINE improves performance when relatively few samples are available and multiple networks are desired, by reducing the complexity of the graphical search space and by taking advantage of shared structural information. We evaluated SHINE on TCGA Pan-Cancer data and found learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.

bioinformatics↗

bioRxiv2021

The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we collected, curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated transcriptomics analysis of public datasets shed light on a number of genes which are frequently deregulated in the psoriatic lesion compared with the unaffected skin in a large number of studies. In particular, CRABP2, LCN2, S100A12 and PDZK1IP1 were found to be deregulated in all of the datasets analyzed. Furthermore, the analysis of co-expression networks highlights genes showing aberrant patterns of connectivity in the lesional network as compared to the network inferred from unaffected skin samples. For instance, we identified co-expression patterns of SERPINB4, KYNU and S100A12 as being the most affected by the disease. Network analysis allowed us to identify YPEL1 and HUS1 as plausible, previously unknown, actors in the expression of the psoriasis phenotype. In addition, by exploiting topological properties of the network models, we highlighted a set of 250 non-deregulated genes, 223 of which have never been associated with the disease before, including CACNA1A, HADH, ATP5MC1 and CBARP among others. Finally, we characterized specific communities of co-expressed genes sustaining relevant molecular functions and specific immune cell types expression signatures playing a role in the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

systems biology↗

bioRxiv2021

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

New affordable therapeutic protocols for COVID-19 are urgently needed despite the increasing number of effective vaccines and monoclonal antibodies. To this end, there is increasing attention towards computational methods for drug repositioning and de novo drug design. Here, we systematically integrated multiple data-driven computational approaches to perform virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the set of prioritized drugs, we selected a subset of representative candidates to test in human cells. Two compounds, 7-hydroxystaurosporine and bafetinib, showed synergistic antiviral effects in our in vitro experiments, and strongly inhibited viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, we extracted and prioritized the chemical substructures of the identified drugs, providing a chemical vocabulary that may help to design new effective drugs.

pharmacology and toxicology↗

bioRxiv2020

animalcules: Interactive Microbiome Analytics 1 and Visualization in R

BackgroundMicrobial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled the study of microbial communities at unprecedented resolution. However, these advances in data generation have presented novel challenges to researchers attempting to analyze and visualize these data. ResultsTo address some of these challenges, we have developed animalcules, an easy-to-use interactive microbiome analysis toolkit for 16S rRNA sequencing data, shotgun DNA metagenomics data, and RNA-based metatranscriptomics profiling data. This toolkit combines novel and existing analytics, visualization methods, and machine learning models. For example, traditional microbiome analyses such as alpha/beta diversity and differential abundance analysis are enhanced in the toolkit, while new methods such as biomarker identification are introduced. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data and discover new insights. animalcules can be used as a standalone command-line R package or users can explore their data with the accompanying interactive R Shiny interface. ConclusionsWe present animalcules, an R package for interactive microbiome analysis through either an interactive interface facilitated by R Shiny or various command-line functions. It is the first microbiome analysis toolkit that supports the analysis of all 16S rRNA, DNA-based shotgun metagenomics, and RNA-sequencing based metatranscriptomics datasets. animalcules can be freely downloaded from GitHub at https://github.com/compbiomed/animalcules or installed through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/animalcules.html.

microbiology↗