The mouse pathology ontology, MPATH; structure and applications

Quilt-1M: One Million Image-Text Pairs for Histopathology

Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has halted comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1,087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate Quilt: a large-scale vision-language dataset consisting of 768,826 image and text pairs. Quilt was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine Quilt with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: Quilt-1M, with 1M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of Quilt-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal retrieval tasks.

Bridging mouse and human anatomies; a knowledge-based approach to comparative anatomy for disease model phenotyping

The laboratory mouse is the foremost mammalian model used for studying human diseases and is closely anatomically related to humans. Whilst knowledge about human anatomy has been collected throughout the history of mankind, the first comprehensive study of the mouse anatomy was published less than 60 years ago. This has been followed by the more recent publication of several books and resources on mouse anatomy. Nevertheless, to date, our understanding and knowledge of mouse anatomy is far from being at the same level as that of humans. In addition, the alignment between current mouse and human anatomy nomenclatures is far from being as developed as those existing between other species, such as domestic animals and humans. To close this gap, more in depth mouse anatomical research is needed and it will be necessary to extent and refine the current vocabulary of mouse anatomical terms.

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67^th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.

A Primer for Assessing the Pathology in Mouse Models of Neuroblastoma

Neuroblastoma, the most common extracranial solid tumor in young children, arises from the sympathetic nervous system. Our understanding of neuroblastoma has been improved by the development of both genetically engineered and xenograft mouse models of the disease. Anatomical pathology is an essential component of the phenotyping of mouse models of cancer, characterizing the morphologic effects of genetic manipulation and drug treatment. The Th-MYCN model, the most widely used of several genetically engineered mouse models of neuroblastoma, was established by targeted expression of the human MYCN gene to murine neural crest cells under the control of the rat tyrosine hydroxylase promoter. Neuroblastoma development in Th-MYCN mice is preceded by neuroblast hyperplasia-the persistence and proliferation of neural crest-derived neuroblasts within the sympathetic autonomic ganglia. The neuroblastomas that subsequently develop morphologically resemble human neuroblastoma and carry chromosomal gains and losses in regions syntenic with those observed in human tumors. In this overview, we describe the essential pathologic features for investigators when assessing mouse models of neuroblastoma. We outline human neuroblastoma as the foundation for understanding the murine disease, followed by details of the murine sympathetic ganglia from which neuroblastoma arises. Sympathetic ganglia, both with and without neuroblast hyperplasia, are described. The macroscopic and microscopic features of murine neuroblastoma are explained, including assessment of xenografts and tumors following drug treatment. An approach to experimental design is also detailed. Increased understanding of the pathology of murine neuroblastoma should improve reproducibility and comparability of research findings and assist investigators working with mouse models of neuroblastoma. © 2021 Wiley Periodicals LLC.

Where to search top-K biomedical ontologies?

Motivation

Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements.

Result

We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries.

Conclusion

The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work.

Availability

The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark

Establishing the Japan-Store house of animal radiobiology experiments (J-SHARE), a large-scale necropsy and histopathology archive providing international access to important radiobiology data

Purpose: Projects evaluating the effects of radiation, within the National Institutes of Quantum and Radiological Science and Technology (QST), National Institute of Radiological Sciences (NIRS), have focused on risk analyses for life shortening and cancer prevalence using laboratory animals. Genetic and epigenetic alterations in radiation-induced tumors have been also analyzed with the aim of better understanding mechanisms of radiation carcinogenesis. As well as the economic and practical limitations of repeating such large-scale experiments, ethical considerations make it vital that we store and share the pathological data and samples of the animal experiments for future use. We are now constructing such an archive called the Japan-Storehouse of Animal Radiobiology Experiments (J-SHARE). Methods: J-SHARE records include information such as detailed experimental protocols, necropsy records and photographs of organs at necropsy. For each animal organs and tumor tissues are dissected, and parts are stored as frozen samples at -80 °C. Samples fixed with formalin are also embedded in paraffin blocks for histopathological analyses. Digital copies of stained tissues are being systematically saved using a virtual slide system linked to original records by barcodes. Embedded and frozen tissues are available for molecular analysis. Conclusion: Similar archive systems for radiation biology have also been under construction in the USA and Europe, the Northwestern University Radiation Archive (NURA), and STORE at the BfS, respectively. The J-SHARE will be linked with the sister-archives and made available for collaborative research to institutions and universities all over the world.

Nail abnormalities identified in an ageing study of 30 inbred mouse strains

In a large-scale ageing study, 30 inbred mouse strains were systematically screened for histologic evidence of lesions in all organ systems. Ten strains were diagnosed with similar nail abnormalities. The highest frequency was noted in NON/ShiLtJ mice. Lesions identified fell into two main categories: acute to chronic penetration of the third phalangeal bone through the hyponychium with associated inflammation and bone remodelling or metaplasia of the nail matrix and nail bed associated with severe orthokeratotic hyperkeratosis replacing the nail plate. Penetration of the distal phalanx through the hyponychium appeared to be the initiating feature resulting in nail abnormalities. The accompanying acute to subacute inflammatory response was associated with osteolysis of the distal phalanx. Evaluation of young NON/ShiLtJ mice revealed that these lesions were not often found, or affected only one digit. The only other nail unit abnormality identified was sporadic subungual epidermoid inclusion cysts which closely resembled similar lesions in human patients. These abnormalities, being age-related developments, may have contributed to weight loss due to impacts upon feeding and should be a consideration for future research due to the potential to interact with other experimental factors in ageing studies using the affected strains of mice.

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a large and well-characterized dataset of anatomic pathology descriptions from a major study of aging mice. We show how different design patterns based on the MPATH and MA ontologies provide orthogonal axes of analysis, and perform differently in over-representation and semantic similarity applications. We discuss how such a data-driven approach might be used generally to generate and evaluate ontology design patterns.

Keeping Score: Semiquantitative and Quantitative Scoring Approaches to Genetically Engineered and Xenograft Mouse Models of Cancer

There is a growing need to quantitate or "score" lesions in mouse models of human disease, for correlation with human disease and to establish their clinical relevance. Several standard semiquantitative scoring schemes have been adapted for nonneoplastic lesions; similarly, the pathologist must carefully select an approach to score mouse models of cancer. Genetically engineered mouse models with a continuum of precancerous and cancerous lesions and xenogeneic models of various derivations present unique challenges for the pathologist. Important considerations include experimental design, understanding of the human disease being modeled, standardized classification of lesions, and approaches for semiquantitative and/or quantitative scoring in the model being evaluated. Quantification should be considered for measuring the extent of neoplasia and expression of tumor biomarkers. Semiquantitative scoring schemes have been devised that include severity, frequency, and distribution of lesions. Although labor-intensive, scoring mouse models of cancer provides numerical data that enable statistical analysis and greater translational impact.

The anatomy of phenotype ontologies: principles, properties and applications

The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.

Integrating phenotype ontologies with PhenomeNET

BACKGROUND

Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.

RESULTS

Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.

CONCLUSIONS

PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.

A histological ontology of the human cardiovascular system

BACKGROUND

In this paper, we describe a histological ontology of the human cardiovascular system developed in collaboration among histology experts and computer scientists.

RESULTS

The histological ontology is developed following an existing methodology using Conceptual Models (CMs) and validated using OOPS!, expert evaluation with CMs, and how accurately the ontology can answer the Competency Questions (CQ). It is publicly available at http://bioportal.bioontology.org/ontologies/HO and https://w3id.org/def/System .

CONCLUSIONS

The histological ontology is developed to support complex tasks, such as supporting teaching activities, medical practices, and bio-medical research or having natural language interactions.

Reproducibility of histopathological findings in experimental pathology of the mouse: a sorry tail

Reproducibility of in vivo research using the mouse as a model organism depends on many factors, including experimental design, strain or stock, experimental protocols, and methods of data evaluation. Gross and histopathology are often the endpoints of such research and there is increasing concern about the accuracy and reproducibility of diagnoses in the literature. To reproduce histopathological results, the pathology protocol, including necropsy methods and slide preparation, should be followed by interpretation of the slides by a pathologist familiar with reading mouse slides and familiar with the consensus medical nomenclature used in mouse pathology. Likewise, it is important that pathologists are consulted as reviewers of manuscripts where histopathology is a key part of the investigation. The absence of pathology expertise in planning, executing and reviewing in vivo research using mice leads to questionable pathology-based findings and conclusions from studies, even in high-impact journals. We discuss the various aspects of this problem, give some examples from the literature and suggest solutions.

Classification and analysis of a large collection of in vivo bioassay descriptions

Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by in vivo bioassays. This is partly due to their complexity and lack of accepted reporting standards-publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL-an open-source literature-based database focused on preclinical drug discovery. We show that in vivo assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different in vivo assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.

Semantic prioritization of novel causative genomic variants

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

We address the problem of how to distinguish which of the many thousands of DNA sequence variants carried by an individual with a rare disease is responsible for the disease phenotypes. This can help clinicians arrive at a diagnosis, but also can be instrumental in improving our understanding of the pathobiology of the disease. Many methods are currently available to help with the problem of determining causative variant, using information about evolutionary conservation and prediction of the functional consequences of the sequence variant. We have developed a novel algorithm (PVP) which augments existing strategies by using the similarity of the patients phenotype to known phenotype-genotype data in human and model organism databases to further rank potential candidate genes. In a retrospective study, we apply PVP to the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism, and find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

The Geropathology Research Network: An Interdisciplinary Approach for Integrating Pathology Into Research on Aging

Geropathology is the study of aging and age-related lesions and diseases in the form of whole necropsies/autopsies, surgical biopsies, histology, and molecular biomarkers. It encompasses multiple subspecialties of geriatrics, anatomic pathology, molecular pathology, clinical pathology, and gerontology. In order to increase the consistency and scope of communication in the histologic and molecular pathology assessment of tissues from preclinical and clinical aging studies, a Geropathology Research Network has been established consisting of pathologists and scientists with expertise in the comparative pathology of aging, the design of aging research studies, biostatistical methods for analysis of aging data, and bioinformatics for compiling and annotating large sets of data generated from aging studies. The network provides an environment to promote learning and exchange of scientific information and ideas for the aging research community through a series of symposia, the development of uniform ways of integrating pathology into aging studies, and the statistical analysis of pathology data. The efforts of the network are ultimately expected to lead to a refined set of sentinel biomarkers of molecular and anatomic pathology that could be incorporated into preclinical and clinical aging intervention studies to increase the relevance and productivity of these types of investigations.

The Vital Role of Pathology in Improving Reproducibility and Translational Relevance of Aging Studies in Rodents

Pathology is a discipline of medicine that adds great benefit to aging studies of rodents by integrating in vivo, biochemical, and molecular data. It is not possible to diagnose systemic illness, comorbidities, and proximate causes of death in aging studies without the morphologic context provided by histopathology. To date, many rodent aging studies do not utilize end points supported by systematic necropsy and histopathology, which leaves studies incomplete, contradictory, and difficult to interpret. As in traditional toxicity studies, if the effect of a drug, dietary treatment, or altered gene expression on aging is to be studied, systematic pathology analysis must be included to determine the causes of age-related illness, moribundity, and death. In this Commentary, the authors discuss the factors that should be considered in the design of aging studies in mice, with the inclusion of robust pathology practices modified after those developed by toxicologic and discovery research pathologists. Investigators in the field of aging must consider the use of histopathology in their rodent aging studies in this era of integrative and preclinical geriatric science (geroscience).

Recommendations for minimum information for publication of experimental pathology data: MINPEPA guidelines

Animal models are essential research tools in modern biomedical research, but there are concerns about their lack of reproducibility and the failure of animal data to translate into advances in human medical therapy. A major factor in improving experimental reproducibility is thorough communication of research methodologies. The recently published ARRIVE guidelines outline basic information that should be provided when reporting animal studies. This paper builds on ARRIVE by providing the minimum information needed in reports to allow proper assessment of pathology data gathered from animal tissues. This guidance covers aspects of experimental design, technical procedures, data gathering, analysis, and presentation that are potential sources of variation when creating morphological, immunohistochemical (IHC) or in situ hybridization (ISH) datasets. This reporting framework will maximize the likelihood that pathology data derived from animal experiments can be reproduced by ensuring that sufficient information is available to allow for replication of the methods and facilitate inter-study comparison by identifying potential interpretative confounders.

Talking welfare: the importance of a common language

Ontologies describing mouse phenotypes and pathology are well established and becoming more universally used (Smith and Eppig in Mamm Genome 23:653, 2012; Scofield et al. in J Biomed Semant 4:18, 2013). However, the language used to describe and disseminate cage-side observations is less well developed. This article explores the hurdles to unifying a language and terminology, and introduces our initial attempt to do so.

ZFIN, The zebrafish model organism database: Updates and new directions

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for genetic and genomic data from zebrafish (Danio rerio) research. ZFIN staff curate detailed information about genes, mutants, genotypes, reporter lines, sequences, constructs, antibodies, knockdown reagents, expression patterns, phenotypes, gene product function, and orthology from publications. Researchers can submit mutant, transgenic, expression, and phenotype data directly to ZFIN and use the ZFIN Community Wiki to share antibody and protocol information. Data can be accessed through topic-specific searches, a new site-wide search, and the data-mining resource ZebrafishMine (http://zebrafishmine.org). Data download and web service options are also available. ZFIN collaborates with major bioinformatics organizations to verify and integrate genomic sequence data, provide nomenclature support, establish reciprocal links, and participate in the development of standardized structured vocabularies (ontologies) used for data annotation and searching. ZFIN-curated gene, function, expression, and phenotype data are available for comparative exploration at several multi-species resources. The use of zebrafish as a model for human disease is increasing. ZFIN is supporting this growing area with three major projects: adding easy access to computed orthology data from gene pages, curating details of the gene expression pattern changes in mutant fish, and curating zebrafish models of human diseases.

The humankind genome: from genetic diversity to the origin of human diseases

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

Phenotype ontologies and cross-species analysis for translational research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.

Histopathology reveals correlative and unique phenotypes in a high-throughput mouse phenotyping screen

The Mouse Genetics Project (MGP) at the Wellcome Trust Sanger Institute aims to generate and phenotype over 800 genetically modified mouse lines over the next 5 years to gain a better understanding of mammalian gene function and provide an invaluable resource to the scientific community for follow-up studies. Phenotyping includes the generation of a standardized biobank of paraffin-embedded tissues for each mouse line, but histopathology is not routinely performed. In collaboration with the Pathology Core of the Centre for Modeling Human Disease (CMHD) we report the utility of histopathology in a high-throughput primary phenotyping screen. Histopathology was assessed in an unbiased selection of 50 mouse lines with (n=30) or without (n=20) clinical phenotypes detected by the standard MGP primary phenotyping screen. Our findings revealed that histopathology added correlating morphological data in 19 of 30 lines (63.3%) in which the primary screen detected a phenotype. In addition, seven of the 50 lines (14%) presented significant histopathology findings that were not associated with or predicted by the standard primary screen. Three of these seven lines had no clinical phenotype detected by the standard primary screen. Incidental and strain-associated background lesions were present in all mutant lines with good concordance to wild-type controls. These findings demonstrate the complementary and unique contribution of histopathology to high-throughput primary phenotyping of mutant mice.

Phenotypic characterization of the KK/HlJ inbred mouse strain

Detailed histopathological diagnoses of inbred mouse strains are important for interpreting research results and defining novel models of human diseases. The aim of this study was to histologically detect lesions affecting the KK/HlJ inbred strain. Mice were examined at 6, 12, and 20 months of age and near natural death (ie, moribund mice). Histopathological lesions were quantified by percentage of affected mice per age group and sex. Predominant lesions were mineralization, hyperplasia, and fibro-osseous lesions. Mineralization was most frequently found in the connective tissue dermal sheath of vibrissae, the heart, and the lung. Mineralization was also found in many other organs but to a lesser degree. Hyperplasia was found most commonly in the pancreatic islets, and fibro-osseous lesions were observed in several bones. The percentage of lesions increased with age until 20 months. This study shows that KK/HlJ mice demonstrate systemic aberrant mineralization, with greatest frequency in aged mice. The detailed information about histopathological lesions in the inbred strain KK/HlJ can help investigators to choose the right model and correctly interpret the experimental results.