Aneuploidy that arises during meiosis and/or mitosis is a major contributor to early embryo loss. We previously showed that human preimplantation embryos encapsulate missegregated chromosomes into micronuclei while undergoing cellular fragmentation and that fragments can contain chromosomal material, but the source of this DNA was unknown. Here, we leveraged the use of a nonhuman primate model and single-cell DNA-sequencing (scDNA-seq) to examine the chromosomal content of 471 individual samples comprising 254 blastomeres, 42 polar bodies, and 175 cellular fragments from a large number (N = 50) of disassembled rhesus cleavage-stage embryos. Our analysis revealed that the aneuploidy and micronucleation frequency is conserved between humans and macaques, and that fragments encapsulate whole and/or partial chromosomes lost from blastomeres. Single-cell/fragment genotyping showed that these chromosome-containing cellular fragments (CCFs) can be maternally or paternally derived and display double-stranded DNA breaks. DNA breakage was further indicated by reciprocal subchromosomal losses/gains between blastomeres and large segmental errors primarily detected at the terminal ends of chromosomes. By combining time-lapse imaging with scDNA-seq, we determined that multipolar divisions at the zygote or two-cell stage were associated with CCFs and generated a random mixture of chromosomally normal and abnormal blastomeres with uniparental or biparental origins. Despite frequent chromosome missegregation at the cleavage-stage, we show that CCFs and nondividing aneuploid blastomeres showing extensive DNA damage are prevented from incorporation into blastocysts. These findings suggest that embryos respond to chromosomal errors by encapsulation into micronuclei, elimination via cellular fragmentation, and selection against highly aneuploid blastomeres to overcome chromosome instability during preimplantation development.

Background Non-human primates (NHPs), particularly macaques, serve as critical and highly relevant pre-clinical models of human disease. The similarity in human and macaque natural disease susceptibility, along with parallel genetic risk alleles, underscores the value of macaques in the development of effective treatment strategies. Nonetheless, there are limited genomic resources available to support the exploration and discovery of macaque models of inherited disease. Notably, there are few public databases tailored to searching NHP sequence variants, and no other database making use of centralized variant calling, or providing genotype-level data and predicted pathogenic effects for each variant. Results The macaque Genotype And Phenotype (mGAP) resource is the first public website providing searchable, annotated macaque variant data. The mGAP resource includes a catalog of high confidence variants, derived from whole genome sequence (WGS). The current mGAP release at time of publication (1.7) contains 17,087,212 variants based on the sequence analysis of 293 rhesus macaques. A custom pipeline was developed to enable annotation of the macaque variants, leveraging human data sources that include regulatory elements (ENCODE, RegulomeDB), known disease- or phenotype-associated variants (GRASP), predicted impact (SIFT, PolyPhen2), and sequence conservation (Phylop, PhastCons). Currently mGAP includes 2767 variants that are identical to alleles listed in the human ClinVar database, of which 276 variants, spanning 258 genes, are identified as pathogenic. An additional 12,472 variants are predicted as high impact (SnpEff) and 13,129 are predicted as damaging (PolyPhen2). In total, these variants are predicted to be associated with more than 2000 human disease or phenotype entries reported in OMIM (Online Mendelian Inheritance in Man). Importantly, mGAP also provides genotype-level data for all subjects, allowing identification of specific individuals harboring alleles of interest. Conclusions The mGAP resource provides variant and genotype data from hundreds of rhesus macaques, processed in a consistent manner across all subjects ( https://mgap.ohsu.edu ). Together with the extensive variant annotations, mGAP presents unprecedented opportunity to investigate potential genetic associations with currently characterized disease models, and to uncover new macaque models based on parallels with human risk alleles. Electronic supplementary material The online version of this article (10.1186/s12864-019-5559-7) contains supplementary material, which is available to authorized users.

Objective To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. Materials and methods PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. Results The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. Discussion Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. Conclusions Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

Embryonic aneuploidy is highly complex, often leading to developmental arrest, implantation failure, or spontaneous miscarriage in both natural and assisted reproduction. Despite our knowledge of mitotic mis-segregation in somatic cells, the molecular pathways regulating chromosome fidelity during the error-prone cleavage-stage of mammalian embryogenesis remain largely undefined. Using bovine embryos and live-cell fluorescent imaging, we observed frequent micro-/multi-nucleation of mis-segregated chromosomes in initial mitotic divisions that underwent unilateral inheritance, re-fused with the primary nucleus, or formed a chromatin bridge with neighboring cells. A correlation between a lack of syngamy, multipolar divisions, and asymmetric genome partitioning was also revealed and single-cell DNA-seq showed propagation of primarily non-reciprocal mitotic errors. Depletion of the mitotic checkpoint protein, BUB1B/BUBR1, resulted in similarly abnormal nuclear structures and cell divisions, as well as chaotic aneuploidy and dysregulation of the kinase-substrate network mediating mitotic progression, all prior to zygotic genome activation. This demonstrates that embryonic micronuclei sustain multiple fates, provides an explanation for blastomeres with uniparental origins, and substantiates defective checkpoints and likely other maternally-derived factors as major contributors to the karyotypic complexity afflicting mammalian preimplantation development.

Adverse event (AE) reports contain notes detailing procedural and guideline deviations, and unwanted incidents that can bring harm to patients. Available datasets mainly focus on vigilance or post-market surveillance of adverse drug reactions or medical device failures. The lack of clinical-related AE datasets makes it challenging to study healthcare-related AEs. AEs affect 10% of hospitalized patients, and almost half are preventable. Having an AE dataset can assist in identifying possible patient safety interventions and performing quality surveillance to lower AE rates. The free-text notes can provide insight into the cause of incidents and lead to better patient care. The objective of this study is to introduce a Norwegian AE dataset and present preliminary processing and analysis for sepsis-related events, specifically peripheral intravenous catheter-related bloodstream infections. Therefore, the methods focus on performing a domain analysis to prepare and better understand the data through screening, generating synthetic free-text notes, and annotating notes.