Expression of collier in the premandibular segment of myriapods: support for the traditional Atelocerata concept or a case of convergence?

<p>Abstract</p> <p>Background</p> <p>A recent study on expression and function of the ortholog of the <it>Drosophila collier </it>(<it>col</it>) gene in various arthropods including insects, crustaceans and chelicerates suggested a <it>de novo </it>function of <it>col </it>in the development of the appendage-less intercalary segment of insects. However, this assumption was made on the background of the now widely-accepted Pancrustacea hypothesis that hexapods represent an in-group of the crustaceans. It was therefore assumed that the expression of <it>col </it>in myriapods would reflect the ancestral state like in crustaceans and chelicerates, i.e. absence from the premandibular/intercalary segment and hence no function in its formation.</p> <p>Results</p> <p>We find that <it>col </it>in myriapods is expressed at early developmental stages in the same anterior domain in the head, the parasegment 0, as in insects. Comparable early expression of <it>col </it>is not present in the anterior head of an onychophoran that serves as an out-group species closely related to the arthropods.</p> <p>Conclusions</p> <p>Our findings suggest either that i) the function of <it>col </it>in head development has been conserved between insects and myriapods, and that these two classes of arthropods may be closely related supporting the traditional Atelocerata (or Tracheata) hypothesis; or ii) alternatively <it>col </it>function could have been lost in early head development in crustaceans, or may indeed have evolved convergently in insects and myriapods.</p

A review of the correlation of tergites, sternites, and leg pairs in diplopods

In some arthropods there is a discrepancy in the number of dorsal tergites compared to the number of ventral sternites and leg pairs. The posterior tergites of the Diplopoda (millipedes) each cover two sternites and two pairs of legs. This segment arrangement is called diplosegmentation. The molecular nature of diplosegmentation is still unknown. There are even conflicting theories on the way the tergites and sternites/leg pairs should be correlated to each other. The different theories are based either on embryological analyses or on studies of the adult morphology and turned out to be not compatible with each other. We have previously used the expression patterns of segmentation genes in the pill millipede Glomeris marginata (Myriapoda: Diplopoda) to study millipede segmentation. Here we review the existing models on the alignment of tergites and leg pairs in millipedes with special emphasis on the implications the gene expression data have on the debate of tergite and leg pair assignment in millipedes. The remarkable outcome of the gene expression analysis was that (1) there is no coupling of dorsal and ventral segmentation and, importantly, that (2) the boundaries delimiting the tergites do neither correlate to the embryonic boundaries of the dorsal embryonic segments nor to the boundaries of the ventral embryonic segments. Using these new insights, we critically reinvestigated the correlation of tergites, sternites, and leg pairs in millipedes. Our model, which takes into account that the tergite boundaries are different from the dorsal embryonic segment boundaries, provides a solution of the problem of tergite to sternite/leg pair correlation in basal milipedes with non-fused exoskeletal elements and also has implications for derived species with exoskeletal rings. Moreover, lack of coupling of dorsal and ventral segmentation may also explain the discrepancy in numbers of dorsal tergites and ventral leg pairs seen in some other arthropods

Duplicated Hox genes in the spider Cupiennius salei

BACKGROUND: Hox genes are expressed in specific domains along the anterior posterior body axis and define the regional identity. In most animals these genes are organized in a single cluster in the genome and the order of the genes in the cluster is correlated with the anterior to posterior expression of the genes in the embryo. The conserved order of the various Hox gene orthologs in the cluster among most bilaterians implies that such a Hox cluster was present in their last common ancestor. Vertebrates are the only metazoans so far that have been shown to contain duplicated Hox clusters, while all other bilaterians seem to possess only a single cluster. RESULTS: We here show that at least three Hox genes of the spider Cupiennius salei are present as two copies in this spider. In addition to the previously described duplicated Ultrabithorax gene, we here present sequence and expression data of a second Deformed gene, and of two Sex comb reduced genes. In addition, we describe the sequence and expression of the Cupiennius proboscipedia gene. The spider Cupiennius salei is the first chelicerate for which orthologs of all ten classes of arthropod Hox genes have been described. The posterior expression boundary of all anterior Hox genes is at the tagma border of the prosoma and opisthosoma, while the posterior boundary of the posterior Hox genes is at the posterior end of the embryo. CONCLUSION: The presence of at least three duplicated Hox genes points to a major duplication event in the lineage to this spider, perhaps even of the complete Hox cluster as has taken place in the lineage to the vertebrates. The combined data of all Cupiennius Hox genes reveal the existence of two distinct posterior expression boundaries that correspond to morphological tagmata boundaries

Expression of myriapod pair rule gene orthologs

Background Segmentation is a hallmark of the arthropods; most knowledge about the molecular basis of arthropod segmentation comes from work on the fly Drosophila melanogaster. In this species a hierarchic cascade of segmentation genes subdivides the blastoderm stepwise into single segment wide regions. However, segmentation in the fly is a derived feature since all segments form virtually simultaneously. Conversely, in the vast majority of arthropods the posterior segments form one at a time from a posterior pre-segmental zone. The pair rule genes (PRGs) comprise an important level of the Drosophila segmentation gene cascade and are indeed the first genes that are expressed in typical transverse stripes in the early embryo. Information on expression and function of PRGs outside the insects, however, is scarce. Results Here we present the expression of the pair rule gene orthologs in the pill millipede Glomeris marginata (Myriapoda: Diplopoda). We find evidence that these genes are involved in segmentation and that components of the hierarchic interaction of the gene network as found in insects may be conserved. We further provide evidence that segments are formed in a single-segment periodicity rather than in pairs of two like in another myriapod, the centipede Strigamia maritima. Finally we show that decoupling of dorsal and ventral segmentation in Glomeris appears already at the level of the PRGs. Conclusions Although the pair rule gene network is partially conserved among insects and myriapods, some aspects of PRG interaction are, as suggested by expression pattern analysis, convergent, even within the Myriapoda. Conserved expression patterns of PRGs in insects and myriapods, however, may represent ancestral features involved in segmenting the arthropod ancestor

Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)

Most clinical specialties have a plethora of studies that develop or validate one or more prediction models, for example, to inform diagnosis or prognosis. Having many prediction model studies in a particular clinical field motivates the need for systematic reviews and meta-analyses, to evaluate and summarise the overall evidence available from prediction model studies, in particular about the predictive performance of existing models. Such reviews are fast emerging, and should be reported completely, transparently, and accurately. To help ensure this type of reporting, this article describes a new reporting guideline for systematic reviews and meta-analyses of prediction model research

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models

Objectives We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques. Study Design and Setting We systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty. Results We included 152 studies: 38% reported diagnostic models and 62% prognostic models. When reported, discrimination was described without precision estimates in 53/71 abstracts (74.6% [95% CI 63.4–83.3]) and 53/81 main texts (65.4% [95% CI 54.6–74.9]). Of the 21 abstracts that recommended the model to be used in daily practice, 20 (95.2% [95% CI 77.3–99.8]) lacked any external validation of the developed models. Likewise, 74/133 (55.6% [95% CI 47.2–63.8]) studies made recommendations for clinical use in their main text without any external validation. Reporting guidelines were cited in 13/152 (8.6% [95% CI 5.1–14.1]) studies. Conclusion Spin practices and poor reporting standards are also present in studies on prediction models using machine learning techniques. A tailored framework for the identification of spin will enhance the sound reporting of prediction model studies

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Objectives In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. Study Design and Setting We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. Results We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. Conclusion The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and Objectives We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. Methods We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes. Results We included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]). Conclusion Our review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models. Systematic review registration PROSPERO, CRD42019161764

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed

Objectives: Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement. Study Design and Setting: We searched PubMed (January 2018–May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs. Results: Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD. Conclusion: Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this

Screening rules for growth to detect celiac disease: A case-control simulation study

Background: It is generally assumed that most patients with celiac disease (CD) have a slowed growth in terms of length (or height) and weight. However, the effectiveness of slowed growth as a tool for identifying children with CD is unknown. Our aim is to study the diagnostic efficiency of several growth criteria used to detect CD children. Methods: A case-control simulation study was carried out. Longitudinal length and weight measurements from birth to 2.5 years of age were used from three groups of CD patients (n = 134) (one group diagnosed by screening, two groups with clinical manifestations), and a reference group obtained from the Social Medical Survey of Children Attending Child Health Clinics (SMOCC) cohort (n = 2,151) in The Netherlands. The main outcome measures were sensitivity, specificity and positive predictive value (PPV) for each criterion. Results: Body mass index (BMI) performed best for the groups with clinical manifestations. Thirty percent of the CD children with clinical manifestations and two percent of the reference children had a BMI Standard Deviation Score (SDS) less than -1.5 and a decrease in BMI SDS of at least -2.5 (PPV = 0.85%). The growth criteria did not discriminate between the screened CD group and the reference group. Conclusion: For the CD children with clinical manifestations, the most sensitive growth parameter is a decrease in BMI SDS. BMI is a better predictor than weight, and much better than length or height. Toddlers with CD detected by screening grow normally at this stage of the disease