RESUMO
Background: Although imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques. Methods: A systematic search was carried out to identify available literature published in PubMed, Web of Science, and the Cochrane Library up to March 2023. Studies that developed and/or validated machine learning models based on radiomics data to identify and/or predict unfavorable cerebral and cardiovascular events in carotid plaque were included. The basic information of each piece of included literature was identified, and the reporting quality, risk of bias, and radiomics methodology quality were assessed according the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist, the Prediction Model Risk of Bias Assessment Tool (PROBAST), and the radiomics quality score (RQS), respectively. Results: A total of 2,738 patients from 19 studies were included. The mean overall TRIPOD adherence rate was 66.1% (standard deviation 12.8%), with a range of 45-87%. All studies had a high overall risk of bias, with the analysis domain being the most common source of bias. The mean RQS was 9.89 (standard deviation 5.70), accounting for 27.4% of the possible maximum value of 36. The mean area under the curve for diagnostic or predictive properties of these included radiomics models was 0.876±0.09, with a range of 0.741-0.989. Conclusions: Radiomics models may have value in the assessment of carotid plaque, the overall scientific validity and reporting quality of current carotid plaque radiomics reports are still lacking, and many barriers must be overcome before these models can be applied in clinical practice.
RESUMO
Drosophila ovary is recognized as one of the best model systems to study stem cell biology in vivo. We had previously identified an autonomous role of the histone H1 in germline stem cell (GSC) maintenance. Here, we found that histone H1 depletion in escort cells (ECs) resulted in an increase of spectrosome-containing cells (SCCs), an ovary tumor-like phenotype. Further analysis showed that the Dpp pathway is excessively activated in these SCC cells, while the expression of bam is attenuated. In the H1-depleted ECs, both transposon activity and DNA damage had increased dramatically, followed by EC apoptosis, which is consistent with the role of H1 in other somatic cells. Surprisingly, H1-depleted ECs acquired cap cell characteristics including dpp expression, and the resulting abnormal Dpp level inhibits SCC further differentiation. Most interestingly, double knockdown of H1 and dpp in ECs can reduce the number of SCCs to the normal level, indicating that the additional Dpp secreted by ECs contributes to the germline tumor. Taken together, our findings indicate that histone H1 is an important epigenetic factor in controlling EC characteristics and a key suppressor of germline tumor.