No abstract

A considerable number of cells expressing typical immature neuronal markers including doublecortin (DCX+) are present around layer II in the cerebral cortex of young and adult guinea pigs and other larger mammals, and their origin and biological implication await further characterization. We show here in young adult guinea pigs that these DCX+ cells are accompanied by in situ cell division around the superficial cortical layers mostly in layer I, but they co-express proliferating cell nuclear antigen and an early neuronal-fate determining factor, PAX6. A small number of these DCX+ cells also colocalize with BrdU following administration of this mitotic indicator. Cranial X-ray irradiation causes a decline of DCX+ cells around layer II, and novel environmental exploration induces c-Fos expression among these cells in several neocortical areas. Together, these data are compatible with a notion that DCX+ cortical neurons around layer II might derive from proliferable neuronal precursors around layer I in young adult guinea pig cerebrum, and that these cells might be modulated by experience under physiological conditions.

No abstract

Background Evaluating tumor‐infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored. Purpose To establish a radiomics nomogram based on dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level. Study Type Retrospective. Population A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results. Field Strength/Sequence 3.0 T; axial T2‐weighted imaging (fast spin echo), diffusion‐weighted imaging (spin echo‐echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo). Assessment A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated. Statistical Tests The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t‐tests and chi‐squared tests or Fisher's exact test, Hosmer–Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant. Results The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9. Data Conclusion The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer. Level of Evidence 4 Technical Efficacy Stage 2

No abstract