Effects of light qualities on growth and physiological-biochemical traits of Scutellaria baicalensis.

Canopy spectral composition significantly affects growth and functional traits of understory plants. In this study, we explored the optimal light condition suitable for enhancing Scutellaria baicalensis's yield and quality, aiming to provide scientific reference for the exploitation and utilization of medicinal plant resources in the understory of forests. We measured the responses of growth, morphology, biomass allocation, physiological traits, and secon-dary metabolites of S. baicalensis to different light qualities. S. baicalensis was cultured under five LED-light treatments including full spectrum light (control), ultraviolet-A (UV-A) radiation, blue, green, and red light. Results showed that UV-A significantly reduced plant height, base diameter, leaf thickness, leaf area ratio, and biomass of each organ. Red light significantly reduced base diameter, biomass, effective quantum yield of photosystem Ⅱ (ФPSⅡ), and total flavonoid concentration. Under blue light, root length and total biomass of S. baicalensis significantly increased by 48.0% and 10.8%, respectively, while leaf number and chlorophyll content significantly decreased by 20.0% and 31.6%, respectively. The other physiological and biochemical traits were consistent with their responses in control. Our results suggested that blue light promoted photosynthesis, biomass accumulation, and secondary metabolite synthesis of S. baicalensis, while red light and UV-A radiation negatively affected physiological and biochemical metabolic processes. Therefore, the ratio of blue light could be appropriately increased to improve the yield and quality of S. baicalensis.

Postnatal treatment and evolution patterns of giant fetal hepatic hemangioma: a case series of 29 patients.

OBJECTIVES: To explore the clinical characteristics, postnatal treatment and prognosis of giant fetal hepatic hemangioma (GFHH). METHOD: Retrospective analysis was performed on children with giant fetal hepatic hemangioma (maximum tumor diameter > 40 mm) diagnosed by prenatal ultrasound and MRI from December 2016 to December 2020. These patients were observed and treated at the Children's Hospital of Fudan University after birth. The clinical data were collected to analyze the clinical characteristics, treatment, and prognosis of GFHH using independent sample t tests or Fisher's exact tests. RESULTS: Twenty-nine patients who were detected by routine ultrasound in the second and third trimester of pregnancy with giant fetal hepatic hemangiomas were included. The first prenatal ultrasound diagnosis of gestational age was 34.0 ± 4.3 weeks, ranging from 22 to 39 weeks. Of the patients, 28 had focal GFHHs and 1 had multifocal GFHHs. Surgery was performed, and the diagnosis was confirmed histopathologically in two patients. There were 8 cases with echocardiography-based evidence of pulmonary hypertension, 11 cases had a cardiothoracic ratio > 0.6, and 4 cases had hepatic arteriovenous fistula (AVF). The median follow-up time was 37 months (range: 14-70 months). During the follow-up, 12 patients received medical treatment with propranolol as the first-line therapy. The treatment group had a higher ratio of cardiothoracic ratio > 0.6 (P = 0.022) and lower albumin levels (P = 0.018). Four (14.8%) lesions showed postnatal growth before involuting. Complete response was observed in 13 (13/29) patients, and partial response was observed in 16 (16/29) patients. CONCLUSIONS: Fetal giant hepatic hemangioma is mainly localized, and its clinical outcome conforms to RICH (rapidly involuting) and PICH (partially involuting), but some fetal giant hepatic hemangiomas will continue to grow after birth and then gradually decrease. For uncomplicated giant fetal hepatic hemangioma, postnatal follow-up is the main concern, while those with complications require aggressive medical treatment. Propranolol may have no effect on the volume change of GFHH.

Application of Machine Learning and Deep EfficientNets in Distinguishing Neonatal Adrenal Hematomas From Neuroblastoma in Enhanced Computed Tomography Images.

Background: The aim of the study was to employ a combination of radiomic indicators based on computed tomography (CT) imaging and machine learning (ML), along with deep learning (DL), to differentiate between adrenal hematoma and adrenal neuroblastoma in neonates. Methods: A total of 76 neonates were included in this retrospective study (40 with neuroblastomas and 36 with adrenal hematomas) who underwent CT and divided into a training group (n = 38) and a testing group (n = 38). The regions of interest (ROIs) were segmented by two radiologists to extract radiomics features using Pyradiomics package. ML classifications were done using support vector machine (SVM), AdaBoost, Extra Trees, gradient boosting, multi-layer perceptron (MLP), and random forest (RF). EfficientNets was employed and classified, based on radiometrics. The area under curve (AUC) of the receiver operating characteristic (ROC) was calculated to assess the performance of each model. Results: Among all features, the least absolute shrinkage and selection operator (LASSO) logistic regression selected nine features. These radiomics features were used to construct radiomics model. In the training cohort, the AUCs of SVM, MLP and Extra Trees models were 0.967, 0.969 and 1.000, respectively. The corresponding AUCs of the test cohort were 0.985, 0.971 and 0.958, respectively. In the classification task, the AUC of the DL framework was 0.987. Conclusion: ML decision classifiers and DL framework constructed from CT-based radiomics features offered a non-invasive method to differentiate neonatal adrenal hematoma from neuroblastoma and performed better than the clinical experts.

Tumor Suppressive Role of MUC6 in Wilms Tumor via Autophagy-Dependent ß-Catenin Degradation.

Wilms tumor is the most common renal malignancy in children. Known gene mutations account for about 40% of all wilms tumor cases, but the full map of genetic mutations in wilms tumor is far from clear. Whole genome sequencing and RNA sequencing were performed in 5 pairs of wilms tumor tissues and adjacent normal tissues to figure out important genetic mutations. Gene knock-down, CRISPR-induced mutations were used to investigate their potential effects in cell lines and in-vivo xenografted model. Mutations in seven novel genes (MUC6, GOLGA6L2, GPRIN2, MDN1, MUC4, OR4L1 and PDE4DIP) occurred in more than one patient. The most prevalent mutation was found in MUC6, which had 7 somatic exonic variants in 4 patients. In addition, TaqMan assay and immunoblot confirmed that MUC6 expression was reduced in WT tissues when compared with control tissues. Moreover, the results of MUC6 knock-down assay and CRISPR-induced MUC6 mutations showed that MUC6 inhibited tumor aggression via autophagy-dependent ß-catenin degradation while its mutations attenuated tumor-suppressive effects of MUC6. Seven novel mutated genes (MUC6, GOLGA6L2, GPRIN2, MDN1, MUC4, OR4L1 and PDE4DIP) were found in WT, among which MUC6 was the most prevalent one. MUC6 acted as a tumor suppressive gene through autophagy dependent ß-catenin pathway.

Single-Cell Characterization of Malignant Phenotypes and Developmental Trajectories of Adrenal Neuroblastoma.

Neuroblastoma (NB), which is a subtype of neural-crest-derived malignancy, is the most common extracranial solid tumor occurring in childhood. Despite extensive research, the underlying developmental origin of NB remains unclear. Using single-cell RNA sequencing, we generate transcriptomes of adrenal NB from 160,910 cells of 16 patients and transcriptomes of putative developmental cells of origin of NB from 12,103 cells of early human embryos and fetal adrenal glands at relatively late development stages. We find that most adrenal NB tumor cells transcriptionally mirror noradrenergic chromaffin cells. Malignant states also recapitulate the proliferation/differentiation status of chromaffin cells in the process of normal development. Our findings provide insight into developmental trajectories and cellular states underlying human initiation and progression of NB.