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Leaf color is a key trait that determines the ornamental quality of landscape tree species such as Acer tutcheri, and anthocyanin is the main pigment for red leaf coloration. Red leaf fading significantly reduces the ornamental value of A. tutcheri leaves in the spring; however, the physiological mechanism that causes red leaf discoloration in this species is still unclear. Only the anabolic or degradative metabolism of anthocyanin has been studied in terms of leaf color changes. In this study, leaves from four color-change stages of A. tutcheri during the spring were selected by the average sampling method, which involves measuring and analyzing the pigment content and physiological factors related to anthocyanin metabolism, aiming to clarify the key physiological factors of spring leaf coloration. Our results show that the reduced anthocyanin/chlorophyll ratio was the direct cause of red leaf fading in spring. The carbohydrates synthesized during the growth of juvenile leaves were mainly used for their growth and development, whereas fewer carbon sources were used for the synthesis of anthocyanin. Phenylalanine ammonia-lyase and chalcone isomerase activities increased in the early stages of juvenile leaf development and decreased in the middle and late stages, whereas peroxidase activity continued to increase. The decrease in anthocyanin synthesis-related enzyme activity reduced the accumulation of anthocyanin, whereas the increase in anthocyanin degradation-related enzyme activity accelerated the depletion of anthocyanin. Increasing vacuole pH was a major factor in the degradation of anthocyanin. The physiological characteristics of A. tutcheri leaves during the spring season suggest that anthocyanin is a key factor affecting the presentation of color in juvenile leaves, and red leaf fading in the spring of A. tutcheri is the result of the joint effect of the decrease in anthocyanin anabolic metabolism and the increase in anthocyanin degradative metabolism. [ABSTRACT FROM AUTHOR]

Objective: To establish and validate a predictive formula for calculating the possibility of developing delayed neurological sequelae (DNS) after acute carbon monoxide (CO) poisoning to facilitate better decision-making about treatment strategies.Methods: This study retrospectively enrolled 605 consecutive patients who had been newly diagnosed with CO poisoning from the Central Hospital of Enshi Prefecture between January 1, 2015 and December 31, 2020. The cohort was randomly divided into two subgroups: the development cohort (n = 104) and validation cohort (n = 44). Univariate analysis and backward elimination of multivariate logistic regression were used to identify predictive factors, and a predictive formula was established. The performance was assessed using the area under the curve (AUC), the mean AUC of five-fold cross-validation, and calibration plots.Results: The formula included four commonly available predictors: initial GCS score, duration of exposure, CK, and abnormal findings on MRI. We next created a formula to calculate the risk score for developing DNS: Risk score = -4.54 + 3.35 * (Abnormal findings on MRI = yes) - 0.51 * (Initial GCS score) + 0.65 * (Duration of exposure) + 0.01 * (CK). Then, the probability of developing DNS could be calculated: Probability of DNS = 1/(1 + e Risk score). The model revealed good discrimination with AUC, and mean AUC of fivefold cross-validation in two cohort, and the calibration plots showed good calibration.Conclusions: This study established a prediction predictive formula for predicting developing of DNS, which could facilitate better decision-making about treatment strategies. [ABSTRACT FROM AUTHOR]

Cerebral oedema is a major pathological change of acute carbon monoxide (CO) poisoning, the pathogenesis of which is still unclear. In the aquaporin (AQP) water channel family, AQP1 and AQP4 play critical roles in the progress of cerebral oedema of various neuropathological events. However, their functions in CO poisoning have not been demonstrated. In this study, we investigated the expressions of AQPs and associated mechanisms of brain injury in an acute CO poisoning rat model. Compared with the control injected intraperitoneally with equal volume of air, the dry weight/wet weight (DW/WW) ratio of brain water content, levels of AQP1, AQP4, phosph‐p38 mitogen‐activated protein kinase (p‐p38 MAPK) and astrocyte marker, glial fibrillary acidic protein (GFAP) in the frontal cortex and hippocampal CA1 of acute CO poisoning group significantly increased at 6, 12, 24 hours after CO injection. Intracerebroventricular injection with a p38 MAPK inhibitor, SB203580 (200 µmol/L/kg/d), before CO injection reduced water content in the brain tissues and significantly decreased levels of AQP1, AQP4, p‐p38 MAPK and GFAP. Therefore, our study showed that the overexpressions of AQP1 and AQP4 were involved in the development of CO poisoning‐induced cerebral oedema, which could be attenuated by inhibition of p‐p38 MAPK signalling. Manipulation of AQPs and p38 MAPK may be a new therapeutic strategy for acute CO poisoning. [ABSTRACT FROM AUTHOR]

Objective: To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety., Methods: A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model., Results: 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model., Conclusion: We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jia, Yang, Li, Chen, Wu, Li, Wang, Wang, Liu and Wu.)