Search

Rhodiola, belonging to the Crassulaceae family, is a perennial herbaceous plant genus. There are about 90 Rhodiola species worldwide, some of which have been reported to have medicinal properties. Rhodiola sachalinensis is a perennial medicinal herb within this genus and, in the present study, its chloroplast genome was sequenced, assembled, annotated and compared with 24 other Rhodiola species. The results obtained show that the chloroplast genome of R. sachalinensis is 151 595 bp long and has a CG content of 37.7%. The inverted repeats (IR) region of the Rhodiola chloroplast genome is the most conserved region, with the main differences being observed in the ycf1 and ndhF genes at the IRb‐small single copy boundary, and rps19 and trnH genes at the IRa‐large single copy boundary. Phylogenetic analysis showed that Rhodiola species form two major clades, and species with recorded medicinal properties, clustered together in one branch except for R. dumulosa. Within the genus, R. sachalinensis is most closely related to Rhodiola rosea, although comparative analyses showed that only R. sachalinensis and Rhodiola subopposita contained the psbZ gene, which encodes a highly conserved protein subunit of the Photosystem II core complex. Overall, the present study contributes to the understanding of the chloroplast genome of Rhodiola species, and provides a theoretical basis for the study of their genetic diversity and possible use as medicinal plants.

Abstract Background The association between exposure to PM2.5 chemical constituents and obesity remains to be elucidated, as most studies have used a single measure of obesity. This study aimed to investigate the associations of exposure to PM2.5 chemical constituents with general, abdominal, and visceral obesity, and the mediation effect of physical activity (PA) in the associations. Methods Based on a total of 49,819 adults from the baseline of the Yunnan Behavior and Disease Surveillance cohort (YBDS) in southern China in 2021, we used multiple linear regression, weighted quantile sum regression, and quantile regression to estimate independent and joint effects of PM2.5 chemical constituents on elevated BMI, waist circumference (WC) and visceral adiposity index (VAI) and whether these effects changed in quantiles. Mediation analysis was used to examine whether physical activity acts as a mediator in these pathways. Results Per IQR μg/m3 increase in all PM2.5 chemical constituents was significantly associated with the elevated BMI (β [95% CI]: 0.170 [0.127, 0.214]), WC (0.316 [0.217, 0.415]) and VAI (0.102 [0.075, 0.129]), with the largest weights from OM (53.89, 81.67, and 89.82%, respectively). The effects of PM2.5 chemical constituents on obesity showed an overall upward trend from quantiles 1–4 of BMI, WC, and VAI, especially with a rapid upward trend from the sixth decile of VAI. Reduced PA mediated 3.16, 7.08, and 3.78% of the associations between PM2.5 chemical constituents and elevated BMI, WC, and VAI, respectively. Conclusions Exposure to PM2.5 chemical constituents, especially OM, was significantly associated with increased risks for obesity in adults. The effects of associations increased with obesity severity, with PA playing a mediation role.

Abstract To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann–Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

IntroductionSynaptic failure, a hallmark of Alzheimer's disease (AD), is correlated with reduced levels of synaptic proteins. Though people with Down syndrome (DS) are at markedly increased risk for AD (AD-DS), few studies have addressed synapse dysfunction.MethodsSynaptic proteins were measured in the frontal cortex of DS, AD-DS, sporadic AD cases, and controls. The same proteins were examined in the Dp16 model of DS.ResultsA common subset of synaptic proteins were reduced in AD and AD-DS, but not in DS or a case of partial trisomy 21 lacking triplication of APP gene. Pointing to compromised synaptic function, the reductions in AD and AD-DS were correlated with reduced SNARE complexes. In Dp16 mice reductions in syntaxin 1A, SNAP25 and the SNARE complex recapitulated findings in AD-DS; reductions were impacted by both age and increased App gene dose.DiscussionSynaptic phenotypes shared between AD-DS and AD point to shared pathogenetic mechanisms.

Abstract Background Healthy lifestyles are crucial for preventing chronic diseases. Nonetheless, approximately 90% of Chinese community residents regularly engage in at least one unhealthy lifestyle. Mobile smart devices-based health interventions (mHealth) that incorporate theoretical frameworks regarding behavioral change in interaction with the environment may provide an appealing and cost-effective approach for promoting sustainable adaptations of healthier lifestyles. We designed a randomized controlled trial (RCT) to evaluate the effectiveness of a socioecological model-guided, smart device-based, and self-management-oriented lifestyles (3SLIFE) intervention, to promote healthy lifestyles among Chinese community residents. Methods This two-arm, parallel, cluster-RCT with a 6-month intervention and 6-month follow-up period foresees to randomize a total of 20 communities/villages from 4 townships in a 1:1 ratio to either intervention or control. Within these communities, a total of at least 256 community residents will be enrolled. The experimental group will receive a multi-level intervention based on the socioecological model supplemented with a multi-dimensional empowerment approach. The control group will receive information only. The primary outcome is the reduction of modifiable unhealthy lifestyles at six months, including smoking, excess alcohol consumption, physical inactivity, unbalanced diet, and overweight/obesity. A reduction by one unhealthy behavior measured with the Healthy Lifestyle Index Score (HLIS) will be considered favorable. Secondary outcomes include reduction of specific unhealthy lifestyles at 3 months, 9 months, and 12 months, and mental health outcomes such as depression measured with PHQ-9, social outcomes such as social support measured with the modified Multidimensional Scale of Perceived Social Support, clinical outcomes such as obesity, and biomedical outcomes such as the development of gut microbiota. Data will be analyzed with mixed effects generalized linear models with family and link function determined by outcome distribution and accounting for clustering of participants in communities. Discussion This study will provide evidence concerning the effect of a mHealth intervention that incorporates a behavioral change theoretical framework on cultivating and maintaining healthy lifestyles in community residents. The study will provide insights into research on and application of similar mHealth intervention strategies to promote healthy lifestyles in community populations and settings. Trial registration number ChiCTR2300070575. Date of registration: April 17, 2023. https://www.chictr.org.cn/index.aspx .

The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. The algorithm utilizes the Mish activation function, known for its superior nonlinear performance capability and smoother curve, enabling the neural network to manage more complex challenges. The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. In addition, an improved K-Means clustering algorithm, KMMP, is employed to select appropriate anchor box for fabric defects. The experimental results show: a reduction in the number of parameters by 45.5% and computational volume by 41.0%, along with increases in precision by 3.9%, recall by 7.0%, and mAP by 3.0%. These results indicated that the improved algorithm achieves a more effective balance between detection performance and the requirement for a lightweight solution.

Abstract In recent years, with the increasing incidence of endometrial carcinoma in women of child-bearing age, to decision of whether to preserve patients’ fertility during treatment has become increasingly complex, presenting a formidable challenge for both physicians and patients. Non-fertility-sparing treatment can remove lesions more thoroughly than fertility-sparing treatment. However, patients will permanently lose their fertility. In contrast, fertility-sparing treatment can treat tumors without impairing fertility, but the risk of disease progression is high as compared with non-fertility-sparing treatment. Therefore, it is extremely important to accurately identify patients who are suitable for fertility-sparing treatments. The evaluation of prognostic factors, including myometrial invasion, the presence of lymph node metastases, and histopathological type, is vital for determining whether a patient can receive fertility-sparing treatment. As a non-invasive and quantitative approach, radiomics has the potential to assist radiologists and other clinicians in determining more precise judgments with regard to the above factors by extracting imaging features and establishing predictive models. In this review, we summarized currently available fertility-sparing strategies and reviewed the performance of radiomics in predicting risk factors associated with fertility-sparing treatment. This review aims to assist clinicians in identifying patients suitable for fertility-sparing treatment more accurately and comprehensively and informs more appropriate and rigorous treatment decisions for endometrial cancer patients of child-bearing age. Critical relevance statement: Radiomics is a promising tool that may assist clinicians identify risk factors about fertility-sparing more accurately and comprehensively. Graphical abstract