Explore the vast range of titles available.

Background The importance of engaging parents in health research as co‐researchers is gaining growing recognition. While a number of benefits of involving parents as co‐researchers have been proposed, guidelines on exactly how effective engagement can be achieved are lacking. The objectives of this scoping review were to (i) synthesize current evidence on engaging parents as co‐researchers in health research; (ii) identify the potential benefits and challenges of engaging parent co‐researchers; and (iii) identify gaps in the literature. Methods A scoping literature review was conducted using established methodology. Four research databases and one large grey literature database were searched, in addition to hand‐searching relevant journals. Articles meeting specific inclusion criteria were retrieved and data extracted. Common characteristics were identified and summarized. Results Ten articles were included in the review, assessed as having low‐to‐moderate quality. Parent co‐researchers were engaged in the planning, design, data collection, analysis and dissemination aspects of research. Structural enablers included reimbursement and childcare. Benefits of engaging parent co‐researchers included enhancing the relevance of research to the target population, maximizing research participation and parent empowerment. Challenges included resource usage, wide‐ranging experiences, lack of role clarity and power differences between parent co‐researchers and researchers. Evaluation of parent co‐researcher engagement was heterogeneous and lacked rigour. Conclusions A robust evidence base is currently lacking in how to effectively engage parent co‐researchers. However, the review offers some insights into specific components that may form the basis of future research to inform the development of best practice guidelines.

Abstract Background: Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. Methods: In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. Results: In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962–1.000 in the training cohort and 0.969–0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. Conclusion: ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. Trial Registration: Chictr.org.cn: ChiCTR2200059599.

Traditional Chinese medicine is gaining prominence in lumbar disc herniation (LDH) management, but the mechanisms of its active compounds and their molecular targets remain largely unclear. Herein, we aim to elucidate the therapeutic mechanism of Gentisic acid by investigating its role in regulating S100A9 in LDH. Clinical analysis reveals that S100A9 expression and inflammatory levels correlat positively with LDH severity. S100A9 is found to promote M1 macrophage polarization and impair dorsal root ganglion (DRG) neuronal activity. Mechanistically, Gentisic acid binds to MAPK14, downregulates S100A9 via MAPK14, and then suppresses M1 polarization, enhances neuronal autophagic flux, and improves neuronal viability through the S100A9/Rac1/2 pathway. In vivo experiments demonstrate that Gentisic acid ameliorates disc injury, improves neurological function, and alleviates pain in a rat LDH model, with efficacy comparable to celecoxib. These results suggest that Gentisic acid could alleviate LDH symptoms by modulating macrophage polarization and autophagy through the MAPK14/S100A9/Rac1/2 axis, offering a promising therapeutic strategy for LDH.

Litchi ( Litchi chinensis ) is an economically important fruit tree in southern China and is widely cultivated in subtropical regions. However, irregular flowering attributed to inadequate floral induction leads to a seriously fluctuating bearing. Litchi floral initiation is largely determined by cold temperatures, whereas the underlying molecular mechanisms have yet to be identified. In this study, we identified four CRT/DRE BINDING FACTORS ( CBF ) homologs in litchi, of which LcCBF1 , LcCBF2 and LcCBF3 showed a decrease in response to the floral inductive cold. A similar expression pattern was observed for the MOTHER OF FT AND TFL1 homolog ( LcMFT ) in litchi. Furthermore, both LcCBF2 and LcCBF3 were found to bind to the promoter of LcMFT to activate its expression, as indicated by the analysis of yeast-one-hybrid (Y1H), electrophoretic mobility shift assays (EMSA), and dual luciferase complementation assays. Ectopic overexpression of LcCBF2 and LcCBF3 in Arabidopsis caused delayed flowering and increased freezing and drought tolerance, whereas overexpression of LcMFT in Arabidopsis had no significant effect on flowering time. Taken together, we identified LcCBF2 and LcCBF3 as upstream activators of LcMFT and proposed the contribution of the cold-responsive CBF to the fine-tuning of flowering time.

In mining environments, ensuring image authenticity is critical for safety monitoring. However, current low-light image enhancement methods often fail to balance optimization and fidelity, resulting in suboptimal image quality. Additionally, existing models trained on general datasets do not meet the unique demands of mining environments, which often feature challenging lighting conditions. To address this, we propose Mining Environment Transformer (MEFormer), a high-fidelity low-light image restoration network with efficient computational performance. MEFormer incorporates an innovative cross-scale feature fusion architecture, which facilitates enhanced image restoration across multiple scales. We also present the Mining Environment Low-Light (MELOL) a new dataset that captures the specific low-light conditions found in mining environments, filling the gap in available data. Experiments on public datasets and MELOL demonstrate that MEFormer achieves a 0.05 increase in the SSIM, a PSNR above 25, and an LPIPS score of 0.15. The model processes 10,000 128 × 128 images in just 2.8 s using an Nvidia H100 GPU.