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Objective: To investigate the association between high-sensitivity C-reactive protein (Hs-CRP) levels and the risk of postoperative delirium (POD) following general anesthesia. Methods: This retrospective cross-sectional study included 644 patients who underwent general anesthesia. Univariate and multivariate logistic regression analyses were performed to evaluate the relationship between Hs-CRP and POD, with subgroup analyses used to assess stratified associations. Receiver operator characteristic (ROC) curve analysis was employed to assess the predictive efficacy of Hs-CRP for POD. Restricted cubic spline (RCS) analysis was conducted to explore the linear relationship between the log-transformed Hs-CRP (Log10Hs-CRP) and POD risk. Results: The total population consisted of 644 individuals with a mean age of 64.02 ± 13.20 years, 506 (78.60%) of whom were male, and 114 patients (17.7%) had POD. Compared to the lower Hs-CRP group, patients in the higher Hs-CRP group exhibited higher age, heart rate, white blood cell count, blood urea nitrogen, creatinine, uric acid, fasting glucose, hemoglobin A1c, fibrinogen, D-dimer, and a higher prevalence of CKD, but lower hemoglobin, high-density lipoprotein cholesterol, albumin and estimated glomerular filtration rate. Additionally, the prevalence of POD was higher in the higher Hs-CRP group (24.7% vs. 9.5%, p < 0.001). Multivariate logistic regression confirmed that elevated Hs-CRP and its forms (Log10Hs-CRP, standardized Hs-CRP, and higher Hs-CRP group) consistently increased the risk of POD across all adjusted models (p < 0.05). Stratified analyses further highlighted significant associations between Hs-CRP and POD in specific subgroups, notably in patients aged ≥65 years, female patients, and those with or without hypertension, diabetes, or stroke history, and without chronic kidney disease (p < 0.05). ROC curve analysis demonstrated that Hs-CRP had a significant predictive ability for POD in the overall population (AUC = 0.646), as well as in male (AUC = 0.644) and female patients (AUC = 0.654). Additionally, RCS analysis indicated a linear positive association between Log10Hs-CRP and POD risk (p = 0.003, nonlinear p = 0.896). Conclusion: Elevated Hs-CRP levels are significantly associated with an increased risk of POD following general anesthesia. [ABSTRACT FROM AUTHOR]

This study focuses on the renewal and iteration mechanisms of aged tea trees in interactions with their soil microbial communities, aiming to elucidate the impact of the planting age of tea trees on the structure and function of soil microbial communities and how these impacts are linked to the formation of tea quality. By conducting a comparative analysis of the cultivation soil from tea trees with varying planting ages ranging from 30 to 200 years, we employed microbial diversity sequencing, a soil physicochemical property analysis, and tea leaf chemical component detection. We combined these methods with redundancy analysis (RDA) and linear discriminant analysis effect size (LEfSe) to reveal significant correlations between the planting age of tea trees and the soil's microbial diversity and function. The results indicate that as the planting age of tea trees increases, there are significant changes in the soil's pH and nutrient content. Concurrently, the components of the tea leaves also change. Most notably, around the 120 years mark of the tea tree planting age, the diversity of the soil microbial community reaches a turning point. Key microbial community analyses revealed shifts in the dominant microbial populations within the soil across the various tea tree planting ages, exemplified by taxa such as Hygrocybe Mycena, Humicola, Bradyrhizobium, and Candidatus Solibacter. These alterations in microbial communities are closely associated with soil nutrient dynamics and the developmental stages of tea trees. These findings not only provide scientific guidance for tea garden management, tea tree cultivation, and tea production but also offer new insights into the impact of tea tree–soil–microbe interactions on tea quality, which is significantly important for enhancing tea quality. [ABSTRACT FROM AUTHOR]

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques. [ABSTRACT FROM AUTHOR]

We aim to examine and reestablish the correlational and linear regression relationships, as well as the predictive value, between the significant facial and tongue features and the metabolic parameters in type 2 diabetes mellitus (T2DM). Materials and Methods: From March to May 2024, we studied 269 patients with T2DM in the endocrinology department of Shanghai Pudong Hospital. The patients' facial and tongue characteristics were sampling by a tongue imaging device equipped with artificial intelligence (AI) (XiMaLife, Sinology, China) of automated and advanced machine learning algorithms. Then, the imaging features were examined in relation to the blood examination. Results: Multiple facial and tongue features, as well as dimensional facial and tongue color parameters, were significantly correlated with glycated hemoglobin A1c (HbA1c) (r < 0.3, p < 0.05), glycated albumin (GA) (− 0.20 < 0.30, p < 0.05), C-peptide (− 0.20.20, p < 0.05), plasma insulin (r < 0.30, p < 0.05), fasting plasma glucose (FPG) (r < 0.3, p < 0.05), significant hepatic and renal function indicators (− 0.30 < r < 0.20, p< 0.05), cardiac injury markers (− 0.30 < r < 0.30, p < 0.05), tumor markers (− 0.5 < r < 0.5, p < 0.05), thyroid function (− 0.15 < r < 0.55, p < 0.05), and blood cell count, including white blood cells (r < 0.2, p < 0.05), and hemoglobin (Hb) (− 0.30 < r < 0.3, 0.0001. The correlational results demonstrated that the tongue's characteristics and signs may be linked with the dynamic of the metabolic status of T2DM. In order to examine the causal relationships, we performed linear regression analyses, which revealed that various facial and tongue imaging parameters partially determined the metabolic indicators. The predictive value of imaging features was evaluated by receiver operating characteristic curve (ROC) to assess metabolic status in T2DM. Conclusion: This study demonstrated that metabolic status, renal and hepatic, cardiac, and thyroid function, the proportion of blood cells, and Hb in T2DM were intimately associated with facial and tongue features. The precise analysis of facial and tongue features through AI and advanced machine learning could be used to predict T2DM's conditions and progression. [ABSTRACT FROM AUTHOR]

Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model's learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan's ecological tea industry and ensures the healthy growth of the tea industry. [ABSTRACT FROM AUTHOR]

Grading tea leaves efficiently in a natural environment is a crucial technological foundation for the automation of tea-picking robots. In this study, to solve the problems of dense distribution, limited feature-extraction ability, and false detection in the field of tea grading recognition, an improved YOLOv8n model for tea grading and counting recognition was proposed. Firstly, the SPD-Conv module was embedded into the backbone of the network model to enhance the deep feature-extraction ability of the target. Secondly, the Super-Token Vision Transformer was integrated to reduce the model's attention to redundant information, thus improving its perception ability for tea. Subsequently, the loss function was improved to MPDIoU, which accelerated the convergence speed and optimized the performance. Finally, a classification-positioning counting function was added to achieve the purpose of classification counting. The experimental results showed that, compared to the original model, the precision, recall and average precision improved by 17.6%, 19.3%, and 18.7%, respectively. The average precision of single bud, one bud with one leaf, and one bud with two leaves were 88.5%, 89.5% and 89.1%. In this study, the improved model demonstrated strong robustness and proved suitable for tea grading and edge-picking equipment, laying a solid foundation for the mechanization of the tea industry. [ABSTRACT FROM AUTHOR]

The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm. [ABSTRACT FROM AUTHOR]