OBJECTIVE To analyze the prototype components absorbed into blood and brain of Lithocarpus litseifolius leaves， so as to provide a reference for clarifying the pharmacological material basis of its prevention and treatment of central nervous system dis eases. METHODS The ethanol extract of L. litseifolius leaves， as well as the gastric lavage fluid and perfusion solution were prepared. Using rats as subjects， plasma samples of intestinal wall metabolism， intestinal flora metabolism and hepatic metabolism were prepared via in situ intestinal perfusion and closed intestinal loop method； while comprehensive metabolic plasma samples， brain tissue samples， and cerebrospinal fluid samples were collected after intragastric administration. UPLC-HRMS technology was utilized to analyze and identify chemical components and prototype components absorbed into blood and brain of L. litseifolius leaves. RESULTS A total of 66 chemical constituents were identified in L. litseifolius leaves， primarily consisting of flavonoids， organic acids， and others. A total of 16， 13， 11， and 5 prototype components were identified in intestinal wall metabolism， intestinal flora metabolism， hepatic metabolism， and comprehensive metabolic plasma samples， respectively. Additionally， 4 prototype components were detected in brain tissue and 9 in cerebrospinal fluid. Phloridzin， trilobatin， phloretin-2- O -malonyl hexoside， and phloretin were identified as common components across all sample types. CONCLUSIONS Prototype components absorbed into blood and brain of L. litseifolius leaves， such as phloridzin， trilobatin， phloretin， and other components may serve as the pharmacological material basis for their therapeutic effects on central nervous system diseases.

BACKGROUND:Currently,treatment method for knee osteoarthritis includes oral medicine,joint cavity drug injection,and physiotherapy,but the curative effect is limited.Existing studies have confirmed that silicon-based bioceramics can promote cartilage and subchondral bone repair and vascular regeneration.OBJECTIVE:To explore the effect of different concentrations of silicon-based bioceramics injected into the knee joint cavity in the treatment of knee osteoarthritis in rats.METHODS:Silicon-based bioceramics-calcium silicate was prepared.Twenty-five SD rats were randomly divided into five groups,with five rats in each group.The healthy group did not receive any intervention,and the modeling group,low-dose calcium silicate group,high-dose calcium silicate group,and saline group used anterior cruciate ligament transection to establish bilateral knee osteoarthritis models.Four weeks after modeling,0.05 mL of 50 and 100 mg/mL calcium silicate solution were injected into the knee joint cavity in the low-dose calcium silicate group and high-dose calcium silicate group,respectively,and 0.05 mL of saline was injected into the knee joint cavity in the saline group,once a week for 4 consecutive weeks.In the fifth week of administration,bilateral knee joint Micro-CT detection,knee joint cartilage hematoxylin-eosin staining,and modified Mankin score were performed.RESULTS AND CONCLUSION:(1)Micro-CT quantitative analysis showed that compared with the healthy group,the volume fraction and number of trabeculae of the medial tibial plateau in the modeling group decreased(P＜0.05),and the separation of trabeculae increased(P＜0.05).Compared with the modeling group,the volume fraction and number of trabeculae of the medial tibial plateau in the low-dose calcium silicate group and the saline group increased(P＜0.05),and the separation of trabeculae decreased(P＜0.05).(2)Hematoxylin-eosin staining showed that the cartilage surface of the healthy group and the low-dose calcium silicate group was relatively smooth and flat,the chondrocytes were evenly distributed,without clustered chondrocytes,the tide line was complete,and the staining was uniform;the cartilage surface of the high-dose calcium silicate group was slightly uneven,the middle and deep cells were disordered,with a small number of clustered chondrocytes,the tide line was discontinuous,and the staining was uneven;the cartilage surface of the saline group and the modeling group was obviously rough,the cells were disordered,with a large number of clustered chondrocytes,the tide line disappeared,and the staining was uneven.The modified Mankin score of the healthy group was lower than that of the high-dose calcium silicate group,the saline group,and the modeling group(P＜0.05).The modified Mankin score of the high-dose calcium silicate group and the low-dose calcium silicate group was lower than that of the saline group and the modeling group(P＜0.05).(3)The results show that calcium silicate knee joint injection has a certain effect in the treatment of knee osteoarthritis.Compared with 100 mg/mL calcium silicate solution,50 mg/mL calcium silicate solution can promote the recovery of subchondral bone and cartilage.

BACKGROUND:Currently,treatment method for knee osteoarthritis includes oral medicine,joint cavity drug injection,and physiotherapy,but the curative effect is limited.Existing studies have confirmed that silicon-based bioceramics can promote cartilage and subchondral bone repair and vascular regeneration.OBJECTIVE:To explore the effect of different concentrations of silicon-based bioceramics injected into the knee joint cavity in the treatment of knee osteoarthritis in rats.METHODS:Silicon-based bioceramics-calcium silicate was prepared.Twenty-five SD rats were randomly divided into five groups,with five rats in each group.The healthy group did not receive any intervention,and the modeling group,low-dose calcium silicate group,high-dose calcium silicate group,and saline group used anterior cruciate ligament transection to establish bilateral knee osteoarthritis models.Four weeks after modeling,0.05 mL of 50 and 100 mg/mL calcium silicate solution were injected into the knee joint cavity in the low-dose calcium silicate group and high-dose calcium silicate group,respectively,and 0.05 mL of saline was injected into the knee joint cavity in the saline group,once a week for 4 consecutive weeks.In the fifth week of administration,bilateral knee joint Micro-CT detection,knee joint cartilage hematoxylin-eosin staining,and modified Mankin score were performed.RESULTS AND CONCLUSION:(1)Micro-CT quantitative analysis showed that compared with the healthy group,the volume fraction and number of trabeculae of the medial tibial plateau in the modeling group decreased(P＜0.05),and the separation of trabeculae increased(P＜0.05).Compared with the modeling group,the volume fraction and number of trabeculae of the medial tibial plateau in the low-dose calcium silicate group and the saline group increased(P＜0.05),and the separation of trabeculae decreased(P＜0.05).(2)Hematoxylin-eosin staining showed that the cartilage surface of the healthy group and the low-dose calcium silicate group was relatively smooth and flat,the chondrocytes were evenly distributed,without clustered chondrocytes,the tide line was complete,and the staining was uniform;the cartilage surface of the high-dose calcium silicate group was slightly uneven,the middle and deep cells were disordered,with a small number of clustered chondrocytes,the tide line was discontinuous,and the staining was uneven;the cartilage surface of the saline group and the modeling group was obviously rough,the cells were disordered,with a large number of clustered chondrocytes,the tide line disappeared,and the staining was uneven.The modified Mankin score of the healthy group was lower than that of the high-dose calcium silicate group,the saline group,and the modeling group(P＜0.05).The modified Mankin score of the high-dose calcium silicate group and the low-dose calcium silicate group was lower than that of the saline group and the modeling group(P＜0.05).(3)The results show that calcium silicate knee joint injection has a certain effect in the treatment of knee osteoarthritis.Compared with 100 mg/mL calcium silicate solution,50 mg/mL calcium silicate solution can promote the recovery of subchondral bone and cartilage.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

The research on the cardiovascular toxicity of air pollutants is in urgent need of collaborative innovation across multiple models. This paper systematically reviewed the advantages and limitations of four principal research models of cardiotoxicity, including epidemiological model, mammalian model, zebrafish model, and in vitro model. Epidemiological models have been used to demonstrate a significant correlation between exposure to PM_2.5 and both the incidence and mortality of cardiovascular diseases within populations; however, these models face challenges in establishing causal inferences and interpreting individual mechanisms. Mammalian models have been applied to elucidate the pathogenic mechanisms of PM_2.5 at both the systemic and organ-specific levels, yet they encounter difficulties related to interspecies differences and throughput constraints. Zebrafish models, with their transparent embryos and observable development, offer a distinctive opportunity for high-throughput screening and mechanistic investigation of PM_2.5-induced cardiac developmental toxicity. Nonetheless, their cardiac physiological structure diverges from that of mammals, limiting their capacity to accurately model chronic conditions such as coronary heart disease. In vitro models, particularly human heart organoids and chip technologies, have provided profound insights into the direct toxic mechanisms of PM_2.5, including disruptions in calcium homeostasis, cellular senescence, and electrophysiological irregularities at the cellular and molecular levels. Despite these advancements, the complexity and developmental maturity of these models present challenges to their broader application. This paper proposed that the key to overcoming the bottlenecks of single models lies in the construction of an integrated evaluation system that combines “epidemiological studies, mammalian models, zebrafish models, and in vitro models”. By focusing on three aspects, namely model integration, technological convergence, and policy support, it is intended to collaboratively address issues such as standardization of multi-model data, simulation of complex exposure scenarios and susceptible life stages, and transformation pathways. This will provide innovative methodological support for the analysis of the cardiotoxic mechanisms of air pollutants, the assessment of environmental health impacts, and the formulation of precise prevention and control strategies.

Objective Focusing on gynecological surgery,we constructed a prediction model for surgical duration by extracting features from unstructured surgical planning texts and integrating multimodal data via artificial intelligence technology.Methods The clinical data of 34 614 patients who underwent gynecologic surgeries at West China Second University Hospital,Sichuan University between January 2022 and October 2024 were collected.An embedding-transformer model was constructed to convert surgical planning texts into a one-dimensional numerical feature,referred to as the step feature.The predictive value of the step feature was assessed by comparing the performance improvements of linear regression,random forest,eXtreme Gradient Boosting(XGBoost),support vector regression,K-nearest neighbor regression,and artificial neural network algorithms in two scenarios—with and without the step feature as an input.The out-of-sample prediction accuracy of the models was assessed using mean absolute error(MAE),root mean squared error(RMSE),and R-squared(R2).Furthermore,the model interpretability was examined using SHapley Additive exPlanations(SHAP)values.Results SHAP results showed that the step feature had the highest predictive contribution.Temporal factors in surgical scheduling also influenced gynecological surgery duration.The XGBoost model demonstrated optimal performance on the test set,significantly improving prediction accuracy with a 40.43%increase in R2,while reducing MAE and RMSE by 21.27%and 20.13%,respectively,compared to the baseline model without the step feature.Conclusion The embedding-transformer model developed in this study effectively extracts features from surgical planning texts and enhances the predictive performance of machine learning models.The XGBoost prediction model can assist hospital administrators in implementing more refined management of gynecological surgeries and improving the utilization efficiency of surgical resources.