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.

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

Cuproptosis, a recently discovered form of regulated cell death involving copper ion metabolism, has emerged as a promising approach for tumor therapy. This pathway not only directly eliminates tumor cells but also promotes immunogenic cell death (ICD), reshaping the tumor microenvironment (TME) and initiating robust anti-tumor immune responses. However, translating cuproptosis-based therapies into clinical applications is hindered by challenges, including complex metabolic regulation, TME heterogeneity, and the precision required for effective drug delivery. To address these limitations, nanoparticles offer transformative solutions by providing precise delivery of cuproptosis-inducing agents, controlled drug release, and enhanced therapeutic efficacy through simultaneous modulation of metabolic pathways and immune responses. This review systematically discusses recent advancements in nanoparticle-based cuproptosis delivery systems, highlighting nanoparticle design principles and their synergistic effects when integrated with other therapeutic modalities such as ICB, PTT, and CDT. Furthermore, we explore the potential of cuproptosis-based nanomedicine for personalized cancer treatment by emphasizing strategies for TME stratification and therapeutic optimization tailored to patient profiles. By integrating current insights from metabolic reprogramming, tumor immunotherapy, and nanotechnology, this review aims to facilitate the clinical translation of cuproptosis nanomedicine and significantly contribute to the advancement of precision oncology.

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*

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.

ObjectiveTo investigate the trends in diabetes mortality and potential years of life lost (PYLL) among residents of Huangpu District, Shanghai from 1993 to 2021, to analyze the long-term trends of diabetic patients with different characteristics and to provide a reference for scientific prevention and control of diabetes in aging urban areas. MethodsDiabetes mortality data were obtained from the Huangpu District cause of death registration records in the Shanghai death cause registration system. Indicators such as crude mortality rate, standardized mortality rate, potential years of life lost (PYLL), average years of life lost (AYLL), annual percentage change (APC), and average annual percentage change (AAPC) were used to analyze diabetes-related mortality and life loss. Statistical analyses were performed using software SPSS 21.0 and Joinpoint 5.0.2. ResultsFrom 1993 to 2021, the average annual crude mortality rate of diabetes in Huangpu District was 46.56/100 000, and the average annual standardized mortality rate was 20.44/100 000. The crude mortality rate and standardized mortality rate of diabetes for female residents were higher than those for males. The crude mortality rate showed an overall increasing trend [AAPC=2.81% (95%CI: 0.20%‒5.49%), P<0.05], while the increase in standardized mortality rate significantly slowed [AAPC=0.15% (95%CI: -2.27%‒2.63%)], P<0.05]. The mortality rate rose rapidly in the 70‒74 years age group and peaked in the 85‒ years age group (607.69/100 000). Diabetes accounted for a cumulative PYLL of22 741 person-years, with an average annual AYLL of 1.88 years and an average annual potential years of life lost rate (PYLLR) of 0.82‰. Male residents had higher PYLL, AYLL, and PYLLR than females. ConclusionDiabetes mortality rates in Huangpu District have increased year by year, resulting in significant life loss. However, the age-standardized mortality rate increase has markedly slowed. Efforts should focus on elderly diabetic patients aged ≥70 years, by leveraging platforms such as community-based chronic disease health support centers, efforts should be made to enhance diabetes screening service for middle-aged and elderly residents. Consequently, elderly diabetic patients’ awareness of diabetes and responce to related complications is improved, which would be conducive to controling the progression of complications and reducing the mortolity risk of diabetes.

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.