Bone marrow mesenchymal stem cells (BMSCs) are widely used for therapeutic applications in tissue engineering and regenerative medicine. Nevertheless, the function of BMSCs is adversely affected by ...senescence. Thus, understanding the molecular mechanisms that contribute to BMSC senescence is critical for the development of BMSC-based tissue engineering and regenerative medicine. In this study, senescent BMSCs were characterized with >80 % of BMSCs stained positive for SA-β-gal, increased expressions of senescence-related genes (p16INK4a and p21Waf1). These senescent characters were accompanied by elevated autophagic activity, up-regulation of interleukin 6 (IL-6), IL-8, and FoxO3a. Autophagic activity inhibition alleviated the senescent state with reduced levels of IL-6 and IL-8 during BMSC senescence. The enhanced autophagic activity upregulated the levels of IL-6 and IL-8 which is associated with up-regulation of FoxO3a, and knockdown of FoxO3a reduced IL-6 and IL-8 expression in senescent BMSCs. Therefore, this study indicated the pivotal role of autophagic activity in the expressions of IL-6 and IL-8 during BMSC senescence, which is regulated by FoxO3a.
•Autophagy affects the progression of BMSC senescence.•Autophagic activity modulates the levels of IL-6 and IL-8 during BMSC senescence.•FoxO3a plays a critical role in the expression of IL6 and IL8 that allows autophagy to enhance senescent phenotype of BMSCs.
Abstract
Background
Our aim in this study was to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of coronavirus disease 2019 (COVID-19), which has ...caused a worldwide pandemic.
Methods
COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. The disease prognosis signature of COVID-19 was validated in the test group.
Results
The signature of COVID-19 was combined with the following 5 indicators: neutrophil count, lymphocyte count, procalcitonin, age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P < .001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test, P < .001). The area under the receiver operating characteristic (ROC) curve showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the 5 indicators alone.
Conclusions
The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.
The coronavirus disease 2019 (COVID-19) signature composed of 5 indicators (neutrophil count, lymphocyte count, procalcitonin, older age, and C-reactive protein) was an effective prognostic biomarker that could provide risk assessment and predict the survival probability of patients with COVID-19.
Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale ...conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.
Background Several observational studies have reported an association between gut microbiota and male erectile dysfunction (ED). However, it remains unclear whether there is a causal relationship ...between gut microbiota and male ED. Thus, we aimed to investigate the potential causal relationship between gut microbiota and male ED through Mendelian randomization (MR) analysis. Objective To assess the causal relationship between gut microbiota and male ED, we performed a two-sample MR analysis. Methods We obtained gut microbiota genome-wide association studies (GWAS) data from the MiBioGen consortium and publicly available GWAS data on male ED from the OPEN GWAS database. Subsequently, we performed a two-sample MR analysis to evaluate the causal relationship between gut microbiota and male ED. Finally, we performed sensitivity analysis, including Cochran’s Q test, MR-Egger intercept analysis, MR-PRESSO, and leave-one-out analysis, to assess the level of heterogeneity and horizontal pleiotropy in the results. Results Our MR analysis revealed a negative causal relationship between the genus Ruminococcaceae UCG013 and male ED (OR = 0.761, 95% CI 0.626–0.926), while the family Lachnospiraceae, genus Lachnospiraceae NC2004 group, genus Oscillibacter, and genus Tyzzerella3 may be associated with an increased risk of male ED, with the highest risk observed for family Lachnospiraceae (OR = 1.264, 95% CI 1.063–1.504). Furthermore, sensitivity analysis confirmed the reliability of our positive findings. Conclusion Our MR analysis revealed a causal relationship between gut microbiota and male ED. This may contribute to a better understanding of the potential applications of gut microbiota in the occurrence and treatment of male ED.
To better promote the research and development of regional crop yield simulations, we review related research on regional crop yield simulations over the past ten years, summarize the research ...progress on regional crop yield simulations at home and abroad from the three aspects of crop growth models, remote sensing technology, and data assimilation technology, and propose three future development directions for regional crop yield simulations: (1) Based on the agronomic mechanism of crop yield estimation, it is necessary to fully consider crop growth characteristics and yield formation mechanisms. (2) With respect to the remote sensing mechanism of crop yield estimation, we can consider combining radar stereo lateral view measurement technology to determine crop characteristics and remote sensing information. (3) From the perspective of combining agronomy and remote sensing, the crop yield spatiotemporal simulation assimilation algorithm should be optimized, and the yield simulation unit should be extended. It is expected that these considerations can provide new ideas for regional crop yield simulations with high accuracy, a large scale, and full coverage.
The crop harvest index (HI) is of great significance for research on the application of crop variety breeding, crop growth simulation, crop management in precision agriculture and crop yield ...estimation, among other topics. To obtain spatial information on the crop dynamic HI (D-HI), taking winter wheat as the research object and fully considering the changes in crop biomass and the grain filling process from the flowering period to the maturity period, the dynamic fG (D-fG) parameter was estimated as the ratio between the aboveground biomass accumulated in different growth periods, from the flowering stage to the maturity stage, and the aboveground biomass in the corresponding periods. Based on the D-fG parameter estimation using unmanned aerial vehicle (UAV) hyperspectral remote sensing data, a technical method for obtaining spatial information on the winter wheat D-HI was proposed and the accuracy of the proposed method was verified. A correlation analysis was performed between the normalized difference spectral index (NDSI), which was calculated using pairs of any two bands of the UAV hyperspectral spectrum, and the measured D-fG. Based on this correlation analysis, the center of gravity of the local maximum region of R2 was used to determine the sensitive band center to accurately estimate D-fG. On this basis, remote sensing estimation of the D-fG was realized by using the NDSI constructed by the sensitive hyperspectral band centers. Finally, based on the D-fG remote sensing parameters and the D-HI estimation model, spatial information on the D-HI of winter wheat was accurately obtained. The results revealed five pairs of sensitive hyperspectral band centers (i.e., λ(476 nm, 508 nm), λ(444 nm, 644 nm), λ(608 nm, 788 nm), λ(724 nm, 784 nm) and λ(816 nm, 908 nm)) for D-fG estimation, and the results of the D-fG remote sensing estimation showed high precision. The root mean square error (RMSE) was between 0.0436 and 0.0604, the normalized RMSE (NRMSE) was between 10.31% and 14.27% and the mean relative error (MRE) was between 8.28% and 12.55%. In addition, the D-fG parameter estimation, using the NDSI constructed by the above five sensitive remote sensing band centers, yielded highly accurate spatial D-HI information with an RMSE between 0.0429 and 0.0546, an NRMSE between 9.87% and 12.57% and an MRE between 8.33% and 10.90%. The D-HI estimation results based on the hyperspectral sensitive band centers λ(724 nm, 784 nm) had the highest accuracy, with RMSE, NRMSE and MRE values of 0.0429, 9.87% and 8.33%, respectively. The proposed method of acquiring spatial information on the winter wheat D-HI in this study was shown to be feasible, and it might provide a technical reference toward developing satellite-based indices to monitor large-scale crop HI information.
Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an ...operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.
Background
To study the risk factors of Gleason score upgrading (GSU) after radical prostatectomy (RP) in a Chinese cohort.
Methods
The data of 637 patients who underwent prostate biopsy and RP in ...our hospital from January 2014 to January 2021 were retrospectively analyzed. The age, body mass index (BMI), prostate‐specific antigen (PSA) level, testosterone (TT) level, neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), eosinophil‐to‐lymphocyte ratio (ELR), aspartate aminotransferase/alanine transaminase (AST/ALT) ratio, clinical stage, the biopsy method, and pathological characteristics of specimens after biopsy and RP were collected for all patients. Univariate analysis and multivariate logistic regression analysis were used to analyze the risk factors of GSU after RP. The predictive efficacy was verified with the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. We performed the analysis separately in the overall cohort and in the cohort with Gleason score (GS) = 6.
Results
In the overall cohort, 177 patients (27.79%) had GSU, and in the GS = 6 cohort, 68 patients (60.18%) had GSU. Multivariate logistic regression analysis showed that in the overall cohort, clinical stage ≥T2c (OR = 3.201, p < 0.001), the number of positive cores ≥3 (OR = 0.435, p = 0.04), and positive rate of biopsy (OR = 0.990, p = 0.016) can affect whether GS is upgraded, and the AUC of the combination of the three indicators for predicting the occurrence of GSU was 0.627. In the GS = 6 cohort, multivariate logistic regression analysis showed that clinical stage ≥T2c (OR = 4.690, p = 0.001) was a risk factor for GSU, and the AUC predicted to occur GSU is 0.675.
Conclusion
Clinical stage ≥T2c, the number of positive cores <3, and lower positive rate of biopsy are the risk factors of GSU. This study may provide some references for clinicians to judge the accuracy of biopsy pathological grading and formulate treatment strategies, but the specific effect still needs clinical practice certification.
Clinical stage ≥T2c, the number of positive cores <3, and lower positive rate of biopsy are the risk factors of Gleason score upgrading.
Soil organic carbon stock (SOCS) changes caused by land use changes are still unclear, and understanding this response is essential for many environmental policies and land management practices. In ...this study, we investigated the temporal–spatial and vertical distribution characteristics of the SOCS in the Western Sichuan Plateau (WSP) using the sparrow search algorithm–random forest regression (SSA-RFR) models with excellent model applicability and accuracy. The temporal–spatial variations in the SOCS were modeled using 1080 soil samples and a set of nine environmental covariates. We analyzed the effect of land use changes on the SOCS in the WSP. The total SOCS increased by 18.03 Tg C from 1990 to 2020. The results of this study confirmed a significant increase in the SOCS in the study area since 2010. There was a 27.88 Tg C increase in the SOCS in 2020 compared to the total SOCS in 2010. We found that the spatial distribution of the SOCS increased from southeast to northwest, and the vertical distribution of the SOCS in the study area decreased with increasing soil depth. Forests and grasslands are the main sources of SOCS the total SOCS in the forest and grassland accounted for 37.53 and 59.39% of the total soil organic carbon (SOC) pool in 2020, respectively. The expansion of the wetlands, forest, and grassland areas could increase the SOCS in the study area. A timely and accurate understanding of the dynamics of SOC is crucial for developing effective land management strategies to enhance carbon sequestration and mitigate land degradation.
To explore if switching intravesical chemotherapeutic agents is beneficial in short-term recurrences of high-risk non-muscle-invasive bladder cancer (NMIBC) following the failure of preceding ...intravesical therapy.
From June 2010 to October 2015, 205 patients with NMIBC who experienced tumor recurrence within a year after receiving first-line intravesical chemotherapy (IVC) were classified into two groups. After a second complete transurethral resection (TUR) process, we immediately altered the intravesical instillation agent for 107 patients (group A). In contrast, the remaining 98 patients (group B) continued using their original intravesical instillation agent. After transurethral resection of the bladder tumor (TURBT), all patients received either an immediate instillation of epirubicin (EPI), gemcitabine (GEM), or hydroxycamptothecin (HCPT), followed by regular induction and maintenance instillations. Recurrence and progression rates were evaluated using the Chi-square test, and recurrence-free survival (RFS) and progression-free survival (PFS) were calculated using the Kaplan-Meier method.
In this study, there was no significant difference in either the 5-year tumor recurrence or progression rates between the two groups (p > 0.05) The Kaplan-Meier plot showed no difference in progression-free or recurrence-free survival between the two groups.
Switching IVC agents does not improve RFS and PFS for patients with short-term recurrent high-risk NMIBC.
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