Search

Recent investigations have shown that the necroptosis of tissue cells in joints is important in the development of osteoarthritis (OA). This study aimed to investigate the potential effects of exogenous skeletal stem cells (SSCs) on the necroptosis of subchondral osteoblasts in OA. Human SSCs and subchondral osteoblasts isolated from human tibia plateaus were used for Western blotting, real-time PCR, RNA sequencing, gene editing, and necroptosis detection assays. In addition, the rat anterior cruciate ligament transection OA model was used to evaluate the effects of SSCs on osteoblast necroptosis in vivo. The micro-CT and pathological data showed that intra-articular injections of SSCs significantly improved the microarchitecture of subchondral trabecular bones in OA rats. Additionally, SSCs inhibited the necroptosis of subchondral osteoblasts in OA rats and necroptotic cell models. The results of bulk RNA sequencing of SSCs stimulated or not by tumor necrosis factor α suggested a correlation of SSCs-derived tumor necrosis factor α-induced protein 3 (TNFAIP3) and cell necroptosis. Furthermore, TNFAIP3-derived from SSCs contributed to the inhibition of the subchondral osteoblast necroptosis in vivo and in vitro. Moreover, the intra-articular injections of TNFAIP3-overexpressing SSCs further improved the subchondral trabecular bone remodeling of OA rats. Thus, we report that TNFAIP3 from SSCs contributed to the suppression of the subchondral osteoblast necroptosis, which suggests that necroptotic subchondral osteoblasts in joints may be possible targets to treat OA by stem cell therapy. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate and compare the performances of the random forest (RF) and support vector regression (SVR) models combined with different feature selection methods, including recursive feature elimination (RFE), simulated annealing feature selection (SAFS), and selection by filtering (SBF) in predicting soil pH in Anhui Province, East China. We also used the ALL original features to build the RF and SVR models as a comparison. A total of 140 samples were selected, following the principles of randomness, uniformity, and representativeness, to consider the combination of landscape elements, such as topography, parent material, and land use. Auxiliary data, including climatic, topographic, and vegetation indexes, were used for predicting soil pH. The results showed that compared with the use the ALL original modeling features (ALL-RF, ALL-SVR), the combination of the three feature selection algorithms with RF and SVR can eliminate some redundant features and effectively improve the prediction accuracy of the soil pH model. For the RF model, the RMSE and the MAE of the calibration of the RFE-RF model were 0.73 and 0.57 and had the highest R2 in four different RF models. The testing set of the RFE-RF model had an R2 of 0.61, which was better than that of the ALL-RF (R2 = 0.45) model and lower than those of the SAFS-RF (R2 = 0.71) and SBF-RF (R2 = 0.69) models. For the SVR model, the RFE-RF model was more robust and had better generalization ability. The accuracy of digital soil mapping can be improved through feature selection. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×10 5 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×10 5 t, with an average annual decrease of 1.22×10 4 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×10 5 , 82.45×10 5 , 82.82×10 5 , and 82.51×10 5 t, respectively.

Skeletal stem cells (SSC) have gained attentions as candidates for the treatment of osteoarthritis due to their osteochondrogenic capacity. However, the immunomodulatory properties of SSC, especially under delivery operations, have been largely ignored. In the study, we found that Pdpn + and Grem1 + SSC subpopulations owned immunoregulatory potential, and the single-cell RNA sequencing (scRNA-seq) data suggested that the mechanical activation of microgel carriers on SSC induced the generation of Pdpn + Grem1 + Ptgs2 + SSC subpopulation, which was potent at suppressing macrophage inflammation. The microgel carriers promoted the YAP nuclear translocation, and the activated YAP protein was necessary for the increased expression of Ptgs2 and PGE 2 in microgels-delivered SSC, which further suppressed the expression of TNF-ɑ, IL-1β and promoted the expression of IL-10 in macrophages. SSC delivered with microgels yielded better preventive effects on articular lesions and macrophage activation in osteoarthritic rats than SSC without microgels. Chemically blocking the YAP and Ptgs2 in microgels-delivered SSC partially abolished the enhanced protection on articular tissues and suppression on osteoarthritic macrophages. Moreover, microgel carriers significantly prolonged SSC retention time in vivo without increasing SSC implanting into osteoarthritic joints. Together, our study demonstrated that microgel carriers enhanced SSC reprogramming towards immunomodulatory phenotype to regulate macrophage phenotype transformation for effectively osteoarthritic therapy by promoting YAP protein translocation into nucleus. The study not only complement and perfect the immunological mechanisms of SSC-based therapy at the single-cell level, but also provide new insight for microgel carriers in stem cell-based therapy., (© 2023 The Authors.)

Premature delivery is one of the direct factors that affect the early development and safety of infants. Its direct clinical manifestation is the change of uterine contraction intensity and frequency. Uterine Electrohysterography(EHG) signal collected from the abdomen of pregnant women can accurately and effectively reflect the uterine contraction, which has higher clinical application value than invasive monitoring technology such as intrauterine pressure catheter. Therefore, the research of fetal preterm birth recognition algorithm based on EHG is particularly important for perinatal fetal monitoring. We proposed a convolution neural network(CNN) based on EHG fetal preterm birth recognition algorithm, and a deep CNN model was constructed by combining the Gramian angular difference field(GADF) with the transfer learning technology. The structure of the model was optimized using the clinical measured term-preterm EHG database. The classification accuracy of 94.38% and F 1 value of 97.11% were achieved. The experimental results showed that the model constructed in this paper has a certain auxiliary diagnostic value for clinical prediction of premature delivery.