An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Recycling of waste printed circuit boards (WPCBs) is an important subject not only for the protection of the environment, but also for the recovery of valuable materials, especially copper. In this paper, an effective and benign process for copper recovery from WPCBs is presented. In the process, copper in the WPCBs was treated through suspension electrolysis to gain copper powder. The effects of process parameters of H2SO4 and CuSO4 concentrations and the current density were studied. Experimental results showed that the suspension electrolysis process is an effective method to recover copper from WPCBs. The X-ray diffraction (XRD) and electronic differential system (EDS) spectra indicated no other impurities in the recycled copper. The copper recovery conditions were optimized using a Box-Behnken design of response surface methodology. The optimum conditions were observed when the solution contained 30 g/L CuSO4, 110 g/L H2SO4, and current density of 3 A/dm2, resulting in a recovery efficiency of 86.46%. The results indicate the process as a promising method to recover copper from WPCBs.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
AbstractBackground Vaginal birth after cesarean section（VBAC） is recommended by international and domestic guidelines or expert consensuses.However ,no valid tools can exactly predict who can succeed in trying vaginal birth among eligible women with a history of cesarean section.Machine learning is gradually used to develop models in obstetrics and midwifery.This study aimed to develop an explainable machine learning model to predict the chance of successful VBAC. Methods The data were collected to establish 7 predicting models from two tertiary hospitals in Guangdong province of China.Training and internal validation data were collected from the First Dongguan Affiliated Hospital Of Guangdong Medical University from January 2012 to December 2022.External validation data were collected from Shenzhen Longhua District Central Hospital from Januray 2011 to December 2017. 7 predicting models based on machine learning were developed and evaluated by area under the operating characteristic curve (AUC).The optimal one was picked out from 7 models according to its AUC and other indices.The outcome of the predictive model was interpreted by Shapley Additive exPlanations(SHAP). Results A total of 2438 pregnant women with trial of labor after cesarean （TOLAC）were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.725 (95% CI: 0.653–0.792), the accuracy for 0.611 (95% CI: 0.557–0.672), sensitivity 0.69 (95% CI: 0.551–0.829), and specificity 0.69 (95% CI: 0.72–0.76). Cervical Bishop score and interval of pregnancy showed the greatest impact on successful vaginal birth, according to SHAP results. Conclusion Models based on machine learning algorithms can be used to predict whether a trail of vaginal birth succeeds. CatBoost model showed more significant performance compared with traditional logistic regression and other machine learning algorithms in this study. Cervical Bishop score and interval of pregnancy are important factors for successful VBAC. More researchs still need to be undertaken to promote the accuracy of ML algorithms and overcome their shortcomings.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The high mortality associated with pancytopenia and multi-organ failure resulting from hematopoietic disorders of acute radiation syndrome (h-ARS) creates an urgent need for developing more effective treatment strategies.Here, we showed that bone marrow multipotent mesenchymal stromal cells (BMMSCs) effectively regulate oxidative stress following radiative injury, which might be on account of irradiation-induced elevation of protein levels of CR6-interacting factor 1(CRIF1) and nuclear factor E2-related factor 2(NRF2).Crif1-knockdown BMMSCs presented increased oxidative stress and apoptosis after irradiation, which were partially due to a suppressed antioxidant response mediated by decreased NRF2 nuclear translocation.Co-immunoprecipitation (Co-IP) experiments indicated that CRIF1 interacted with protein kinase C-δ (PKC-δ).NRF2 Ser40 phosphorylation was inhibited in Crif1-deficient BMMSCs even in the presence of three kinds of PKC agonists, suggesting that CRIF1 might co-activate PKC-δ to phosphorylate NRF2 Ser40.After radiative injury, the supporting effect of BMMSCs for the colony forming ability of HSCs in vitro was reduced, and the deficiency of CRIF1 aggravated such damage.Thus, CRIF1 plays an essential role in PKC-δ/NRF2 pathway modulation to alleviate oxidative stress in BMMSCs after irradiative injury, and at some level it may maintain the HSCs-supporting effect of BMMSCs after radiative injuries.
This study investigated the influence of periprocedural hemorrhage and clinical outcomes with an endovascular therapeutic strategy for cerebellar arteriovenous malformations (cAVMs).
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