We isolated and characterized a D-lactic acid-producing lactic acid bacterium (D-LAB), identified as Lactobacillus delbrueckii subsp. lactis QU 41. When compared to Lactobacillus coryniformis subsp. torquens JCM 1166 (T) and L. delbrueckii subsp. lactis JCM 1248 (T), which are also known as D-LAB, the QU 41 strain exhibited a high thermotolerance and produced D-lactic acid at temperatures of 50 °C and higher. In order to optimize the culture conditions of the QU 41 strain, we examined the effects of pH control, temperature, neutralizing reagent, and initial glucose concentration on D-lactic acid production in batch cultures. It was found that the optimal production of 20.1 g/l D-lactic acid was acquired with high optical purity (>99.9% of D-lactic acid) in a pH 6.0-controlled batch culture, by adding ammonium hydroxide as a neutralizing reagent, at 43 °C in MRS medium containing 20 g/l glucose. As a result of product inhibition and low cell density, continuous cultures were investigated using a microfiltration membrane module to recycle flow-through cells in order to improve D-lactic acid productivity. At a dilution rate of 0.87 h(-1), the high cell density continuous culture exhibited the highest D-lactic acid productivity of 18.0 g/l/h with a high yield (ca. 1.0 g/g consumed glucose) and a low residual glucose (<0.1 g/l) in comparison with systems published to date.
BackgroundTimely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).MethodsThis was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance.ResultsA total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely.ConclusionsIn this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions.Clinical Trial Registrationhttp://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.
Identifying the mechanism of glioma progression is critical for diagnosis and treatment. Although studies have shown that guanylate-binding protein 2(GBP2) has critical roles in various cancers, its function in glioma is unclear. In this work, we demonstrate that GBP2 has high expression levels in glioma tissues. In glioma cells, depletion of GBP2 impairs proliferation and migration, whereas overexpression of GBP2 enhances proliferation and migration. Regarding the mechanism, we clarify that epidermal growth factor receptor (EGFR) signaling is regulated by GBP2, and also demonstrate that GBP2 interacts directly with kinesin family member 22(KIF22) and regulates glioma progression through KIF22/EGFR signaling in vitro and in vivo. Therefore, our study provides new insight into glioma progression and paves the way for advances in glioma treatment.
Intracranial aneurysm is a severe cerebral disorder involving complicated risk factors and endovascular coiling is a common therapeutic selection for intracranial aneurysm. The recurrence is a clinical challenge in intracranial aneurysms after coil embolization. With this study, we provided a meta-analysis of the risk factors for the recurrence of intracranial aneurysm after coil embolization. Nine studies were included with a total of 1,270 studies that were retrieved from the database. The sample size of patients with intracranial aneurysms ranged from 241 to 3,530, and a total of 9,532 patients were included in the present meta-analysis. The intracranial aneurysms that occurred in middle cerebral artery (MCA) (OR = 1.09, 95% CI: 1.03–1.16, P = 0.0045) and posterior circulation (OR = 2.01, 95% CI: 1.55–2.60, P = 0.000) presented the significantly higher risk of recurrence after coil embolization. Meanwhile, intracranial aneurysms of size > 7 mm (OR = 5.38, 95%CI: 3.76–7.70, P = 0.000) had a significantly higher risk of recurrence after coil embolization. Moreover, ruptured aneurysm (OR = 2.86, 95% CI: 2.02–4.04, P = 0.000) and subarachnoid hemorrhage (SAH) (OR = 1.57, 95% CI: 1.20–2.06, P = 0.001) was positively correlated with the risk of recurrence after coil embolization. In conclusion, this meta-analysis identified the characteristics of intracranial aneurysms with MCA, posterior circulation, size > 7 mm, ruptured aneurysm, and SAH as the risk factors of recurrence after coil embolization for intracranial aneurysms.
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