Fluidized bed photobioreactor based on diatomite powder and high light intensity improved microalgae harvesting, nutrient removal and lipid accumulation: Performance and microscopic mechanism.

Cultivation of microalgae using anaerobic digestate is a gain-win strategy for algal biomass production and achieving environmental benefits. However, the low biomass concentration and high harvest cost of the conventional suspended microalgae culture system are troublesome issues. In this study, a novel fluidized bed photobioreactor (FBPBR) based on diatomite powder was constructed for cultivating Scenedesmus quadricauda and treating diluted anaerobic digestate. The optimized diatomite carrier dosage of 750 mg/L increased microalgal biomass concentration to 1.58 g/L compared to suspended microalgae without carrier (0.99 g/L). When the light intensity was increased from 100 to 200 µmol/m2/s, the microalgal biomass in the FBPBR increased to 1.84 g/L and the settling efficiency increased to 93.58 %. This was due to the 1.60-fold enhancement of extracellular polymeric substance (EPS) secretion and changes in EPS properties. The increase in hydrophobic functional groups of EPS under high light intensity, coupled with the reconstitution of protein secondary structure, facilitated the initial attachment of algae to diatomite and the thickening of microalgal biofilm. Moreover, transcriptomic analysis demonstrated that diatomite promoted antioxidant defense and photosynthesis in S. quadricauda cells, alleviating the adverse effect of anaerobic digestate stress. The diatomite addition and elevated light intensity contributed to the highest lipid content (60.37 %), which was owing to the upregulated genes encoding fatty acid and triacylglycerol synthesis under the stress of localized nutrient starvation in the inner layer of microalgae biofilms. Furthermore, the regulation of phosphorus metabolism and NH4+-N assimilation improved nutrient removal (93.24 % and 96.86 % for NH4+-N and TP removal). This work will provide guidance for the development of FBPBR based on diatomite powder.

Nomogram for the preoperative prediction of Ki-67 expression and prognosis in stage IA lung adenocarcinoma based on clinical and multi-slice spiral computed tomography features.

OBJECTIVE: This study developed and validated a nomogram utilizing clinical and multi-slice spiral computed tomography (MSCT) features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma. Additionally, we assessed the predictive accuracy of Ki-67 expression levels, as determined by our model, in estimating the prognosis of stage IA lung adenocarcinoma. MATERIALS AND METHODS: We retrospectively analyzed data from 395 patients with pathologically confirmed stage IA lung adenocarcinoma. A total of 322 patients were divided into training and internal validation groups at a 6:4 ratio, whereas the remaining 73 patients composed the external validation group. According to the pathological results, the patients were classified into high and low Ki-67 labeling index (LI) groups. Clinical and CT features were subjected to statistical analysis. The training group was used to construct a predictive model through logistic regression and to formulate a nomogram. The nomogram's predictive ability and goodness-of-fit were assessed. Internal and external validations were performed, and clinical utility was evaluated. Finally, the recurrence-free survival (RFS) rates were compared. RESULTS: In the training group, sex, age, tumor density type, tumor-lung interface, lobulation, spiculation, pleural indentation, and maximum nodule diameter differed significantly between patients with high and low Ki-67 LI. Multivariate logistic regression analysis revealed that sex, tumor density, and maximum nodule diameter were significantly associated with high Ki-67 expression in stage IA lung adenocarcinoma. The calibration curves closely resembled the standard curves, indicating the excellent discrimination and accuracy of the model. Decision curve analysis revealed favorable clinical utility. Patients with a nomogram-predicted high Ki-67 LI exhibited worse RFS. CONCLUSION: The nomogram utilizing clinical and CT features for the preoperative prediction of Ki-67 expression in stage IA lung adenocarcinoma demonstrated excellent performance, clinical utility, and prognostic significance, suggesting that this nomogram is a noninvasive personalized approach for the preoperative prediction of Ki-67 expression.

Comprehensive molecular analyses of a 7-m6A-related lncRNAs signature for prognosis, tumor immunity and therapeutic effect in patients with hepatocellular carcinoma.

Hepatocellular carcinoma is the most common form of liver tumor. m6A modification and noncoding RNA show indispensable roles in HCC. We sought to establish and verify an appropriate m6A-related long noncoding RNA prognostic tool for predicting hepatocellular carcinoma progression. We extracted the RNA expression levels and the clinicopathologic data from GTEx and TCGA databases. Multivariate Cox regression analysis and receiver operating characteristic curves were performed to test the model's predictive ability. We further built a nomogram for overall survival according to the risk score and clinical features. A competing endogenous RNA network and Gene Ontology assessment were implemented to identify related biological mechanisms and processes. By bioinformatics analysis, a risk model comprising GABPB1-AS1, AC025580.1, LINC01358, AC026356.1, AC009005.1, HCG15, and AC026368.1 was built to offer a prognostic prediction for hepatocellular carcinoma independently. The prognostic tool could better prognosticate hepatocellular carcinoma patients' survival than other clinical characteristics. Then, a nomogram with risk score and clinical characteristics was created, which had strong power to calculate the survival probability in hepatocellular carcinoma. The immune-associated processes involving the differentially expressed genes between the two subgroups were displayed. Analyses of prognosis, clinicopathological characteristics, tumor mutation burden, immune checkpoint molecules, and drug response showed significant differences among the two risk subtypes, hinting that the model could appraise the efficacy of immunotherapy and chemotherapy. The tool can independently predict the prognosis in patients with hepatocellular carcinoma, which benefits drug selection in hepatocellular carcinoma patients.