Highly effective removal of 4-chloroaniline in water by nano zero-valent iron cooperated with microbial degradation.

The misuse of aromatic amines like 4-chloroaniline (4-CA) has led to severe environmental and health issues. However, it's difficult to be utilized by microorganisms for degradation. Nano-zero-valent iron (nZVI) is a promising material for the remediation of chloroaniline pollution, however, the synergistic effect and mechanism of nZVI with microorganisms for the degradation of 4-CA are still unclear. This study investigated the potential of 4-CA removal by the synergistic system involving nZVI and 4-CA degrading microbial flora. The results indicate that the addition of nZVI significantly enhanced the bio-degradation rate of 4-CA from 43.13 % to 62.26 %. Under conditions involving 0.1 % nZVI addition at a 24-hour interval, pH maintained at 7, and glucose as an external carbon source, the microbial biomass, antioxidant enzymes, and dehydrogenase were significantly increased, and the optimal 4-CA degradation rate achieved 68.79 %. Additionally, gas chromatography-mass spectrometry (GC-MS) analysis of intermediates indicated that the addition of nZVI reduced compounds containing benzene rings and enhanced the dechlorination efficiency. The microbial community remained stable during the 4-CA degradation process. This study illustrates the potential of nZVI in co-microbial remediation of 4-CA compounds in the environment.

The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery.

Objective: To explore the value of multiparametric magnetic resonance imaging(MRI) radiomics in the preoperative prediction of isocitrate dehydrogenase (IDH) genotype for gliomas. Methods: The preoperative routine MRI sequences of 114 patients with pathologically confirmed grade II-IV gliomas were retrospectively analysed. All patients were randomly divided into training cohort(n=79) and validation cohort(n=35) in the ratio of 7:3. After feature extraction, we eliminated covariance by calculating the linear correlation coefficients between features, and then identified the best features using the F-test. The Logistic regression was used to build the radiomics model and the clinical model, and to build the combined model. Assessment of these models by subject operating characteristic (ROC) curves, area under the curve (AUC), sensitivity and specificity. Results: The multiparametric radiomics model was built by eight selected radiomics features and yielded AUC values of 0.974 and 0.872 in the training and validation cohorts, which outperformed the conventional models. After incorporating the clinical model, the combined model outperformed the radiomics model, with AUCs of 0.963 and 0.892 for the training and validation cohorts. Conclusion: Radiomic models based on multiparametric MRI sequences could help to predict glioma IDH genotype before surgery.

Androgen Metabolism and Response in Prostate Cancer Anti-Androgen Therapy Resistance.

All aspects of prostate cancer evolution are closely related to androgen levels and the status of the androgen receptor (AR). Almost all treatments target androgen metabolism pathways and AR, from castration-sensitive prostate cancer (CSPC) to castration-resistant prostate cancer (CRPC). Alterations in androgen metabolism and its response are one of the main reasons for prostate cancer drug resistance. In this review, we will introduce androgen metabolism, including how the androgen was synthesized, consumed, and responded to in healthy people and prostate cancer patients, and discuss how these alterations in androgen metabolism contribute to the resistance to anti-androgen therapy.