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In this study, the phase composition, morphology, and element distribution of Mg impurities in a low-grade titanium slag and in its chlorinated residue after carbochlorination were identified using XRD, SEM-EDS, and MLA. Most Mg impurities existed as anosovite, rutile, titanaugite, and Ti silicate phases before the carbochlorination process. The thermodynamic analysis revealed that the chlorination tendencies of Mg, Ti, Ca, Fe, and Mn oxides in the original titanium slag was much higher than those of Si and Al oxides in the temperature range of 700–850 °C. In addition, the binary phase diagram analysis indicates that the magnesium chloride (MgCl2) byproduct formed Na2MgCl4 eutectic salts in the NaCl-based salt bath. The carbochlorination experiments of the Mg-bearing titanium slag show that the chlorination ratio of magnesium oxide was above 93% under the suitable industrial conditions used for the extraction of titanium from Mg-bearing titanium slag. In addition, the formation of Na2MgCl4 eutectic salts after carbochlorination was confirmed, indicating that the negative effect of MgCl2 on the chlorination process was effectively eliminated. The Mg impurities in the chlorinated residue existed as titanaugite, quartz, chlorite, and rutile phases. Furthermore, we found that the carbochlorination of Mg-bearing titanium slag in chloride media effectively improved the comprehensive utilization efficiency of the Mg-bearing titanium slag. We believe that the proposed carbochlorination mechanism of the Mg-bearing titanium slag will provide thorough understanding and useful guidance on the practical industrial application of Mg-bearing titanium slag.

The current study proposes a novel method of preparing Ti–Al–V alloy by electrochemical co-deposition by adding Ti2O3 and V2O3 into the molten salt system. The influence of chlorination of Ti2O3 and V2O3 in KCl–LiCl–MgCl2–AlCl3 melt was studied by inductively-coupled plasma emission spectrometer (ICP-AES) and X-ray diffractometer (XRD). The results revealed that the optimal chlorination temperature ranged from 600 °C to 700 °C and the main chlorination products were TiCl3, VCl3, and Al2O3 byproduct. The electrochemical behavior of Ti3+, V3+ and Al3+ in a molten AlCl3-containing chloride salt was investigated by cyclic voltammetry. The stepwise reduction in the cathode could be given as: V3+→V2+, Ti3+→Ti2+, V2+→V, Ti2+→Ti, Ti2+/V2+/Al3+→Ti–Al–V alloy. The electrolytic products were also characterized by XRD and scanning electron microscopy (SEM), equipped with energy dispersive spectroscopy (EDS). The results demonstrated that, as metallic Al was used as an anode, a dendritic Al3Ti alloy and Al sheet were obtained after the addition of Ti2O3 into the KCl–LiCl–MgCl2–AlCl3 melt. However, the presence of both Ti2O3 and V2O3 resulted in fishbone-like Al3Ti, Al3Ti0.8V0.2 and Al3Ti0.666V0.333 alloys, as well as dense granules of metallic Al.

Background: The dynamic monitoring of perioperative carcinoembryonic antigen (CEA) is recommended by current colorectal cancer (CRC) guidelines, while the benefits of additional measurements of carbohydrate antigen 19-9 (CA19-9) and carbohydrate antigen 125 (CA125) have remained controversial. Methods: This retrospective longitudinal cohort included 3539 CRC patients who underwent curative resection. Distinct trajectory groups were identified by the latent class growth mixed model. Patients were grouped into subgroups jointly by CEA, CA19-9, and CA125 according to preoperative levels and longitudinal trajectories, respectively. The end points were overall survival (OS) and recurrence-free survival (RFS). Findings: Three distinct trajectory groups were characterized for serum CEA, CA19-9, and CA125: low-stable, early-rising, and later-rising. Jointly, patients were grouped into six preoperative (trajectory) joint groups. Compared with the three-low group, the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) associated with death were 1.87 (1.29-2.70), 3.82 (2.37-6.17), 1.87 (0.97-3.61), 2.81 (1.93-4.11), and 4.99 (2.80-8.86) for the CEA-high, CA19-9-high, CA125-high, two-high, and three-high group, respectively. And compared with the three-stable trajectory group, the corresponding HRs (95% CIs) were 1.59 (1.10-2.30), 1.55 (0.77-3.10), 6.25 (4.02-9.70), 4.05 (2.73-6.02), and 12.40 (5.77-26.70) for the five rising trajectory groups, respectively. Similar associations between joint groups and RFS were observed. Notably, the trajectory joint group still had prognostic significance after adjusting for preoperative levels. The CA19-9-high group (HR: 3.82, 95% CI: 2.37-6.17) was associated with higher risk of death than the two-high group (HR: 2.81, 95% CI: 1.93-4.11). Likewise, for the CA125-rising trajectory group and two-rising trajectory group, the HRs (95% CIs) were 6.13 (3.75-10.00) and 3.99 (2.63-6.05) for death, and 3.08 (2.07-4.58) and 2.10 (1.52-2.90) for recurrence. Interpretation: In addition to CEA, the dynamic measurements of CA19-9 and CA125 are recommended to monitor the prognosis of CRC patients. Funding: National Natural Science Foundation of China [81973147, 82001986, 81960592, 82073569, 81660545].

Background: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. Methods: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. Findings: DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05). Interpretation: MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. Funding: The funding bodies that contributed to this study are listed in the Acknowledgements section.