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Abstract Background The correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear. Methods A retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS. Results The discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52–66) years) and 608 patients (403 (66.28%) males; age 69 (62–76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02). Conclusion Preoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

Selenium-enriched Lentinus edodes (SL) is a kind of edible fungi rich in organic selenium and nutrients. Monascus purpureus with high monacolin K (MK) production and Saccharomyces cerevisiae were selected as the fermentation strains. A single-factor experiment and response surface methodology were conducted to optimize the production conditions for MK with higher contents from selenium-enriched Lentinus edodes fermentation (SLF). Furthermore, we investigated the nutritional components, antioxidant capacities, and volatile organic compounds (VOCs) of SLF. The MK content in the fermentation was 2.42 mg/g under optimal fermentation conditions. The organic selenium content of SLF was 7.22 mg/kg, accounting for 98% of the total selenium content. Moreover, the contents of total sugars, proteins, amino acids, reducing sugars, crude fiber, fat, and ash in SLF were increased by 9%, 23%, 23%, 94%, 38%, 44%, and 25%, respectively. The antioxidant test results demonstrated that 1.0 mg/mL of SLF exhibited scavenging capacities of 40%, 70%, and 79% for DPPH, ABTS, and hydroxyl radicals, respectively. Using gas chromatography–ion mobility spectrometry technology, 34 unique VOCs were identified in SLF, with esters, alcohols, and ketones being the main components of its aroma. This study showed that fungal fermentation provides a theoretical reference for enhancing the nutritional value of SL.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel–rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel–rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.

Abstract Background Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Methods 121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T1WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D17) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate. Results After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. Conclusions These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.

Abstract Background Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction. Methods The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements. Results In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery. Conclusions Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Abstract Background Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis. Methods A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. Results The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively. Conclusion These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.

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.

The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.

This study optimized the slope and slope length factor (LS) and crop management factor (P) of the RUSLE model and then introduced the gravity centre model to analyze the spatial–temporal variation patterns of soil erosion in Yellow River Basin from a new perspective. Results showed that: (1) The improved model of RUSLE with optimized factors of LS and P had better applicability in Yellow River Basin; (2) The average erosion intensity was 2777.5 t/a, which belonged to moderate erosion. The soil erosion intensity of the Yellow River Basin showed an overall trend of increasing firstly (1990–2005) and then decreasing (2005–2015). (3) During 1990–2015, the gravity centre of soil erosion moved to the southwest, indicating that the increment and increasing rate of soil erosion in the southwest parts of the Yellow River Basin were greater than that in the northeast parts. (4) The intensity of soil erosion aggravated with the increasing slope. The sandy soil, chestnut soil, light-grey calcium soil and fluvo aquic soil had severe erosion intensity due to the regional climate and their own physical–chemical structure. The woodland and shrubbery land were more susceptible to soil erosion.

BackgroundSensitivity to neoadjuvant chemotherapy in locally advanced gastric cancer patients varies; however, an effective predictive marker is currently lacking. We aimed to propose and validate a practical treatment efficacy prediction method based on contrast-enhanced computed tomography (CECT) radiomics.MethodData of l24 locally advanced gastric carcinoma patients who underwent neoadjuvant chemotherapy were acquired retrospectively between December 2012 and August 2020 from three different cancer centers. In total, 1216 radiomics features were initially extracted from each lesion’s pretreatment portal venous phase computed tomography image. Subsequently, a radiomics predictive model was constructed using machine learning software. Clinicopathological data and radiological parameters of the enrolled patients were collected and analyzed retrospectively. Univariate and multivariate logistic regression analyses were performed to screen for independent predictive indices. Finally, we developed an integrated model combining clinicopathological predictive parameters and radiomics features.ResultIn the training set, 10 (14.9%) patients achieved a good response (GR) after preoperative neoadjuvant chemotherapy (n = 77), whereas in the testing set, seven (17.5%) patients achieved a GR (n = 47). The radiomics predictive model showed competitive prediction efficacy in both the training and independent external validation sets. The areas under the curve (AUC) values were 0.827 (95% confidence interval [CI]: 0.609–1.000) and 0.854 (95% CI: 0.610–1.000), respectively. Similarly, when only the single hospital data were included as an independent external validation set (testing set 2), AUC values of the models were 0.827 (95% CI: 0.650–0.952) and 0.889 (95% CI: 0.663–1.000) in the training set and testing set 2, respectively.ConclusionOur study is the first to discover that CECT radiomics could provide powerful and consistent predictions of therapeutic sensitivity to neoadjuvant chemotherapy among gastric cancer patients across different hospitals.

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].

BackgroundWhether elevated postoperative serum carcinoembryonic antigen (CEA) levels are prognostic in patients with stage II colorectal cancer (CRC) remains controversial.Patients and MethodsPrimary and sensitivity analysis populations were obtained from a retrospective, multicenter longitudinal cohort including consecutive patients without neoadjuvant treatment undergoing curative resection for stage I–III CRC. Serum CEA levels before (CEApre-m1) and within 1 (CEApost-m1), 2–3 (CEApost-m2–3), and 4–6 months (CEApost-m4–6) after surgery were obtained, and their associations with recurrence-free survival (RFS) and overall survival (OS) were assessed using Cox regression. Sensitivity and subgroup analyses were performed.ResultsPrimary and sensitivity analysis populations included 710 [415 men; age, 54.8 (11.6) years] and 1556 patients [941 men; age, 56.2 (11.8) years], respectively. Recurrence hazard ratios (HRs) in the elevated CEApre-m1, CEApost-m1, CEApost-m2–3, and CEApost-m4–6 groups were 1.30 (95% CI: 0.91–1.85), 1.53 (95% CI: 0.89–2.62), 1.88 (95% CI: 1.08–3.28), and 1.15 (95% CI: 0.91–1.85), respectively. The HRs of the elevated CEApre-m1, CEApost-m1, CEApost-m2–3, and CEApost-m4–6 groups for OS were 1.09 (95% CI: 0.60–1.97), 2.78 (95% CI: 1.34–5.79), 2.81 (95% CI: 1.25–6.30), and 3.30 (95% CI: 1.67–.536), respectively. Adjusted multivariate analyses showed that both in the primary and sensitivity analysis populations, elevated CEApost-m2–3, rather than CEApre-m1, CEApost-m1, and CEApost-m4–6, was an independent risk factor for recurrence, but not for OS. The RFS in the elevated and normal CEApost-m2–3 groups differed significantly among patients with stage II disease [n = 266; HR, 2.89; 95% CI, 1.02–8.24 (primary analysis); n = 612; HR, 2.69; 95% CI, 1.34–5.38 (sensitivity analysis)].ConclusionsElevated postoperative CEA levels are prognostic in patients with stage II CRC, with 2–3 months after surgery being the optimal timing for CEA measurement.

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.

In this study, the adsorption mechanisms of dodecylamine hydrochloride(DDAHC), sodium dodecyl sulfate (SDS), sodium dodecyl benzene sulfonate(SDBS), and their mixed anionic/cationic collectors at ten different molar ratios on a muscovite (Mcv) surface in neutral aqueous solution were assessed by molecular dynamics simulations (MDS). According to the snapshot, interaction energy, radial distribution function (RDF), and density profile between the Mcv surface and collector molecules, the individual DDAHC collector was an effective collector for the flotation of Mcv. The molar ratio of anionic/cationic collectors was determined to be an essential factor in the flotation recovery of Mcv. The DDAHC collector was involved in the adsorption of the mixed anionic/cationic collectors on the Mcv (001) surface, whereas SDS and SDBS collectors were co-adsorbed with DDAHC. The mixed cationic/anionic collector showed the best adsorption on the Mcv surface in a molar ratio of 2. Additionally, SDBS, which has one more benzene ring than SDS, was more likely to form spherical micelles with DDAHC, thus resulting in better adsorption on the Mcv surface. The results of micro-flotation experiments indicated that the DDAHC collector could improve the flotation recovery of Mcv in neutral aqueous solution, which was in agreement with MDS-derived findings. In conclusion, DDAHC alone is the optimum collector for Mcv flotation under the neutral aqueous conditions, while the mixture of DDAHC and SDBS collectors (molar ratio = 2:1) exhibits the similar flotation performance.