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Abstract The XRF, XRD, polarizing microscopy and SEM-EDS were used to study the alteration mechanism of copper-bearing biotite and the leachable property of copper-bearing minerals in Mulyashy Copper Mine, Zambia. It was found that biotite can be divided into copper-bearing biotite and copper-free biotite. Some copper-bearing biotite existed in the form of monomer, and others aggregated with copper-bearing chlorite, malachite or copper-bearing limonite. The main reason for the occurrence of biotite aggregations was that copper-bearing biotite underwent two kinds of alteration mechanisms as follows: altering into copper-bearing chlorite and malachite, and altering into copper-bearing chlorite and copper-bearing limonite. The order of factors effecting the copper leaching rate of the ores in acid leaching experiments was temperature > sample size > H2SO4 concentration > leaching time > stirring speed. In addition, the copper leaching rate of copper-bearing minerals at different temperatures was in the following order: malachite, chrysocolla and pseudomalachite > copper-bearing chlorite > copper-bearing muscovite > copper-bearing biotite > copper-bearing limonite. The leachable property of biotite is closely related to its special structure.

In arsenopyrite bioleaching, the interfacial reaction between mineral and cells is one of the most important factors. The energy of the interface is influenced by the mineralogical and microbiological characteristics. In this paper, the interfacial energy was calculated, and the surface of arsenopyrite during the bioleaching process was characterized by 3D laser microscopy, scanning electron microscopy with energy-dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy, in order to assess the dissolution and oxidation behavior of arsenopyrite during bioleaching. The results showed that the contact angles of arsenopyrite were 22 ± 2° when covered with biofilms, but the reaction surface of arsenopyrite turned 103 ± 2°. However, the angle was 45–50° when covered by passive layer, which was half as that of arsenopyrite surface. The interfacial energy of arsenopyrite without biofilms increased from 45 to 62 mJ/m2, while it decreased to 5 ± 1 mJ/m2 when covered by biofilms during the leaching process. The surface was separated into fresh surface, oxidized surface, and (corrosion) pits. The interfacial energy was influenced by the fresh and oxidized surfaces. Surface roughness increased from 0.03 ± 0.01 to 5.89 ± 1.97 μm, and dissolution volume increased from 6.31 ± 0.47 × 104 to 2.72 ± 0.49 × 106 μm3. The dissolution kinetics of arsenopyrite followed the model of Kt = lnX, and the dissolution mechanisms were mixed controlled: surface reaction control and diffusion through sulfur layer. On the surface of arsenopyrite crystal, the oxidation steps of each element can be described as: for Fe, Fe(II)–(AsS)→Fe(III)–(AsS)→Fe(III)–OH or Fe(III)–SO; for S, As–S(-1) or Fe–S(-1)→polysulfide S→intermediate S–O→sulfate; and for As, As–1–S→As0→As+1–O→As+3–O→As+5–O.

OBJECTIVE: Over the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis. METHODS: Two different datasets were used: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7(th) edition of the American Joint Committee on Cancer TNM staging system. RESULTS: When the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05). CONCLUSIONS: The TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.

OBJECTIVE: At present, only the number of metastatic lymph nodes (LNs+) is used for the pN category of AJCC TNM system for colon cancer. Recently, the ratio of metastatic to examined lymph nodes (LNR) has been reported to represent powerful independent predictive capacity in colon cancer. We sought to propose a novel category (nLN) which intergrades LNR and LNs+ into the AJCC staging system for colon cancer. DESIGN: 34476 patients from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset with stage III colon cancer were reviewed. Harrell's C statistic was used to evaluate the predictive capacity. The Cox proportional hazards model was used to construct a novel category. RESULTS: The LNR category had more predictive capacity than the pN category in whole groups of patients (Harrell's C index: 0.6194 vs 0.6113, p = 0.003). Subgroup analysis showed that the LNR category was not better than pN category in predictive capacity if the number of lymph nodes examined was more than 13. We also found that there was significant survival heterogeneity among different pN categories at the same LNR category (P

OBJECTIVE: The aim of the current study was to investigate which is the most suitable classification for colorectal cancer, log odds of positive lymph nodes (LODDS) classification or the classifications based on the number of positive lymph nodes (pN) and positive lymph node ratio(LNR) in a Chinese single institutional population. DESIGN: Clinicopathologic and prognostic data of 1297 patients with colorectal cancer were retrospectively studied. The log-rank statistics, Cox's proportional hazards model, the Nagelkerke R(2) index and a Harrell's C statistic were used. RESULTS: Univariate and three-step multivariate analyses identified that LNR was a significant prognostic factor and LNR classification was superior to both the pN and LODDS classifications. Moreover, the results of the Nagelkerke R(2) index (0.130) and a Harrell's C statistic (0.707) of LNR showed that LNR and LODDS classifications were similar and LNR was a little better than the other two classifications. Furthermore, for patients in each LNR classification, prognosis was homologous between those in different pN or LODDS classifications. However, for patients in pN1a, pN1b, LODDS2 and LODDS3 classifications, significant differences in survival were observed among patients in different LNR classifications. CONCLUSIONS: For patients with colorectal cancer, the LNR classification is more suitable than pN and LODDS classifications for prognostic assessment in a Chinese single institutional population.

Background: Whether the 7th edition of American Joint Committee on Cancer (AJCC) TNM staging system (AJCC-7) is a successful revision remains debatable. We aimed to compare the predictive capacity of the AJCC-7 for colorectal cancer with the 6th edition of the AJCC TNM staging system (AJCC-6). Methods: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) dataset consisting of 158,483 records was used in this study. We evaluated the predictive capacity of the two editions of the staging system using Harrell's C index and Bayesian Information Criterion (BIC). Results: There was a significant prognostic difference between patients at stage IIB and IIC (P < 0.001). Stage III patients with similar prognoses were adequately sub-grouped in the same stage according to AJCC-7. The Harrell's C index revealed a value of 0.7692 for AJCC-7, which was significantly better than 0.7663 for AJCC-6 (P < 0.001). BIC analysis provided consistent results (P < 0.001). Conclusions: This study demonstrates that AJCC-7 is superior to the AJCC-6 staging system in predictive ca [ABSTRACT FROM AUTHOR]

Background: Dexmedetomidine (DEX) is a selective α2-adrenergic receptor agonist with anxiolytic and analgesic properties. In the present study, we aimed primarily to assess the effects of DEX on sedation, cognitive function and cardiovascular reflex responses before, during and after the tracheal intubation in the elderly patients., Materials and Methods: Eighty patients undergoing elective abdominal surgery were randomly assigned to four Groups: Group A(saline, n=20), Group B (0.25μg/kg DEX, n=20), Group C (0.50μg/kg DEX, n=20) and Group D (1.00μg/kg DEX, n=20). With the constant speed infusion of saline and a loading different doses of DEX (diluted with saline to 50ml) for 10min respectively before induction of anaesthesia, the values of arterial pressure {systolic blood pressure (SBP), diastolic blood pressure (DBP)}, heart rate (HR) and bispectral index (BIS) at the time point of before pump DEX (T0), at the end of infusing DEX (T1), before tracheal intubation (T2), at the moment of tracheal intubation (T3) and 5min after trachea intubation (T4) were observed, oxygen saturation (SPO2) and the Modified Observers Assessment of Alertness/Sedation Scale (OAA/S) score were observed at the time of T1 and T0., Results: Comparison among Groups, compared with Group A, SBP and DBP values in Group C at T2 showed significant differences (p<0.05), SBP and DBP values in Group D at T1, T2 and T4 indicated significant differences (p<0.05), HR values in Group D at T1, T2, T3 and T4 showed significant differences (p<0.05); Compared with Group A, BIS values in Group C at T2 and T3 indicated significant differences (p<0.05), BIS values in Group D at T1, T2, T3 and T4 showed significant differences (p<0.05); Comparison between T3 andT2, means of SBP, DBP and HR in Group A and in Group B showed significant differences (p<0.05); Group D showed significant differences in SPO2 and (OAA/S) betweenT1 and T0 (p<0.05)., Conclusion: Comparison within Groups and between Groups in different doses DEX, the present result showed that 0.5μg/kg DEX had an effective inhibition, without respiratory depression, on tracheal intubation evoked cardiovascular response in the elderly patients.