The series Bi2Sr2Ca(n-1) Cu(n)O(2n+4) (1 less than or equal to n less than or equal to 5): Phase stability and superconducting properties

Phase relations at 850 and 870 C, melting transitions in air, oxygen, and helium were studied for Bi(2.1)Sr(1.9) CuO6 and for the Bi2Sr2Ca(n-1) Cu(n)O(2n+4) for n = 1, 2, 3, 4, 5, and infinity (CaCuO2). Up to 870 C, the n = 2 composition resides in the compatibility tetrahedron bounded by Bi(2+x)(Sr,Ca)(3-y) Cu2O8, (Sr,Ca)14 Cu24O41, Ca2CuO3, and a Bi-Sr-Ca-O phase. The n is greater than or equal to 3 compositions reside in the compatibility tetrahedron Bi(2+x)(Sr,Ca)(3-y) Cu2O8 - (Sr,Ca)14 Cu24O41 - Ca2CuO3 - CuO up to 850 C. However, Bi(2+x)Sr(4-y) Cu3O10 forms for n is greater than or equal to 3 after extended heating at 870 C. Bi(2+x)Sr(2-y) CuO6 and Bi(2+x)(Sr,Ca)(3-y) Cu2O8 melt in air at 914 C and 895 C respectively. During melting, all of the compositions studied lose 1 to 2 percent by weight of oxygen from the reduction of copper. Bi(2+x)Sr(2-y) CuO6, Bi(2+n)(Sr,Ca)(3-y) Cu2O8, and Bi(2+x)(Sr,Ca)(4-y) Cu3O10 exhibit crystallographic alignment in a magnetic field, with the c-axes orienting parallel to the field

The use of tumour markers CEA, CA-195 and CA-242 in evaluating the response to chemotherapy in patients with advanced colorectal cancer.

Tumour markers CEA, CA-195 and CA-242 were measured in 33 patients undergoing chemotherapy for advanced colorectal cancer. The aim was to determine whether they could be used to accurately monitor the course of the disease, and reduce the need for imaging. Treatment with a 5-fluorouracil based regimen resulted in a partial response in nine patients (27%), whereas the remainder either had disease stabilisation or suffered from progression. Before treatment the CEA was elevated in 85% of patients and the CA-195 and CA-242 in 78%. All three markers were elevated in 70% and at least one elevated in 93%. CA-195 and CA-242 appeared to be co-expressed, by contrast with the CEA. When compared to the results of serial CT scanning the CEA correlated best with the course of the disease, the positive predictive value being 54% for a partial response, 77% for minor and partial responses combined and 100% for progressive disease. The corresponding values for CA-195 were 46%, 62% and 100% respectively and for CA-242, 50%, 67% and 100% respectively. Thus, although falling levels of markers overestimate the number of responses demonstrated by imaging, rising tumour markers invariably herald progressive disease. This was often evident up to 16 weeks before progression was observed on scanning. CEA is the most useful of the three markers in the monitoring of patients being treated for advanced colorectal cancer, but other markers may prove valuable if the CEA is normal. The use of tumour markers should reduce the need for regular scanning

Identifying patients at risk of emergency admission for colorectal cancer.

BACKGROUND: Patients whose colorectal cancer is treated after an emergency admission tend to have late-stage cancer and a poor prognosis. We identified risk factors for an emergency admission by linking data from the National Bowel Cancer Audit (NBCA) and the English Hospital Episode Statistics (HES), an administrative database of all admissions to English National Health Service hospitals, which includes data on mode of admission. METHODS: We identified all adults included in the NBCA with a primary diagnosis of bowel cancer, excluding cancer of the appendix, between August 2007 and July 2011 whose record could be linked to HES. Multivariable logistic regression was used to estimate adjusted odds ratios (OR) for an emergency admission for colorectal cancer. All risk factors were adjusted for cancer site and calendar year. RESULTS: 97,909 adults were identified with a primary diagnosis of bowel cancer and 82,777 patients could be linked to HES. Patients who were older, female, of a non-white ethnic background, and more socioeconomically deprived, and those with dementia or cardiac, neurologic and liver disease had an increased risk of presenting as an emergency admission. The strongest risk factors were age (90 compared with 70 years: OR 2.99, 95% CI 2.84 to 3.15), dementia (OR 2.46, 2.18 to 2.79), and liver disease (OR 1.87, 1.69 to 2.08). CONCLUSIONS: Our study identifies risk factors that may impair health-seeking behaviour and access to healthcare. An earlier recognition of symptoms in patients with these risk factors may contribute to better outcomes

Early mortality from colorectal cancer in England: a retrospective observational study of the factors associated with death in the first year after diagnosis

Background: The United Kingdom performs poorly in international comparisons of colorectal cancer survival with much of the deficit owing to high numbers of deaths close to the time of diagnosis. This retrospective cohort study investigates the patient, tumour and treatment characteristics of those who die in the first year after diagnosis of their disease. Methods: Patients diagnosed with colon (n=65,733) or rectal (n=26,123) cancer in England between 2006 and 2008 were identified in the National Cancer Data Repository. Multivariable logistic regression was used to investigate the odds of death within 1 month, 1-3 months and 3-12 months after diagnosis. Results: In all, 11.5% of colon and 5.4% of rectal cancer patients died within a month of diagnosis: this proportion decreased significantly over the study period. For both cancer sites, older age, stage at diagnosis, deprivation and emergency presentation were associated with early death. Individuals who died shortly after diagnosis were also more likely to have missing data about important prognostic factors such as disease stage and treatment. Conclusion: Using routinely collected data, at no inconvenience to patients, we have identified some important areas relating to early deaths from colorectal cancer, which merit further research

Improving the odds of drug development success through human genomics: modelling study.

Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases - the 'disease-ome' - represented as columns; and all protein coding genes - 'the protein-coding genome'- represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate