The influence of systemic inflammation, dietary intake and stage of disease on rate of weight loss in patients with gastro-oesophageal cancer

Although weight loss is often a dominant symptom in patients with upper gastrointestinal malignancy, there is a lack of objective evidence describing changes in nutritional status and potential associations between weight loss, food intake, markers of systemic inflammation and stage of disease in such patients. Two hundred and twenty patients diagnosed with gastric/oesophageal cancer were studied. Patients underwent nutritional assessment consisting of calculation of body mass index, measurement of weight loss, dysphagia scoring and estimation of dietary intake. Serum acute-phase protein concentrations were determined by enzyme-linked immunosorbent assay. In all, 182 (83%) patients had lost weight at diagnosis (median loss, 7% body weight). Weight loss was associated with poor performance status, advanced disease stage, dysphagia, reduced dietary intake and elevated serum C-reactive protein (CRP) concentrations. Multiple regression identified dietary intake (estimate of effect, 38%), serum CRP concentrations (estimate of effect, 34%) and stage of disease (estimate of effect, 28%) as independent variables in determining degree of weight loss. Mechanisms other than reduced dietary intake or mechanical obstruction by the tumour appear to be involved in the nutritional decline in patients with gastro-oesophageal malignancy. Recognition that systemic inflammation plays a role in nutritional depletion may inform the development of appropriate therapeutic strategies to ameliorate weight loss, making patients more tolerant of cancer-modifying treatments such as chemotherapy

Specific detection of fungal pathogens by 18S rRNA gene PCR in microbial keratitis

<p>Abstract</p> <p>Background</p> <p>The sensitivity and specificity of 18S rRNA polymerase chain reaction (PCR) in the detection of fungal aetiology of microbial keratitis was determined in thirty patients with clinical diagnosis of microbial keratitis.</p> <p>Methods</p> <p>Corneal scrapings from patients were used for Gram stain, culture and PCR analysis. PCR was performed with primer pairs targeted to the 18S rRNA gene. The result of the PCR was compared with conventional culture and Gram staining method. The PCR positive samples were identified by DNA sequencing of the internal transcribed spacer (ITS) region of the rRNA gene. Main outcome measures were sensitivity and specificity of PCR in the detection of fungus in corneal keratitis.</p> <p>Results</p> <p>Combination of microscopy and culture gave a positive result in 11 of 30 samples of microbial keratitis. PCR detected 10 of 11 samples that were positive by conventional method. One of the 19 samples that was negative by conventional method was positive by PCR. Statistical analysis revealed that the PCR to have a sensitivity of 90.9% and specificity of 94.7% in the detection of a fungal aetiology in microbial keratitis.</p> <p>Conclusion</p> <p>PCR is a rapid, sensitive and useful method to detect fungal aetiology in microbial keratitis.</p

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Pathway-based identification of SNPs predictive of survival

In recent years, several association analysis methods for case-control studies have been developed. However, as we turn towards the identification of single nucleotide polymorphisms (SNPs) for prognosis, there is a need to develop methods for the identification of SNPs in high dimensional data with survival outcomes. Traditional methods for the identification of SNPs have some drawbacks. First, the majority of the approaches for case-control studies are based on single SNPs. Second, SNPs that are identified without incorporating biological knowledge are more difficult to interpret. Random forests has been found to perform well in gene expression analysis with survival outcomes. In this paper we present the first pathway-based method to correlate SNP with survival outcomes using a machine learning algorithm. We illustrate the application of pathway-based analysis of SNPs predictive of survival with a data set of 192 multiple myeloma patients genotyped for 500 000 SNPs. We also present simulation studies that show that the random forests technique with log-rank score split criterion outperforms several other machine learning algorithms. Thus, pathway-based survival analysis using machine learning tools represents a promising approach for the identification of biologically meaningful SNPs associated with disease