Chemotherapy versus supportive care in advanced non-small cell lung cancer: improved survival without detriment to quality of life

BACKGROUND: In 1995 a meta-analysis of randomised trials investigating the value of adding chemotherapy to primary treatment for non-small cell lung cancer (NSCLC) suggested a small survival benefit for cisplatin-based chemotherapy in each of the primary treatment settings. However, the metaanalysis included many small trials and trials with differing eligibility criteria and chemotherapy regimens. METHODS: The aim of the Big Lung Trial was to confirm the survival benefits seen in the meta-analysis and to assess quality of life and cost in the supportive care setting. A total of 725 patients were randomised to receive supportive care alone (n = 361) or supportive care plus cisplatin-based chemotherapy (n = 364). RESULTS: 65% of patients allocated chemotherapy (C) received all three cycles of treatment and a further 27% received one or two cycles. 74% of patients allocated no chemotherapy (NoC) received thoracic radiotherapy compared with 47% of the C group. Patients allocated C had a significantly better survival than those allocated NoC: HR 0.77 (95% CI 0.66 to 0.89, p = 0.0006), median survival 8.0 months for the C group v 5.7 months for the NoC group, a difference of 9 weeks. There were 19 (5%) treatment related deaths in the C group. There was no evidence that any subgroup benefited more or less fromchemotherapy. No significant differences were observed between the two groups in terms of the pre-defined primary and secondary quality of life end points, although large negative effects of chemotherapy were ruled out. The regimens used proved to be cost effective, the extra cost of chemotherapy being offset by longer survival. CONCLUSIONS: The survival benefit seen in this trial was entirely consistent with the NSCLC meta-analysis and subsequent similarly designed large trials. The information on quality of life and cost should enablepatients and their clinicians to make more informed treatment choices

A conserved fungal glycosyltransferase facilitates pathogenesis of plants by enabling hyphal growth on solid surfaces

Article Authors Metrics Comments Related Content Abstract Author summary Introduction Results and discussion Conclusions Methods Supporting information Acknowledgments References Reader Comments (0) Media Coverage (0) Figures Abstract Pathogenic fungi must extend filamentous hyphae across solid surfaces to cause diseases of plants. However, the full inventory of genes which support this is incomplete and many may be currently concealed due to their essentiality for the hyphal growth form. During a random T-DNA mutagenesis screen performed on the pleomorphic wheat (Triticum aestivum) pathogen Zymoseptoria tritici, we acquired a mutant unable to extend hyphae specifically when on solid surfaces. In contrast “yeast-like” growth, and all other growth forms, were unaffected. The inability to extend surface hyphae resulted in a complete loss of virulence on plants. The affected gene encoded a predicted type 2 glycosyltransferase (ZtGT2). Analysis of >800 genomes from taxonomically diverse fungi highlighted a generally widespread, but discontinuous, distribution of ZtGT2 orthologues, and a complete absence of any similar proteins in non-filamentous ascomycete yeasts. Deletion mutants of the ZtGT2 orthologue in the taxonomically un-related fungus Fusarium graminearum were also severely impaired in hyphal growth and non-pathogenic on wheat ears. ZtGT2 expression increased during filamentous growth and electron microscopy on deletion mutants (ΔZtGT2) suggested the protein functions to maintain the outermost surface of the fungal cell wall. Despite this, adhesion to leaf surfaces was unaffected in ΔZtGT2 mutants and global RNAseq-based gene expression profiling highlighted that surface-sensing and protein secretion was also largely unaffected. However, ΔZtGT2 mutants constitutively overexpressed several transmembrane and secreted proteins, including an important LysM-domain chitin-binding virulence effector, Zt3LysM. ZtGT2 likely functions in the synthesis of a currently unknown, potentially minor but widespread, extracellular or outer cell wall polysaccharide which plays a key role in facilitating many interactions between plants and fungi by enabling hyphal growth on solid matrices

Serum magnesium and calcium levels in relation to ischemic stroke : Mendelian randomization study

ObjectiveTo determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach.MethodsAnalyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases).ResultsIn standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 7 10-4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 7 10-4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype.ConclusionsThis study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype

A generic knowledge-based approach to the analysis of partial discharge data

Partial discharge (PD) diagnosis is a recognized technique to detect defects within high voltage insulation in power system equipment. A variety of methods exist to capture the signals that are emitted during PD, and this paper focuses on the ultra high frequency (UHF) and IEC 60270 techniques. Phase-resolved patterns can be constructed from the PD data captured using either of these techniques and due to the individual signatures that different defects generate, experts can examine the phase-resolved pattern to classify the defect that created it. In recent years, knowledge regarding PD phenomena and phase-resolved patterns has increased, providing an opportunity to employ a knowledge-based system (KBS) to automate defect classification. Due to the consistent physical nature of PD across different high voltage apparatus and the ability to construct phase-resolved patterns from various sensors, the KBS offers a generic approach to the analysis of PD by taking the phase-resolved pattern as its input and identifying the physical PD processes associate with the pattern. This paper describes the advances of this KBS, highlighting its generic application through the use of several case studies, which present the diagnosis of defects captured through both the IEC 60270 and UHF techniques. This paper also demonstrates, in one of the case studies, how a limitation of previous pattern recognition techniques can be overcome by mimicking the approach of a PD expert when the pulses occur over the zero crossings of the voltage waveform of the phase-resolved pattern

An incremental knowledge based approach to the analysis of partial discharge data

Defects in transformer insulation cause partial discharges (PD) which over time can progressively deteriorate the insulating material and possibly lead to electrical breakdown. Therefore, the early detection of the PD is crucial. A PD emits an electromagnetic signal in the ultra high frequency range, and current digital hardware has made it possible to transform this raw data into Phase-Resolved Partial Discharge (PRPD) pattern. Automated PD diagnositc systems previously employed pattern recognition techniques. However, specialists are now able to identify features of the PRPD pattern and deduce different behaviours and therefore physical geometical aspects of the defect. Using this knowledge within a knowledge-based system provides and explanation, and therefore reassurance of the diagnosed fault. This paper describes how to capture and model the knowledge from experts, along with the construction of a rule-based system. It presents a case study of the system's use and the introduction of explanation when diagnosing a defect. The next stage of the monitoring process involves linking the online PD data capture to further diagnostic algorithms and user interfaces. This paper illustrates how this knowledge-based system integrates into an overall transformer monitoring system to provide further data handling and interpretation

An incremental knowledge based approach to the analysis of partial discharge data

Defects in transformer insulation cause partial discharges (PD) which over time can progressively deteriorate the insulating material and possibly lead to electrical breakdown. Therefore, the early detection of the PD is crucial. A PD emits an electromagnetic signal in the ultra high frequency range, and current digital hardware has made it possible to transform this raw data into Phase-Resolved Partial Discharge (PRPD) pattern. Automated PD diagnositc systems previously employed pattern recognition techniques. However, specialists are now able to identify features of the PRPD pattern and deduce different behaviours and therefore physical geometical aspects of the defect. Using this knowledge within a knowledge-based system provides and explanation, and therefore reassurance of the diagnosed fault. This paper describes how to capture and model the knowledge from experts, along with the construction of a rule-based system. It presents a case study of the system's use and the introduction of explanation when diagnosing a defect. The next stage of the monitoring process involves linking the online PD data capture to further diagnostic algorithms and user interfaces. This paper illustrates how this knowledge-based system integrates into an overall transformer monitoring system to provide further data handling and interpretation