Pseudomonas aeruginosa is one of the major causative agents of mortality and morbidity in hospitalized patients due to a multiplicity of virulence factors associated with both chronic and acute infections. Acute P. aeruginosa infection is primarily mediated by planktonic bacteria expressing the type III secretion system (TTSS), a surface-attached needle-like complex that injects cytotoxins directly into eukaryotic cells, causing cellular damage. Lipopolysaccharide (LPS) is the principal surface-associated virulence factor of P. aeruginosa. This molecule is known to undergo structural modification (primarily alterations in the

A group of patients with large burdens of P. aeruginosa who did not meet clinical criteria for ventilator-associated pneumonia had an increased risk of death when compared with patients who had high P. aeruginosa burdens and met ventilator-associated pneumonia criteria. Patients with high P. aeruginosa burden seemed to possess more virulent strains.

PAI-1 concentrations in bBALs correlated with mortality in ventilated patients with positive cultures for P. aeruginosa. Elevated bBAL PAI-1 concentrations also correlated with the secretion of type III exotoxins by P. aeruginosa.

Pseudomonas aeruginosa is an opportunistic pathogen that can, like other bacterial species, exist in antimicrobial resistant sessile biofilms and as free-swimming, planktonic cells. Specific virulence factors are typically associated with each lifestyle and several two-component response regulators have been shown to reciprocally regulate transition between biofilm-associated chronic, and free-swimming acute infections. Quorum sensing (QS) signal molecules belonging to the las and rhl systems are known to regulate virulence gene expression by P. aeruginosa. However the impact of a recently described family of novel quorum sensing signals produced by the Pseudomonas Quinolone Signal (PQS) biosynthetic pathway, on the transition between these modes of infection is less clear. Using clonal isolates from a patient developing ventilator-associated pneumonia, we demonstrated that clinical observations were mirrored by an in vitro temporal shift in isolate phenotype from a non-secreting, to a Type III cytotoxin secreting (TTSS) phenotype and further, that this phenotypic change was PQS-dependent. While intracellular type III cytotoxin levels were unaffected by PQS concentration, cytotoxin secretion was dependent on this signal molecule. Elevated PQS concentrations were associated with inhibition of cytotoxin secretion coincident with expression of virulence factors such as elastase and pyoverdin. In contrast, low concentrations or the inability to biosynthesize PQS resulted in a reversal of this phenotype. These data suggest that expression of specific P. aeruginosa virulence factors appears to be reciprocally regulated and that an additional level of PQS-dependent posttranslational control, specifically governing type III cytotoxin secretion, exists in this species.

We consider the task of automatically annotating free texts describing clinical trials with concepts from a controlled, structured medical vocabulary. Specifically we aim to build a model to infer distinct sets of (ontological) concepts describing complementary clinically salient aspects of the underlying trials: the populations enrolled, the interventions administered and the outcomes measured, i.e., the PICO elements. This important practical problem poses a few key challenges. One issue is that the output space is vast, because the vocabulary comprises many unique concepts. Compounding this problem, annotated data in this domain is expensive to collect and hence sparse. Furthermore, the outputs (sets of concepts for each PICO element) are correlated: specific populations (e.g., diabetics) will render certain intervention concepts likely (insulin therapy) while effectively precluding others (radiation therapy). Such correlations should be exploited. We propose a novel neural model that addresses these challenges. We introduce a Candidate-Selector architecture in which the model considers setes of candidate concepts for PICO elements, and assesses their plausibility conditioned on the input text to be annotated. This relies on a ‘candidate set’ generator, which may be learned or relies on heuristics. A conditional discriminative neural model then jointly selects candidate concepts, given the input text. We compare the predictive performance of our approach to strong baselines, and show that it outperforms them. Finally, we perform a qualitative evaluation of the generated annotations by asking domain experts to assess their quality.

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BackgroundLung cancer is the most common type of cancer worldwide with an estimation of 1.82 million new cancer cases diagnosed; and it is the leading cause of cancer-related deaths. Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase identified as being highly expressed in cancer cells including lung cancers. The aim of the study is to determine the EGFR mutation status in non-small cell lung cancer (NSCLC) patients to investigate the association between the EGFR mutation status and clinicopathological characters of patients.MethodsThe tissue samples of the lung cancer patients were collected bronchoscopically. The EGFR mutations of 70 NSCLC patients were determined by the immunohistochemistry (IHC).ResultsEGFR mutations were present in 24 cases (34.29%), including 19 (79.13%) cases of exon 19 and five (20.83%) cases of exon 21 mutation. EGFR mutations were frequently associated with adenocarcinoma and non-smoker. Statistically significant association of EGFR mutations with adenocarcinoma subtypes and non-smokers was found (P < 0.05); and no significant association of EGFR mutation with the age of the patient (P = 0.4647) and the stage (P = 0.4578) of the tumor was found. When we compared between these two mutations, no significant association with age (P=0.614) and smoking status (P=0.127) was found in this study.ConclusionsEGFR mutations were significantly associated with female sex, non-smoker and adenocarcinoma subtypes. The analysis of EGFR mutation by the IHC method is a potentially useful tool to guide clinicians for personalized treatment of NSCLC patients of adenocarcinoma subtype.

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