BackgroundTobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure.Methodology/Principal FindingsWe performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.Conclusions/SignificanceOur work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.

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We consider the problem of detecting features in spatial point processes in the presence of substantial clutter. One example is the detection of mine elds using reconnaissance aircraft images that erroneously identify many objects that are not mines. Another is the detection of seismic faults on the basis of earthquake catalogs: earthquakes tend to be clustered close to the faults, but there are many that are farther away.Our solution uses model-based clustering based on a mixture model for the process, in which features are assumed to generate points according to highly linear multivariate normal densities, and the clutter arises according to a spatial Poisson process. Very nonlinear features are represented by several highly linear multivariate normal densities, giving a piecewise linear representation.The model is estimated in two stages. In the rst stage, hierarchical model-based clustering is used to provide a rst estimate of the features. In the second stage, this clustering is re ned using the EM algorithm. The number of features is found using an approximation to the posterior probability of each n umber of features.For the mine eld problem, this method yields high detection rates and low false positive rates. For the seismic fault problem, the method accurately recovers the documented faults in the San Francisco Bay a r e a .

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BackgroundIn contrast with normal cells, most of the cancer cells depend on aerobic glycolysis for energy production in the form of adenosine triphosphate (ATP) bypassing mitochondrial oxidative phosphorylation. Moreover, compared to normal cells, cancer cells exhibit higher consumption of glucose with higher production of lactate. Again, higher rate of glycolysis provides the necessary glycolytic intermediary precursors for DNA, protein and lipid synthesis to maintain high active proliferation of the tumor cells. In this scenario, classical control theory based approach may be useful to explore the altered dynamics of the cancer cells. Since the dynamics of the cancer cells is different from that of the normal cells, understanding their dynamics may lead to development of novel therapeutic strategies.MethodWe have developed a model based on the state space equations of classical control theory along with an order reduction technique to mimic the actual dynamic behavior of mammalian central carbon metabolic (CCM) pathway in normal cells. Here, we have modified Michaelis Menten kinetic equation to incorporate feedback mechanism along with perturbations and cross talks associated with a metabolic pathway. Furthermore, we have perturbed the proposed model to reduce the mitochondrial oxidative phosphorylation. Thereafter, we have connected proportional-integral (PI) controller(s) with the model for tuning it to behave like the CCM pathway of a cancer cell. This methodology allows one to track the altered dynamics mediated by different enzymes.Results and DiscussionsThe proposed model successfully mimics all the probable dynamics of the CCM pathway in normal cells. Moreover, experimental results demonstrate that in cancer cells, a coordination among enzymes catalyzing pentose phosphate pathway and intermediate glycolytic enzymes along with switching of pyruvate kinase (M2 isoform) plays an important role to maintain their altered dynamics.

Aims:To evaluate clinical profile and short-term outcome of psychogenic non-epileptic seizures (PNES) in Indian adult population.Setting and Design:A prospective observational study, conducted at tertiary teaching institute at New Delhi.Materials and Methods:Sixty-three patients with confirmed PNES were enrolled. The diagnosis was based on witnessing the event during video-electroencephalography (Video-EEG) monitoring. A detailed clinical evaluation was done including evaluation for coexistent anxiety or depressive disorders. Patients were divided into two groups on the basis of excessive or paucity of movements during PNES attacks. Patients were followed-up to 12 months for their PNES frequency.Statistical Analysis:Means and standard deviations were calculated for continuous variables. Chi-square and Students t-test were used to compare categorical and continuous variables respectively.Results:The mean age at onset of PNES was 25.44 years; with F:M ratio of 9.5:1. Coexistent epilepsy was present in 13 (20.63%) cases. Twenty-two patients (44%) with only PNES (n = 50) had received antiepileptic drugs. Out of 63 patients of PNES 24 (38.1%) had predominant motor phenomenon, whereas 39 (61.9%) had limp attacks. The common features observed were pre-ictal headache, ictal eye closure, jaw clenching, resistant behavior, ictal weeping, ictal vocalization, and unresponsiveness during episodes. Comorbid anxiety and depressive disorders was seen in 62.3% and 90.16% patients, respectively. Short-term (6-12 months) outcome of 45 patients was good (seizure freedom in 46.66% and >50% improvement in 24.44% cases).Conclusion:PNES is common, but frequently misdiagnosed and treated as epileptic seizures. A high index of suspicion is required for an early diagnosis. Proper disclosure of diagnosis and management of the psychiatric comorbidities can improve their outcome.Limitation:Limited sample size and change in seizures frequency as the only parameter for the assessment of the outcome are the two major limitations of our study.

We evaluated Hybrid Capture 2 (HC2) and polymerase chain reaction (PCR) results for paired specimens collected at 19,187 visits from 5,026 of 5,060 women participating in the Atypical Squamous Cells of Undetermined Significance/Low-Grade Squamous Intraepithelial Lesion Triage Study (ALTS). We examined the test agreement between HC2 and PCR detection for any of 13 carcinogenic human papillomavirus types targeted by HC2 and compared clinical performance of the 2 tests for detecting concurrent and follow-up cervical intraepithelial neoplasia (CIN) 3 or cancer. The k value for the 2 assays was 0.65 (95% confidence interval, 0.64-0.66), with 82.7% crude agreement. HC2 was more sensitive (93.6% vs 89.3%; P < .0005) but less specific (41.2% vs 48.5%; P < .0005) than PCR for detecting 2-year cumulative CIN 3 or cancer (n = 503). The presence of multiple types as detected by PCR and/or cytologic abnormality increased the likelihood of an HC2+ result. Increased sensitivity of HC2 compared with PCR was surprising, given the theoretical advantages of PCR-based methods for analytic sensitivity. Smaller amounts of material used in PCR could have limited its sensitivity, but our results demonstrate the importance of optimization and standardization of PCR-based assays for clinical applications.