Immunohistochemical molecular phenotypes of gastric cancer based on SOX2 and CDX2 predict patient outcome

Background Gastric cancer remains a serious health concern worldwide. Patients would greatly benefit from the discovery of new biomarkers that predict outcome more accurately and allow better treatment and follow-up decisions. Here, we used a retrospective, observational study to assess the expression and prognostic value of the transcription factors SOX2 and CDX2 in gastric cancer. Methods SOX2, CDX2, MUC5AC and MUC2 expression were assessed in 201 gastric tumors by immunohistochemistry. SOX2 and CDX2 expression were crossed with clinicopathological and follow-up data to determine their impact on tumor behavior and outcome. Moreover, SOX2 locus copy number status was assessed by FISH (N = 21) and Copy Number Variation Assay (N = 62). Results SOX2 was expressed in 52% of the gastric tumors and was significantly associated with male gender, T stage and N stage. Moreover, SOX2 expression predicted poorer patient survival, and the combination with CDX2 defined two molecular phenotypes, SOX2+CDX2- versus SOX2-CDX2+, that predict the worst and the best long-term patients’ outcome. These profiles combined with clinicopathological parameters stratify the prognosis of patients with intestinal and expanding tumors and in those without signs of venous invasion. Finally, SOX2 locus copy number gains were found in 93% of the samples reaching the amplification threshold in 14% and significantly associating with protein expression. Conclusions We showed, for the first time, that SOX2 combined with CDX2 expression profile in gastric cancer segregate patients into different prognostic groups, complementing the clinicopathological information. We further demonstrate a molecular mechanism for SOX2 expression in a subset of gastric cancer cases.

FASL polymorphism is associated with response to bacillus Calmette-Guérin immunotherapy in bladder cancer

Objective Deregulation of FAS/FASL system may lead to immune escape and influence bacillus Calmette-Guérin (BCG) immunotherapy outcome, which is currently the gold standard adjuvant treatment for high-risk non–muscle invasive bladder tumors. Among other events, functional promoter polymorphisms of FAS and FASL genes may alter their transcriptional activity. Therefore, we aim to evaluate the role of FAS and FASL polymorphisms in the context of BCG therapy, envisaging the validation of these biomarkers to predict response. Patients and methods DNA extracted from peripheral blood from 125 patients with bladder cancer treated with BCG therapy was analyzed by Polymerase Chain Reaction—Restriction Fragment Length Polymorphism for FAS-670 A/G and FASL-844 T/C polymorphisms. FASL mRNA expression was analyzed by real-time Polymerase Chain Reaction. Results Carriers of FASL-844 CC genotype present a decreased recurrence-free survival after BCG treatment when compared with FASL-844 T allele carriers (mean 71.5 vs. 97.8 months, P = 0.030) and have an increased risk of BCG treatment failure (Hazard Ratio = 1.922; 95% Confidence Interval: [1.064–3.471]; P = 0.030). Multivariate analysis shows that FASL-844 T/C and therapeutics scheme are independent predictive markers of recurrence after treatment. The evaluation of FASL gene mRNA levels demonstrated that patients carrying FASL-844 CC genotype had higher FASL expression in bladder tumors (P = 0.0027). Higher FASL levels were also associated with an increased risk of recurrence after BCG treatment (Hazard Ratio = 2.833; 95% Confidence Interval: [1.012–7.929]; P = 0.047). FAS-670 A/G polymorphism analysis did not reveal any association with BCG therapy outcome. Conclusions Our results suggest that analysis of FASL-844 T/C, but not FAS-670 A/G polymorphisms, may be used as a predictive marker of response to BCG immunotherapy.

Using a cohesive damage model to predict the tensile behaviour of CFRP single-strap repairs

This work addresses both experimental and numerical analyses regarding the tensile behaviour of CFRP single-strap repairs. Two fundamental geometrical parameters were studied: overlap length and patch thickness. The numerical model used ABAQUS® software and a developed cohesive mixed-mode damage model adequate for ductile adhesives, and implemented within interface finite elements. Stress analyses and strength predictions were carried out. Experimental and numerical comparisons were performed on failure modes, failure load and equivalent stiffness of the repair. Good correlation was found between experimental and numerical results, showing that the proposed model can be successfully applied to bonded joints or repairs.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Deteção e previsão de falhas em equipamentos de produção industrial

As empresas nacionais deparam-se com a necessidade de responder ao mercado com uma grande variedade de produtos, pequenas séries e prazos de entrega reduzidos. A competitividade das empresas num mercado global depende assim da sua eficiência, da sua flexibilidade, da qualidade dos seus produtos e de custos reduzidos. Para se atingirem estes objetivos é necessário desenvolverem-se estratégias e planos de ação que envolvem os equipamentos produtivos, incluindo: a criação de novos equipamentos complexos e mais fiáveis, alteração dos equipamentos existentes modernizando-os de forma a responderem às necessidades atuais e a aumentar a sua disponibilidade e produtividade; e implementação de políticas de manutenção mais assertiva e focada no objetivo de “zero avarias”, como é o caso da manutenção preditiva. Neste contexto, o objetivo principal deste trabalho consiste na previsão do instante temporal ótimo da manutenção de um equipamento industrial – um refinador da fábrica de Mangualde da empresa Sonae Industria, que se encontra em funcionamento contínuo 24 horas por dia, 365 dias por ano. Para o efeito são utilizadas medidas de sensores que monitorizam continuamente o estado do refinador. A principal operação de manutenção deste equipamento é a substituição de dois discos metálicos do seu principal componente – o desfibrador. Consequentemente, o sensor do refinador analisado com maior detalhe é o sensor que mede a distância entre os dois discos do desfibrador. Os modelos ARIMA consistem numa abordagem estatística avançada para previsão de séries temporais. Baseados na descrição da autocorrelação dos dados, estes modelos descrevem uma série temporal como função dos seus valores passados. Neste trabalho, a metodologia ARIMA é utilizada para determinar um modelo que efetua uma previsão dos valores futuros do sensor que mede a distância entre os dois discos do desfibrador, determinando-se assim o momento ótimo da sua substituição e evitando paragens forçadas de produção por ocorrência de uma falha por desgaste dos discos. Os resultados obtidos neste trabalho constituem uma contribuição científica importante para a área da manutenção preditiva e deteção de falhas em equipamentos industriais.

Aerobic Exercise Does Not Predict Brain Derived Neurotrophic Factor And Cortisol Alterations in Depressed Patients

The pathophysiology of depression is related to neurobiological changes that occur in the monoamine system, hypothalamic-pituitary-adrenal axis, neurogenesis system and the neuroimmune system. In recent years, there has been a growing interest in the research of the effects of exercise on brain function, with a special focus on its effects on brainderived neurotrophic factor (BDNF), cortisol and other biomarkers. Thus, the aim of this study is to present a review investigating the acute and chronic effects of aerobic exercise on BDNF and cortisol levels in individuals with depression. It was not possible to establish an interaction between aerobic exercise and concentration of BDNF and cortisol, which may actually be the result of the divergence of methods, such as type of exercises, duration of the sessions, and prescribed intensity and frequency of sessions.