Temperature Changes between Neighboring Days and Mortality in Summer: A Distributed Lag Non-Linear Time Series Analysis

Background

Many studies have shown that high temperatures or heat waves were associated with mortality and morbidity. However, few studies have examined whether temperature changes between neighboring days have any significant impact on human health.

Method

A distributed lag non-linear model was employed to investigate the effect of temperature changes on mortality in summer during 2006–2010 in two subtropical Chinese cities. The temperature change was defined as the difference of the current day’s and the previous day’s mean temperature.

Results

We found non-linear effects of temperature changes between neighboring days in summer on mortality in both cities. Temperature increase was associated with increased mortality from non-accidental diseases and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality in both cities. Significant association between temperature changes and respiratory mortality was only found in Guangzhou.

Conclusion

This study suggests that temperature changes between neighboring days might be an alternative temperature indicator for studying temperature-mortality relationship.     

Model-Based Assessment of Estuary Ecosystem Health Using the Latent Health Factor Index,with Application to the Richibucto Estuary

The ability to quantitatively assess ecological health is of great interest to those tasked with monitoring and conserving ecosystems. For decades, biomonitoring research and policies have relied on multimetric health indices of various forms. Although indices are numbers, many are constructed based on qualitative procedures, thus limiting the quantitative rigor of the practical interpretations of such indices. The statistical modeling approach to construct the latent health factor index (LHFI) was recently developed. With ecological data that otherwise are used to construct conventional multimetric indices, the LHFI framework expresses such data in a rigorous quantitative model, integrating qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modeling approach allows unified statistical inference of health for observed sites (along with prediction of health for partially observed sites, if desired) and of the relevance of ecological drivers, all accompanied by formal uncertainty statements from a single, integrated analysis. Thus far, the LHFI approach has been demonstrated and validated in a freshwater context. We adapt this approach to modeling estuarine health, and illustrate it on the previously unassessed system in Richibucto in New Brunswick, Canada, where active oyster farming is a potential stressor through its effects on sediment properties. Field data correspond to health metrics that constitute the popular AZTI marine biotic index and the infaunal trophic index, as well as abiotic predictors preconceived to influence biota. Our paper is the first to construct a scientifically sensible model that rigorously identifies the collective explanatory capacity of salinity, distance downstream, channel depth, and silt–clay content–all regarded a priori as qualitatively important abiotic drivers–towards site health in the Richibucto ecosystem. This suggests the potential effectiveness of the LHFI approach for assessing not only freshwater systems but aquatic ecosystems in general.     

Extracting Drug-Drug Interaction from the Biomedical Literature Using a Stacked Generalization-Based Approach

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.     

Directed In Vitro Myogenesis of Human Embryonic Stem Cells and Their In Vivo Engraftment

Development of human embryonic stem cell (hESC)-based therapy requires derivation of in vitro expandable cell populations that can readily differentiate to specified cell types and engraft upon transplantation. Here, we report that hESCs can differentiate into skeletal muscle cells without genetic manipulation. This is achieved through the isolation of cells expressing a mesodermal marker, platelet-derived growth factor receptor-α (PDGFRA), following embryoid body (EB) formation. The ESC-derived cells differentiated into myoblasts in vitro as evident by upregulation of various myogenic genes, irrespective of the presence of serum in the medium. This result is further corroborated by the presence of sarcomeric myosin and desmin, markers for terminally differentiated cells. When transplanted in vivo, these pre-myogenically committed cells were viable in tibialis anterior muscles 14 days post-implantation. These hESC-derived cells, which readily undergo myogenic differentiation in culture medium containing serum, could be a viable cell source for skeletal muscle repair and tissue engineering to ameliorate various muscle wasting diseases.     

2-Methoxyestradiol Induces Mitotic Arrest,Apoptosis, and Synergistic Cytotoxicity with Arsenic Trioxide in Human Urothelial Carcinoma Cells

2-Methoxyestradiol (2-ME), an endogenous derivative of 17β-estradiol, has been reported to elicit antiproliferative responses in various tumors. In this study, we investigated the effects of 2-ME on cell viability, proliferation, cell cycle, and apoptosis in human urothelial carcinoma (UC) cell lines. We used two high-grade human bladder UC cell lines (NTUB1 and T24). After treatment with 2-ME, the cell viability and apoptosis were measured by MTT assay and flow cytometry (fluorescence-activated cell sorting), with annexin V-FITC staining and propidium iodide (PI) labeling. DNA fragmentation was analyzed by agarose gel electrophoresis. Flow cytometry with PI labeling was used for the cell cycle analyses. The protein levels of caspase activations, poly (ADP-ribose) polymerase (PARP) cleavage, phospho-histone H2A.X, phospho-Bad, and cell cycle regulatory molecules were measured by Western blot. The effects of the drug combinations were analyzed using the computer software, CalcuSyn. We demonstrated that 2-ME effectively induces dose-dependent cytotoxicity and apoptosis in human UC cells after 24 h exposure. DNA fragmentation, PARP cleavage, and caspase-3, 7, 8, 9 activations can be observed with 2-ME-induced apoptosis. The decreased phospho-Bad (Ser136 and Ser155) and mitotic arrest of the cell cycle in the process of apoptosis after 2-ME treatment was remarkable. In response to mitotic arrest, the mitotic forms of cdc25C, phospho-cdc2, cyclin B1, and phospho-histone H3 (Ser10) were activated. In combination with arsenic trioxide (As₂O₃), 2-ME elicited synergistic cytotoxicity (combination index <1) in UC cells. We concluded that 2-ME significantly induces apoptosis through decreased phospho-Bad and arrests bladder UC cells at the mitotic phase. The synergistic antitumor effect with As₂O₃ provides a novel implication in clinical treatment of UC.     

Autophagy Enhances Bacterial Clearance during P. aeruginosa Lung Infection

Pseudomonas aeruginosa is an opportunistic bacterial pathogen which is the leading cause of morbidity and mortality among cystic fibrosis patients. Although P. aeruginosa is primarily considered an extacellular pathogen, recent reports have demonstrated that throughout the course of infection the bacterium acquires the ability to enter and reside within host cells. Normally intracellular pathogens are cleared through a process called autophagy which sequesters and degrades portions of the cytosol, including invading bacteria. However the role of autophagy in host defense against P. aeruginosa in vivo remains unknown. Understanding the role of autophagy during P. aeruginosa infection is of particular importance as mutations leading to cystic fibrosis have recently been shown to cause a blockade in the autophagy pathway, which could increase susceptibility to infection. Here we demonstrate that P. aeruginosa induces autophagy in mast cells, which have been recognized as sentinels in the host defense against bacterial infection. We further demonstrate that inhibition of autophagy through pharmacological means or protein knockdown inhibits clearance of intracellular P. aeruginosa in vitro, while pharmacologic induction of autophagy significantly increased bacterial clearance. Finally we find that pharmacological manipulation of autophagy in vivo effectively regulates bacterial clearance of P. aeruginosa from the lung. Together our results demonstrate that autophagy is required for an effective immune response against P. aeruginosa infection in vivo, and suggest that pharmacological interventions targeting the autophagy pathway could have considerable therapeutic potential in the treatment of P. aeruginosa lung infection.     

13-Methyltetradecanoic Acid Exhibits Anti-Tumor Activity on T-Cell Lymphomas In Vitro and In Vivo by Down-Regulating p-AKT and Activating Caspase-3

13-Methyltetradecanoic acid (13-MTD), a saturated branched-chain fatty acid purified from soy fermentation products, induces apoptosis in human cancer cells. We investigated the inhibitory effects and mechanism of action of 13-MTD on T-cell non-Hodgkin’s lymphoma (T-NHL) cell lines both in vitro and in vivo. Growth inhibition in response to 13-MTD was evaluated by the cell counting kit-8 (CCK-8) assay in three T-NHL cell lines (Jurkat, Hut78, EL4 cells). Flow cytometry analyses were used to monitor the cell cycle and apoptosis. Proteins involved in 13-MTD-induced apoptosis were examined in Jurkat cells by western blotting. We found that 13-MTD inhibited proliferation and induced the apoptosis of T-NHL cell lines. 13-MTD treatment also induced a concentration-dependent arrest of Jurkat cells in the G₁-phase. During 13-MTD-induced apoptosis in Jurkat cells, the cleavage of caspase-3 and poly ADP-ribose polymerase (PARP, a caspase enzymolysis product) were detected after incubation for 2 h, and increased after extending the incubation time. However, there was no change in the expression of Bcl-2 or c-myc proteins. The appearance of apoptotic Jurkat cells was accompanied by the inhibition of AKT and nuclear factor-kappa B (NF-κB) phosphorylation. In addition, 13-MTD could also effectively inhibit the growth of T-NHL tumors in vivo in a xenograft model. The tumor inhibition rate in the experimental group was 40%. These data indicate that 13-MTD inhibits proliferation and induces apoptosis through the down-regulation of AKT phosphorylation followed by caspase activation, which may provide a new approach for treating T-cell lymphomas.