Bioactive compounds from Artemisia dracunculus L. activate AMPK signaling in skeletal muscle.

An extract from Artemisia dracunculus L. (termed PMI-5011) improves glucose homeostasis by enhancing insulin action and reducing ectopic lipid accumulation, while increasing fat oxidation in skeletal muscle tissue in obese insulin resistant male mice. A chalcone, DMC-2, in PMI-5011 is the major bioactive that enhances insulin signaling and activation of AKT. However, the mechanism by which PMI-5011 improves lipid metabolism is unknown. AMPK is the cellular energy and metabolic sensor and a key regulator of lipid metabolism in muscle. This study examined PMI-5011 activation of AMPK signaling using murine C2C12 muscle cell culture and skeletal muscle tissue. Findings show that PMI-5011 increases Thr172-phosphorylation of AMPK in muscle cells and skeletal muscle tissue, while hepatic AMPK activation by PMI-5011 was not observed. Increased AMPK activity by PMI-5011 affects downstream signaling of AMPK, resulting in inhibition of ACC and increased SIRT1 protein levels. Selective deletion of DMC-2 from PMI-5011 demonstrates that compounds other than DMC-2 in a "DMC-2 knock out extract" (KOE) are responsible for AMPK activation and its downstream effects. Compared to 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR) and metformin, the phytochemical mixture characterizing the KOE appears to more efficiently activate AMPK in muscle cells. KOE-mediated AMPK activation was LKB-1 independent, suggesting KOE does not activate AMPK via LKB-1 stimulation. Through AMPK activation, compounds in PMI-5011 may regulate lipid metabolism in skeletal muscle. Thus, the AMPK-activating potential of the KOE adds therapeutic value to PMI-5011 and its constituents in treating insulin resistance or type 2 diabetes.

Using the health beliefs model to explore children's attitudes and beliefs on air pollution.

OBJECTIVES: Traffic-related air pollution (TRAP) negatively impacts children's health. Self-protective measures are available, but population uptake is variable. It is essential to understand human beliefs and behaviours related to air pollution in order to understand the lack of self-protection in communities. As a prelude to undertaking a comprehensive assessment of children's attitudes and beliefs on the health effects of TRAP exposure, we sought to develop and validate an appropriate instrument. STUDY DESIGN: This study used exploratory sequential mixed methods. METHODS: This instrument, based on the constructs of the health belief model (HBM), aimed to determine factors predicting wearing a mask to protect against TRAP exposure. An initial literature-based questionnaire was modified using in-depth interviews, focus group discussions, and a quantitative survey pilot. This study included 121 school students and nine professional experts in Vietnam. The questionnaire was tested for content validity, agreement, test-retest reliability, and internal consistency. RESULTS: The concordance of questionnaire items between two repeated assessments ranged from 47.2% to 78.3%, intraclass correlation coefficients ranged from 0.16 to 0.87 and Cronbach's internal reliability coefficient for the instrument was 0.60. CONCLUSION: The self-administered instrument, based on the HBM, is suitable to understand health attitudes and beliefs related to self-protective behaviours to reduce TRAP exposure.

Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends.

Zika virus infection in humans has been linked to severe neurological sequels and foetal malformations. The rapidly evolving epidemics and serious complications made the frequent updates of Zika virus mandatory. Web search query has emerged as a low-cost real-time surveillance system to anticipate infectious diseases' outbreaks. Hence, we developed a prediction model that could predict Zika-confirmed cases based on Zika search volume in Google Trends. We extracted weekly confirmed Zika cases of two epidemic countries, Brazil and Colombia. We got the weekly Zika search volume in the two countries from Google Trends. We used standard time-series regression (TSR) to predict the weekly confirmed Zika cases based on the Zika search volume (Zika query). The basis TSR model - using 1-week lag of Zika query and using 1-week lag of Zika cases as a control for autocorrelation - was the best for predicting Zika cases in Brazil and Colombia because it balanced the performance of the model and the advance time in the prediction. Our results showed that we could use Google search queries to predict Zika cases 1 week earlier before the outbreak. These findings are important to help healthcare authorities evaluate the outbreak and take necessary precautions.