Administration of Jerusalem artichoke reduces the postprandial plasma glucose and glucose-dependent insulinotropic polypeptide (GIP) concentrations in humans.

Background: The consumption of Jerusalem artichoke has multiple beneficial effects against diabetes and obesity. Objective: The aim of this study was to determine the effect of a single administration of Jerusalem artichoke tubers on postprandial glycemia and the concentrations of incretin hormones in humans. Method: Grated Jerusalem artichoke was administered prior to a meal (Trial 1; white rice for prediabetic participants, n = 10). Dose-dependent effect of Jerusalem artichoke (Trial 2; white rice for prediabetic participants, n = 4) and effect prior to the fat-rich meal were also investigated (Trial 3; healthy participants, n = 5) in this pilot study. Circulating glucose, insulin, triglyceride, glucagon, active glucagon-like peptide-1 (GLP-1), and active glucose-dependent insulinotropic polypeptide (GIP) concentrations were subsequently measured in all the trials. Results: Jerusalem artichoke significantly reduced the glucose and GIP concentrations after the consumption of either meal in Trial 1 and Trial 3, whereas there were no differences in the insulin, glucagon, and active GLP-1 concentrations. Also, there was no significant difference in the triglyceride concentration after the ingestion of the fat-rich meal in Trial 3. The glucose and GIP-lowering effects were dose-dependent, and the consumption of at least 100 g of Jerusalem artichoke was required to have these effects in Trial 2. Conclusion: This study demonstrates that a single administration of Jerusalem artichoke tubers reduces postprandial glucose and active GIP concentrations in prediabetic and healthy individuals.

Modeling for Change of Daily Nurse Calls After Surgery in an Orthopedics Ward Using Bayesian Statistics.

Nurse call data may be used to evaluate the quality of nursing. However, traditional frequency-based statistics may not easily apply to nurse calls due to the large individual variability and daily call changes. We intended to propose a probabilistic modeling of nurse calls based on Bayesian statistics. We constructed the model including nurse call daily changes, individual variability, and adjustment according to characteristics (age and sex). Nurse call differences after surgery were analyzed based on data from the orthopedic ward from April 2014 to October 2017. Results show that there were differences in nurse calls from day 1 to day 10 after surgery between patients who had undergone orthopedic surgery and those who had undergone other surgeries such as tumor surgery. Furthermore, there were differences in nurse calls from day 1 to day 8 after surgery between patients who used extra pain relief medicine and those who did not. Although the analysis required multiple comparisons regarding daily nurse call changes and fixed data samples per day, our approach using Bayesian statistics could detect the periods and significant differences. This indicates that our nurse call modeling based on Bayesian statistics may be used to analyze nurse call changes.

Bayesian statistic model for nurse call data considering time-series, individual patient variabilities and massive zero-count call data.

Analysis of nurse call data is important to evaluate nursing management, because nurse calls reflect the fundamental demand of patients. However, the nurse call data include time-series properties and individual patient variabilities. In addition, the calls do not necessarily follow the common single distributions such as normal and Poisson distribution. These characteristics of the nurse call data cause the difficulty of applying traditional frequent statistics. To resolve this problem, we introduced Bayesian statistics and proposed a model including three elements: 1) transition, which represents time-series change of nurse calls, 2) random effect, which handles individual patient variabilities, and 3) zero inflated Poisson distribution, which is suitable for nurse call data including massive zero data. To evaluate the model, nurse call dataset containing total 3324 patients in orthopedics ward was used and the differences of nurse calls between the patients who had undergone orthopedics surgery and those who had undergone other surgeries were analyzed. The result in comparing all combinations of elements suggested that our model including all elements was the most fitting model to the dataset. In addition, the model could detect longer duration of nurse call difference existence than the other models. These results indicated that our proposed model based on Bayesian statistics may contribute to analyzing nurse call dataset.

All ZZ male Xenopus laevis provides a clear sex-reversal test for feminizing endocrine disruptors.

We investigated the possibility of using all ZZ male Xenopus laevis tadpoles produced by mating normal ZZ males with feminized ZZ males to detect estrogenic chemical activity. We examined the effects of 17beta-estradiol (E2) on sex differentiation by treating NF stage 49/50 to stage 57 tadpoles with 0.1, 1, 10, and 20 nM E2 for 4 weeks. Following this, the tadpoles were allowed to develop in clean water until the animals reached stage 66. Increased developmental abnormalities and mortality were not observed in all E2-exposed groups during metamorphosis. Feminization of gonads was detected at all E2 concentrations, whereas nonexposed controls developed testes. Morphological and histological analyses showed that feminized gonads were ovaries. Five and one hermaphroditic frogs were found in the 0.1 and 1 nM E2 groups, respectively, showing testicular as well as ovarian regions within one gonad. These results indicate that phenotypically normal females can be produced from genetic males and demonstrate the utility of a sex-reversal test based on all ZZ males for examining in vivo effects of chemicals with estrogenic activity. The testing of all ZZ male tadpoles might be a useful tool for assessment of feminizing compounds not only estrogenic substance.