生物识别与生物统计学杂志

Background: Tobacco use is a manmade manner which causes severe chronic diseases and Bangladesh is one of the most tobacco prevalent countries in the world. Advertisement and promotion events may have a big contribution to accelerate this. Therefore, this study aimed to analyze the advertisement and promotion events that encouraged the tobacco user.
Data and methods: Secondary data of sample size 9629 collected by the Global Adult Tobacco Survey (GATS), 2010 has been used. Along with descriptive analysis, binary logistic regression has been used to analyze the sociodemographic and economic correlates to be encouraged by marketing policy.
Results: The most common site for noticing cigarette, bidi and smokeless tobacco product advertisements was in stores (49.90%, 26.25% and 13.97%). From logistic regression it has been found that rural respondents are 1.17 times more inspired to smoke (OR=1.17, 95% CI=1.06, 1.30) from marketing policy than urban respondents. Female respondents are less inspired to smoke (OR=0.24, 95% CI=0.20, 0.28) than male respondents. Older respondents are less inspired to smoke by marketing policy than younger respondents (OR=0.98, 95% CI=0.98, 0.99). On the other hand, Rural respondents are 1.15 times more likely to be inspired to use smokeless product than urban respondents (OR=1.15, 95% CI=1.02, 1.31). Female respondents are 0.63 times less inspired to use smokeless tobacco product than male respondents (OR=0.63, 95% CI=0.51, 0.77) by marketing policy. Older respondents are less inspired to use smokeless tobacco products by marketing policy than younger respondents (OR=0.99, 95% CI=0.98, 0.99).
Conclusion: To reduce tobacco use in Bangladesh, Government, policy makers and research institutions that are working for reduction of tobacco use should pay attention more on young, student and female to advocate more. Also, Government could take action to limit advertisement in selling store.

A support vector classification wrapper feature elimination approach was used to find the most relevant pairs of molecular features that adequately and accurately can predict acute aquatic toxicity. These pairs were then used to derive chemical thresholds or boundaries between chemical properties for toxic and nontoxic organic chemicals that can be used as a “rule of thumb” to design less toxic chemicals. The most relevant pairs were determined to be: Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS), Difference between the LUMO and HOMO (dE) and Octonal-Water Partition Coefficient (QPlogo.w), and Difference between the LUMO and HOMO (dE) and Van der Waals surface area of polar nitrogen and oxygen atoms (PSA). Projected hyper planes were constructed for each pair and the following thresholds were found: for Lowest Unoccupied Molecular Orbital (LUMO) and Aqueous Solubility (QPlogS) they roughly correspond to QPlogS>-1 and LUMO>1, and for Octonal-Water Partition Coefficient (QPlogo.w) vs. difference between the LUMO and HOMO (dE) they roughly correspond to QPlogo.w<1 and dE>9. This study shows how a statistical approach such as support vector machines can be applied to the rational design of chemicals with reduced toxicity.

Kolmogorov-Zurbenko filters can be utilized in the public health context analyzing mortality data. This paper aims to expand upon the robust methodology of the KZ filters and their many applications. As a low-pass filter the KZ filters are proven to be the optimal means of analysis for non-stationary data such as mortality data which usually contains various underlying signals: seasonality, long-term trend, and short-term fluctuations. As diabetes incidence and prevalence increases, the burden of health care cost increases, thus prompting the need to understand patterns underlying adverse events related to diabetes, such as mortality. Increasing incidence and prevalence of diabetes prompts the need for preventative measures and understanding what environmental factors are related to adverse events as a result of diabetes. Diabetes mortality across time analyzed with non-parametric models has not previously been studied, thus this extension to the KZ filters is utilized as a preliminary analysis to address the gap in knowledge of diabetes mortality in the United States. Non-parametric time series analysis methods identify an 8.5-year long-term trend as well as annual seasonality of diabetes mortality. Spectral and time analysis of diabetes mortality introduces the relationship between solar activity and diabetes mortality, which is quantified utilizing the cross-correlation between diabetes mortality and total solar irradiation. The strong correlation between solar activity and diabetes mortality confirms the environmental role related specifically to diabetes mortality.

Ordinal scales can be used in latent growth curve modeling in three ways: mean, weighted mean scores, and factors measured by scale items. Sum and mean scores are commonly used in growth curve modeling in spite of certain discouragement. It was unclear how much bias these practices could produce in terms of the change rates and patterns. This study compared three methods with Monte Carlo Simulations under different number of response categories of the items, in terms of five key parameters of growth curve modeling. The hypothetical population models were derived from real empirical data to generate datasets of binary, trichotomous, five- and seven-point scales with sample size of 300. Latent growth curve modeling of mean, weighted mean, and factors measured by the ordinal scales were respectively fit to these datasets. Results indicated that modeling the factors that are measured with ordinal scales yield the fewest biases. Biases of modeling the means and weighted of the scales were under one decimal point in the change rates, whereas biases in the variances and covariance of the intercept and slope factors were large. In conclusion, it is inadvisable to use means or weighted means of ordinal scales for latent growth curve modeling. It produces the best results modeling the factors that are measured with the ordinal scales.

We describe a dose escalation algorithm for drug combinations in cancer phase I clinical trials. Parametric models for describing the association between the doses and the probability of dose limiting toxicity are used assuming univariate monotonicity of the dose-toxicity relationship. Trial design proceeds using the continual reassessment method, where at each stage of the trial, we seek the dose of one agent with estimated probability of toxicity closest to a target probability of toxicity given the current dose of the other agent. A Bayes estimate of the maximum tolerated dose (MTD) curve is proposed at the conclusion of the trial for continuous doses or a set of MTDs is determined in the case of discrete dose levels. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD under various model generated scenarios and misspecification. The method is further assessed for varying algorithms enrolling cohorts of two and three patients receiving different doses and compared to previous approaches such as escalation with overdose control and two-dimensional design.

In this paper, analysis was done for patients diagnosed of cancer from the Kilifi county hospital. The presence or absence of breast cancer had been done by the medical personnel and data documented. The objective was to determine the cancer prevalence rates of in the county. Data was obtained from survey questions and diagnosis by the medical personnel within the observation and follow up period of the patients. Data was also obtained for patients that had undergone testing to ascertain the type of tumor they had. Chi-square tests were carried out to check whether there was association between cancer and the smoking and between cancer and alcohol intake. The test show there was no association between Cancer and smoking (χ2=0.70938, df=2, pvalue=0.7014). Similarly a chi-square test showed no association between breast cancer and alcohol intake (χ2=0.42101, df=2, pvalue=0.8102). A logistic regression was fit to adjust for confounding. The table below shows the results after fitting this model. The results confirm that smoking and alcohol intake was not associated with breast cancer.

Tooth growth is essential to health and survival. In humans the growth rate can be inferred from the width of perikymata growth intervals. We hypothesized that in ancient times teeth grew faster than in modern humans. We measured the intervals between perikymata ridges on the surfaces of teeth and in thin sections of molars (which we used as standards) in ancient, prehistoric and modern humans. We compared statistically the results from ancient and modern specimens and assessed the impact of dietary factors and sociality on tooth growth. We found that ancient teeth grew faster than modern teeth (wider intervals) because of environmental, nutritional and life style influences. This apparently conferred evolutionary advantages for human survival. Our results gleaned from combining measurements of sections of teeth with modeling of web-available images suggest that life styles of modern humans have lead to smaller teeth.