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At least 1,279 records · Page 71Linked to original sources
PubMed2005

Forecasting seasonal population growth of Aphis glycines (Hemiptera: Aphididae) in soybean in Illinois.

From 2001 to 2004, 252 fifty-plant samples were collected from commercial soybean, Glycine max L., fields in three townships (93-km2 area) in Illinois. Townships were sampled every 3 wk from late June or early July when aphids (Aphis glycines Matsumura) first invaded the townships to early August. We used linear regression of 18 mean township field densities to calibrate several simple models to predict the change in aphid population density in a township from one sampling date to the next. The best exponential model for the complete data set has an r2 = 0.54, Y2 = Ylexp (0.09659 x DAY), where Y1 and Y2 are the first and second samples of aphids separated by a 3-wk period (the number of days, DAY). Our intrinsic rate of increase for the population is much lower than rates calculated in other studies. The best single-variable linear model has an r2 = 0.88, Y2 = Y1 + 0.1084 x Y1 x DAY. The latter model indicates the value of including monitoring data in the prediction. The best two-variable model has an R2 = 0.98, Y2 = Y1 + 0.08136 x Y1 x DAY + 0.000080 x N1(2) x DAY, where N1(2) x DAY is the interaction term for initial, squared, sample density of the season multiplied by the number of days between samples. The latter two models indicate that the change in the population density is greater for more dense populations. Degree-days were generally inferior to days as the time component in the simple models.

Animals↗
PubMed1989

Bayesian forecasting of serum lithium concentrations. Comparison with traditional methods.

Twelve pharmacokinetic methods of estimating lithium maintenance dosage requirements were compared in 21 patients with bipolar illness. Methods which were compared included the single- and multiple-point methods of Perry, 4 non-linear regression and 6 Bayesian methods. The REVOL algorithm was employed for converging on to estimates of clearance and apparent volume of distribution for the non-linear regression and Bayesian methods. Data analysis was based on an evaluation of prediction error as a measure of bias, and absolute prediction error as a measure of precision. In a direct comparison, there were no statistically significant differences in bias or precision between any of the methods.

Adolescent↗
PubMed2000

Forecasting roles of combinatorial chemistry in the age of genomically derived drug discovery targets.

Genomics has caused an explosion in the number of potential therapeutic targets with varying degrees of validated pathophysiology. Among the first applications of combinatorial chemistry in genomics-driven drug discovery is the search for surrogate ligands or substrates. In the event that no surrogate is found for molecular assays, more exotic functional screens in whole cells or model organisms are used. Protein-protein interaction mapping by yeast and mammalian two-hybrid systems dominates empirical functional genomics, and this will lead to a bias for screening projects targeting this type of interaction. Drug discovery for protein-protein interactions has a poor track record, and this will challenge prevailing views on the design of combinatorial libraries. Genomics based on structural homology will yield many putative kinases, receptors, enzymes, transporter proteins, ion channels and GPCRs. Most of these projects will require new surrogate agonists, ligands or substrates, and then pharmaceutically useful agonists or antagonists will need to be found. Again, combinatorial chemistry might be essential to these studies. Given the need to screen hundreds of targets at great risk of irrelevance to pathophysiology, combined with the challenge of finding surrogate or natural ligands for these new targets, there is an urgent need for efficiency. Different groups are addressing these concerns by developing biologically-driven combinatorial libraries in order to achieve a higher density of bioactivity. Early efforts in this regard will be described.

Combinatorial Chemistry Techniques↗
PubMedDate not supplied

Multivariate Markovian modeling of tuberculosis: forecast for the United States.

We have developed a computer-implemented, multivariate Markov chain model to project tuberculosis (TB) incidence in the United States from 1980 to 2010 in disaggregated demographic groups. Uncertainty in model parameters and in the projections is represented by fuzzy numbers. Projections are made under the assumption that current TB control measures will remain unchanged for the projection period. The projections of the model demonstrate an intermediate increase in national TB incidence (similar to that which actually occurred) followed by continuing decline. The rate of decline depends strongly on geographic, racial, and ethnic characteristics. The model predicts that the rate of decline in the number of cases among Hispanics will be slower than among white non-Hispanics and black non-Hispanics a prediction supported by the most recent data.

Adolescent↗
PubMed2006

Real-time forecast of multiphase outbreak.

We used a single equation with discrete phases to fit the daily cumulative case data from the 2003 severe acute respiratory syndrome outbreak in Toronto. This model enabled us to estimate turning points and case numbers during the 2 phases of this outbreak. The 3 estimated turning points are March 25, April 27, and May 24. The estimated case number during the first phase of the outbreak between February 23 and April 26 is 140.53 (95% confidence interval [CI] 115.88-165.17) if we use the data from February 23 to April 4; and 249 (95% CI: 246.67-251.25) at the end of the second phase on June 12 if we use the data from April 28 to June 4. The second phase can be detected by using case data just 3 days past the beginning of the phase, while the first and third turning points can be identified only approximately 10 days afterwards. Our modeling procedure provides insights into ongoing outbreaks that may facilitate real-time public health responses.

Canada↗
PubMed2004

Drug forecast--solifenacin: an investigational anticholinergic for overactive bladder.

OBJECTIVE: To review a new anticholinergic for overactive bladder undergoing review by the U.S. Food and Drug Administration, solifenacin. DATA SOURCE: A MEDLINE/PUBMED search was conducted to identify pertinent studies in the English language. In addition, proceedings of meetings of the Incontinence Society, European Association of Urology, American Urological Association, and American College of Obstetrics and Gynecology were reviewed for relevant abstracts. Additional references were obtained from the bibliographies of these sources. Data over the time period of 1986 through October 2003 were reviewed. STUDY SELECTION: All studies evaluating any aspect of solifenacin in animals and humans. DATA SYNTHESIS: Preclinical studies demonstrated that solifenacin was an antagonist at muscarinic cholinergic M1, M2, and M3 receptors. On the basis of comparative preclinical studies evaluating the effects of solifenacin and oxybutynin on guinea pig detrusor muscle cells, mouse submandibular gland cells, and the intact rat, solifenacin was felt to be a "uroselective" antimuscarinic. Solifenacin is predominantly eliminated via metabolism, with urinary excretion of parent compound being less than 10%. Solifenacin, dosed once daily, is significantly superior to placebo in reducing the number of micturitions, urge episodes, and urge incontinence episodes per day and increasing the volume voided per micturition. In two active-controlled trials, solifenacin was at least equivalent to tolterodine in efficacy and tolerability. The most problematic adverse effects of solifenacin are the anticholinergic effects of dry mouth, blurred vision, and constipation. CONCLUSION: Although promising in preclinical studies, the uroselectivity of the anticholinergic activity of solifenacin has not been validated in clinical trials. No comparative efficacy/tolerability data with oxybutynin are available. On the basis of available data, solifenacin does not appear to be a substantial advance upon existing anticholinergics in the management of overactive bladder.

Journal Article↗