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PubMed2000

A model to predict water intake of a pig growing in a known environment on a known diet.

A model to predict voluntary water intake (WI) of a pig fed a known diet in a known environment is described. The daily retentions of protein, lipid, water and ash were estimated over time using a published pig growth model. Food intakes were estimated using published methods. WI was estimated by adding the amounts required for digestion (WD), faecal excretion (Wfec), growth (WG), evaporation (WE), urinary excretion (WU) and by then subtracting the water arising from feed (WF), from nutrient oxidation (WO) and synthesis of body constituents (WS). WD was predicted assuming an absorption of water of 0.10, 0.16 and 0.07 kg/kg digestible carbohydrate, crude protein and lipid respectively. Wfec was estimated taking into account the water associated with the undigested protein (0.86 kg/kg), diethyl ether extract (-12.11 kg/kg), crude fibre (1.86 kg/kg), ash (-0.42 kg/kg) and N-free extract (4.4 kg/kg). The basal level of WE was estimated from the heat production of the pig fed ad libitum (MJ/d) as: 0.25 x (metabolizable energy - energy retained as protein and lipid) x 0.4, where 0.25 is the assumed proportion of the insensible heat loss at the comfort temperature and 0.4 is the water lost per MJ dissipated heat. WE in a hot environment was predicted by assuming that evaporation increased up to three times the basal level to offset the decreased sensible heat loss. To predict WU a water requirement for renal excretion of 2.05 and 3.40 kg/osmol excreted N as urea and minerals respectively was assumed. The urinary load of N and minerals was predicted from the intake of digestible nutrients and their retention. From the oxidation of 1 kg carbohydrate, protein, and fat it was assumed that 0.6, 0.42 and 1.07 kg water (WO) were released respectively. WS was predicted by assuming a release of 0.16, 0.07 and 0.57 kg water per kg retained protein, retained lipid coming from digestible lipid, and retained lipid coming from digestible carbohydrate respectively. The model is strongly rooted in a theoretical structure. When its predictions were compared with data from suitable experiments, the results were not significantly different. Both the pattern and the magnitude of responses of the model to changes in body weight, feed intake and environmental temperature are sensible and it allows a fuller prediction of voluntary water intake than the methods currently available.

Animal Feed↗
PubMed2001

Hydrophobicity and prediction of the secondary structure of membrane proteins and peptides.

Reliability of the hydropathy method to predict the formation of membrane-spanning alpha-helices by integral membrane proteins and peptides whose structure is known from X-ray crystallography is analysed. It is shown that Kyte-Doolittle hydropathy plots do not predict accurately 22 transmembrane alpha-helices in the reaction centres (RC) of the photosynthetic bacteria Rhodopseudomonas viridis and Rhodobacter sphaeroides (R-26). The accuracy of prediction for these proteins was improved using an optimised Kyte-Doolittle hydrophobicity scale. However, this hydrophobicity scale did not improve the predictions for the alphabeta-peptides of the B800-850 (LH2) complexes of the photosynthetic bacteria Rhodopseudomonas acidophila and Rhodospirillum molischianum, which were excluded from the optimisation procedure. The best and worst predictions of membrane-spanning alpha-helices for the RC proteins and LH2 peptides, respectively, were obtained with a propensity scale (PRC) calculated from the amino acid sequences and X-ray data for the RC proteins. A propensity scale (PLH) obtained using the amino acid sequences and X-ray data for the alphabeta-peptides of the LH2 complexes did not give an acceptable prediction of the transmembrane segments in the LH2 peptides; moreover, it markedly contradicted the PRC scale. Amino acids have been concluded to have no significant preference to localisation in transmembrane segments. Therefore, the predictive ability of the hydropathy methodology appears to be limited: the number of transmembrane segments can be correctly calculated for the best case only, and the lengths and positions of membrane-spanning alpha-helices in a protein amino acid sequence can not be predicted exactly.

Algorithms↗
PubMed2002

[Predictive factors affect the choice of strategy for breast cancer surgery].

Predictive factors are those factors that predict the effects of chemotherapeutic or other agents on the tumor or the host. Many factors have been investigated for their predictive value of the effect of chemotherapeutic or hormonal agents. Estrogen or progesterone receptors are the most established predictive factors for hormonal therapy. Her-2/neu is a predictive of the effect of the monoclonal antibody trastuzumab and of certain chemotherapeutic agents. Other predictive factors remain under clinical investigation. Most cases of breast cancer are initially considered to be systemic disease. Cure can only achieved with surgery that leaves no residual cancer cells, followed by an appropriate form of systemic therapy. In the clinical situation, local therapy and systemic therapy for breast cancer have been considered independently. However, preoperative chemotherapy has become common recently. The interaction between chemotherapy and surgery should be considered because the results of preoperative chemotherapy affect the choice of operative technique. Predictive factors for the effect of radiation therapy should also be taken into account after breast-conserving surgery. It remains to be determined which predictive factors should be considered at which time.

Antineoplastic Agents, Hormonal↗
PubMed2002

An overview on predicting the subcellular location of a protein.

The present paper overviews the issue on predicting the subcellular location of a protein. Five measures of extracting information from the global sequence based on the Bayes discriminant algorithm are reviewed. 1) The auto-correlation functions of amino acid indices along the sequence; 2) The quasi-sequence-order approach; 3) the pseudo-amino acid composition; 4) the unified attribute vector in Hilbert space, 5) Zp parameters extracted from the Zp curve. The actual performance of the predictive accuracy is closely related to the degree of similarity between the training and testing sets or to the average degree of pairwise similarity in dataset in a cross-validated study. Many scholars considered that the current higher predictive accuracy still cannot ensure that some available algorithms are effective in practice prediction for the higher pairwise sequence identity of the datasets, but some of them declared that construction of the dataset used for developing software should base on the reality determined by the Mother Nature that some subcellular locations really contain only a minor number of proteins of which some even have a high percentage of sequence similarity. Owing to the complexity of the problem itself, some very sophisticated and special programs are needed for both constructing dataset and improving the prediction. Anyhow finding the target information in mature protein sequence and properly cooperating it with sorting signals in prediction may further improve the overall predictive accuracy and make the prediction into practice.

Algorithms↗
PubMed2003

Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE.

BACKGROUND: The gaining interest in the use of clinical prediction guides as an aid for helping clinicians make effective front-line decisions, together with the increasing emphasis on evidence-based practice, underscores the need for accurate identification of sound clinical prediction studies. Despite the growing use of clinical prediction guides, little work has been done on identifying optimal literature search filters for retrieving these types of studies. The current study extends our earlier work, on developing optimal search strategies, to include clinical prediction guides. OBJECTIVE: To develop optimal search strategies for detecting methodologically sound clinical prediction studies in MEDLINE in the publishing year 2000. DESIGN: Comparison of the retrieval performance of methodologic search strategies in MEDLINE with a manual review ("gold standard") of each article for each issue of 162 core health care journals for the year 2000. METHODS: 6 experienced research assistants who had been trained and intensively calibrated reviewed all issues of 162 journals for the publishing year 2000. Each article was classified for format, interest, purpose, and methodologic rigor. Search strategies were developed for all purpose categories, including studies of clinical prediction guides. MAIN OUTCOME MEASURES: The sensitivity (recall), specificity, precision, and accuracy of single and combinations of search terms. RESULTS: 39% of original studies classified as a clinical prediction guide were methodologically sound. Combinations of terms reached peak sensitivities of 95%. Compared with the best single term, a three-term strategy increased sensitivity for sound studies by 17% (absolute increase), but with some loss of specificity when sensitivity was maximized. When search terms were combined to optimize sensitivity and specificity, these values reached or were close to 90%. CONCLUSION: Several search strategies can enhance the retrieval of sound clinical prediction studies.

Causality↗
PubMedDate not supplied

A simple method to predict dissolved phosphorus in runoff from surface-applied manures.

Computer models are a rapid, inexpensive way to identify agricultural areas with a high potential for P loss, but most models poorly simulate dissolved P release from surface-applied manures to runoff. We developed a simple approach to predict dissolved P release from manures based on observed trends in laboratory extraction of P in dairy, poultry, and swine manures with water over different water to manure ratios. The approach predicted well dissolved inorganic (R2 = 0.70) and organic (R2 = 0.73) P release from manures and composts for data from leaching experiments with simulated rainfall. However, it predicted poorly (R2 = 0.18) dissolved inorganic P concentrations in runoff from soil boxes where dairy, poultry, and swine manures had been surface-applied and subjected to simulated rainfall. Multiplying predicted runoff P concentrations by the ratio of runoff to rainfall improved the relationship between measured and predicted runoff P concentrations, but runoff P was still overpredicted for dairy and swine manures. We attributed this overprediction to immediate infiltration of dissolved P in the freely draining water of dairy and swine manure slurries upon their application to soils. Further multiplying predicted runoff dissolved inorganic P concentrations by 0.35 for dairy and 0.60 for swine manures resulted in an accurate prediction of dissolved P in runoff (R2 = 0.71). The ability of our relatively simple approach to predict dissolved inorganic P concentrations in runoff from surface-applied manures indicates its potential to improve water quality models, but field testing of the approach is necessary first.

Animals↗
PubMed2004

PreSPI: design and implementation of protein-protein interaction prediction service system.

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

Computational Biology↗
PubMed2003

A domain combination based probabilistic framework for protein-protein interaction prediction.

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices are constructed. Each matrix holds information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, respectively. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting sets of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in a Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP (Database of Interacting Proteins) is used as a learning set of interacting protein pairs, very high sensitivity (86%) and moderate specificity (56%) are achieved within our framework.

Amino Acid Sequence↗
PubMed2001

[The predictive value of plasma fibronectin concentration on fetal growth retardation and preeclampsia at earlier stage of the third trimester].

OBJECTIVE: To evaluate the predictive value of maternal plasma fibronectin concentration at 24-34 weeks on fetal growth retardation (FGR) and preeclampsia. METHODS: The maternal plasma fibronectin concentrations were measured by rate nephelometric procedure in the 130 initial normal nulliparous pregnant woman at 24-34 gestational weeks. The outcomes of pregnancies were followed. The predictive values of FN predicting on 3 kinds of outcome of pregnancy with FGR, preeclampsia, or FGR complicated preeclampsia were compared. RESULTS: (1) In a cohort of 130 initially normal nulliparous pregnant women, 10 cases of FGR, 10 cases of preeclampsia, and 4 cases of FGR complicated with preeclampsia were developed, and 106 cases with no complication. The maternal age, gravidity, sampling gestational ages, delivering gestational ages were not significantly different among the 4 groups (P > 0.05). (2) The plasma fibronectin levels in women with FGR, preeclampsia or FGR complicated preeclampsia [(486.45 +/- 122.69) mg/L, (428.38 +/- 118.71) mg/L and (443.66 +/- 68.13) mg/L, respectively] were significantly higher than that in the control [(284.41 +/- 93.83) mg/ L, P < 0.01]. (3) The areas under ROC of predicting on three outcomes of FGR, preeclampsia, or FGR complicated preeclampsia were 0.893, 0.818 and 0.867 respectively. (4) At the best cut point, the sensitivity, specificity, positive predictive value, negative predictive value and Kappa index of FN level predicting on the outcomes of pregnancy with three groups were 57.14%, 95.69%, 61.54%, 94.87%, 0.545 5 (on FGR); 91.27%, 50.00%, 15.38%, 98.29%, 0.1975 (on preeclampsia) and 42.86%, 91.38%, 37.50%, 92.98%, 0.3221 (on FGR complicated with preeclampsia), respectively. CONCLUSION: The maternal plasma fibronectin may be used as an earlier predictor for screening of FGR and the predictive value of FN on FGR is better than on preeclampsia.

Area Under Curve↗
PubMed2005

Outcome prediction after moderate and severe head injury using an artificial neural network.

Many studies have constructed predictive models for outcome after traumatic brain injury. Most of these attempts focused on dichotomous result, such as alive vs dead or good outcome vs poor outcome. If we want to predict more specific levels of outcome, we need more sophisticated models. We conducted this study to determine if artificial neural network modeling would predict outcome in five levels of Glasgow Outcome Scale (death, persistent vegetative state, severe disability, moderate disability, and good recovery) after moderate to severe head injury. The database was collected from a nation-wide epidemiological study of traumatic brain injury in Taiwan from July 1, 1995 to June 30, 1998. There were total 18583 records in this database and each record had thirty-two parameters. After pruning the records with minor cases (GCS 13) and missing data in the 132 variables, the number of cases decreased from 18583 to 4460. A step-wise logistic regression was applied to the remaining data set and 10 variables were selected as being statically significant in predicting outcome. These 10 variables were used as the input neurons for constructing neural network. Overall, 75.8% of predictions of this model were correct, 14.6% were pessimistic, and 9.6% optimistic. This neural network model demonstrated a significant difference of performance between different levels of Glasgow Outcome Scale. The prediction performance of dead or good recovery is best and the prediction of vegetative state is worst. An artificial neural network may provide a useful "second opinion" to assist neurosurgeon to predict outcome after traumatic brain injury.

Brain Injuries↗
PubMed2006

Association between split selection instability and predictive error in survival trees.

OBJECTIVES: To evaluate split selection instability in six survival tree algorithms and its relationship with predictive error by means of a bootstrap study. METHODS: We study the following algorithms: logrank statistic with multivariate p-value adjustment without pruning (LR), Kaplan-Meier distance of survival curves (KM), martingale residuals (MR), Poisson regression for censored data (PR), within-node impurity (WI), and exponential log-likelihood loss (XL). With the exception of LR, initial trees are pruned by using split-complexity, and final trees are selected by means of cross-validation. We employ a real dataset from a clinical study of patients with gallbladder stones. The predictive error is evaluated using the integrated Brier score for censored data. The relationship between split selection instability and predictive error is evaluated by means of box-percentile plots, covariate and cutpoint selection entropy, and cutpoint selection coefficients of variation, respectively, in the root node. RESULTS: We found a positive association between covariate selection instability and predictive error in the root node. LR yields the lowest predictive error, while KM and MR yield the highest predictive error. CONCLUSIONS: The predictive error of survival trees is related to split selection instability. Based on the low predictive error of LR, we recommend the use of this algorithm for the construction of survival trees. Unpruned survival trees with multivariate p-value adjustment can perform equally well compared to pruned trees. The analysis of split selection instability can be used to communicate the results of tree-based analyses to clinicians and to support the application of survival trees.

Algorithms↗
PubMed2006

Predictive biomarkers for drug-resistant Acinetobacter baumannii isolates with bla(TEM-1), AmpC-type bla and integrase 1 genotypes.

BACKGROUND AND PURPOSE: We tested whether antibiotic susceptibilities of drug-resistant Acinetobacter baumannii isolates could be used to predict the clinically important genotypes bla(TEM-1), AmpC-type bla, and integrase 1 gene (IntI1). METHODS: We analyzed 401 A. baumannii isolates obtained at Changhua Christian Hospital between April 2001 and March 2002. The isolates were all from blood cultures, and identification of A. baumannii was confirmed by API-20NE. Antibiotic susceptibility testing (phenotype) was performed by disk diffusion method. Polymerase chain reaction was used to detect the genes bla(TEM-1), AmpC-type bla and IntI1. RESULTS: Of 32 A. baumannii isolates, 10 possessed bla(TEM-1), 21 AmpC-type bla, and 26 IntI1. Resistance to ceftazidime (CAZ) predicted bla(TEM-1) genotype with 63.6% sensitivity, 100% specificity, 55.6% positive predictive value (PPV) and 0% negative predictive value (NPV). Trimethoprim-sulfamethoxazole (SXT) and gentamicin (GM) resistance predicted IntI1 genotype with 83.3% sensitivity, 71.4% specificity, 95.2% PPV and 45.4% NPV. No resistance phenotype could predict the AmpC-type bla genotype. CONCLUSIONS: CAZ resistance predicted the bla(TEM-1) genotype with 100% specificity, and SXT and GM resistance predicted the IntI1 genotype with 92.5% PPV. Therefore, antibiotic susceptibilities to CAZ, SXT, and GM can be utilized clinically to detect critical genotypes in A. baumannii.

Acinetobacter Infections↗
PubMed1988

Predicting life span for applicants to inpatient hospice.

The advent of hospice programs and their funding under Medicare has recently made eligibility for substantial insured services turn on whether a patient has less than three or six months to live. The implicit assumption is that physicians can provide this prediction accurately. To test this assumption and to improve predictions, the life spans of 108 consecutive applications for inpatient hospice care were estimated independently by two oncologists, an internist, an oncology nurse, and a hospice social worker, based on data in a ten-page multidisciplinary application packet. The applicants were followed up until death. Actual life span was correlated with predictions. The median (+/- SD) life span was 3.5 +/- 12.4 weeks. The predictions as a group were overly optimistic about survival by an average of 3.4 weeks. The best prognosticator's prediction was only moderately correlated with actual life span, and no two prognosticator's predictions correlated closely with one another. Predicting actual interval until death was more accurate than predicting a 90% confidence interval around the time of death, though the latter procedure was better at avoiding the error of unpredicted long-term survivors. This imprecision in "expert" estimation of life span poses substantial problems for hospice programs and policymakers.

Aged↗
PubMed1988

Warfarin dosage predictions assisted by the analog computer.

This paper describes an analog computer program used to predict warfarin dosages following an initial three daily doses of 10 mg. The program simulates the patient's response to warfarin and suggested dosages can be entered into the program daily to predict the dose necessary for the patient. Warfarin dosage predictions were made for 29 patients. There was a statistically significant correlation between predicted prothrombin time (PT) response and actual PT response (p less than 0.005) for all predictions made. However, when actual and predicted responses were compared with a paired t test, they were significantly different (p less than 0.05). The program described here has been useful for predicting initial warfarin requirements for the majority of patients. Continued research is necessary to identify useful computer methods for predicting warfarin dosages.

Computers, Analog↗
PubMed1987

A prospective study of the accuracy of ultrasound in predicting fetal microcephaly.

A prospective study of the diagnostic accuracy of ultrasound in the prediction of fetal microcephaly was performed on a study population of 24 patients. An occipitofrontal diameter larger than the predicted mean -2 standard deviations (SD), a head perimeter larger than the predicted mean -2 SD, and a head perimeter/abdominal perimeter larger than the predicted mean -1 SD were found to exclude fetal microcephaly. An occipitofrontal diameter smaller than the predicted mean -4 SD, a head perimeter smaller than the predicted mean -5 SD, a head perimeter/abdominal perimeter smaller than the predicted mean -3 SD, and a femur length/head perimeter larger than the predicted mean +3 SD were found to cause no errors in the diagnosis of microcephaly. If neither of these two groups of tests is satisfied, fetal microcephaly cannot be reliably diagnosed or excluded on the basis of a single ultrasound examination.

Anthropometry↗
PubMed1985

Accuracy of residual volume prediction--effects on body composition estimation in pulmonary dysfunction.

The accuracy of predicting residual volume (RV) and the effect of employing such predictions in the assessment of body composition by hydrostatic weighing was examined. Prediction equations derived by Wilmore (Med. Sci. Sports, 1969) and Boren et al. (Am. J. Med., 1966) were cross-validated on 400 male subjects with varying degrees of pulmonary dysfunction. Statistical analysis included comparison of predicted vs measured RV and regression analysis of measured on predicted values. Neither equation met acceptable statistical criteria (p greater than .05). Underwater weights were calculated based upon the measured RV and an assumed standard body weight, body density, and percent body fat (i.e., 70 kg, 1.0647 g/cc and 15.0%). Predicted RV was then substituted in the calculation and the effect on standardized values examined, between the calculated and standardized percent fat value with both equations. Results suggest in the present subjects, RV cannot be accurately predicted and the use of such prediction equations introduces sizable errors in body composition estimation.

Adult↗
PubMed1979

A comparison between predicted VO2 max from the Astrand procedure and the Canadian Home Fitness Test.

The purpose of this study was to compare the predicted maximal oxygen concumption derived from the Canadian Home Fitness Test (CHFT) and the Astrand ergometer test to the observed VO2 max determined from a progressive multi-stage treadmill test. Sixty-four sedentary subjects (35 males and 29 females) ranging in age from 20 to 54 years participated in the study. The mean VO2 max measured on the treadmill for males and females was 34.6 +/- 6.0 ml/kg/min while the Astrand procedure predicted a mean VO2max of 29.6 +/- 6.5 ml/kg/min and the CHFT predicted a mean VO2 max of 34.8 +/- 5.0 ml/kg/min. Statistical analysis revealed a significant under-prediction (P less than 0.001) of the VO2 predicted by the Astrand test to the VO2 max derived from the treadmill test while there were no differences between the treadmill VO2max and that predicted by the CHFT. When the male and female values were analyzed separately, the same results were seen in the males. For the females, however, there were no significant differences among predicted and observed values. It concluded that the CHFT provided an adequate prediction of cardio-respiratory fitness as well as, if not superior to, the Astrand procedure.

Adult↗
PubMedDate not supplied

Prediction of steady-state concentrations of valproic acid as determined from single plasma concentrations after the first dose.

A mathematical equation that predicts the maintenance dose needed to achieve a desired steady-state plasma concentration (Css) for first-order drugs from a single plasma concentration drawn after a single dose was tested for valproic acid. Six healthy volunteers received a single oral 500-mg dose of valproic acid and plasma concentrations were determined at 0, 1, 2, 4, 6, 8, 10, 24, and 32 hours. Each subject then received 750 mg of valproic acid daily in three divided doses for four days and a Css was determined. The equation was rearranged to predict the Css of valproic acid from plasma concentrations drawn at 1, 2, 4, 6, 8, and 10 hours after the single dose of valproic acid, using individually determined and mean terminal rate constant (k) values. The predicted Css were then compared with the actual Css. There was no difference in the predicted and actual Css using plasma concentrations collected at 6, 8, or 10 hours regardless of the derivation of the k values. The four-hour concentration predicted a lower Css if the mean (rather than the individual) k values were used. The one- and two-hour concentrations were not accurate predictors. While a 6-, 8-, or 10-hour plasma concentration would be adequate for predicting the Css, the 10-hour concentration had the least difference between the predicted and actual values. The time and cost of titrating valproic acid to a desired Css may be decreased by using this mathematical equation to predict the necessary maintenance dose.

Adult↗