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PubMed1989

Predictability of left heart dysfunction from right heart performance--cardiac index-venous pressure plots and cardiac index-mean pulmonary artery wedge pressure plots at rest and their shift during dynamic exercise.

In an attempt to examine the extent to which the right heart performance can predict the left heart performance in heart diseases primarily affecting the left heart, we recorded cardiac index-venous pressure (CI-VP) plot and cardiac index-mean pulmonary artery wedge pressure (CI-PAW) plot at rest and investigated the shift of CI-VP plot and CI-PAW plot that occurred during mild dynamic exercise of the lower limbs. Six patients had normal heart function and 20 patients had heart diseases primarily affecting the left heart. The sensitivity, specificity, positive predictive value and negative predictive value in the estimation of the left heart function with delta CI/delta VP were 80%, 100%, 89% and 100%, respectively, when delta CI/delta PAW was regarded as the golden standard for the estimation of the left heart function. The sensitivity, specificity, positive predictive value and negative predictive value in estimating the left heart function with delta CI/delta RA were 86%, 93%, 92% and 86%, respectively. When the left heart function was estimated by delta VP alone without measuring delta CI, the sensitivity, specificity positive predictive value and negative predictive value were 40%, 100%, 73% and 100%, respectively. In short, it was possible to predict the left heart dysfunction with delta CI/delta VP or delta CI/delta RA, in the presence of the right heart dysfunction. It was also possible to predict a steep heart slope from normal delta CI/delta VP with error of 2/18 (11%), when steep left heart slope was predicted, based on the presence of steep right heart slope. In comparison, delta VP alone was a less sensitive index of the performance of the left heart.

Adolescent↗
PubMed1992

Duodenal neoplasms: predictive value of CT for determining malignancy and tumor resectability.

CT examinations of 25 patients with proved primary or metastatic duodenal neoplasms were retrospectively reviewed to determine if morphologic features seen on CT scans could be used to predict the benign or malignant nature of these neoplasms and to assess the effectiveness of using CT findings to predict tumor resectability. We studied 19 malignant and six benign tumors. Histologic proof was obtained by means of surgery in 20 patients and by endoscopic biopsy in five. CT features of tumor morphology were assessed in the 22 cases in which a duodenal tumor was seen on CT. These features included central necrosis, ulceration or excavation, and the location of the tumor with respect to the bowel wall. The specific morphologic features used to predict that a tumor was malignant included the presence of an exophytic or intramural mass, central necrosis, and ulceration. The only criterion used to predict that a tumor was benign was that the mass be entirely intraluminal. Whenever vascular encasement, invasion of contiguous organs other than the head of the pancreas, distant lymphadenopathy, or metastases were present, the tumor was predicted to be unresectable for cure. With the exception of three benign smooth muscle tumors, all tumors with one or more CT morphologic features indicative of a malignant neoplasm were malignant (n = 16). Three of four intraluminal masses were benign. In three cases of polypoid tumors smaller than 2 cm, a duodenal tumor was not seen on CT. Whenever extraduodenal disease was found (15 cases), the neoplasms were malignant. In the 22 cases in which a tumor was detected on CT, the sensitivity of using the presence of one or more morphologic features associated with a malignant neoplasm as a predictor was 94%; the specificity was 50%, and the accuracy was 82%. If the presence of any morphologic feature indicative of a malignant neoplasm was combined with the presence of any finding of extraduodenal disease, CT was 100% sensitive and 86% accurate for predicting that the tumor was malignant. CT appears to be reliable for predicting duodenal tumor resectability. On the basis of CT findings, 10 tumors were correctly predicted as being unresectable for cure, and 12 were predicted as being resectable; no surgery was performed in the remaining three cases. In conclusion, evaluation of the morphologic features of duodenal neoplasms is a sensitive, but nonspecific, method for predicting that a tumor is malignant.(ABSTRACT TRUNCATED AT 400 WORDS)

Adenocarcinoma↗
PubMed1991

Prediction of reversible ischemia after coronary artery bypass grafting by positron emission tomography.

Metabolic imaging using positron emission tomography (PET) facilitates the identification of ischemic but viable myocardium. In this study, the predictive value of PET for identifying improvement in regional function after coronary artery bypass grafting (CABG) was assessed. PET perfusion and metabolic imagings using N-13 ammonia and F-18 deoxyglucose (FDG) were performed before and 5-7 weeks after CABG in 25 patients with coronary artery disease. Each of the 5 myocardial segments of the left ventricle was categorized as normal, ischemic and infarcted based on the findings of perfusion and PET metabolic images. Among 58 hypoperfused segments, abnormal perfusion in 17 of 25 ischemic segments was correctly predicted to be reversible (68% prediction accuracy), and that in 25 of 33 infarcted segments were correctly predicted to be irreversible (76% prediction accuracy) (p < 0.001). Similarly, among 53 asynergy segments assessed by radionuclide ventriculography, abnormal wall motion in 21 of 27 asynergy segments was correctly predicted to be reversible (78% prediction accuracy), and that in 21 of 26 PET viable segments was correctly predicted to be irreversible (81% prediction accuracy) (p < 0.001). Thus, preoperative metabolic imaging using PET appears to be useful for predicting responses to CABG.

Adult↗
PubMed1989

Fetal monitoring and predictions by clinicians: observations during a randomized clinical trial in very low birth weight infants.

Predictions about perinatal outcome in very low birth weight infants were studied in a randomized clinical trial of electronic fetal monitoring and periodic auscultation to assess the effect of diagnostic monitoring information on clinicians' ability to predict perinatal outcomes. The only predictions consistently correct before monitoring information was available were those regarding infant survival (88% correct, kappa [kappa] = 0.40, P less than .001 for the electronic fetal monitoring group; 80% correct, kappa = 0.35, P less than .01 for the periodic auscultation group). After monitoring, predictions of 5-minute Apgar scores and arterial cord pH were significantly more accurate, and clinicians' confidence in their predictions increased significantly in both the electronic fetal monitoring and the auscultation groups. Predictions of 5-minute Apgar scores were significantly more accurate in the electronic fetal monitoring group (92% correct, kappa = 0.80) than in the periodic auscultation group (61% correct, kappa = 0.28) (Z difference = 3.04; P less than .01). We conclude that clinicians gain information during intrapartum monitoring that generally leads to improved predictions and increased confidence in predictions. In this study, they made more accurate predictions about 5-minute Apgar scores with electronic fetal monitoring, suggesting that electronic fetal monitoring may provide better information about neonatal well-being than does periodic auscultation. Improved information, as measured by clinical predictions, is probably highly valued by patients and clinicians and may be an important determinant of acceptance of this diagnostic technology.

Apgar Score↗
PubMed1991

A prospective evaluation of elevated serum theophylline concentrations to determine if high concentrations are predictable.

PURPOSE: To evaluate prospectively whether serum theophylline concentrations of 25 mg/L and greater were predictable (and presumably preventable) by use of basic pharmacokinetic calculations. DESIGN: Prospective study. PATIENTS: Fifty-five patients with a serum theophylline concentration of at least 25.0 mg/L were evaluated initially and if subsequent elevated theophylline concentrations occurred. INTERVENTIONS: The predicted steady-state serum theophylline concentration was calculated from the dosage rate divided by the predicted clearance to determine how many elevated concentrations (greater than 20 mg/L) were predictable. Predicted clearances were 0.04 L/kg/hour for normal subjects less than 70 years of age and 0.02 L/kg/hour for patients with congestive heart failure, chronic obstructive pulmonary disease, or liver disease. Estimated clearances were determined and compared with predicted clearances. If patients did not have steady-state concentrations, additional calculations were made. MAIN RESULTS: From 6,368 consecutive theophylline determinations, 69 (1.08%) samples from 55 patients were 25 mg/L or higher. Predictably high concentrations occurred in 23 of 33 (69.7%) fully evaluable cases. These concentrations occurred because of a failure to consider decreased elimination clearance from congestive heart failure, chronic obstructive pulmonary disease, or hepatic disease. Five fatalities occurred, and in two cases, theophylline appeared to contribute to the patient's death. Three other patients experienced syncope. The predicted elimination clearance of theophylline of 0.02 L/kg/hour was too high in eight patients over 70 years old with cardiac or pulmonary disease. Nursing and pharmacy oversights were identified as three patients were given two theophylline products simultaneously. CONCLUSIONS: Most elevated theophylline concentrations are predictable (and preventable) by basic pharmacokinetic calculations. Patients experiencing elevated theophylline concentrations often had comorbid conditions and were greater than 60 years of age. The dosage rate of theophylline (mg/hour) can be estimated from predicted clearance (L/kg/hour) times desired steady-state serum concentration (mg/L).

Adult↗
PubMed1975

Comparison of the predicted and observed secondary structure of T4 phage lysozyme.

Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both beta-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140-142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.

Adenylate Kinase↗
PubMed1987

An evaluation of a computer program to predict the outcome of hyaline membrane disease.

A computerized model was developed to predict the severity of hyaline membrane disease. This study compared the program's predictive ability with that of three neonatologists. Blood gas and respiratory support data of 33 infants were entered into the computer which then predicted the severity of disease. The computer predicted 18 outcomes correctly. The neonatologists, provided with the same data, predicted 24 outcomes correctly. Both sets of predictions--computer and physician--found that patients predicted to have mild outcomes had the shortest oxygen requirements, duration of mechanical ventilation, and length of hospitalization, and infants predicted to have severe outcomes had the greatest oxygen requirements, duration of mechanical ventilation, and length of hospitalization. While actual severity was linked to birth weight, the model did not utilize birth weight in its predictive algorithm. Consequently, although the computer program predicted the outcomes with moderate success, it was less accurate than the neonatologists.

Blood Gas Analysis↗
PubMed1988

Bayesian forecasting improves the prediction of intraoperative plasma concentrations of alfentanil.

To achieve therapeutic plasma concentrations of the opioid alfentanil, one must administer the drug as a variable rate continuous infusion. For most patients, using population pharmacokinetic parameters of alfentanil for dosing regimen allows accurate prediction of the plasma concentration of the drug over time. However, for some patients, using such parameters results in systematic over- or underprediction of the concentration. Retrospectively studying a data set (dosage history and measured concentrations) for 34 patients, the authors examined how Bayesian forecasting could improve the precision of prediction. For each patient, a Bayesian regression was performed to estimate "individualized" pharmacokinetic parameters, using population pharmacokinetic values for alfentanil and the measurement of alfentanil in one or more plasma samples from each patient. These individualized parameters were then used to predict the subsequent plasma concentrations of alfentanil over time. By comparing the value of each measured point with its corresponding predicted value, the authors calculated the prediction error as a percentage of the measured value. The precision of the prediction was assessed by the percent mean absolute prediction error. After Bayesian forecasting using a single point sampled at 80 min after start of anesthesia, the average precision of the prediction was 13.8 +/- 6.1% (SD). Using no Bayesian forecasting and only population values of the pharmacokinetic parameters for the prediction of the concentration, the precision was 24.3 +/- 16.9%. The improvement in precision brought by Bayesian forecasting was especially noticeable for those patients whose prediction of alfentanil was poor using population pharmacokinetic values (i.e., "outlier" patients).(ABSTRACT TRUNCATED AT 250 WORDS)

Alfentanil↗
PubMed1986

Prediction of steady-state trough serum procainamide concentrations after administration of a sustained-release procainamide preparation.

Four methods of predicting steady-state trough serum procainamide concentrations (SPC) were compared in 15 patients receiving sustained-release procainamide (Procan-SR) therapy. All methods were based on a one-compartment pharmacokinetic model. Method 1 utilized nine initial measured SPC and individualized pharmacokinetic parameters for prediction of the steady-state SPC. Method 2 utilized three SPC and individualized pharmacokinetic parameters. Method 3 utilized two SPC and an individualized apparent elimination rate constant plus other average pharmacokinetic parameters. Method 4 utilized all averaged pharmacokinetic parameters (required no initial SPC). The predicted and measured SPCs for each method were analyzed by linear regression. Regression equations and correlation coefficients (r) for Methods 1, 2, 3, and 4 were as follows: predicted SPC = 0.72 measured SPC + 1.60 (r = 0.86), predicted SPC = 0.67 measured SPC + 1.74 (r = 0.82), predicted SPC = 0.13 measured SPC + 2.57 (r = 0.58), and predicted SPC = 0.15 measured SPC + 2.47 (r = 0.52), respectively. The precision, as measured by the mean squared prediction error (95% confidence interval) for Methods 1, 2, 3, and 4 was 1.44 (0.61, 2.27), 1.75 (0.93, 2.57), 2.81 (1.3, 4.49), and 3.68 (1.12, 6.26), respectively. (Eighty-five percent of the predictions were within +/- 1.5 micrograms/ml of the measured SPC by Methods 1 and 2, as compared with 69% by Methods 3 and 4.) Bias, as measured by the mean prediction error (95% confidence interval) for Methods 1, 2, 3, and 4 were -0.44 (-0.90, 0.03), -0.34 (-0.87, 0.18), -0.43 (-1.09, 0.24), and -0.98 (-1.66, -0.31).(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗
PubMed1990

An evaluation of Bayesian microcomputer predictions of theophylline concentrations in newborn infants.

Determination of appropriate theophylline maintenance doses in preterm infants is confounded by interpatient variability. This study evaluated the performance of an IBM PC computer program applying Bayesian regression before and during steady state in 37 preterm infants. Prior population estimates of clearance and distribution volume in preterm infants and Bayesian estimates of clearance and distribution volume based on one to three theophylline plasma concentrations were used to predict subsequent concentrations (drawn 1-17 days later). We assessed the accuracy and precision of the predictive performance of the Bayesian program with the mean prediction error and the mean absolute prediction error. The absolute prediction error (mean absolute error +/- SEM) significantly decreased with increasing feedback concentrations from 3.54 +/- 0.45 micrograms/ml (population estimates) to 2.74 +/- 0.42 (one feedback) and 2.02 +/- 0.35 micrograms/ml (two feedback concentrations). Mean prediction errors (+/- SEM) based on one to three feedbacks (-1.5 +/- 0.40 micrograms/ml) were significant improvements over population predictions (-2.63 +/- 0.72 micrograms/ml, p less than 0.05), although a small but significant average overprediction remained. Absolute prediction error was correlated with postconceptional and postnatal age when zero or one but not two feedback concentrations were available. Computer program predictions based on one measured feedback concentration were more accurate and precise than population-based predictions. Refinement of population parameters or two feedback concentrations further improved performance.

Bayes Theorem↗
PubMed1988

Predicting serum lithium concentration using Bayesian method: a comparison with other methods.

Two pharmacokinetic approaches (single-point Bayesian and two fixed volume of distribution-iterative methods) for predicting serum lithium concentrations in patients treated with lithium carbonate for manic-depressive illness or cyclic neutropenia in Kyushu University Hospital were evaluated and compared retrospectively. Prior to these analyses, three methods (prediction using mean parameters reported by Mason et al., the Pepin method, and the Zetin method) without measuring serum concentrations were also compared. In the Bayesian analysis, the effect of population mean parameters (reported by Mason et al. and Pepin et al.), which were used as initial estimates in a fitting process, on predictive performance was also studied. Forty five patients (21 male, 24 female) were included in this study. The average number of determinations per patient was 6.3, and the sampling times ranged from 2 to 18 h after the last dose. Serum lithium concentrations were measured by atomic absorption spectrometer. The Bayesian method used a computer program (PEDA) developed previously by one of us. The prediction using the population mean values from Mason's report gave the least root mean squared error (RMSE; a composite measure for bias and precision of prediction), and was considered to be the most precise among the methods without measuring serum concentrations. Among the methods using a single measured concentration, the Bayesian prediction was less biased and more precise than that by the two fixed volume of distribution-iterative methods. The Bayesian method reduced prediction error in serum concentration prediction compared with those obtained from population mean parameters in both cases: A high reduction of RMSE was observed when the values from Pepin method were used as initial estimates (from 0.320 to 0.219 meq/l), while, Mason's values gave less reduction (from 0.219 to 0.213 meq/l). In the Bayesian prediction of serum lithium concentration, the selection of population-based initial estimates gave no effect on predictive ability of the Bayesian method in terms of RMSE. In conclusion, the Bayesian method was robust and flexible with regard to dosing schedule, sampling time and number of blood samples, and gave the most clinically acceptable precision among the methods evaluated.

Adolescent↗
PubMed1990

Predicting the course of AIDS in Australia.

There have been urgent demands for knowledge about the epidemic of the acquired immunodeficiency syndrome (AIDS) in Australia. Accurate predictions are important for efficient allocation and planning of limited health-care resources. Ideal data for this purpose would be reliable knowledge of the past and present incidence of human immunodeficiency virus (HIV) infection. However, since the incidence of the infection is unknown predictions can only be based on historical data of the incidence of AIDS. In this article, we show the limitations of such predictions by examining a broad range of mathematical models that successfully track the observed data (1187 cases diagnosed to December 31, 1988). In addition, we describe a simple method for prediction in subgroups where the numbers of cases observed so far are small. Four models representing different forms of departure from the simple exponential model provide the best fits to the Australian AIDS data. Regional variability and a possible effect resulting from the introduction of zidovudine were incorporated into the models. Significant regional variability in the course of the epidemic was observed between New South Wales, Victoria and the rest of the country. For Australia as a whole, the doubling time changed from less than one year before mid 1987 to more than two years after this time. Model fits were improved by fitting the models to just the four years of data from 1985. The models give comparable predictions for the first year (1989) of around 600 new cases. However, by 1993 the predictions vary considerably, ranging from 500 to 2300 new cases. It is predicted that between 3100 and 6700 cases are likely to be diagnosed in Australia between 1989 and 1993. The results from the subgroup prediction demonstrate that when the observed number of cases is small, then the range of predictions for a future time interval is very wide. For reliable long-term predictions that are necessary for public health planning, basic information on the past and present incidence of HIV infection is urgently needed.

Acquired Immunodeficiency Syndrome↗
PubMed1975

The prediction of maximal oxygen uptake before and after physical training in children.

Maximal oxygen consumption (V O2 max) expressed in ml/kg/min and predicted V O2 max were determined before and after 8 weeks of training in 24 boys 10-12 years. Training involved 13 of them while 11 were controls. Predicted V O2 max was based on submaximal cycling heart rate according to the Astrand-Rhyming procedure. Pre-training, V O2 max was underpredicted by 12 per cent. This resulted mainly from an apparently low cycling efficiency in these subjects compared to that implicit in the prediction equation. Although adjustments in the prediction equation could equalize the means for V O2 max and predicted V O2 max, the rather low correlation (r = .55) between these measures precluded the accurate prediction of individual scores. V O2 max remained unchanged with training while submaximal heart rate during bicycle and treadmill exercise showed a significant decrease, resulting in predicted increases in V O2 max in children. Since V O2 max was actually unchanged, the prediction falsely indicated an improvement. Furthermore, despite a significantly lower heart rate in the trained group, there was no difference in predicted V O2 max between the groups post-training. These findings indicate that if V O2 max is the parameter of interest, it would seem to be more satisfactory to measure it directly until more reliable methods of prediction are developed.

Analysis of Variance↗
PubMed1988

Predictive value of Glasgow coma score for awakening after out-of-hospital cardiac arrest. Cerebral Resuscitation Study Group of the Belgian Society for Intensive Care.

The Glasgow coma score (GCS) during days 1-6 after cardiac arrest was used to predict neurological outcome in 360 resuscitated victims of out-of-hospital cardiac arrest. A predictive rule based on the best GCS of 216 patients resuscitated in 1983-84 (prediction group) was constructed, and its predictive power was tested on 133 patients treated in 1985 (test group). Neurological outcome was correctly predicted 2 days after cardiac arrest in 80% of the prediction group, with a best GCS of 10 or above and 4 or below as cutoff points. For patients with a best GCS of 5-9, prediction of outcome was possible 6 days after cardiac arrest, with a best GCS of 8 during the first 6 days as the single cutoff point. The rule was then validated in the test group: the sensitivity was 96%; the specificity 86%; the negative predictive value 97%; and the positive predictive value 77%. These data suggest that this simple GCS-based rule can be helpful in predicting outcome in patients resuscitated after out-of-hospital cardiac arrest, but confirmation of these data is required in a prospective study in a larger number of patients.

Belgium↗
PubMed2026

CLASPP: A unified model for predicting post-translational modifications.

Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular pathways and increasing the functional diversity of the proteome. Accurately predicting the PTM types that are likely to occur at a given site in the primary sequence is a key challenge in functional proteomics. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types, making it difficult to predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-based Stratified PTM Predictor (CLASPP), a unified PTM prediction model. CLASPP addresses imbalance challenges by leveraging unsupervised clustering-based undersampling and a novel contrastive learning framework tailored to PTM data. Additionally, our hierarchical data organization and curation are shown to improve CLASPP's performance by balancing the representation of individual PTM types and provides a standardized dataset to train and validate future model designs. Drawing inspiration from advancements in image and natural language processing, the CLASPP model employs a multi-stage training strategy and a high-quality, curated training dataset to improve PTM prediction performance. To uncover what is learned during the contrastive learning stage, the CLASPP model is shown to distinguish known protein kinase substrate specificity profiles as a form of explainability. Finally, we evaluate the application of CLASPP in predicting PTMs in different model organisms and experimentally validated ubiquitination sites in the understudied DCLK3 kinase. Overall, CLASPP represents a unified model for PTM prediction that addresses key bottlenecks in data imbalance and offers new strategies for biological data curation, thereby improving PTM-type prediction performance across diverse organisms.

Protein Processing, Post-Translational↗
PubMed1992

Differences between predictive characteristics of signal-averaged electrocardiographic variables for postinfarction sudden death and ventricular tachycardia.

Several studies indicate that the electrophysiologic substrate for sustained ventricular tachycardia differs from that of ventricular fibrillation. This prospective study examined whether there were clinically relevant differences between the predictive values of the standard time-domain signal-averaged (SA) electrocardiographic (ECG) variables for ventricular tachycardia and sudden death after myocardial infarction. Predischarge SA electrocardiograms were recorded in 332 patients after infarction. During a follow-up period of greater than or equal to 6 months, there were 12 sudden deaths (3.6%), 14 patients (4.2%) developed spontaneous sustained ventricular tachycardia and 20 patients (6%) died of circulatory failure. The sensitivity, specificity and positive predictive accuracy of the numerical values of the time-domain SA electrocardiographic variables for predicting sudden death and ventricular tachycardia were compared. The optimal criteria for predicting ventricular tachycardia required the positivity of greater than or equal to 2 of the standard time-domain SA variables, whereas the optimal criteria for predicting sudden death required the positivity of all 3 variables. A high specificity was sustained over a wider range of sensitivity for sudden death than it was for ventricular tachycardia and the values of the variables which provided the same sensitivity for sudden death and ventricular tachycardia were different. For a sensitivity of 70%, the positive predictive accuracy was 31% for predicting sudden death and 13% for predicting ventricular tachycardia. The study concludes that differences in the predictive characteristics of variables for ventricular tachycardia and sudden death may be used to refine postinfarction risk stratification.

Adult↗
PubMed2026

Genome-based predictions of metabolic preferences and substrate phenotypes in psychrotrophic bacteria from permafrost environments.

Genomes reveal vast functional potential, but harbor genomic noise that obscures prediction of metabolic and environmental preferences. Genomic databases are skewed towards clinically relevant and easily cultivated bacteria, limiting predictions for diverse and underrepresented environmental taxa. Psychrotrophic bacteria, which can survive and grow in cold, nutrient-limited, dry, and saline environments, are especially underrepresented despite their relevance for understanding microbial responses to changing cold environments and potential biotechnological value given growth at low temperatures. Assembling complete genomes of 48 isolates from Alaskan permafrost, seasonally frozen active layer soils, and terrestrial ice, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog annotations to evaluate the predictability of metabolic resource-use traits observed using phenotypic tests. Genome-predicted values for glycolytic versus gluconeogenic catabolic preference index, or sugar-acid preference (SAP), explained over 50% of the variance in empirically observed SAP. SAP was inversely correlated to genomic GC content, which follows phylum-level trends, indicating that coarse metabolic preference covaries with phylogeny. Regularized elastic net models offered a more granular view, linking KEGG genes to specific substrate utilization and sensitivity phenotypes and yielding moderate but reproducible accuracy (AUC 0.70-0.79) for 11 substrates, demonstrating that specific substrate responses may be predictable from relatively small subsets of KO genes. These results extend recent advances, such as the SAP metric, and highlight associations among genomic GC content, phylum, and broad metabolic strategy. Linking genomic content to phenotype using isolates is a necessary step toward predictive models of microbial function in environmental communities, and this work can be used for hypothesis generation, with applications towards more expansive data sets.IMPORTANCECold region soils and ice host psychrotrophic bacteria with metabolic traits and adaptations that enable persistence in harsh, resource-limited environments. However, these taxa are underrepresented in genomic reference databases dominated by well-studied, mesophilic organisms. This gap limits inference of ecological strategies and our ability to predict how these microbes may influence the large, thaw-vulnerable carbon reservoirs in permafrost. Here, we show that genomic GC content is associated with the sugar-versus-acid catabolic preference (SAP) of isolates across major phyla, suggesting that broad genomic features may provide a coarse signal of metabolic strategy. We demonstrate that a modified SAP metric, using binary (positive/negative) substrate utilization rather than detailed growth rate measurements, is moderately predictive, thus extending its application to slow-growing or difficult-to-culture taxa. Together, these advances broaden the toolkit for linking genome content to resource-use traits (phenotype) in poorly characterized, cold-adapted bacteria and offer a tractable entry point to broad prediction and hypothesis generation.

Genome, Bacterial↗
PubMed1978

Predicting hearing level from the acoustic reflex. A comparison of three methods.

Three prediction methods based on the acoustic reflex noise-tone difference (NTD) were used to predict hearing level in 370 subjects. The methods were two versions of the sensitivity prediction by acoustic reflex (SPAR) and a formula for estimating hearing threshold level. With each method, hearing loss was correctly predicted in more than one half of the subjects, while serious predictive error occurred in less than 10%. Age was found to be an important factor in hearing level prediction. Predictive accuracy for each method decreased systematically as a function of age. Predictive accuracy for the two SPAR methods decreased dramatically in subjects with minor middle ear and/or tympanogram abnormalities. Nevertheless, the value of the acoustic reflex NTD in predicting hearing level was confirmed.

Acoustic Impedance Tests↗