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At least 1,315 records · Page 73Linked to original sources
PubMed2005

Conversion from mild cognitive impairment to probable Alzheimer's disease predicted by brain magnetic resonance spectroscopy.

OBJECTIVE: Mild cognitive impairment has been regarded as a pre-Alzheimer condition, but some patients do not develop dementia. Given the available therapies for Alzheimer's disease, early diagnosis is of paramount importance. The authors' objective was to determine whether findings from magnetic resonance spectroscopy (MRS) of the hippocampus and other cortical areas would predict conversion from amnestic mild cognitive impairment to probable Alzheimer's disease. METHOD: A longitudinal inception cohort of 53 consecutive and incident subjects fulfilling the criteria of amnestic mild cognitive impairment was followed for a mean period of 3 years. At baseline, a neuropsychological examination (Mini-Mental State Examination, Blessed Dementia Rating Scale, Clinical Dementia Rating, verbal fluency test, and memory tests) and standard blood tests were performed, and three cortical areas were examined by proton MRS: left hippocampus, right parietal cortex, and left occipital cortex. The patients were evaluated periodically to detect conversion to probable Alzheimer's disease. The statistical analysis of predictions was based on receiver operating characteristic curves. RESULTS: By the follow-up assessment that occurred on average after 3 years, 29 patients (55%) had developed probable Alzheimer's disease. An occipital cortex N-acetylaspartate/creatine ratio < or =1.61 predicted dementia at 100% sensitivity and 75% specificity (area under the curve=0.91, 95% CI=0.80-0.97). The positive predictive value was 83%, and the negative predictive value was 100%, with an overall cross-validated classification accuracy of 88.7%. None of the values in the hippocampus and parietal cortex had significant predictive value. CONCLUSIONS: MRS of the brain performed on patients with mild cognitive impairment is a valuable tool in predicting conversion to probable Alzheimer's disease. Occipital values were more reliable than hippocampal values in this prediction.

Aged↗
PubMed1999

Clinical prediction of assaultive behavior among male psychiatric patients at a maximum-security forensic facility.

OBJECTIVE: Patient characteristics associated with the clinical prediction of assaultive behavior in a forensic psychiatric hospital were compared with characteristics associated with actual assaultive behavior. METHODS: Treating psychiatrists at a New York forensic psychiatric hospital were asked to predict which of a sample of 183 recently admitted male patients were likely to show assaultive behavior during a three-month period. The predictions were compared with incident reports of actual assaultive behavior. Several patient characteristics, including race, legal status, age, education, criminal history, psychiatric symptoms rated independently by raters other than the treating psychiatrists, and ward behavior, were examined for their association with predicted and actual assaultive behavior. RESULTS: Clinicians' rate of correct prediction of assaultive behavior was 71 percent, with a diagnostic sensitivity of 54 percent and a diagnostic specificity of 79 percent. Characteristics associated with the prediction of assaultive behavior were race, transfer from a civil facility because of violence or dangerousness, age, education, arrests for violent offenses, childhood physical abuse, hostility, temper (or nurses' assessment of the patient's irritability), and inability to follow ward routine. Characteristics associated with actual assaultive behavior were transfer from a civil hospital, dual diagnosis of schizophrenia and substance abuse or dependence, childhood physical abuse, age, thought disorder, and temper. CONCLUSIONS: Clinicians were significantly more accurate than chance in prospectively predicting which male forensic patients would show assaultive behavior. However, some of the factors associated with clinical prediction, such as race, ability to follow ward routine, and arrest history, were not associated with actual assaultive behavior. In addition, clinicians failed to use dual diagnosis of schizophrenia and substance use disorder as a predictor.

Adult↗
PubMedDate not supplied

Predicting in-hospital mortality for stroke patients: results differ across severity-measurement methods.

OBJECTIVE: To see whether severity-adjusted predictions of likelihoods of in-hospital death for stroke patients differed among severity measures. METHODS: The study sample was 9,407 stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Probability of death was calculated for each patient using logistic regression with age-sex and each of five severity measures as the independent variables: admission MedisGroups probability-of-death scores; scores based on 17 physiologic variables on admission; Disease Staging's probability-of-mortality model; the Seventy Score of Patient Management Categories (PMCs); and the All Patient-Refined Diagnosis Groups (APR-DRGs). For each patient, the odds of death predicted by the severity measures were compared. The frequencies of seven clinical indicators of poor prognosis in stroke were examined for patients with very different odds of death predicted by different severity measures. Odds ratios were considered very different when the odds of death predicted by one severity measure was less than 0.5 or greater than 2.0 of that predicted by a second measure. RESULTS: MedisGroups and the physiology scores predicted similar odds of death for 82.2% of the patients. MedisGroups and PMCs disagreed the most, with very different odds predicted for 61.6% of patients. Patients viewed as more severely III by MedisGroups and the physiology score were more likely to have the clinical stroke findings than were patients seen as sicker by the other severity measures. This suggests that MedisGroups and the physiology score are more clinically credible. CONCLUSIONS: Some pairs of severity measures ranked over 60% of patients very differently by predicted probability of death. Studies of severity-adjusted stroke outcomes may produce different results depending on which severity measure is used for risk adjustment.

Adolescent↗
PubMed2005

Prediction of twin-arginine signal peptides.

BACKGROUND: Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides. RESULTS: We have retrieved sequence data for Tat substrates in order to train a computational method for discrimination of Sec and Tat signal peptides. The TatP method is able to positively classify 91% of 35 known Tat signal peptides and 84% of the annotated cleavage sites of these Tat signal peptides were correctly predicted. This method generates far less false positive predictions on various datasets than using simple pattern matching. Moreover, on the same datasets TatP generates less false positive predictions than a complementary rule based prediction method. CONCLUSION: The method developed here is able to discriminate Tat signal peptides from cytoplasmic proteins carrying a similar motif, as well as from Sec signal peptides, with high accuracy. The method allows filtering of input sequences based on Perl syntax regular expressions, whereas hydrophobicity discrimination of Tat- and Sec-signal peptides is carried out by an artificial neural network. A potential cleavage site of the predicted Tat signal peptide is also reported. The TatP prediction server is available as a public web server at http://www.cbs.dtu.dk/services/TatP/.

Arginine↗
PubMed2005

An SVM-based system for predicting protein subnuclear localizations.

BACKGROUND: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computational tool for the prediction of subnuclear and subcellular localizations generally applicable to protein sequences. The information on localization may reveal the molecular function of novel proteins, in addition to providing insight on the biological pathways in which they function. The bulk of past work has been focused on protein subcellular localizations. Furthermore, no specific tool has been dedicated to prediction at the subnuclear level, despite its high importance. In order to design a suitable predictive system, the extraction of subtle sequence signals that can discriminate among proteins with different subnuclear localizations is the key. RESULTS: New kernel functions used in a support vector machine (SVM) learning model are introduced for the measurement of sequence similarity. The k-peptide vectors are first mapped by a matrix of high-scored pairs of k-peptides which are measured by BLOSUM62 scores. The kernels, measuring the similarity for sequences, are then defined on the mapped vectors. By combining these new encoding methods, a multi-class classification system for the prediction of protein subnuclear localizations is established for the first time. The performance of the system is evaluated with a set of proteins collected in the Nuclear Protein Database (NPD). The overall accuracy of prediction for 6 localizations is about 50% (vs. random prediction 16.7%) for single localization proteins in the leave-one-out cross-validation; and 65% for an independent set of multi-localization proteins. This integrated system can be accessed at http://array.bioengr.uic.edu/subnuclear.htm. CONCLUSION: The integrated system benefits from the combination of predictions from several SVMs based on selected encoding methods. Finally, the predictive power of the system is expected to improve as more proteins with known subnuclear localizations become available.

Algorithms↗
PubMed2006

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains.

BACKGROUND: The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. RESULTS: A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. CONCLUSION: This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

Amino Acid Sequence↗
PubMed2006

Improving the accuracy of protein secondary structure prediction using structural alignment.

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4-5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally.

Algorithms↗
PubMed2005

Optimal search strategies for identifying sound clinical prediction studies in EMBASE.

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies.

Databases, Bibliographic↗
PubMed2006

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

BACKGROUND: Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. METHODS: A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. RESULTS: CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. CONCLUSION: State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Birth Weight↗
PubMed2005

Predictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signatures.

BACKGROUND: Our goal was to identify gene signatures predictive of response to preoperative systemic chemotherapy (PST) with epirubicin/cyclophosphamide (EC) in patients with primary breast cancer. METHODS: Needle biopsies were obtained pre-treatment from 83 patients with breast cancer and mRNA was profiled on Affymetrix HG-U133A arrays. Response ranged from pathologically confirmed complete remission (pCR), to partial remission (PR), to stable or progressive disease, "No Change" (NC). A primary analysis was performed in breast tissue samples from 56 patients and 5 normal healthy individuals as a training cohort for predictive marker identification. Gene signatures identifying individuals most likely to respond completely to PST-EC were extracted by combining several statistical methods and filtering criteria. In order to optimize prediction of non responding tumors Student's t-test and Wilcoxon test were also applied. An independent cohort of 27 patients was used to challenge the predictive signatures. A k-Nearest neighbor algorithm as well as two independent linear partial least squares determinant analysis (PLS-DA) models based on the training cohort were selected for classification of the test samples. The average specificity of these predictions was greater than 74% for pCR, 100% for PR and greater than 62% for NC. All three classification models could identify all pCR cases. RESULTS: The differential expression of 59 genes in the training and the test cohort demonstrated capability to predict response to PST-EC treatment. Based on the training cohort a classifier was constructed following a decision tree. First, a transcriptional profile capable to distinguish cancerous from normal tissue was identified. Then, a "favorable outcome signature" (31 genes) and a "poor outcome signature" (26 genes) were extracted from the cancer specific signatures. This stepwise implementation could predict pCR and distinguish between NC and PR in a subsequent set of patients. Both PLS-DA models were implemented to discriminate all three response classes in one step. CONCLUSION: In this study signatures were identified capable to predict clinical outcome in an independent set of primary breast cancer patients undergoing PST-EC.

Journal Article↗
PubMed2004

Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1.

BACKGROUND: Large fractions of all fully sequenced genomes code for proteins of unknown function. Annotating these proteins of unknown function remains a critical bottleneck for systems biology and is crucial to understanding the biological relevance of genome-wide changes in mRNA and protein expression, protein-protein and protein-DNA interactions. The work reported here demonstrates that de novo structure prediction is now a viable option for providing general function information for many proteins of unknown function. RESULTS: We have used Rosetta de novo structure prediction to predict three-dimensional structures for 1,185 proteins and protein domains (<150 residues in length) found in Halobacterium NRC-1, a widely studied halophilic archaeon. Predicted structures were searched against the Protein Data Bank to identify fold similarities and extrapolate putative functions. They were analyzed in the context of a predicted association network composed of several sources of functional associations such as: predicted protein interactions, predicted operons, phylogenetic profile similarity and domain fusion. To illustrate this approach, we highlight three cases where our combined procedure has provided novel insights into our understanding of chemotaxis, possible prophage remnants in Halobacterium NRC-1 and archaeal transcriptional regulators. CONCLUSIONS: Simultaneous analysis of the association network, coordinated mRNA level changes in microarray experiments and genome-wide structure prediction has allowed us to glean significant biological insights into the roles of several Halobacterium NRC-1 proteins of previously unknown function, and significantly reduce the number of proteins encoded in the genome of this haloarchaeon for which no annotation is available.

Archaeal Proteins↗
PubMed2006

Using several pair-wise informant sequences for de novo prediction of alternatively spliced transcripts.

BACKGROUND: As part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus. RESULTS: MARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted. CONCLUSION: The MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small.

Algorithms↗
PubMed1998

Validity of predictions of residual retroperitoneal mass histology in nonseminomatous testicular cancer.

PURPOSE: To validate predictions of the histology (necrosis, mature teratoma, or cancer) of residual retroperitoneal masses in patients treated with chemotherapy for metastatic nonseminomatous testicular germ cell tumor. PATIENTS AND METHODS: We studied 172 testicular cancer patients who underwent resection while tumor markers were normal. Predictive characteristics for the residual histology were registered, including the presence of teratoma elements in the primary tumor, the prechemotherapy level of tumor markers (alpha-fetaprotein [AFP], human chorionic gonadotropin [HCG], lactate dehydrogenase [LDH]), the size of the residual mass, and the percentage of shrinkage in mass diameter. We calculated the predicted probability of necrosis and the ratio of cancer and mature teratoma with previously published logistic regression formulas. RESULTS: The distribution of the residual histology was necrosis in 77 (45%), mature teratoma in 72 (42%), and cancer in 23 (13%). Necrosis could be well distinguished from other tissue, with an area under the receiver operating characteristic (ROC) curve of 82%. No tumor was found in 15 patients with a predicted probability of necrosis over 90%. The predicted probabilities corresponded reliably with the observed probabilities (goodness-of-fit tests, P > .20), although a somewhat higher probability of necrosis was observed in patients treated with chemotherapy containing etoposide. Conversely, cancer could not reliably be predicted or adequately discriminated from mature teratoma. CONCLUSION: The predicted probabilities of necrosis have adequate reliability and discriminative power. These predictions may validly support the decision-making process regarding the need and extent of retroperitoneal lymph node dissection.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

PURPOSE: This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. PATIENTS AND METHODS: A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. RESULTS: Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. CONCLUSION: Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression-based predictive index can be used to select patients for PMRT.

Adult↗
PubMed1984

Comparison of predicted and adult heights in short boys: effect of androgen therapy.

We evaluated the accuracy of height predictions based on the tables of Bayley and Pinneau (2) in 43 boys with short stature. Sixteen boys were treated with androgens and 27 received no treatment. In 17 boys whose bone ages were within normal limits, and who received no treatment, the mean +/- SE predicted height of 164.9 +/- 1.5 cm was not significantly different from the mean adult height (166.5 +/- 1.5 cm). The predicted height exceeded the actual adult height by more than 5.1 cm in only one instance [5.1 cm is the degree of accuracy reported by Bayley and Pinneau (2)]. In 10 boys, whose bone ages were severely delayed (more than 2 SD below their chronologic age) and also were not treated, predicted height overestimated adult height by more than 5.1 cm in five of them. This difference was statistically significant (P less than 0.05). In five boys with normal bone ages, androgen therapy had no significant effect on either predicted height (168.1 +/- 4.1 before, 166.8 +/- 4.4 cm after) or actual adult height (166.5 +/- 4.1 cm). The 11 boys with severely delayed bone ages had a significant increase in predicted height during androgen therapy (165.4 +/- 1.5 to 169.8 +/- 1.7 cm, P less than 0.01), but actual adult height (162.4 +/- 2.4 cm) was not significantly greater than pretreatment predicted height. Further, the number of boys whose predicted height exceeded their adult height by 5.1 cm was not significantly different in treated (4/11) or untreated (5/10) boys.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed2006

Prediction of hepatic clearance using cryopreserved human hepatocytes: a comparison of serum and serum-free incubations.

Cryopreserved human hepatocytes have been used to predict hepatic in-vivo clearance. Physiologically-based direct scaling methods generally underestimate human in-vivo hepatic clearance. Cryopreserved human hepatocytes were incubated in 100% serum and in serum-free medium to predict the in-vivo hepatic clearance of six compounds (phenazone (antipyrine), bosentan, mibefradil, midazolam, naloxone and oxazepam). Monte Carlo simulations were performed in an attempt to incorporate the variability and uncertainty in the measured parameters to the prediction of hepatic clearance. The intrinsic clearance (CL(int)) and the associated variability of the six compounds decreased in the presence of serum and the values were reproducible across donors. The predicted CL(hep, in-vivo) obtained with hepatocytes from donors incubated in serum was more accurate than the prediction obtained in the absence of serum. For example, the CL(hep, in-vivo) of mibefradil in donor GNG was 4.27 mL min(-1) kg(-1) in the presence of serum and 0.46 mL min(-1) kg(-1) in the absence of serum (4.88 mL min(-1) kg(-1) observed in-vivo). Using the results obtained in this study together with an extended data set (26 compounds), the clearance of 77% of the compounds was predicted within a 2-fold error in the absence of serum. In the presence of serum, 85% of the compounds were successfully predicted within a 2-fold error. In conclusion, cryopreserved human hepatocyte suspensions represented a convenient and predictive model to assess human drug clearance.

Antipyrine↗
PubMed2005

Prediction of cerebral vasospasm in patients presenting with aneurysmal subarachnoid hemorrhage: a review.

OBJECTIVE: Cerebral vasospasm is a devastating medical complication of aneurysmal subarachnoid hemorrhage (SAH). It is associated with high morbidity and mortality rates, even after the aneurysm has been treated. A substantial amount of experimental and clinical research has been conducted in an effort to predict and prevent its occurrence. This research has contributed to significant advances in the understanding of the mechanisms leading to cerebral vasospasm. The ability to accurately and consistently predict the onset of cerebral vasospasm, however, has been challenging. This topic review describes the various methodologies and approaches that have been studied in an effort to predict the occurrence of cerebral vasospasm in patients presenting with SAH. METHODS: The English-language literature on the prediction of cerebral vasospasm after aneurysmal SAH was reviewed using the MEDLINE PubMed (1966-present) database. RESULTS: The risk factors, diagnostic imaging, bedside monitoring approaches, and pathological markers that have been evaluated to predict the occurrence of cerebral vasospasm after SAH are presented. CONCLUSION: To date, a large blood burden is the only consistently demonstrated risk factor for the prediction of cerebral vasospasm after SAH. Because vasospasm is such a multifactorial problem, attempts to predict its occurrence will probably require several different approaches and methodologies, as is done at present. Future improvements in the prevention of cerebral vasospasm from aneurysmal SAH will most likely require advances in our understanding of its pathophysiology and our ability to predict its onset.

Humans↗
PubMed2006

Accuracy of the combination of mammography and sonography in predicting tumor response in breast cancer patients after neoadjuvant chemotherapy.

BACKGROUND: Residual tumor size after neoadjuvant chemotherapy is an important consideration in surgical planning. We examined the accuracy of the combination of mammography and sonography in predicting pathologic residual tumor size. METHODS: Tumor size was evaluated by physical examination, mammography, and sonography at diagnosis and before surgery in 162 breast cancer patients who received neoadjuvant chemotherapy. Agreement between the predicted and the pathologic responses and the predicted and the pathologic tumor sizes was calculated. The effect of invasive lobular carcinoma, high nuclear grade, hormone receptor positivity, and the presence of an extensive intraductal component on the accuracy of mammography and sonography in predicting pathologic residual tumor size was analyzed. RESULTS: Forty-two patients (25.9%) had a pathologic complete response (pCR). Overall agreement between predicted and pathologic responses was 53% for physical examination, 67% for mammography plus sonography, and 63% for physical examination plus mammography and sonography. The sensitivity of mammography and sonography in predicting pCR was 78.6%, and the specificity was 92.5%; the accuracy was 88.9%. Residual tumor size determined by mammography and sonography correlated with pathologic residual tumor size (r = .662); pathologic tumor size was within .5 cm of predicted in 69.1% of patients. Multivariate analysis showed that pathologic residual tumor size was underestimated for lobular carcinoma and overestimated for poorly differentiated tumors. CONCLUSIONS: The combination of mammography and sonography has a high accuracy in predicting pCR after neoadjuvant chemotherapy. Agreement of residual tumor size in mammography and sonography with pathologic residual tumor size was moderate.

Adult↗