Aprotinin prolongs activated and nonactivated whole blood clotting time and potentiates the effect of heparin in vitro

This study was designed to evaluate the effect of aprotinin on activated versus nonactivated whole blood clotting time using two different on-site methods and to quantify these anticoagulant properties when compared to heparin in a controlled, in vitro environment. Blood specimens were obtained prior to heparin administration from 56 patients undergoing cardiac surgery. Specimens obtained from the first consecutive 20 patients were mixed with either normal saline (NS) or aprotinin (400 kallikrein inhibiting units (KIU)/mL), inserted into Hemochron tubes containing either NS or heparin (0.3 or 0.6 U/mL) and then used to measure celite-activated (celite ACT) and nonactivated whole blood clotting time (WBCT1) using four Hemochron instruments. Accordingly, specimens obtained from the second consecutive 20 patients were mixed with either NS or aprotinin, inserted into Automated Clot Timer cartridges containing either NS or heparin (0.06, 0.13, or 0.25 U/mL) and then used to measure kaolin-activated (kaolin ACT) or nonactivated whole blood clotting times (WBCT2) using four Automated Clot Timer instruments. Specimens obtained from the last 16 patients were mixed with either incrementally larger doses of aprotinin (0, 100, 200, 300, or 400 KIU/mL) or heparin (0, 0.12, 0.24, 0.36, 0.48, or 0.72 U/mL) and were then used for measurement of whole blood clotting time (WBCT2) using six Automated Clot Timer instruments. Aprotinin significantly prolonged activated or nonactivated whole blood clotting time and potentiated the prolongation of whole blood clotting time by heparin. The linear relationship between whole blood clotting time and either heparin concentration (WBCT2 = H x 357 + 280, mean adjusted r2 = 0.88) or aprotinin concentration (WBCT2 = A x 0.97 + 300, mean adjusted r2 = 0.94) was variable among patients. On average, 200 KIU/mL of aprotinin prolonged WBCT2 to the same extent as 0.69 +/- 0.28 U/mL of heparin using linear regression models within each patient. Aprotinin significantly prolongs activated or nonactivated whole blood clotting time measurements in a dose-dependent manner. Since prolongation of whole blood clotting time by heparin is potentiated by aprotinin in vitro, aprotinin's anticoagulant properties may in part account for the prolonged celite activated clotting time values observed in the presence of aprotinin.     

Postoperative changes in the coagulation and fibrinolytic systems in endoluminal stent-graft treatment of thoracic aortic aneurysms

Length of stay (LOS) predictions in acute pancreatitis could be used to stratify patients with severe acute pancreatitis, make treatment and resource allocation decisions, and for quality assurance. Artificial neural networks have been used to predict LOS in other conditions but not acute pancreatitis. The hypothesis of this study was that a neural network could predict LOS in patients with acute pancreatitis. The medical records of 195 patients admitted with acute pancreatitis were reviewed. A backpropagation neural network was developed to predict LOS >7 days. The network was trained on 156 randomly selected cases and tested on the remaining 39 cases. The neural network had the highest sensitivity (75%) for predicting LOS >7 days. Ranson criteria had the highest specificity (94%) for making this prediction. All methods incorrectly predicted LOS in two patients with severe acute pancreatitis who died early in their hospital course. An artificial neural network can predict LOS >7 days. The network and traditional prognostic indices were least accurate for predicting LOS in patients with severe acute pancreatitis who died early in their hospital course. The neural network has the advantage of making this prediction using admission data.     

Heparinization for routine hemodialysis

Routine hemodialysis requires anticoagulation with heparin to prevent clotting in the extracorporeal circuit. Activated whole blood clotting times are used to measure the level of anticoagulation during dialysis. The magnitude of the clotting time obtained for a given level of heparin depends on the test used, and each dialysis unit must establish its own target range. The preferred dosage regimen for heparin is an initial loading dose, followed by a constant infusion. Dosage regimens based on intermittent boluses of heparin are associated with periods of under- and over-anticoagulation and require more staff attention during dialysis. Because patient responses to heparin differ markedly, the doses required to achieve suitable anticoagulation must be determined on an individual basis. The doses of heparin for an individual patient are best determined using a simple pharmacodynamic model, although they can be found empirically through trial and error. In spite of careful anticoagulation, an unsatisfactory outcome may still result. Patient-specific factors may be responsible for some of these adverse outcomes; however, in most cases they result from poor technique, including failure to deliver the prescribed dose of heparin, turbulence and foam formation in the extracorporeal circuit, and excessive access recirculation.     

Cervical endometriosis: a potential pitfall in the evaluation of glandular cells in cervical smears

More reliable prediction of outcome would be helpful for clinicians who treat severely head-injured patients. To determine if neural network modeling would improve outcome prediction compared with standard logistic regression analysis and to determine if data available 24 h after severe head injury allows better prediction than data obtained within 6 h, we tested the ability of both techniques at these two times to predict outcome (dead versus alive) at 6 months. One thousand sixty-six consecutive patients with Glasgow Coma Scale scores of 8 or less during the first 24 h after injury were randomly divided into two groups. Data from the first group (n = 799) were used to develop the models; data from the second group (n = 267) were used to test the accuracy, sensitivity, and specificity of the models by comparing predicted and actual outcomes. The 6-month mortality rate was 63.5%. Our findings confirm the importance of age, Glasgow Coma Scale scores, and hypotension in predicting outcome. Using data available at 24 h improved the predictive power of both models compared with admission data; at both time points, however, the differences in the results obtained with the two models were negligible. We conclude that outcome (dead versus alive) at 6 months after severe head injury can be predicted with logistic regression or neural network models based on data available at 24 h. Critical therapeutic decisions, such as cessation of therapy, should be based on the patient's status 1 day after injury and only rarely on admission status alone.     

Modeling and Predicting Biological Performance of Contact Stabilization Process Using Artificial Neural Networks

In this paper, the microfauna distribution data of a contact stabilization process were used in a neural network system to model and predict the biological activity of the effluent. Five uncorrelated components of the microfauna were used as the artificial neural network model input to predict the dehydrogenase activity of the effluent (DAE) using back-propagation and general regression algorithms. The models’ optimum architectures were determined for the back-propagation neural network (BPNN) model by varying the number of hidden layers, hidden transfer functions, test set size percentages, and initial weights. Comparison of the two model prediction results showed that the genetic general regression neural network model demonstrated the ability to calibrate the multicomponent microfauna, and yielded reliable DAE close to that resulting from direct experimentation, and thus was judged superior to BPNN models.     

Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket

The estimation of scour downstream of a ski-jump bucket has remained inconclusive, despite analysis of numerous prototypes as well as hydraulic model studies in the past. It is partly due to the complexity of the phenomenon involved and partly because of limitations of the traditional analytical tool of statistical regression. This paper addresses the latter part and presents an alternative to the regression in the form of neural networks. The depth of the scour hole developed along with its width and length is predicted using neural network models. A network architecture complete with trained values of connection weight and bias and requiring input of grouped parameters pertaining to discharge head, tail water channel depth, bucket radius, lip angle, and median sediment size is recommended in order to predict the depth, the location of maximum scour, as well as the width of scour hole. The neural network predictions have been compared with traditional statistical schemes. Although the common and simple feed forward back propagation network took a very long time to train as compared to some advanced schemes, it was found to impart equally reliable training as the latter. Use of causative variables in grouped forms was found to be more rewarding than that of their raw forms probably due to lesser scaling effect.     

The application of neural networks in predicting the outcome of in-vitro fertilization

Infertility affects one in six couples at some time in their lives, with 48% of these couples requiring assisted conception techniques in order to achieve a pregnancy. Whilst the overall clinical pregnancy rate per embryo transfer is 23%, this varies widely between clinics. The Human Fertilisation and Embryology Authority has attempted to analyse the results of all units, with weighting of different factors affecting assisted conception, and the published data have invariably led to comparisons between units. However, statistical models need to be developed to eliminate bias for valid comparisons. Neural networks offer a novel approach to pattern recognition. In some instances neural networks can identify a wider range of associations than other statistical techniques due in part to their ability to recognize highly non-linear associations. It was hoped that a neural network approach may be able to predict success for individual couples about to undergo in-vitro fertilization (IVF) treatment. A neural network was constructed using the variables of age, number of eggs recovered, number of embryos transferred and whether there was embryo freezing. Overall the network managed to achieve an accuracy of 59%.     

Filling hemodialysis catheters in the interdialytic period: heparin versus citrate versus polygeline: a prospective randomized study

Heparin and saline are commonly used to fill hemodialysis central venous catheters to prevent their thrombosis during the interdialytic period. The purpose of this prospective clinical study was to evaluate whether replacing heparin with citrate or polygeline could ensure satisfactory catheter function without exposing patients to the risk of systemic heparinization. Thirty end-stage renal disease (ESRD) patients with subclavian or jugular single lumen catheters as temporary vascular access for hemodialysis were enrolled. After the insertion of the catheters, the patients were randomly assigned to one of the following three filling groups: Group A, heparin; Group B, citrate; Group C, polygeline. Before each dialysis, the filling solution was aspirated and clot volume, if present, was measured. The catheter usage time and the clot volume were 23 +/- 24 days and 0.052 +/- 0.035 ml in Group A, 51 +/- 36 days and 0.059 +/- 0.032 ml in Group B, and 32 +/- 10 days and 0.056 +/- 0.038 ml in Group C, respectively. Our results indicate that citrate or polygeline can replace heparin effectively as a filling solution for single lumen temporary hemodialysis catheters.     

Viral infections and asthma

PURPOSE: The aim of this study was to classify the human IgG autoantibody repertoire of sera from patients suffering from endocrine ophthalmopathy (EOP) and healthy subjects (CTRL) for diagnostic purposes using the recently developed Megablot technique. This technique allows for the simultaneous and quantitative screening of a large set of antigens and uses multivariate statistical techniques and an artificial neural network. METHODS: Sera were tested against Western blots (WBs) of SDS-PAGE preparations of proteins from human extraorbital eye muscle (EOP: n = 16; CTRL: n = 11). Digital image analysis was performed. The blots were subsequently analyzed by multivariate statistical techniques (analysis of discriminance) and an artificial neural network (probalistic neural network). RESULTS: The sera of both the EOP and CTRL groups showed a complex staining pattern against WBs of SDS-PAGEs from human eye muscle. Using the multivariate statistical technique for classification, all of the known samples and 85% of the unknown samples (not presented during calculation) were assigned to their correct clinical group. Using the artificial neural network as classifier, all of the samples presented during training and 96.3% of the unknown samples (not trained) were assigned correctly. CONCLUSIONS: The artificial neural network exceeds the ability of multivariate statistical techniques such as analysis of discriminance to assign unknown samples to their correct predefined group. Thus, the neural network exceeds other methods in generalizing some similarities of blots used for classification. This study reveals that our new technique and its evaluation using a neural network can be used as a helpful diagnostic tool in autoimmune diseases such as endocrine ophthalmopathy.     

Application of two neural network paradigms to the study of voluntary employee turnover.

Two neural network paradigms—multilayer perceptron and learning vector quantization—were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Two types of intraoral distribution of fluorotic enamel

Assessment of body composition remains a goal for the routine assessment of nutritional status of patients on long-term dialysis. Methods generally available for estimation of body fat in healthy individuals are limited by practicality and availability for use in this patient population. Anthropometry, which is cost effective and easy to perform, is limited by the lack of appropriate reference standards for patients on dialysis and artifact caused by hydration status. Bioelectrical impedance affords new opportunities for non-invasive assessment of fluid volume, its distribution, and body cell mass; estimation of fat-free mass and body fat can be affected by hydration status. Dual x-ray absorptiometry permits estimation of bone status and fat mass because changes in hydration status are reflected in estimates of fat-free mass. Evaluation of validity of techniques for fluid status and body composition assessment requires the use of appropriate reference methods and proper statistical procedures to examine error, not only between groups, but by individual. Use of body composition assessment methods together with biochemical measurements will enhance the nutritional assessment of end-stage renal disease patients on long-term hemodialysis.     

Validation of the Metacomprehension Scale

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.     

Anticoagulation in hemodialysis with a single dose of low molecular weight heparin

The aim of this work was to compare the benefits and problems of low molecular weight heparin use in chronic hemodialysis, compared to conventional heparin. We studied 35 patients that received low molecular weight heparin (Enoxaparine, molecular weight 4000) during 115 consecutive hemodialysis procedures and conventional heparin during the subsequent 35 procedures. We assess the heparin dose, partial thromboplastin time before dialysis and at 3 and 120 min during the procedure, arterio-venous fistula compression time, clot formation in the circuit and residual volume of filters. Median total dose of conventional heparin was 6289 U (range 3000-10000) compared to 5555 U (range 2000-8000) of low molecular weight heparin. When the dose was calculated per kg of body weight, it was lower for low molecular weight heparin than for conventional heparin (87.8 U (range 33-100) vs 100 U (range 50-176)). Partial thromboplastin time achieved was lower with low molecular weight heparin, compared with conventional heparin, at 3 (64.26 vs 125.2 sec) and 120 min (39.1 vs 84.45 sec). Clot formation, arteriovenous fistula compression time and residual volume of filters were similar for both types of heparin. It is concluded that a single dose of low molecular weight heparin simplifies anticoagulation during hemodialysis, modifies less the partial thromboplastin time and does not alter filter re-utilization.     

Monitoring anticoagulant treatment with the KEM-O-MAT 2HP autoanalyzer

Synthetic chromogenic substrates were adapted to KEM-O-MAT 2HP for an automated monitoring of anticoagulant treatments, determination of factor X (S 2337) in oral anticoagulation, anti-Xa (Hepachrom) and anti-lla (S 2238) activities of heparin in prophylactic and curative heparin therapies. In all cases, these methods revealed high sensitivity, reproducibility, and a good correlation of their results with those of classical clotting assays. The present cost of synthetic substrates seriously limits the potential value of chromogenic assays for routine controls. However, the development of automated amidolytic techniques should provide an easier standardization in the laboratory control of anticoagulant treatments.     

Cholesterol crystal embolism during treatment with low-molecular-weight heparin

BACKGROUND: Cholesterol crystal embolism is often an iatrogenic complication in ulcerated atherosclerosis of the aorta. CASE REPORTS: Two cases of multi-organ embolism of cholesterol crystals were histologically proven in patients treated with low-molecular-weight heparin. Both patients had acute renal failure, hypertension with acute pulmonary edema, skin necrosis and ischemia of the digestive tract. Outcome was favorable after discontinuing anticoagulants, symptomatic treatment, definitive hemodialysis and low-dose corticosteroids. DISCUSSION: These two cases are the first reported in the literature of cholesterol crystal embolism occurring during prophylactic treatment with low-molecular-weight heparin. They demonstrate that there is a risk of severe cholesterol embolism in high-risk patients after administration of low-molecular-weight heparin as for non-fractionated heparin, fibrinolytics, arteriography and cardiovascular surgery. Low-molecular-weight heparin thus should not be used in patients with a diagnosis of cholesterol crystal embolism.     

Efficacy of pancreatic transplantation on cardiovascular alterations in diabetic rats: an ultrastructural and immunohistochemical study

OBJECTIVES: To determine whether a neural network is superior to standard computational methods in predicting stone regrowth after shock wave lithotripsy (SWL) and to determine whether the presence of residual fragments, as an independent variable, increases risk. METHODS: We reviewed the records of 98 patients with renal or ureteral calculi treated by primary SWL at a single institution and followed up for at least 1 year; residual stone fragment growth or new stone occurrence was determined from abdominal radiographs. A neural network was programmed and trained to predict an increased stone volume over time utilizing input variables, including previous stone events, metabolic abnormality, directed medical therapy, infection, caliectasis, and residual fragments after SWL. Patient data were partitioned into a training set of 65 examples and a test set of 33. The neural network did not encounter the test set until training was complete. RESULTS: The average follow-up period was 3.5 years (range 1 to 10). Of 98 patients, 47 had residual stone fragments 3 months after SWL; of these 47, 8 had increased stone volume at last follow-up visit. Of 51 patients stone free after SWL, 4 had stone recurrence. Coexisting risk factors were incorporated into a neural computational model to determine which of the risk factors was individually predictive of stone growth. The classification accuracy of the neural model in the test set was 91%, with a sensitivity of 91%, a specificity of 92%, and a receiver operating characteristic curve area of 0.964, results significantly better than those yielded by linear and quadratic discriminant function analysis. CONCLUSIONS: A computational tool was developed to predict accurately the risk of future stone activity in patients treated by SWL. Use of the neural network demonstrates that none of the risk factors for stone growth, including the presence of residual fragments, is individually predictive of continuing stone formation.     

Anticoagulation with low molecular weight heparin (Fragmin) during continuous hemodialysis in the intensive care unit

A preparation of low molecular weight heparin (Fragmin) was administered to patients with multiorgan failure receiving continuous venovenous hemodialysis. Three patients received a high-dose regimen (35 IU/kg bolus followed by 13 IU/kg infusion), and 7 received a low-dose regimen (8 and 5 IU/kg, respectively) for 36 h. High-dose Fragmin was associated with minimal clotting in the extracorporeal circuit. Plasma fibrinopeptide A levels declined, and mean anti-Xa activity was in the range 0.47-0.79 IU/ml. The urea equilibration coefficient (UEC) (100% at initiation) remained above 90% throughout. All 3 patients had mild bleeding episodes, which led to discontinuation of Fragmin in 1. During all low-dose treatments, marked thrombus formation occurred in the extracorporeal circuit, and in 2, the circuit clotted within the study period. Fibrinopeptide A levels further increased in 4 patients, and mean anti-Xa activity was in the range 0.27-0.53 IU/ml. UEC declined appreciably in 3 treatments (including the 2 in which early circuit clotting occurred). One patient experienced a mild bleeding episode. The low-dose Fragmin regimen produced safer anticoagulation in patients at risk from bleeding and is suitable for prolonged renal support although the tendency to thrombosis may necessitate more frequent circuit changes.     

铜闪速熔炼过程操作参数预测模型及应用

基于某厂实际铜闪速熔炼工艺和控制过程,对神经网络模型在铜闪速熔炼过程在线控制进行了研究。在分析影响溶剂率、熔炼氧单耗、反应塔总风量操作参数因素的基础上,提出一种基于BP神经网络的操作参数的预测方法,分别建立了输入向量只包含主要元素和考虑杂质元素的BP神经网络模型。网络的训练和测试结果表明,两种神经网络的输出值与实际值的最大相对误差均小于1.0%,输出值与实际样本值吻合得较好,模型输入参数中包括杂质元素时具有更高的计算精度。     

Probabilistic Failure Prediction for Deteriorating Pipelines: Nonparametric Approach

Component failures in water distribution systems are usually predicted by parametric models where the model parameters are determined by projecting the past failure rates of the component to the future. This paper shows that in such techniques, failures are implicitly assumed to be stationary random processes. However, due to the nonstationary nature of some influencing factors, this assumption may lead to inaccurate predictions. A new nonparametric technique is developed for failure prediction of classes of pipes considering this nonstationary process. The presented technique uses limited data that are typical to the databases of water distribution systems. In this method, maximum likelihood estimates of the probability of future failures are calculated and used, both to predict the number of failures occurring within a specified period of time in future, and to provide some lower and upper bounds (confidence intervals) for the estimations. This technique is applied to predict the failures of water pipes in western suburbs of Melbourne. Results of the predictions are compared with the empirical results from a failure record. Deviation of these predictions from empirical measures in terms of both rejection rates and mean-square errors of predictions are acceptable.     

Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from MRI appearances

Several MRI features of supratentorial astrocytomas are associated with high histologic grade by statistically significant p values. We sought to apply this information prospectively to a group of astrocytomas in the prediction of tumor grade. We used 10 MRI features of fibrillary astrocytomas from 52 patient studies to develop neural network and multiple linear regression models for practical use in predicting tumor grade. The models were tested prospectively on MR images from 29 patients studies. The performance of the models was compared against that of a radiologist. Neural network accuracy was 61% in distinguishing between low and high grade tumors. Multiple linear regression achieved an accuracy of 59%. Assessment of the images by a radiologist yielded 57% accuracy. We conclude that while certain MRI parameters may be statistically related to astrocytoma histologic grade, neural network and linear regression models cannot reliably use them to predict tumor grade.