LC-MS/MS method for simultaneous analysis of uracil, 5,6-dihydrouracil, 5-fluorouracil and 5-fluoro-5,6-dihydrouracil in human plasma for therapeutic drug monitoring and toxicity prediction in cancer patients

The chemotherapeutic drug 5-fluorouracil (5-FU) is widely used for treating solid tumors. Response to 5-FU treatment is variable with 10-30% of patients experiencing serious toxicity partly explained by reduced activity of dihydropyrimidine dehydrogenase (DPD). DPD converts endogenous uracil (U) into 5,6-dihydrouracil (UH(2) ), and analogously, 5-FU into 5-fluoro-5,6-dihydrouracil (5-FUH(2) ). Combined quantification of U and UH(2) with 5-FU and 5-FUH(2) may provide a pre-therapeutic assessment of DPD activity and further guide drug dosing during therapy. Here, we report the development of a liquid chromatography-tandem mass spectrometry assay for simultaneous quantification of U, UH(2) , 5-FU and 5-FUH(2) in human plasma. Samples were prepared by liquid-liquid extraction with 10:1 ethyl acetate-2-propanol (v/v). The evaporated samples were reconstituted in 0.1% formic acid and 10 μL aliquots were injected into the HPLC system. Analyte separation was achieved on an Atlantis dC(18) column with a mobile phase consisting of 1.0 mm ammonium acetate, 0.5 mm formic acid and 3.3% methanol. Positively ionized analytes were detected by multiple reaction monitoring. The analytical response was linear in the range 0.01-10 μm for U, 0.1-10 μm for UH(2) , 0.1-75 μm for 5-FU and 0.75-75 μm for 5-FUH(2) , covering the expected concentration ranges in plasma. The method was validated following the FDA guidelines and applied to clinical samples obtained from ten 5-FU-treated colorectal cancer patients. The present method merges the analysis of 5-FU pharmacokinetics and DPD activity into a single assay representing a valuable tool to improve the efficacy and safety of 5-FU-based chemotherapy.

Prediction of whole-genome DNA-DNA similarity, determination of G+C content and phylogenetic analysis within the family Pasteurellaceae by multilocus sequence analysis (MLSA)

Genome predictions based on selected genes would be a very welcome approach for taxonomic studies, including DNA-DNA similarity, G+C content and representative phylogeny of bacteria. At present, DNA-DNA hybridizations are still considered the gold standard in species descriptions. However, this method is time-consuming and troublesome, and datasets can vary significantly between experiments as well as between laboratories. For the same reasons, full matrix hybridizations are rarely performed, weakening the significance of the results obtained. The authors established a universal sequencing approach for the three genes recN, rpoA and thdF for the Pasteurellaceae, and determined if the sequences could be used for predicting DNA-DNA relatedness within the family. The sequence-based similarity values calculated using a previously published formula proved most useful for species and genus separation, indicating that this method provides better resolution and no experimental variation compared to hybridization. By this method, cross-comparisons within the family over species and genus borders easily become possible. The three genes also serve as an indicator of the genome G+C content of a species. A mean divergence of around 1 % was observed from the classical method, which in itself has poor reproducibility. Finally, the three genes can be used alone or in combination with already-established 16S rRNA, rpoB and infB gene-sequencing strategies in a multisequence-based phylogeny for the family Pasteurellaceae. It is proposed to use the three sequences as a taxonomic tool, replacing DNA-DNA hybridization.

Prediction of hip fracture risk by quantitative ultrasound in more than 7000 Swiss women > or =70 years of age: comparison of three technologically different bone ultrasound devices in the SEMOF study

To compare the prediction of hip fracture risk of several bone ultrasounds (QUS), 7062 Swiss women > or =70 years of age were measured with three QUSs (two of the heel, one of the phalanges). Heel QUSs were both predictive of hip fracture risk, whereas the phalanges QUS was not. INTRODUCTION: As the number of hip fracture is expected to increase during these next decades, it is important to develop strategies to detect subjects at risk. Quantitative bone ultrasound (QUS), an ionizing radiation-free method, which is transportable, could be interesting for this purpose. MATERIALS AND METHODS: The Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk (SEMOF) study is a multicenter cohort study, which compared three QUSs for the assessment of hip fracture risk in a sample of 7609 elderly ambulatory women > or =70 years of age. Two QUSs measured the heel (Achilles+; GE-Lunar and Sahara; Hologic), and one measured the heel (DBM Sonic 1200; IGEA). The Cox proportional hazards regression was used to estimate the hazard of the first hip fracture, adjusted for age, BMI, and center, and the area under the ROC curves were calculated to compare the devices and their parameters. RESULTS: From the 7609 women who were included in the study, 7062 women 75.2 +/- 3.1 (SD) years of age were prospectively followed for 2.9 +/- 0.8 years. Eighty women reported a hip fracture. A decrease by 1 SD of the QUS variables corresponded to an increase of the hip fracture risk from 2.3 (95% CI, 1.7, 3.1) to 2.6 (95% CI, 1.9, 3.4) for the three variables of Achilles+ and from 2.2 (95% CI, 1.7, 3.0) to 2.4 (95% CI, 1.8, 3.2) for the three variables of Sahara. Risk gradients did not differ significantly among the variables of the two heel QUS devices. On the other hand, the phalanges QUS (DBM Sonic 1200) was not predictive of hip fracture risk, with an adjusted hazard risk of 1.2 (95% CI, 0.9, 1.5), even after reanalysis of the digitalized data and using different cut-off levels (1700 or 1570 m/s). CONCLUSIONS: In this elderly women population, heel QUS devices were both predictive of hip fracture risk, whereas the phalanges QUS device was not.

A simple asthma prediction tool for preschool children with wheeze or cough

BACKGROUND Many preschool children have wheeze or cough, but only some have asthma later. Existing prediction tools are difficult to apply in clinical practice or exhibit methodological weaknesses. OBJECTIVE We sought to develop a simple and robust tool for predicting asthma at school age in preschool children with wheeze or cough. METHODS From a population-based cohort in Leicestershire, United Kingdom, we included 1- to 3-year-old subjects seeing a doctor for wheeze or cough and assessed the prevalence of asthma 5 years later. We considered only noninvasive predictors that are easy to assess in primary care: demographic and perinatal data, eczema, upper and lower respiratory tract symptoms, and family history of atopy. We developed a model using logistic regression, avoided overfitting with the least absolute shrinkage and selection operator penalty, and then simplified it to a practical tool. We performed internal validation and assessed its predictive performance using the scaled Brier score and the area under the receiver operating characteristic curve. RESULTS Of 1226 symptomatic children with follow-up information, 345 (28%) had asthma 5 years later. The tool consists of 10 predictors yielding a total score between 0 and 15: sex, age, wheeze without colds, wheeze frequency, activity disturbance, shortness of breath, exercise-related and aeroallergen-related wheeze/cough, eczema, and parental history of asthma/bronchitis. The scaled Brier scores for the internally validated model and tool were 0.20 and 0.16, and the areas under the receiver operating characteristic curves were 0.76 and 0.74, respectively. CONCLUSION This tool represents a simple, low-cost, and noninvasive method to predict the risk of later asthma in symptomatic preschool children, which is ready to be tested in other populations.

Development and validation of energy expenditure prediction models based on GT3X accelerometer data in 5- to 9-year-old children

Background: Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children. Methods: Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model. Results: All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches. Conclusions: The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.

Prediction of outcome in patients with low-grade squamous intraepithelial lesions by fluorescence in situ hybridization analysis of human papillomavirus, TERC, and MYC

BACKGROUND Cytology is an excellent method with which to diagnose preinvasive lesions of the uterine cervix, but it suffers from limited specificity for clinically significant lesions. Supplementary methods might predict the natural course of the detected lesions. The objective of the current study was to test whether a multicolor fluorescence in situ hybridization (FISH) assay might help to stratify abnormal results of Papanicolaou tests. METHODS A total of 219 liquid-based cytology specimens of low-grade squamous intraepithelial lesions (LSIL), 49 atypical squamous cells of undetermined significance (ASCUS) specimens, 52 high-grade squamous intraepithelial lesion (HSIL) specimens, and 50 normal samples were assessed by FISH with probes for the human papillomavirus (HPV), MYC, and telomerase RNA component (TERC). Subtyping of HPV by polymerase chain reaction (PCR) was performed in a subset of cases (n=206). RESULTS There was a significant correlation found between HPV detection by FISH and PCR (P<.0001). In patients with LSILs, the presence of HPV detected by FISH was significantly associated with disease progression (P<.0001). An increased MYC and/or TERC gene copy number (>2 signals in>10% of cells) prevailed in 43% of ASCUS specimens and was more frequent in HSIL (85%) than in LSIL (33%) (HSIL vs LSIL: P<.0001). Increased TERC gene copy number was significantly correlated with progression of LSIL (P<.01; odds ratio, 7.44; area under the receiver operating characteristic curve, 0.73; positive predictive value, 0.30; negative predictive value, 0.94) CONCLUSIONS: The detection of HPV by FISH analysis is feasible in liquid-based cytology and is significantly correlated with HPV analysis by PCR. The analysis of TERC gene copy number may be useful for risk stratification in patients with LSIL.

A Stratified Model for Psychosis Prediction in Clinical Practice

Objective: Impaired cognition is an important dimension in psychosis and its at-risk states. Research on the value of impaired cognition for psychosis prediction in at-risk samples, however, mainly relies on study-specific sample means of neurocognitive tests, which unlike widely available general test norms are difficult to translate into clinical practice. The aim of this study was to explore the combined predictive value of at-risk criteria and neurocognitive deficits according to test norms with a risk stratification approach. Method: Potential predictors of psychosis (neurocognitive deficits and at-risk criteria) over 24 months were investigated in 97 at-risk patients. Results: The final prediction model included (1) at-risk criteria (attenuated psychotic symptoms plus subjective cognitive disturbances) and (2) a processing speed deficit (digit symbol test). The model was stratified into 4 risk classes with hazard rates between 0.0 (both predictors absent) and 1.29 (both predictors present). Conclusions: The combination of a processing speed deficit and at-risk criteria provides an optimized stratified risk assessment. Based on neurocognitive test norms, the validity of our proposed 3 risk classes could easily be examined in independent at-risk samples and, pending positive validation results, our approach could easily be applied in clinical practice in the future.

Improving the clinical prediction of psychosis by combining ultra-high risk criteria and cognitive basic symptoms

OBJECTIVE Cognitive impairments are regarded as a core component of schizophrenia. However, the cognitive dimension of psychosis is hardly considered by ultra-high risk (UHR) criteria. Therefore, we studied whether the combination of symptomatic UHR criteria and the basic symptom criterion "cognitive disturbances" (COGDIS) is superior in predicting first-episode psychosis. METHOD In a naturalistic 48-month follow-up study, the conversion rate to first-episode psychosis was studied in 246 outpatients of an early detection of psychosis service (FETZ); thereby, the association between conversion, and the combined and singular use of UHR criteria and COGDIS was compared. RESULTS Patients that met UHR criteria and COGDIS (n=127) at baseline had a significantly higher risk of conversion (hr=0.66 at month 48) and a shorter time to conversion than patients that met only UHR criteria (n=37; hr=0.28) or only COGDIS (n=30; hr=0.23). Furthermore, the risk of conversion was higher for the combined criteria than for UHR criteria (n=164; hr=0.56 at month 48) and COGDIS (n=158; hr=0.56 at month 48) when considered irrespective of each other. CONCLUSIONS Our findings support the merits of considering both COGDIS and UHR criteria in the early detection of persons who are at high risk of developing a first psychotic episode within 48months. Applying both sets of criteria improves sensitivity and individual risk estimation, and may thereby support the development of stage-targeted interventions. Moreover, since the combined approach enables the identification of considerably more homogeneous at-risk samples, it should support both preventive and basic research.

Intra- and interlaboratory reliability of a cryopreserved trout hepatocyte assay for the prediction of chemical bioaccumulation potential

Measured rates of intrinsic clearance determined using cryopreserved trout hepatocytes can be extrapolated to the whole animal as a means of improving modeled bioaccumulation predictions for fish. To date, however, the intra- and interlaboratory reliability of this procedure has not been determined. In the present study, three laboratories determined in vitro intrinsic clearance of six reference compounds (benzo[a]pyrene, 4-nonylphenol, di-tert-butyl phenol, fenthion, methoxychlor and o-terphenyl) by conducting substrate depletion experiments with cryopreserved trout hepatocytes from a single source. O-terphenyl was excluded from the final analysis due to nonfirst-order depletion kinetics and significant loss from denatured controls. For the other five compounds, intralaboratory variability (% CV) in measured in vitro intrinsic clearance values ranged from 4.1 to 30%, while interlaboratory variability ranged from 27 to 61%. Predicted bioconcentration factors based on in vitro clearance values exhibited a reduced level of interlaboratory variability (5.3-38% CV). The results of this study demonstrate that cryopreserved trout hepatocytes can be used to reliably obtain in vitro intrinsic clearance of xenobiotics, which provides support for the application of this in vitro method in a weight-of-evidence approach to chemical bioaccumulation assessment.

Don't forget asking the others! - The added value of significant other's reports on patient interpersonal style for the prediction of therapy process and outcome

Theory: Interpersonal factors play a major role in causing and maintaining depression. It is unclear, however, to what degree significant others of the patient need to be involved for characterizing the patient's interpersonal style. Therefore, our study sought to investigate how impact messages as perceived by the patients' significant others add to the prediction of psychotherapy process and outcome above and beyond routine assessments, and therapist factors. Method: 143 outpatients with major depressive disorder were treated by 24 therapists with CBT or Exposure-Based Cognitive Therapy. Interpersonal style was measured pre and post therapy with the informant‐based Impact Message Inventory (IMI), in addition to the self‐report Inventory of Interpersonal Problems (IIP‐32). Indicators for the patients' dominance and affiliation as well as interpersonal distress were calculated from these measures. Depressive and general symptomatology was assessed at pre, post, and at three months follow‐up, and by process measures after every session. Results: Whereas significant other's reports did not add significantly to the prediction of the early therapeutic alliance, central mechanisms of change, or post‐therapy outcome including therapist factors, the best predictor of outcome 3 months post therapy was an increase in dominance as perceived by significant others. Conclusions: The patients' significant others seem to provide important additional information about the patients' interpersonal style and therefore should be included in the diagnostic process. Moreover, practitioners should specifically target interpersonal change as a potential mechanism of change in psychotherapy for depression.

Prediction of Post-Weaning Fibrinogen Status during Cardiopulmonary Bypass: An Observational Study in 110 Patients.

BACKGROUND After cardiac surgery with cardiopulmonary bypass (CPB), acquired coagulopathy often leads to post-CPB bleeding. Though multifactorial in origin, this coagulopathy is often aggravated by deficient fibrinogen levels. OBJECTIVE To assess whether laboratory and thrombelastometric testing on CPB can predict plasma fibrinogen immediately after CPB weaning. PATIENTS / METHODS This prospective study in 110 patients undergoing major cardiovascular surgery at risk of post-CPB bleeding compares fibrinogen level (Clauss method) and function (fibrin-specific thrombelastometry) in order to study the predictability of their course early after termination of CPB. Linear regression analysis and receiver operating characteristics were used to determine correlations and predictive accuracy. RESULTS Quantitative estimation of post-CPB Clauss fibrinogen from on-CPB fibrinogen was feasible with small bias (+0.19 g/l), but with poor precision and a percentage of error >30%. A clinically useful alternative approach was developed by using on-CPB A10 to predict a Clauss fibrinogen range of interest instead of a discrete level. An on-CPB A10 ≤10 mm identified patients with a post-CPB Clauss fibrinogen of ≤1.5 g/l with a sensitivity of 0.99 and a positive predictive value of 0.60; it also identified those without a post-CPB Clauss fibrinogen <2.0 g/l with a specificity of 0.83. CONCLUSIONS When measured on CPB prior to weaning, a FIBTEM A10 ≤10 mm is an early alert for post-CPB fibrinogen levels below or within the substitution range (1.5-2.0 g/l) recommended in case of post-CPB coagulopathic bleeding. This helps to minimize the delay to data-based hemostatic management after weaning from CPB.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.