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 codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers

Substantial variation exists in response to standard doses of codeine ranging from poor analgesia to life-threatening central nervous system (CNS) depression. We aimed to discover the genetic markers predictive of codeine toxicity by evaluating the associations between polymorphisms in cytochrome P450 2D6 (CYP2D6), UDP-glucuronosyltransferase 2B7 (UGT2B7), P-glycoprotein (ABCB1), mu-opioid receptor (OPRM1), and catechol O-methyltransferase (COMT) genes, which are involved in the codeine pathway, and the symptoms of CNS depression in 111 breastfeeding mothers using codeine and their infants. A genetic model combining the maternal risk genotypes in CYP2D6 and ABCB1 was significantly associated with the adverse outcomes in infants (odds ratio (OR) 2.68; 95% confidence interval (CI) 1.61-4.48; P(trend) = 0.0002) and their mothers (OR 2.74; 95% CI 1.55-4.84; P(trend) = 0.0005). A novel combination of the genetic and clinical factors predicted 87% of the infant and maternal CNS depression cases with a sensitivity of 80% and a specificity of 87%. Genetic markers can be used to improve the outcome of codeine therapy and are also probably important for other opioids sharing common biotransformation pathways.

Modeling of red blood cell life-spans in hematologically normal populations

Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction--in the above mentioned areas--was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.

The association between life events and suicide intent in self-poisoners with and without a history of deliberate self-harm: a preliminary study

The associations between life events in the 12 months preceding an episode of self-poisoning resulting in hospital attendance (the index episode), and the suicide intent of this episode were compared in individuals for whom the index episode was their first, episode and in individuals in whom it was a recurrence of DSH. Results indicated a significant interaction between independent life events, repetition status, and gender in the prediction of suicide intent, the association between life events and intent being moderated by repetition status in women only. The results provide preliminary evidence to suggest the presence of a suicidal process in women, in which the impact of negative life events on suicide intent diminishes across episodes.

A prediction model for personal radio frequency electromagnetic field exposure

Radio frequency electromagnetic fields (RF-EMF) in our daily life are caused by numerous sources such as fixed site transmitters (e.g. mobile phone base stations) or indoor devices (e.g. cordless phones). The objective of this study was to develop a prediction model which can be used to predict mean RF-EMF exposure from different sources for a large study population in epidemiological research. We collected personal RF-EMF exposure measurements of 166 volunteers from Basel, Switzerland, by means of portable exposure meters, which were carried during one week. For a validation study we repeated exposure measurements of 31 study participants 21 weeks after the measurements of the first week on average. These second measurements were not used for the model development. We used two data sources as exposure predictors: 1) a questionnaire on potentially exposure relevant characteristics and behaviors and 2) modeled RF-EMF from fixed site transmitters (mobile phone base stations, broadcast transmitters) at the participants' place of residence using a geospatial propagation model. Relevant exposure predictors, which were identified by means of multiple regression analysis, were the modeled RF-EMF at the participants' home from the propagation model, housing characteristics, ownership of communication devices (wireless LAN, mobile and cordless phones) and behavioral aspects such as amount of time spent in public transports. The proportion of variance explained (R2) by the final model was 0.52. The analysis of the agreement between calculated and measured RF-EMF showed a sensitivity of 0.56 and a specificity of 0.95 (cut-off: 90th percentile). In the validation study, the sensitivity and specificity of the model were 0.67 and 0.96, respectively. We could demonstrate that it is feasible to model personal RF-EMF exposure. Most importantly, our validation study suggests that the model can be used to assess average exposure over several months.

Kinetics of maternally acquired anti-hepatitis A antibodies: prediction of waning based on maternal or cord blood antibody levels

BACKGROUND Timing is critical for efficient hepatitis A vaccination in high endemic areas as high levels of maternal IgG antibodies against the hepatitis A virus (HAV) present in the first year of life may impede the vaccine response. OBJECTIVES To describe the kinetics of the decline of anti-HAV maternal antibodies, and to estimate the time of complete loss of maternal antibodies in infants in León, Nicaragua, a region in which almost all mothers are anti-HAV seropositive. METHODS We collected cord blood samples from 99 healthy newborns together with 49 corresponding maternal blood samples, as well as further blood samples at 2 and 7 months of age. Anti-HAV IgG antibody levels were measured by enzyme immunoassay (EIA). We predicted the time when antibodies would fall below 10 mIU/ml, the presumed lowest level of seroprotection. RESULTS Seroprevalence was 100% at birth (GMC 8392 mIU/ml); maternal and cord blood antibody concentrations were similar. The maternal antibody levels of the infants decreased exponentially with age and the half-life of the maternal antibody was estimated to be 40 days. The relationship between the antibody concentration at birth and time until full waning was described as: critical age (months)=3.355+1.969 × log(10)(Ab-level at birth). The survival model estimated that loss of passive immunity will have occurred in 95% of infants by the age of 13.2 months. CONCLUSIONS Complete waning of maternal anti-HAV antibodies may take until early in the second year of life. The here-derived formula relating maternal or cord blood antibody concentrations to the age at which passive immunity is lost may be used to determine the optimal age of childhood HAV vaccination.

International palliative care experts' view on phenomena indicating the last hours and days of life

BACKGROUND Providing the highest quality care for dying patients should be a core clinical proficiency and an integral part of comprehensive management, as fundamental as diagnosis and treatment. The aim of this study was to provide expert consensus on phenomena for identification and prediction of the last hours or days of a patient's life. This study is part of the OPCARE9 project, funded by the European Commission's Seventh Framework Programme. METHOD The phenomena associated with approaching death were generated using Delphi technique. The Delphi process was set up in three cycles to collate a set of useful and relevant phenomena that identify and predict the last hours and days of life. Each cycle included: (1) development of the questionnaire, (2) distribution of the Delphi questionnaire and (3) review and synthesis of findings. RESULTS The first Delphi cycle of 252 participants (health care professionals, volunteers, public) generated 194 different phenomena, perceptions and observations. In the second cycle, these phenomena were checked for their specific ability to diagnose the last hours/days of life. Fifty-eight phenomena achieved more than 80% expert consensus and were grouped into nine categories. In the third cycle, these 58 phenomena were ranked by a group of palliative care experts (78 professionals, including physicians, nurses, psycho-social-spiritual support; response rate 72%, see Table 1) in terms of clinical relevance to the prediction that a person will die within the next few hours/days. Twenty-one phenomena were determined to have "high relevance" by more than 50% of the experts. Based on these findings, the changes in the following categories (each consisting of up to three phenomena) were considered highly relevant to clinicians in identifying and predicting a patient's last hours/days of life: "breathing", "general deterioration", "consciousness/cognition", "skin", "intake of fluid, food, others", "emotional state" and "non-observations/expressed opinions/other". CONCLUSION Experts from different professional backgrounds identified a set of categories describing a structure within which clinical phenomena can be clinically assessed, in order to more accurately predict whether someone will die within the next days or hours. However, these phenomena need further specification for clinical use.

Finite element analysis for prediction of bone strength

Article preview View full access options BoneKEy Reports | Review Print Email Share/bookmark Finite element analysis for prediction of bone strength Philippe K Zysset, Enrico Dall'Ara, Peter Varga & Dieter H Pahr Affiliations Corresponding author BoneKEy Reports (2013) 2, Article number: 386 (2013) doi:10.1038/bonekey.2013.120 Received 03 January 2013 Accepted 25 June 2013 Published online 07 August 2013 Article tools Citation Reprints Rights & permissions Abstract Abstract• References• Author information Finite element (FE) analysis has been applied for the past 40 years to simulate the mechanical behavior of bone. Although several validation studies have been performed on specific anatomical sites and load cases, this study aims to review the predictability of human bone strength at the three major osteoporotic fracture sites quantified in recently completed in vitro studies at our former institute. Specifically, the performance of FE analysis based on clinical computer tomography (QCT) is compared with the ones of the current densitometric standards, bone mineral content, bone mineral density (BMD) and areal BMD (aBMD). Clinical fractures were produced in monotonic axial compression of the distal radii, vertebral sections and in side loading of the proximal femora. QCT-based FE models of the three bones were developed to simulate as closely as possible the boundary conditions of each experiment. For all sites, the FE methodology exhibited the lowest errors and the highest correlations in predicting the experimental bone strength. Likely due to the improved CT image resolution, the quality of the FE prediction in the peripheral skeleton using high-resolution peripheral CT was superior to that in the axial skeleton with whole-body QCT. Because of its projective and scalar nature, the performance of aBMD in predicting bone strength depended on loading mode and was significantly inferior to FE in axial compression of radial or vertebral sections but not significantly inferior to FE in side loading of the femur. Considering the cumulated evidence from the published validation studies, it is concluded that FE models provide the most reliable surrogates of bone strength at any of the three fracture sites.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

A multifactorial histopathologic score for the prediction of prognosis of resected esophageal adenocarcinomas after neoadjuvant chemotherapy

BACKGROUND For esophageal adenocarcinoma treated with neoadjuvant chemotherapy, postoperative staging classifications initially developed for non-pretreated tumors may not accurately predict prognosis. We tested whether a multifactorial TNM-based histopathologic prognostic score (PRSC), which additionally applies to tumor regression, may improve estimation of prognosis compared with the current Union for International Cancer Control/American Joint Committee on Cancer (UICC) staging system. PATIENTS AND METHODS We evaluated esophageal adenocarcinoma specimens following cis/oxaliplatin-based therapy from two separate centers (center 1: n = 280; and center 2: n = 80). For the PRSC, each factor was assigned a value from 1 to 2 (ypT0-2 = 1 point; ypT3-4 = 2 points; ypN0 = 1 point; ypN1-3 = 2 points; ≤50 % residual tumor/tumor bed = 1 point; >50 % residual tumor/tumor bed = 2 points). The three-tiered PRSC was based on the sum value of these factors (group A: 3; group B: 4-5; group C: 6) and was correlated with patients' overall survival (OS). RESULTS The PRSC groups showed significant differences with respect to OS (p < 0.0001; hazard ratio [HR] 2.2 [95 % CI 1.7-2.8]), which could also be demonstrated in both cohorts separately (center 1 p < 0.0001; HR 2.48 [95 % CI 1.8-3.3] and center 2 p = 0.015; HR 1.7 [95 % CI 1.1-2.6]). Moreover, the PRSC showed a more accurate prognostic discrimination than the current UICC staging system (p < 0.0001; HR 1.15 [95 % CI 1.1-1.2]), and assessment of two goodness-of-fit criteria (Akaike Information Criterion and Schwarz Bayesian Information Criterion) clearly supported the superiority of PRSC over the UICC staging. CONCLUSION The proposed PRSC clearly identifies three subgroups with different outcomes and may be more helpful for guiding further therapeutic decisions than the UICC staging system.

Novel biomarkers for the prediction of metastasis in colorectal cancer

INTRODUCTION In patients with metastatic colorectal cancers, multimodal management and the use of biological agents such as monoclonal antibodies have had major positive effects on survival. The ability to predict which patients may be at 'high risk' of distant metastasis could have major implications on patient management. Histomorphological, immunohistochemical or molecular biomarkers are currently being investigated in order to test their potential value as predictors of metastasis. AREAS COVERED Here, the author reviews the clinical and functional data supporting the investigation of three novel promising biomarkers for the prediction of metastasis in patients with colorectal cancer: tumor budding, Raf1 kinase inhibitor protein (RKIP) and metastasis-associated in colon cancer-1 (MACC1). EXPERT OPINION The lifespan of most potential biomarkers is short as evidenced by the rare cases that have successfully made their way into daily practice such as KRAS or microsatellite instability (MSI) status. Although the three biomarkers reviewed herein have the potential to become important predictive biomarkers of metastasis, they have similar hurdles to overcome before they can be implemented into clinical management: standardization and validation in prospective patient cohorts.

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.

Prediction of the thermal release of transactinide elements (112 ≤ Z ≤ 116) from metals

Metallic catcher foils have been investigated on their thermal release capabilities for future superheavy element studies. These catcher materials shall serve as connection between production and chemical investigation of superheavy elements (SHE) at vacuum conditions. The diffusion constants and activation energies of diffusion have been extrapolated for various catcher materials using an atomic volume based model. Release rates can now be estimated for predefined experimental conditions using the determined diffusion values. The potential release behavior of the volatile SHE Cn (E112), E113, Fl (E114), E115, and Lv (E116) from polycrystalline, metallic foils of Ni, Y, Zr, Nb, Mo, Hf, Ta, and W is predicted. Example calculations showed that Zr is the best suited material in terms of on-line release efficiency and long-term operation stability. If higher temperatures up to 2773 K are applicable, tungsten is suggested to be the material of choice for such experiments.

Prediction of Cranio-Maxilofacial Surgical Planning using an Inverse Soft Tissue Modelling Approach

In cranio-maxillofacial surgery, the determination of a proper surgical plan is an important step to attain a desired aesthetic facial profile and a complete denture closure. In the present paper, we propose an efficient modeling approach to predict the surgical planning on the basis of the desired facial appearance and optimal occlusion. To evaluate the proposed planning approach, the predicted osteotomy plan of six clinical cases that underwent CMF surgery were compared to the real clinical plan. Thereafter, simulated soft-tissue outcomes were compared using the predicted and real clinical plan. This preliminary retrospective comparison of both osteotomy planning and facial outlook shows a good agreement and thereby demonstrates the potential application of the proposed approach in cranio-maxillofacial surgical planning prediction.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).