Determination of perchlorate in infant formula by isotope dilution ion chromatography/tandem mass spectrometry

A sensitive and selective isotope dilution ion chromatography/tandem mass spectrometry (ID IC-MS/MS) method was developed and validated for the determination of perchlorate in infant formula. The perchlorate was extracted from infant formula by using 20 ml of methanol and 5 ml of 1% acetic acid. All samples were spiked with 18O4 isotope-labelled perchlorate internal standard prior to extraction. After purification on a graphitised carbon solid-phase extraction column, the extracts were injected into an ion chromatography system equipped with an Ionpac AS20 column for separation of perchlorate from other anions. The presence of perchlorate in samples was quantified by isotope dilution mass spectrometry. Analysis of both perchlorate and its isotope-labelled internal standard was carried out on a Waters Quattro Ultima triple quadrupole mass spectrometer operating in a multiple reaction monitoring (MRM) negative ionisation mode. The method was validated for linearity and range, accuracy, precision, sensitivity, and matrix effects. The limit of quantification (LOQ) was 0.4 μg 1−1 for liquid infant formula and 0.95 μg kg−1 for powdered infant formula. The recovery ranged from 94% to 110% with an average of 98%. This method was used to analyse 39 infant formula, and perchlorate concentrations ranging from <LOQ to 13.5 μg 1−1

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte

Systematic review of prognostic models in traumatic brain injury

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability world-wide. The ability to accurately predict patient outcome after TBI has an important role in clinical practice and research. Prognostic models are statistical models that combine two or more items of patient data to predict clinical outcome. They may improve predictions in TBI patients. Multiple prognostic models for TBI have accumulated for decades but none of them is widely used in clinical practice. The objective of this systematic review is to critically assess existing prognostic models for TBI METHODS: Studies that combine at least two variables to predict any outcome in patients with TBI were searched in PUBMED and EMBASE. Two reviewers independently examined titles, abstracts and assessed whether each met the pre-defined inclusion criteria. RESULTS: A total of 53 reports including 102 models were identified. Almost half (47%) were derived from adult patients. Three quarters of the models included less than 500 patients. Most of the models (93%) were from high income countries populations. Logistic regression was the most common analytical strategy to derived models (47%). In relation to the quality of the derivation models (n:66), only 15% reported less than 10% pf loss to follow-up, 68% did not justify the rationale to include the predictors, 11% conducted an external validation and only 19% of the logistic models presented the results in a clinically user-friendly way CONCLUSION: Prognostic models are frequently published but they are developed from small samples of patients, their methodological quality is poor and they are rarely validated on external populations. Furthermore, they are not clinically practical as they are not presented to physicians in a user-friendly way. Finally because only a few are developed using populations from low and middle income countries, where most of trauma occurs, the generalizability to these setting is limited

Expanding global access to essential medicines: investment priorities for sustainably strengthening medical product regulatory systems.

Access to quality-assured medical products improves health and save lives. However, one third of the world's population lacks timely access to quality-assured medicines while estimates indicate that at least 10% of medicine in low- and middle-income countries (LMICs) are substandard or falsified (SF), costing approximately US$ 31 billion annually. National regulatory authorities are the key government institutions that promote access to quality-assured medicines and combat SF medical products but despite progress, regulatory capacity in LMICs is still insufficient. Continued and increased investment in regulatory system strengthening (RSS) is needed. We have therefore reviewed existing global normative documents and resources and engaged with our networks of global partners and stakeholders to identify three critical challenges being faced by NRAs in LMICs that are limiting access to medical products and impeding detection of and response to SF medicines. The challenges are; implementing value-added regulatory practices that best utilize available resources, a lack of timely access to new, quality medical products, and limited evidence-based data to support post-marketing regulatory actions. To address these challenges, we have identified seven focused strategies; advancing and leveraging convergence and reliance initiatives, institutionalizing sustainability, utilizing risk-based approaches for resource allocation, strengthening registration efficiency and timeliness, strengthening inspection capacity and effectiveness, developing and implementing risk-based post-marketing quality surveillance systems, and strengthening regulatory management of manufacturing variations. These proposed solutions are underpinned by 13 focused recommendations, which we believe, if financed, technically supported and implemented, will lead to stronger health system and as a consequence, positive health outcomes

Clinical decision-making: physicians' preferences and experiences

BACKGROUND: Shared decision-making has been advocated; however there are relatively few studies on physician preferences for, and experiences of, different styles of clinical decision-making as most research has focused on patient preferences and experiences. The objectives of this study were to determine 1) physician preferences for different styles of clinical decision-making; 2) styles of clinical decision-making physicians perceive themselves as practicing; and 3) the congruence between preferred and perceived style. In addition we sought to determine physician perceptions of the availability of time in visits, and their role in encouraging patients to look for health information. METHODS: Cross-sectional survey of a nationally representative sample of U.S. physicians. RESULTS: 1,050 (53% response rate) physicians responded to the survey. Of these, 780 (75%) preferred to share decision-making with their patients, 142 (14%) preferred paternalism, and 118 (11%) preferred consumerism. 87% of physicians perceived themselves as practicing their preferred style. Physicians who preferred their patients to play an active role in decision-making were more likely to report encouraging patients to look for information, and to report having enough time in visits. CONCLUSION: Physicians tend to perceive themselves as practicing their preferred role in clinical decision-making. The direction of the association cannot be inferred from these data; however, we suggest that interventions aimed at promoting shared decision-making need to target physicians as well as patients

Role of the mesoamygdaloid dopamine projection in emotional learning

Amygdala dopamine is crucially involved in the acquisition of Pavlovian associations, as measured via conditioned approach to the location of the unconditioned stimulus (US). However, learning begins before skeletomotor output, so this study assessed whether amygdala dopamine is also involved in earlier 'emotional' learning. A variant of the conditioned reinforcement (CR) procedure was validated where training was restricted to curtail the development of selective conditioned approach to the US location, and effects of amygdala dopamine manipulations before training or later CR testing assessed. Experiment 1a presented a light paired (CS+ group) or unpaired (CS- group) with a US. There were 1, 2 or 10 sessions, 4 trials per session. Then, the US was removed, and two novel levers presented. One lever (CR+) presented the light, and lever pressing was recorded. Experiment 1b also included a tone stimulus. Experiment 2 applied intra-amygdala R(+) 7-OH-DPAT (10 nmol/1.0 A mu l/side) before two training sessions (Experiment 2a) or a CR session (Experiment 2b). For Experiments 1a and 1b, the CS+ group preferred the CR+ lever across all sessions. Conditioned alcove approach during 1 or 2 training sessions or associated CR tests was low and nonspecific. In Experiment 2a, R(+) 7-OH-DPAT before training greatly diminished lever pressing during a subsequent CR test, preferentially on the CR+ lever. For Experiment 2b, R(+) 7-OH-DPAT infusions before the CR test also reduced lever pressing. Manipulations of amygdala dopamine impact the earliest stage of learning in which emotional reactions may be most prevalent

Direct Measurement of Perchlorate Exposure Biomarkers in a Highly Exposed Population: A Pilot Study

Exposure to perchlorate is ubiquitous in the United States and has been found to be widespread in food and drinking water. People living in the lower Colorado River region may have perchlorate exposure because of perchlorate in ground water and locally-grown produce. Relatively high doses of perchlorate can inhibit iodine uptake and impair thyroid function, and thus could impair neurological development in utero. We examined human exposures to perchlorate in the Imperial Valley among individuals consuming locally grown produce and compared perchlorate exposure doses to state and federal reference doses. We collected 24-hour urine specimen from a convenience sample of 31 individuals and measured urinary excretion rates of perchlorate, thiocyanate, nitrate, and iodide. In addition, drinking water and local produce were also sampled for perchlorate. All but two of the water samples tested negative for perchlorate. Perchlorate levels in 79 produce samples ranged from non-detect to 1816 ppb. Estimated perchlorate doses ranged from 0.02 to 0.51 µg/kg of body weight/day. Perchlorate dose increased with the number of servings of dairy products consumed and with estimated perchlorate levels in produce consumed. The geometric mean perchlorate dose was 70% higher than for the NHANES reference population. Our sample of 31 Imperial Valley residents had higher perchlorate dose levels compared with national reference ranges. Although none of our exposure estimates exceeded the U. S. EPA reference dose, three participants exceeded the acceptable daily dose as defined by bench mark dose methods used by the California Office of Environmental Health Hazard Assessment