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Background: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in transwith a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. Conclusions: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

L’ipertrigliceridemia è una condizione patologica identificata da una concentrazione plasmatica di trigliceridi superiore a 150 mg/dL. Le ipertrigliceridemie primarie hanno alla base mutazioni genetiche, mentre le forme secondarie hanno un’origine multifattoriale, spesso in un contesto poligenico predisponente. L’ipertrigliceridemia è un fattore di rischio per patologie cardiovascolari aterotrombotiche e pancreatite acuta. Le terapie volte a migliorare questa condizione comprendono sia l’adozione di un corretto stile di vita, sia trattamenti farmacologici per ridurre i livelli circolanti di trigliceridi.

ABSTRACTIntroduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories – computational models (e.g., quantitative structure–property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models – have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70–90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

ABSTRACTIntroductionCardiovascular disorders are one of the leading causes of mortality and morbidity worldwide. Recent advances showed a promising role of proprotein convertase subtilisin/kexin type 9 (PCSK9) as a critical player in regulating plasma LDL levels and lipid metabolism.Areas coveredThis review addresses the molecular functions of PCSK9 with a vision on the clinical progress of utilizing monoclonal antibodies and other biological approaches to block PCSK9 activity. The successful clinical trials with monoclonal antibodies are reviewed. Recent advances in (pre)clinical trials of other biological approaches, such as small interfering RNAs, are also discussed.Expert opinionDiscovery of PCSK9 and clinical use of its inhibitors to manage lipid metabolism is a step forward in hypolipidaemic therapy. A better understanding of the molecular activity of PCSK9 can help to identify new approaches in the inhibition of PCSK9 expression/activity. Whether if PCSK9 plays a role in other cardiometabolic conditions may provide grounds for further development of therapies.

In a condition of dysfunctional visceral fat depots, as in the case of obesity, alterations in adipokine levels may be detrimental for the cardiovascular system. The proinflammatory leptin and resistin adipokines have been described as possible links between obesity and atherosclerosis. The present study was aimed at evaluating whether proprotein convertase subtilisin/kexin type 9 (PCSK9), a key regulator of low-density lipoprotein metabolism, is induced by leptin and resistin through the involvement of the inflammatory pathway of STAT3. In HepG2 cells, leptin and resistin up-regulated PCSK9gene and protein expression, as well as the phosphorylation of STAT3. Upon STAT3 silencing, leptin and resistin lost their ability to activate PCSK9. The knockdown of STAT3 did not affect the expression of leptin and resistin receptors or that of PCSK9. The analysis of the human PCSK9promoter region showed that the two adipokines raised PCSK9 promoter activity via the involvement of a sterol regulatory element motif. In healthy males, a positive association between circulating leptin and PCSK9 levels was found only when the body mass index was <25 kg/m2. In conclusion, this study identified STAT3 as one of the molecular regulators of leptin- and resistin-mediated transcriptional induction of PCSK9.

Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice. This Consensus Statement proposes that measurements of waist circumference afford practitioners with an important opportunity to improve the management and health of patients. We argue that BMI alone is not sufficient to properly assess or manage the cardiometabolic risk associated with increased adiposity in adults and provide a thorough review of the evidence that will empower health practitioners and professional societies to routinely include waist circumference in the evaluation and management of patients with overweight or obesity. We recommend that decreases in waist circumference are a critically important treatment target for reducing adverse health risks for both men and women. Moreover, we describe evidence that clinically relevant reductions in waist circumference can be achieved by routine, moderate-intensity exercise and/or dietary interventions. We identify gaps in the knowledge, including the refinement of waist circumference threshold values for a given BMI category, to optimize obesity risk stratification across age, sex and ethnicity. We recommend that health professionals are trained to properly perform this simple measurement and consider it as an important ‘vital sign’ in clinical practice.

Findings from epidemiological studies over the past 30 years have shown that visceral adipose tissue, accurately measured by CT or MRI, is an independent risk marker of cardiovascular and metabolic morbidity and mortality. Emerging evidence also suggests that ectopic fat deposition, including hepatic and epicardial fat, might contribute to increased atherosclerosis and cardiometabolic risk. This joint position statement from the International Atherosclerosis Society and the International Chair on Cardiometabolic Risk Working Group on Visceral Obesity summarises the evidence for visceral adiposity and ectopic fat as emerging risk factors for type 2 diabetes, atherosclerosis, and cardiovascular disease, with a focus on practical recommendations for health professionals and future directions for research and clinical practice. We discuss the measurement of visceral and ectopic fat, pathophysiology and contribution to adverse health outcomes, response to treatment, and lessons from a public health programme targeting visceral and ectopic fat. We identify knowledge gaps and note the need to develop simple, clinically applicable tools to be able to monitor changes in visceral and ectopic fat over time. Finally, we recognise the need for public health messaging to focus on visceral and ectopic fat in addition to excess bodyweight to better combat the growing epidemic of obesity worldwide.

AbstractThe most recent scientific evidence supports the consumption of cow’s milk and dairy products as part of a balanced diet. However, these days, the public and practicing physicans are exposed to a stream of inconsistent (and often misleading) information regarding the relationship between cow’s milk intake and health in the lay press and in the media. The purpose of this article, in this context, is to facilitate doctor–patient communication on this topic, providing physicians with a series of structured answers to frequently asked patient questions. The answers range from milk and milk-derived products’ nutritional function across the life span, to their relationship with diseases such as osteoporosis and cancer, to lactose intolerance and milk allergy, and have been prepared by a panel of experts from the Italian medical and nutritional scientific community.When consumed according to appropriate national guidelines, milk and its derivatives contribute essential micro- and macronutrients to the diet, especially in infancy and childhood where bone mass growth is in a critical phase. Furthermore, preliminary evidence suggests potentially protective effects of milk against overweight, obesity, diabetes, and cardiovascular disease, while no clear data suggest a significant association between milk intake and cancer. Overall, current scientific literature suggests that an appropriate consumption of milk and its derivatives, according to available nutritional guidelines, may be beneficial across all age groups, with the exception of specific medical conditions such as lactose intolerance or milk protein allergy.Key teaching points:Milk and its derivatives contribute essential micro and macronutrients to the diet, when consumed according to appropriate national guidelines, especially in infancy and childhood where bone mass growth is in a critical phase.Preliminary evidence suggests potentially protective effects of milk against overweight, obesity, diabetes and cardiovascular diseaseNo clear data are available about the association between milk intake and cancer.Current scientific literature suggests that an appropriate consumption of milk and its derivatives may be beneficial at all ages, with the exception of specific medical conditions such as lactose intolerance or milk protein allergy.

ABSTRACTIntroduction: Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development.Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures.Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

ABSTRACTIntroduction: Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development.Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application.Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

A large body of evidence supports the notion that incorrect or insufficient nutrition contributes to disease development. A pivotal goal is thus to understand what exactly is appropriate and what is inappropriate in food ingestion and the consequent nutritional status and health. The effective application of these concepts requires the translation of scientific information into practical approaches that have a tangible and measurable impact at both individual and population levels. The agenda for the future is expected to support available methodology in nutrition research to personalize guideline recommendations, properly grading the quality of the available evidence, promoting adherence to the well-established evidence hierarchy in nutrition, and enhancing strategies for appropriate vetting and transparent reporting that will solidify the recommendations for health promotion. The final goal is to build a constructive coalition among scientists, policy makers, and communication professionals for sustainable health and nutritional policies. Currently, a strong rationale and available data support a personalized dietary approach according to personal variables, including sex and age, circulating metabolic biomarkers, food quality and intake frequency, lifestyle variables such as physical activity, and environmental variables including one's microbiome profile. There is a strong and urgent need to develop a successful commitment among all the stakeholders to define novel and sustainable approaches toward the management of the health value of nutrition at individual and population levels. Moving forward requires adherence to well-established principles of evidence evaluation as well as identification of effective tools to obtain better quality evidence. Much remains to be done in the near future.

An artificial pancreas (AP) that can be worn at home from dinner to waking up in the morning might be safe and efficient for first routine use in patients with type 1 diabetes. We assessed the effect on glucose control with use of an AP during the evening and night plus patient-managed sensor-augmented pump therapy (SAP) during the day, versus 24 h use of patient-managed SAP only, in free-living conditions.

Background: The Mars-105 project was aimed at simulating crew's activities, workload, and communication during a mission to Mars, evaluating the homeostatic adaptations to prolonged confinement and cohabitation. Methods:Fasting plasma glucose (FPG) and insulin, C-peptide, leptin, cortisol, and NGF and BDNF plasma levels were monitored in six healthy nonsmoking male subjects taking part in a 105-d Mars mission simulation. Samples were collected from each subject before (0 wk), during (2.5 wk; 5 wk; 10 wk; 15 wk), and after confinement (+1 wk). Results: Confinement resulted in impaired glucometabolic parameters, since FPG increased during the first 5 wk (baseline: 85.2 ± 10.8 mg · dl-1; 2.5 wk: 98.4 ± 4.7 mg · dl-1; 5 wk: 92.5 ± 6.0 mg · dl-1) and insulin dropped at 2.5 wk (baseline: 14.4 ± 4.8 mU · L-1; 2.5 wk: 7.7 ± 2.1 mU · L-1), subsequently returning to baseline values. HOMA-IR paralleled plasma insulin, dropping to 1.8 ± 0.5 at 2.5 wk (baseline: 3.0 ± 1.2). At all time-points tested, plasma leptin levels were decreased (baseline: 4.4 ± 3.3 ng · dl-1; 2.5 wk: 1.6 ± 1.2 ng · dl-1; 5 wk: 1.3 ± 0.8 ng · dl-1; 10 wk: 1.5 ± 1.1 ng · dl-1; 15 wk:1.7 ± 0.8 ng · dl-1), whereas cortisol levels were increased (baseline: 10.8 ± 4.9 ng ·dl-1; 2.5 wk: 16.8 ± 3.5 ng · dl-1; 5 wk: 18.1 ± 7.6 ng · dl-1; 10 wk: 18.1 ± 8.3 ng · dl-1; 15 wk:14.2 ± 4.4 ng · dl-1), resulting in a negative correlation between these hormones. BDNF levels increased only at 5 and 10 wk (baseline: 67.1 ± 36.0 pg · ml-1; 5 wk: 164 ± 54 pg · ml-1; and 10 wk: 110.2 ± 28.9 pg · ml-1).Discussion: The data obtained with the Mars-105 experiment suggest that environmental stress has a strong impact upon metabolic and stress response, indicating the need for further studies and the implementation of specific countermeasures. [ABSTRACT FROM AUTHOR]

Increased iron stores associated with elevated levels of the iron hormone hepcidin are a frequent feature of the metabolic syndrome. The aim of this study was to assess the effect of dietary iron supplementation on insulin resistance and the role of hepcidin in C57Bl/6 male mice fed a standard or iron-enriched diet for 16 weeks. Iron supplementation increased hepatic iron and serum hepcidin fivefold and led to a 40% increase in fasting glucose due to insulin resistance, as confirmed by the insulin tolerance test, and to threefold higher levels of triglycerides. Iron supplemented mice had lower visceral adipose tissue mass estimated by epididymal fat pad, associated with iron accumulation in adipocytes. Decreased insulin signaling, evaluated by the phospho-Akt/Akt ratio, was detected in the visceral adipose tissue of iron overloaded mice, and gene expression analysis of visceral adipose tissue showed that an iron-enriched diet up-regulated iron-responsive genes and adipokines, favoring insulin resistance, whereas lipoprotein lipase was down-regulated. This resulted in hyperresistinemia and increased visceral adipose tissue expression of suppressor of cytokine signaling-3 (Socs3), a target of resistin and hepcidin implicated in insulin resistance. Acute hepcidin administration down-regulated lipoprotein lipase and up-regulated Socs3 in visceral adipose tissue. In conclusion, we characterized a model of dysmetabolic iron overload syndrome in which an iron-enriched diet induces insulin resistance and hypertriglyceridemia and affects visceral adipose tissue metabolism by a mechanism involving hepcidin up-regulation.

The analysis of gene expression temporal profiles is a topic of increasing interest in functional genomics. Model-based clustering methods are particularly interesting because they are able to capture the dynamic nature of these data and to identify the optimal number of clusters. We have defined a new Bayesian method that allows us to cope with some important issues that remain unsolved in the currently available approaches: the presence of time dislocations in gene expression, the non-stationarity of the processes generating the data, and the presence of data collected on an irregular temporal grid. Our method, which is based on random walk models, requires only mild a prioriassumptions about the nature of the processes generating the data and explicitly models inter-gene variability within each cluster. It has first been validated on simulated datasets and then employed for the analysis of a dataset relative to serum-stimulated fibroblasts. In all cases, the results have been promising, showing that the method can be helpful in functional genomics research.

The identification of the insulin minimal model (MM) for the estimation of insulin secretion rate (ISR) and physiological indexes (e.g. β-cell sensitivity) requires the knowledge of C-peptide (CP) kinetics. The four parameters of the two-compartment model of CP kinetics in a given individual can be derived either from an additional bolus experiment or, more frequently, from a population model. However, in both situations, the CP kinetics is uncertain and, in MM identification, it should be treated as such. This paper shows how to handle CP kinetics uncertainty by using a Bayesian methodology. In seven subjects, MM indexes and ISR were estimated together with their confidence intervals, using either the bolus data or the population model to assess CP kinetics. The two main results that arise from the application of the new methodology are: (i) the use of the population model in place of the bolus data to determine CP kinetics does not affect, on average, the point estimates of ISR profile and MM parameters but only the confidence intervals which becomes wider (less than 50%); (ii) in both the bolus and population situation neglecting the uncertainty of CP kinetics, as done in MM literature so far, introduces no bias, on average, on point estimates of MM indexes but only an underestimation of confidence intervals.

Concurrent Solvent Recondensation Large Sample Volume (CRS-LV) splitless injection overcomes the limitation of the maximum sample volume to 1–2 μL valid for classical splitless injection. It is based on control of the evaporation rate in the vaporizing chamber, utilization of a strong pressure increase in the injector resulting from solvent evaporation, and greatly accelerated transfer of the sample vapors from the injector into the inlet of an uncoated precolumn by recondensation of the solvent. The sample vapors are transferred into the column as rapidly as they are formed in the injector (concurrent transfer). 20–50 μL of liquid sample is injected with liquid band formation. The sample liquid is received by a small packing of deactivated glass wool positioned slightly above the column entrance at the bottom of the vaporizing chamber. Solvent evaporation strongly increases the pressure in the injector (auto pressure surge), provided the septum purge outlet is closed and the accessible volumes around the vaporizing chamber are small, driving the first vapors into the precolumn. Transfer continues to be fast because of recondensation of the solvent, obtained by keeping the oven temperature below the pressure-corrected solvent boiling point. The uncoated precolumn must have sufficient capacity to retain most of the sample as a liquid. The experimental data show virtually complete absence of discrimination of volatile or high boiling components as well as high reproducibility.

Concurrent Solvent Recondensation Large Sample Volume (CRS‐LV) splitless injection overcomes the limitation of the maximum sample volume to 1–2 μL valid for classical splitless injection. It is based on control of the evaporation rate in the vaporizing chamber, utilization of a strong pressure increase in the injector resulting from solvent evaporation, and greatly accelerated transfer of the sample vapors from the injector into the inlet of an uncoated precolumn by recondensation of the solvent. The sample vapors are transferred into the column as rapidly as they are formed in the injector (concurrent transfer). 20–50 μL of liquid sample is injected with liquid band formation. The sample liquid is received by a small packing of deactivated glass wool positioned slightly above the column entrance at the bottom of the vaporizing chamber. Solvent evaporation strongly increases the pressure in the injector (auto pressure surge), provided the septum purge outlet is closed and the accessible volumes around the vaporizing chamber are small, driving the first vapors into the precolumn. Transfer continues to be fast because of recondensation of the solvent, obtained by keeping the oven temperature below the pressure‐corrected solvent boiling point. The uncoated precolumn must have sufficient capacity to retain most of the sample as a liquid. The experimental data show virtually complete absence of discrimination of volatile or high boiling components as well as high reproducibility.

Factors affecting the plasma concentrations of the R- and S-enantiomers of fluoxetine and norfluoxetine were investigated in 131 adult patients receiving long-term fluoxetine, of 10 to 60 mg/d (mean, 24 ± 10 mg/d). Plasma concentration values (geometric means, CI 95) in these patients were 186 (156, 223) nmol/L for S-fluoxetine, 67 (58, 77) nmol/L for R-fluoxetine, 247 (212, 287) nmol/L for S-norfluoxetine, and 118 (102, 137) nmol/L for R-norfluoxetine. The difference between the concentrations of the respective R- and S-enantiomers was statistically significant (P< 0.0001) for both the parent drug and the demethylated metabolite. A significant correlation was found between the concentrations of each enantiomer and the prescribed daily dosage (r 0.44, P< 0.0001 for S-fluoxetine; r 0.48, P< 0.0001 for R-fluoxetine; r 0.36, P< 0.0001 for S-norfluoxetine; r 0.32, P0.0003 for R-norfluoxetine), but the variability in concentration at any given dosage was considerable. When an iterative model based on multiple polynomial regressions was applied to determine the potential contributions of dosage, age, gender, body weight, and concomitant medication to the variability in the plasma concentration of the enantiomers, dosage was consistently found to provide the greatest predictive value. The predictive value of the model could be consistently improved when concentrations of other enantiomers were included as covariates. Of 58 patients with depressive symptoms for whom evaluation of clinical response (CGI scale) was available, 33 (57) responded favorably to treatment. The plasma levels of individual enantiomers and of the active moiety (ActM, sum of the concentrations of R-fluoxetine, S-fluoxetine, and S-norfluoxetine) in these patients did not differ significantly from those found in patients with unsatisfactory therapeutic response. Likewise, the concentrations of individual enantiomers and of the ActM were similar in patients with or without adverse effects. Overall, these results demonstrate that the pharmacokinetics of fluoxetine and norfluoxetine exhibit marked stereoselectivity and considerable interpatient variability, which could not be explained by differences in gender, age, or comedication. In addition, a considerable variability was found in the enantiomers' concentrations associated with a favorable therapeutic response.

Abstract: The physical and chemical variability of the water column at subtidal station of an estuary in the Seto Inland Sea, Japan, was studied over a 24-hour period during a spring tide (tidal range ca. 2 m) in May 1995. Surface water and several depths through the water column were monitored every one and two hours, respectively. At each occasion, water temperature, salinity and dissolved oxygen concentration were measured and water samples were collected for the determination of nutrients and suspended particulate matter (SPM). Disruptive changes in the physical and chemical characteristics of the water was produced by the tidal cycle and the mixing of water masses of different origin. These changes were highly significant both spatially and temporally, yet with varying effects on physical parameters, nutrients and the different components of SPM. Significant differences in nutrient concentrations were also observed when the data-set was divided into ebb and flood components, irrespective of the depth. Nitrate and nitrite rose to 1.8 times higher during the flood. Spatial differences of SPM were less marked than those of nutrients, only particulate organic carbon (POC) being significantly higher at the surface than in the intermediate and the lower layer. Both POC and pheopigment concentrations increased markedly through the water column, being highest shortly before the lower low tide. In contrast, suspended solid (SS) content increased sharply after the lower low tide (>40 mg l−1) and this coincided with a marked decrease of the C/SS content (<20 mg g−1). The lagtime between POC and SS tidal transport was caused by particle resuspension from the exposed intertidal sediments as the tidal level rose, and particle transport selection in relation to the tidal state.

When models are used to measure or predict physiological variables and parameters in a given individual, the experiments needed are often complex and costly. A valuable solution for improving their cost effectiveness is represented by population models. A widely used population model in insulin secretion studies is the one proposed by Van Cauter et al. (Diabetes41:368–377, 1992), which determines the parameters of the two compartment model of C-peptide kinetics in a given individual from the knowledge of his/her age, sex, body surface area, and health condition (i.e., normal, obese, diabetic). This population model was identified from the data of a large training set (more than 200 subjects) via a deterministic approach. This approach, while sound in terms of providing a point estimate of C-peptide kinetic parameters in a given individual, does not provide a measure of their precision. In this paper, by employing the same training set of Van Cauter et al., we show that the identification of the population model into a Bayesian framework (by using Markov chain Monte Carlo) allows, at the individual level, the estimation of point values of the C-peptide kinetic parameters together with their precision. A successful application of the methodology is illustrated in the estimation of C-peptide kinetic parameters of seven subjects (not belonging to the training set used for the identification of the population model) for which reference values were available thanks to an independent identification experiment. © 2000 Biomedical Engineering Society. PAC00: 8715Aa, 0250Ga, 8714-g

AUC is an important index of exposure to a drug. Being able to estimate the population AUC without passing through the individual AUCs is of interest because the number of plasma samples that can be collected in each subject is often limited by technical, ethical and cost reasons. In the literature, ad hoc solutions have been proposed toestimate the population AUC when particular sampling schemes are adopted. However, restrictive assumptions are made in order to derive the precision of the estimated AUC. In this work, a new approach is proposed and tested on real data. AUC estimation is performed through a Markov Chain Monte Carlo algorithm.

This paper describes the use of stochastic simulation techniques to reconstruct biomedical signals not directly measurable. In particulart a deconvolution problem with an uncertain clearance parameter is considered. The problem is addressed using a Monte Carlo Markov Chain method, called the Gibbs Sampling, in which the joint posterior probability distribution of the stochastic parameters is estimated through sampling from conditional distributions. This method provides a fully bayesian solution to signal smoothing and deconvolution

In this article we deal with the problem of interpreting data coming from a dynamic system by using causal probabilistic (CPN), a probabilistic graphical model particularly appealing in Intelligent Data Analysis. We discuss the different approaches presented in the literature, outlining their pros and cons through a simple training example. Then, we present a new method for reconstructing the state of the dynamic system, based on Markov Chain Monte Carlo algorithms, called dynamic probabilistic network smoothing (DPN-smoothing). Finally, we present an example of the application of DPN-smoothing in the field of signal deconvolution.

In this work a comparison is made between values of the metal surface areas of several alumina-supported Ru, Rh, and Pt catalysts obtained by hydrogen adsorption isotherms, here defined as the “Multiple Introductions” method, and those calculated by a very simple technique defined as the “Single Introduction” method. With the systematic application of the procedure designated “Back-Sorption,” particularly good agreement is observed between the two techniques if dispersion is calculated on the basis of the irreversibly adsorbed hydrogen. “Back-Sorption” is a necessary practice because of the presence on the above-mentioned metals of more than one adsorbed species not to be considered for the calculation of dispersion, as shown in a more detailed manner for a PtAl2O3catalyst using temperature-programmed desorption. Whenever the conditions for its applicability are satisfied, the Single Introduction method provides a faster and more reliable way to measure the dispersion of metal-supported catalysts than the Multiple Introductions technique.

The Single Introduction (S.I.) method for the measurement by hydrogen chemisorption of metal dispersion in supported metal catalysts has been tested on two commercial 5% PdAl2O3catalysts. The S.I. results are compared with those obtained by the classical method of determining the adsorption isotherm (i.e., the multiple introduction method) and by O2H2titration. As in Part I of this work, both chemisorption and thermal desorption have been used to find the best experimental conditions for the “Back-Sorption” procedure. It is shown that α- and β-palladium hydrides can be decomposed by outgassing the samples for less then 5 min at 305 K and 10−6Torr and that 30 min pumping at the same temperature and pressure is sufficient to eliminate from the catalyst surface the weakly adsorbed hydrogen as well. It has also been verified that chemisorption of hydrogen on these catalysts at room temperature is complete at pressures lower than those required for the α → β phase transition, so that it is possible to perform dispersion measurements by means of a simple S.I. of hydrogen coupled with Back-Sorption below 15 Torr at 305 K. Temperature-programmed desorption of hydrogen on one of the samples outlines the need to discriminate among the different adsorbed species for a more detailed characterization of supported metal catalysts.

A 24 hour time series survey was carried out during a spring tide (tidal range ca.2 m) of May 1995 on a tidal estuary in the Seto Inland Sea, Japan, in the context of an integrated program planned to quantify the dynamics of biophilic elements (carbon, nitrogen and phosphorus) and the roles played by the macrobenthos on the processes. Three stations were set along a transect line of about 1.4 km, which linked the river to the rear to the innermost part of the subtidal zone. Every hour, at each station, measurements were made of surface water temperature, salinity and dissolved oxygen concentration, and surface water was collected for the determination of nutrients [NH4+−N, (NO3−+NO2−)−N, PO43−−P and Si (OH)4−Si]. During the ebb flow, riverine input of silicate and nitrate+nitrite significantly increased the concentrations of both the intertidal and the subtidal stations. Conversely, during the high tide, river nutrient concentrations were lowered by the mixing of fresh water with sea water. As a result, best (inverse) correlations were found at the river station for salinity against silicate (y=-2.9 Sal.+110.7,r2=0.879) and nitrate+nitrite (y=-1.3 Sal.+48.4,r2=0.796). In contrast, ammonium nitrogen concentrations were higher at intermediate salinities. Indeed, no significant correlation was found between salinity and ammonium. The effect of the macrobenthos, which is abundant on the intertidal flat, is discussed as a biological component that influences the processes of nutrient regeneration within the estuary. The effect of the tidal amplitude is an important one in determining the extent of the variations in nutrient concentrations at all three stations, which were stronger between the lower low tide and the higher high tide.

The evaluation of the leptin:adiponectin ratio (L:A) has been suggested as an atherosclerotic index in patients with type 2 diabetes and a useful parameter to assess insulin resistance in patients with and without diabetes.

Obesity is recognized as a disease of epidemic proportions worldwide; as a consequence, a strong demand is now put on the development of novel effective therapies for this condition. The current multidimensional treatment of obesity includes lifestyle changes, behavioural and surgical approaches, as well as pharmacological options. The drugs approved for the clinical treatment of obesity are, however, quite few at the moment and not always effective or well tolerated. This situation seems to evolve today, since the discovery, over the last decade, of a wide range of novel biological targets relevant to food intake and energy metabolism and to obesity has stimulated a great research effort in this field. For these reasons, several promising molecules are now under evaluation at different experimental levels by numerous public and private research centers. This Hot Topic issue of Mini-Reviews in Medicinal Chemistry has therefore been focused on novel approaches to the drug therapy of obesity and collects six updated reviews from experts involved in the development of this field. The first article, by Chaput et al., is a critical review of the use of two drugs, sibutramine and orlistat, currently available to the clinics. The following articles of the issue will then cover in detail the families of molecules proposed and studied to target specific biological systems involved in the development and maintenance of the state of obesity. The contribution by Dozio et al. reviews the hypothalamic neuropeptide systems involved in the control of food intake and energy metabolism, such as the neuropeptide Y and the melanocortin systems, and the agonist and antagonist molecules developed to specifically modulate these targets, acting as anti-obesity drugs. The mini-review by Cervino et al. reports the state-of-the-art of cannabinoid receptor antagonists and their promising use in the treatment of obesity and the metabolic syndrome. The molecular biology of leptin, one of the most studied adipokines, is reviewed by Correia within the perspective of the potential therapeutic actions of recombinant leptin and leptin-related compounds, whereas other adipokines and adipocyte targets, useful for the future management of obesity and the metabolic syndrome, is the area reviewed by Kralisch et al.. Ghrelin and other gastrointestinal peptides involved in the control of food intake, another important field of research in the pharmacology of obesity, is the topic covered by Tassone et al. Obesity is known to arise from the combination of environmental and genetic factors, like many highly prevalent chronic degenerative diseases. Thus, together with lifestyle changes, a future pharmacological approach will need to combine novel safe and effective drugs, several of which are reported or envisioned in this issue, together with the study of the specific genetic/genomic pattern of each individual obese subject, in the logic of pharmacogenomics. I would like to acknowledge Ms. Afshan Siddiq for undertaking the important tasks of correspondence and publication management.