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The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.

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The risk assessments during the COVID-19 pandemic were primarily based on dose-response models derived from the pooled datasets for infection of animals susceptible to SARS-CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose-response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated ß-Poisson (BP) models. The modified version of the one-parameter exponential model or the Wells-Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two-parameter (α and ß) Stirling approximated BP model is often recommended compared to the exponential dose-response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of ߠ≫ 1 and α â‰ª ß, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV-229E) and human rhinovirus (HRV-16 and HRV-39) are used to compare the four dose-response models. Based on goodness-of-fit criteria, the exponential model was the best fitting model for the HCoV-229E (k = 0.054) and for HRV-39 datasets (k = 1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV-16 (α = 0.152 and ß = 0.021 for Laplace BP) and the HRV-16 and HRV-39 pooled datasets (α = 0.2247 and ß = 0.0215 for Laplace BP).

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Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

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BACKGROUND: The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose. METHODS: A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear. RESULTS: We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent. CONCLUSIONS: Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.

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Meeting dose prescription is critical to control tumors in radiation therapy. Interfraction dose variations (IDVs) from the prescribed dose in high dose rate brachytherapy (HDR) would cause the target dose to deviate from the prescription but their clinical effect has not been widely discussed in the literature. Our previous study found that IDVs followed a left-skewed distribution. The clinical effect of the IDVs in 100 cervical cancer HDR patients will be addressed in this paper. An in-house Monte Carlo (MC) program was used to simulate clinical outcomes by convolving published tumor dose response curves with IDV distributions. The optimal dose and probability of risk-free local control (RFLC) were calculated using the utility model. The IDVs were well-fitted by the left-skewed Beta distribution, which caused a 3.99% decrease in local control probability and a 1.80% increase in treatment failure. Utility with respect to IDV uncertainty increased the RFLC probability by 6.70% and predicted an optimal dose range of 83 Gy-91 Gy EQD2. It was also found that a 10 Gy dose escalation would not affect toxicity. In conclusion, HRCTV IDV uncertainty reduced LC probabilities and increased treatment failure rates. A dose escalation may help mitigate such effects.

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The adsorbent MIL-101, a metal-organic framework material, was synthesized, characterized, and tested for removal of relatively low concentrations of benzene and toluene adsorbates (200 ppm) from a gas phase in a continuous flow system. Breakthrough studies were modeled based on Thomas, Yoon-Nelson, Yan, Clark, Bohart-Adams, bed-depth service time, modified dose response, Wolborska, and Gompertz in the continuous fixed-bed operation. Through statistical analysis, it was determined which type of regression is most suitable for the studied models, linear or nonlinear. By comparing the values of error functions, it was possible to infer that the Thomas model is the best match for the experimental breakthrough curves for benzene (with maximum solid-phase concentration qT=126,750 mg/g) and the Gompertz model for toluene (parameter ß=0.01 min-1). Overall, when compared to the model parameters of the linear regression, those obtained through nonlinear regression show a stronger correlation with the results found experimentally. Thus, this type of regression is more suitable for the adsorption model analysis. The liquid film and intraparticle diffusion analysis was described, and it was suggested that both types of diffusion contribute to the adsorption mechanism of benzene and toluene on MIL-101. As for the isotherms, the adsorption process was better fitted by the Freundlich isotherm. The reusability of MIL-101 after six cycles was 76.5% for benzene and 62.4% for toluene, indicating that MIL-101 was a better adsorbent for the removal of benzene in comparison with toluene.

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This research focused on the use of residual fiber from oil palm (Elaeis guineensis) for Ni (II) adsorption in a packed bed column. An analysis was conducted on the effect and statistical incidence of changes in temperature, adsorbent particle size, and bed height on the adsorption process. The results showed that particle size and bed height significantly affect the adsorption of Ni (II) ions, reaching adsorption efficiencies between 87.24 and 99.86%. A maximum adsorption capacity of 13.48 mg/g was obtained in the bed with a break time of 180 min. The Ni (II) adsorption in the dynamic system was evaluated by the analysis of the breakage curve with different theoretical models: Yoon-Nelson, dose-response, and Adams-Bohart; the dose-response model was the most appropriate to describe the behavior of the packed bed with an R2 of 84.56%. The breakthrough curve obtained from Aspen Adsorption® appropriately describes the experimental data with an R2 of 0.999. These results indicate that the evaluated bioadsorbent can be recommended for the elimination of Ni (II) in aqueous solutions in a dynamic system, and the simulation of the process can be a tool for the scalability of the process.

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Dose-finding trials play a key role in the entire drug development process to determine optimal doses for regulatory approval. We address confirmatory efficacy testing for individual dose-placebo comparisons in the context of a dose-finding trial designed with multiple comparison procedures-modeling (MCP-Mod). An extension of the MCP-Mod, called closed MCP-Mod, has been proposed to carry out the MCP-Mod in conjunction with pairwise dose-placebo comparisons; however, an issue associated with the misspecification of candidate dose-response models remains. We consider another way to combine the MCP-Mod and the individual dose-placebo comparisons using serial gatekeeping procedures with fixed sequence, Holm, Hochberg, and step-down Dunnett procedure. The method controls the family-wise error rate in the strong sense and is simple enough to be implemented by existing software. Simulation studies suggested that the serial gatekeeping procedure was comparable with the closed MCP-Mod in terms of statistical power to detect the efficacy of at least one dose, and both methods were capable of pursuing the efficacy claim rather than just establishing the dose-response signal with less than a 20% increase in sample size when assuming monotonic dose-response shapes. The serial gatekeeping procedure would have advantages in the simplicity of implementation and ease of interpretation. The dose-finding trials aiming to declare the dose-response signal, as well as the efficacy of individual doses, would be worth considering as an option to accelerate the drug development program in certain situations.

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Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells-Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.

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A cross-sectional study evaluated the risk of zoonotic Streptococcus suis (S. suis) illness from consuming raw pork and swine blood in Nakhon Sawan Province. A four-step risk assessment recommended by the Codex Alimentarius Commission was used to evaluate the risk along the pork supply chain. A total of 480 pork and swine blood samples were collected from the abattoir (n = 120) and retail (n = 360) during December 2020 and January 2021. Streptococcus suis in samples was enumerated using a culture-based technique and then confirmed by the biochemical and molecular technique. Streptococcus suis was serotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Two positive swine blood samples were contaminated with non-zoonotic S. suis serotype 23 at retail. In the case of all negative samples, the deterministic prevalence becomes zero and then the risk could not be estimated. Otherwise, the beta probability distribution was used to describe the probabilistic prevalence, while the maximum likelihood estimator was applied to estimate the upper limit of a probability distribution of concentration. The district averages of probabilistic prevalences of zoonotic S. suis in pork products at abattoir and retail were 9.9% and 4.1%, respectively. The district averages of concentrations of zoonotic S. suis in pork and blood samples from abattoir were 6.8 × 10-3  cfu/g and 6.83 cfu/ml and in pork and blood samples from retail were 2.3 × 10-3  cfu/g and 2.30 cfu/ml, respectively. The overall annual risk estimate per 100,000 population in pork and swine blood from abattoir and retail were 9.8 × 10-11 , 2.2 × 10-6 , 5.4 × 10-13 , and 8.3 × 10-8 . These risk estimates were negligible (<10-6 ) except for the annual risk estimate in swine blood from the abattoir. The results from this cross-sectional risk assessment should prompt the food safety regulator to cautiously sample by taking into account the duration of sampling and sample size.

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Background The optimal model method for estimation of benchmark dose (BMD) does not consider the uncertainty of model selection. There is a lack of studies on using Bayesian model averaging (BMA) to estimate BMD. Objective To apply BMA to the exposure assessment of cadmium pollution in China, discuss the role of BMA in estimating BMD based on dose-response models, and to provide methodological support for health risk assessment of hazardous substances. Methods The parameters of five dose-response models (Gamma, Log-logistic, Log-probit, Two-stage, and Weibull models) estimated from the data from a cadmium-contaminated area in Baiyin City of Gansu Province and the urinary cadmium ranges in five cadmium-contaminated areas in China were used to simulate the data of varied correct models with different numbers of dosage groups (5 and 8) and different sample sizes (50, 100, and 200), then the performance of BMA and traditional optimal model were compared. The case analysis used the cadmium exposure data in Baiyin, Gansu Province. All analyses set urinary cadmium as the indicator of cadmium exposure, the abnormal rate of β2-microglobulin as the effect indicator, and the benchmark response to 10%. The correct model (the model used when simulating data), optimal model [the model with smallest Akaike information criterion (AIC)], and BMA were used to estimate BMD and lower confidence limit of benchmark dose (BMDL); the BMDs, BMDLs, and relative deviations from different methods were compared. Results In the simulation study, with increasing sample size or the number of dosage groups, the intervals of the 5th percentile and the 90th percentile of BMD tended to be narrower; when the correct model was a single model, the relative deviation of BMD estimation by BMA was greater than that of the traditional optimal model; when the correct model was an equal weight mixed model, the relative deviation of BMD estimation by BMA was less than that by the traditional optimal model. For the data of cadmium-contaminated areas, the optimal model was a Log-probit model (AIC=1814.46), followed by a Log-logistic model (AIC=1814.57); the BMDs (BMDLs) estimated by the Log-probit model, the Log-logistic model, and BMA were 3.46 (2.68), 3.16 (2.33), and 2.92 (2.07) μg·g−1, respectively. Conclusion The traditional optimal model is still recommended when the correct model is known. However, when the dose-response relationship of a hazardous substance is uncertain or with different sources or exposure grouping, compared with the traditional optimal model, BMA theoretically provides more stable estimation of BMD and BMDL by considering multiple possible alternative models.

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Ag particles were precipitated on an activated carbon fiber (ACF) surface using a liquid phase plasma (LPP) method to prepare a Ag/ACF composite. The efficiency was examined by applying it as an adsorbent in the acetaldehyde adsorption experiment. Field-emission scanning electron microscopy and energy-dispersive X-ray spectrometry confirmed that Ag particles were distributed uniformly on an ACF surface. X-ray diffraction and X-ray photoelectron spectroscopy confirmed that metallic silver (Ag0) and silver oxide (Ag2O) precipitated simultaneously on the ACF surface. Although the precipitated Ag particles blocked the pores of the ACF, the specific surface area of the Ag/ACF composite material decreased, but the adsorption capacity of acetaldehyde was improved. The AA adsorption of ACF and Ag/ACF composites performed in this study was suitable for the Dose-Response model.

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When a dose-response relationship is monotonic, the EMAX model has been shown to provide a good empirical fit for designing and analyzing dose-response data across a wide range of pharmaceutical studies. However, the EMAX model has never been applied to a finite mixture distribution. Motivated by a proposal investigating DHA dose effect on preterm birth (PTB, <37 weeks gestation) rate, we developed a Bayesian EMAX mixture model incorporating the three normal components finite mixture model into the EMAX framework. The proposed Bayesian EMAX mixture model analyzes gestational age as a continuous variable, which allows for statistically efficient estimates of PTB rate using various cut point with the same parsimonious model. For example, we can estimate the rate of early PTB (ePTB, <34 weeks gestation), PTB (<37 weeks gestation), and late-term birth (>41 weeks gestation) using the same model. We compared our proposed EMAX mixture model with an EMAX logistic model and an independent doses logistic model for a dichotomized endpoint using extensive simulations. Across the scenarios under consideration, the EMAX mixture model achieved higher power than the EMAX logistic model and the independent doses logistic model in detecting the effect of DHA supplementation on the PTB rate. The EMAX mixture model also resulted in smaller mean squared errors (MSE) in PTB rate estimates.

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Microbial risk assessment in food safety is a valuable tool to reduce the risks of infection by pathogens. The dose-response relation is aimed to establish the relationship between the dose of a pathogen that populations are exposed to and the probability of the adverse health effect by the pathogen. Among many dose-response models ever proposed, the exponential and beta-Poisson models have been internationally applied, but the decision on which model is selected between them solely depends on the goodness of fit to specific data sets. On the other hands, the log-logistic model, one of the alternative models, has been little studied on the dose-response relation. In the present study, thus, the application of the log-logistic model to dose-response relation was studied with hypothetical and experimental data sets of infection (or death), comparing to the above two models. Here the experimental data sets were for pathogenic organisms such as pathogenic Escherichia coli, Listeria monocytogenes, and Cryptosporidium pavrum. Consequently, this model successfully fit to those data sets in comparison to the two models. These results suggested that log-logistic model would have the potential to apply to the dose-response relation, similar to the exponential and beta-Poisson models.

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Saccharomyces yeasts are able to ferment simple sugars to generate levels of ethanol that are toxic to other yeasts and bacteria. The tolerance to ethanol of different yeasts depends also on the incubation temperature. In this study, the ethanol stress responses of S. cerevisiae and the probiotic yeast S. boulardii CNCM I-745 were evaluated at two temperatures. The growth kinetics parameters were obtained by fitting the Baranyi and Roberts model to the experimental data. The four-parameter logistic Hill equation was used to describe the ethanol tolerance of the yeasts at the temperatures of 28 and 37 °C. Adequate determination coefficients were obtained (R2 > 0.91) in all cases. S. boulardii grown at 28 °C was selected as the yeast with the best ethanol tolerance (6-8%) for use in the elaboration of functional craft beers.

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This research provides a framework for the human health risk assessment due to exposure of AR (antibiotic resistance) E. coli from recreational water (swimming activity). Literature-based epidemiological studies were used for f-value formulation (i.e., AR E. coli/total number of E. coli isolates) and the theoretical calculation of AR and non-AR E. coli concentrations. Risk was estimated using calculated values by considering four different dose-response (D-R) scenarios with known characteristics due to current lack of availability of D-R for AR bacteria. f-values ranged between 0.14 and 0.59 and the order of calculated theoretical values of maximum AR E. coli are as follows: ampicillin or amoxicillin (38 CFU/dip) > co-trimoxazole (19 CFU/dip) ~ tetracycline (18 CFU/dip) > ceftriaxone or cefotaxime or ceftazidime (10 CFU/dip) ~ ciprofloxacin or ofloxacin (9 CFU/dip). The risk of infection was considerably high for theoretical calculated concentration values regardless of the chosen D-R model (annual risk of infection (95th percentile) = 1, Spearman's rank correlation coefficient = -0.06 to 0.94), under the conditions studied. Further, AR levels of human gastrointestinal-tract were determined using literature-reported data in stool samples and indicated that the resistance level was very high in healthy human (range: 3.7 × 107-8.4 × 107 CFU/g of wet lumen content). The maximum allowable concentration values for AR E. coli and non-ARB (0.0075 CFU/dip and 2.56 CFU/dip) were found to be smaller than the USEPA recreational water quality guidelines (≤126 CFU/100 mL), which can help the USEPA and other regulatory bodies in revisiting the current guidelines. So based on the noted results, we can conclude that the maintenance of inventory of actual measured concentration of ARB in the recreational water sites is needed to prevent unwanted complication related to the treatment of infectious sustained by resistant microbes.

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BACKGROUND: Treatment inertia and prescription complexity are among reasons that people with type 2 diabetes (T2D) do not reach glycemic targets. This study investigated feasibility of a new approach to basal insulin initiation, where the dose needed to reach a glycemic target is estimated from two weeks of insulin and continuous glucose monitoring (CGM) data. METHODS: This was an exploratory single arm study with a maximum length of 84 days. Eight insulin naïve people with T2D, planning to initiate basal insulin, wore a CGM throughout the study period. A predetermined regime was followed for the first two weeks after which the end dose was estimated. The clinician decided whether to follow this advice and continued the titration until target was reached using a twice weekly stepwise titration algorithm. The primary outcome was the comparison between the estimated and the actual end doses. RESULTS: Median age of participants was 57 years (range: 50-77 years), duration of diabetes was 16 years (range: 5-29 years), and Bodi Mass Index (BMI) was 30.2 kg/m2 (range: 22.0-36.0 kg/m2). The median study end dose was 37 U (range: 20-123 U). The estimated end dose was smaller than or equal to the study end dose in all cases, with median error of 26.7% (range: 0.0%-75.8% underestimation). No self-monitoring of blood glucose values were below 70 mg/dL and no severe hypoglycemia occurred. CONCLUSION: While accuracy may be improved, it was found safe to predict the study end dose of insulin degludec from two weeks of data.

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Current approaches used for dose-response modeling of low-dose exposures of pathogens rely on assumptions and extrapolations. These models are important for quantitative microbial risk assessment of food. A mechanistic framework has been advocated as an alternative approach for evaluating dose-response relationships. The objectives of this study were to investigate the invasion behavior of Campylobacter jejuni, which could arise as a foodborne illness even if there are low counts of pathogens, into Caco-2 cells as a model of intestinal cells and to develop a mathematical model for invading cell counts to reveal a part of the infection dose-response mechanism. Monolayer-cultured Caco-2 cells and various concentrations of C. jejuni in culture were cocultured for up to 12 h. The numbers of C. jejuni bacteria invading Caco-2 cells were determined after coculture for different time periods. There appeared to be a maximum limit to the invading bacterial counts, which showed an asymptotic exponential increase. The invading bacterial counts were higher with higher exposure concentrations (maximum, 5.0 log CFU/cm2) than with lower exposure concentrations (minimum, 0.6 log CFU/cm2). In contrast, the ratio of invading bacteria (number of invading bacteria divided by the total number of bacteria exposed) showed a similar trend regardless of the exposure concentration. Invasion of C. jejuni into intestinal cells was successfully demonstrated and described by the developed differential equation model with Bayesian inference. The model accuracy showed that the 99% prediction band covered more than 97% of the observed values. These findings provide important information on mechanistic pathogen dose-response relationships and an alternative approach for dose-response modeling.IMPORTANCE One of the infection processes of C. jejuni, the invasion behavior of the bacteria in intestinal epithelial cells, was revealed, and a mathematical model for prediction of the cell-invading pathogen counts was developed for the purpose of providing part of a dose-response model for C. jejuni based on the infection mechanism. The developed predictive model showed a high accuracy of more than 97% and successfully described the C. jejuni invading counts. The bacterial invasion predictive model of this study will be essential for the development of a dose-response model for C. jejuni based on the infection mechanism.

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Resumo Este estudo avaliou o impacto do tempo de adesão ao termo de compromisso de gestão (TCG), no âmbito do programa Pacto pela Saúde, sobre o nível de eficácia da política municipal de atenção básica, no período de 2008 a 2012. O TCG objetivou aprimorar a governança de política de saúde pelos entes federados, com especial atenção à gestão por resultados. O programa vigorou no Brasil entre 2006 e 2012, tendo recebido a adesão de 4.587 municípios (80% do total). Esta pesquisa buscou responder à seguinte questão: "qual foi o efeito causal do tempo de participação (em anos) no programa Pacto pela Saúde sobre o nível da eficácia da política local de atenção básica, para os municípios participantes?". Para tanto, adotou-se um desenho de pesquisa quase experimental, mediante estimação de um modelo de dose-resposta com escore de propensão generalizado. Estimou-se, via análise de componentes principais, um indicador de eficácia da política de atenção básica (IDEAB), tendo como referência as metas preconizadas pelo programa. Os resultados da estimação da função de dose-resposta evidenciaram que o tempo de adesão ao Pacto pela Saúde teve impacto positivo e estatisticamente significativo sobre o nível de eficácia das políticas de atenção básica nos municípios participantes. Para cada ano adicional de permanência da política, o IDEAB aumentou, em média, entre 0,011 e 0,019 unidades. Portanto, os resultados sugerem que as metas importam para a governança de política de saúde municipal brasileira.

Resumen El presente estudio evaluó el impacto del tiempo de membresía al Término de Compromiso de Gestión (TCG) sobre el nivel de efectividad de la política municipal de salud en Brasil, de 2008 a 2012. El TCG fue parte del programa Pacto por la Salud, y tenía como objetivo mejorar la gobernanza de la política de salud por parte de los estados federados, con especial atención a la gestión basada en resultados. El programa se ejecutó en Brasil entre 2006 y 2012, y fue adoptado por 4.587 municipios (80 por ciento del total). Esta investigación buscó responder a la siguiente pregunta: ¿Cuál fue el efecto causal del tiempo de participación en el programa (en número de años) sobre la efectividad de la política de atención primaria para los municipios participantes? Para ello, se adoptó un diseño de investigación cuasiexperimental, estimando un modelo de dosis-respuesta con puntaje de propensión generalizada. Se estimó un indicador de efectividad de la política de atención primaria (IDEAB) a través del análisis de componentes principales, con base en los objetivos recomendados por el programa. Los resultados de la estimación de la función dosis-respuesta mostraron que el número de años en el programa Pacto por la Salud tuvo un impacto positivo y estadísticamente significativo en el indicador de efectividad de la política de atención primaria para los municipios participantes. Por cada año adicional en la política, el IDEAB aumentó en un promedio de 0.011 a 0.019 unidades. Por lo tanto, los resultados sugieren que los objetivos son importantes para la gobernanza de la política de salud municipal brasileña.

Abstract This study evaluated the impact of the time a Brazilian local government stays as member of the program "Pacto pela Saúde" (Pact for Health) - by signing a Management Agreement -, and its efficiency to provide primary health care for the population. The research observed the period from 2008 to 2012. The program was an initiative of the Federal Government operated by municipalities through the Management Agreement and aimed to improve healthcare policy management adopting a results-based managerial approach. The program was in place between 2006 and 2012 and was operated by 4,587 local governments (80 percent of the Brazilian municipalities). The research question guiding the study was 'What was the effect of the time of a local government in the program (in years) on the efficiency of health care delivery to local populations? A quasi-experimental research design was adopted, estimating a dose-response model with generalized propensity score. An efficiency indicator of the primary care policy (IDEAB) was estimated via principal component analysis, based on the targets recommended by the program. The results of the dose-response model showed that the number of years in the Management Agreement had a positive and statistically significant impact on the efficiency of health care delivery in participating municipalities. For each additional year in the agreement, IDEAB increased by an average of 0.011 to 0.019 units. Therefore, the results suggest that establishing targets are important for the governance of the Brazilian health care policy.

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We exposed zebrafish (Danio rerio) to different concentrations of lead and cadmium, and monitored them for survival at 24, 48, 72, and 96 h. Metal toxicity was predicted and compared using the dose-response and general threshold survival models in terms of required data sets, fit performance, and applicability. Environ Toxicol Chem 2019;38:2169-2177. © 2019 SETAC.

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