IgE-Mediated Sensitization to Blo t 21 and Blo t 5 Is Associated With Asthma in the Tropics: A Case-Control Study.

BACKGROUND AND OBJECTIVE: Sensitization to Blomia tropicalis is associated with asthma in various tropical and subtropical countries; however, information about the specific molecular components associated with this disease is scarce. Using molecular diagnosis, we sought to identify B tropicalis allergens associated with asthma in Colombia. METHODS: Specific IgE (sIgE) to 8 B tropicalis recombinant allergens (Blo t 2, 5, 7, 8, 10, 12, 13, and 21) was determined using an in-house ELISA system in asthma patients (n=272) and controls (n=298) recruited in a national prevalence study performed in several Colombian cities (Barranquilla, Bogotá, Medellín, Cali, and San Andrés). The study sample included children and adults (mean [SD] age, 28 [17] years). Cross-reactivity between Blo t 5 and Blo t 21 was evaluated using ELISA-inhibition. RESULTS: Specific IgE (sIgE) to 8 B tropicalis recombinant allergens (Blo t 2, 5, 7, 8, 10, 12, 13, and 21) was determined using an in-house ELISA system in asthma patients (n=272) and controls (n=298) recruited in a national prevalence study performed in several Colombian cities (Barranquilla, Bogotá, Medellín, Cali, and San Andrés). The study sample included children and adults (mean [SD] age, 28 [17] years). Cross-reactivity between Blo t 5 and Blo t 21 was evaluated using ELISA-inhibition. CONCLUSION: Although Blo t 5 and Blo t 21 are considered common sensitizers, this is the first report of their association with asthma. Both components should be included in molecular panels for diagnosis of allergy in the tropics.

Possible Risk Factors and Their Potential Associations with Combined Heavy Metal Exposures in Pregnant Women in the Republic of Suriname.

Background: The exposure of pregnant women to multiple environmental pollutants may be more disadvantageous to birth outcomes when compared to single-compound contaminations. Objective: This study investigated the mixed exposures to mercury, manganese, or lead in 380 pregnant Surinamese women. The factors that might be associated with the heavy metal exposures and the relative risk of the potential factors to cause the mixed exposures were explored. The influencing factors of exposures to mixed contaminants assessed were living in Suriname's rural regions, several parts of which are contaminated with heavy metals emitted from artisanal and small-scale gold mining and agricultural activities; the consumption of potentially contaminated foods; advanced maternal age; as well as a relatively low formal educational level and monthly household income. Methods: Descriptive statistics were used to calculate frequency distributions and χ2-contingency analyses to calculate associations and relative risks (RR) with 95% confidence intervals (CI). Findings: Blood levels of two or three of the heavy metals above public health limits were observed in 36% of the women. These women were more often residing in the rural regions, primarily consumed potentially contaminated food items, were 35 years or older, were lower educated, and more often had a lower household income. However, only living in the rural regions (RR = 1.48; 95% CI 1.23-1.77) and a low household income (RR = 1.38; 95% CI 1.15-1.66) significantly increased the risk of exposure exceeding levels of concern to two or three of the heavy metals (by 48% and 38%, respectively). Conclusion: More comprehensive pharmacological, ecological, and epidemiological studies about exposures to mixed heavy metal contaminations in pregnant women are warranted.

Reinforcement learning for in silico determination of adsorbate-substrate structures.

Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2-amino-acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host-guest interactions between phenylboronic acid and ß-cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)-(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q-matrix or Q-table for better decision making (policy) on next actions. Finally, we have developed a transfer-learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels.

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery.

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination in silico for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials. QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained. The software has different acquisition functions for decision making (e.g., expected improvement and lower confidence bound). Also, the Gaussian process is available in the AI framework for regression, where the uncertainty is obtained analytically from Bayesian statistics. For the artificial neural network and support vector regressor algorithms, the uncertainty can be obtained by K-fold cross-validation or nonparametric bootstrap resampling methods. The software is interfaced with several quantum chemistry codes and atomic descriptors, such as the many-body tensor representation. QMLMaterial's capabilities are highlighted in the current work by its applications in the following systems: Na20, Mo6C3 (where the spin multiplicity was considered), H2O@CeNi3O5, Mg8@graphene, Na3Mg3@CNT (carbon nanotube).

IgE sensitization to Blo t 21 and Blo t 5 is associated with asthma in the tropics: a case-control study.

BACKGROUND AND OBJECTIVES: Blomia tropicalis sensitization is associated with asthma in different tropical and sub-tropical countries; however, information about the specific molecular components associated with this disease is scarce. Using molecular diagnosis, we sought to identify B. tropicalis allergens associated with asthma in Colombia. METHODS: Specific IgE (sIgE) to eight B. tropicalis recombinant allergens (Blo t 2/5/7/8/10/12/13 and 21) was determined using an in-house developed ELISA system in asthmatic patients (n=272) and control subjects (n=298) recruited in a national prevalencestudy performed in Colombian cities (Barranquilla, Bogotá, Medellín, Cali and San Andrés). Sample study included children and adults (mean age: 28±SD 17 years old). Cross-reactivity between Blot 5 and Blo t 21 was evaluated by ELISA-inhibition. RESULTS: Sensitization to Blo t 21 (aOR: 1.9; 95% CI: 1.2 - 2.9) and Blo t 5 (aOR: 1.6; 95%CI: 1.1 - 2.5), but not Blo t 2, was associated with asthma. sIgE levels to Blo t 21 and to Blo t 5 were significantly higher in the disease group. Cross-reactivity between Blo t 21 and Blo t 5 is on average moderate; however, individual analysis indicates that may be high (>50%) in some cases. CONCLUSIONS: Although Blo t 5 and Blo t 21 has been described as common sensitizers, this is the first report of their association with asthma. Both components should be included in molecular panels for allergy diagnosis in the tropics.

GAMaterial-A genetic-algorithm software for material design and discovery.

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6 O12 cluster, doping Al in Si11 (4Al@Si11 ) and Na10 supported on graphene (Na10 @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8 C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.

Automatic structural elucidation of vacancies in materials by active learning.

Finding the optimum structures of non-stoichiometric or berthollide materials, such as (1D, 2D, 3D) materials or nanoparticles (0D), is challenging due to the huge chemical/structural search space. Computational methods coupled with global optimization algorithms have been used successfully for this purpose. In this work, we have developed an artificial intelligence method based on active learning (AL) or Bayesian optimization for the automatic structural elucidation of vacancies in solids and nanoparticles. AL uses machine learning regression algorithms and their uncertainties to take decisions (from a policy) on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations. The methodology allows an accurate and automated structural elucidation for vacancies, which are common in non-stoichiometric (berthollide) materials, helping to understand chemical processes in catalysis and environmental sciences, for instance. The AL vacancies method was implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial). Also, two additional acquisition functions for decision making were implemented, besides the expected improvement (EI): the lower confidence bound (LCB) and the probability of improvement (PI). The new software was applied for the automatic structural search for graphite (C36) with 3 (C36-3) and 4 (C36-4) carbon vacancies and C60 (C60-4) fullerene with 4 carbon vacancies. DFTB calculations were used to build the complex search surfaces with reasonably low computational cost. Furthermore, with the AL method for vacancies, it was possible to elucidate the optimum oxygen vacancy distribution in CaTiO3 perovskite by DFT, where a semiconductor behavior results from oxygen vacancies. Throughout the work, a Gaussian process with its uncertainty was employed in the AL framework using different acquisition functions (EI, LCB and PI), and taking into account different descriptors: Ewald sum matrix and sine matrix. Finally, the performance of the proposed AL method was compared to random search and genetic algorithm.

Prenatal Exposure to Mercury, Manganese, and Lead and Adverse Birth Outcomes in Suriname: A Population-Based Birth Cohort Study.

Globally, adverse birth outcomes are increasingly linked to prenatal exposure to environmental contaminants, such as mercury, manganese, and lead. This study aims to assess an association between prenatal exposure to mercury, manganese, and lead and the occurrence of adverse birth outcomes in 380 pregnant women in Suriname. The numbers of stillbirths, preterm births, low birth weights, and low Apgar scores were determined, as well as blood levels of mercury, manganese, lead, and relevant covariates. Descriptive statistics were calculated using frequency distributions. The associations between mercury, manganese, and lead blood levels, on the one hand, and adverse birth outcomes, on the other hand, were explored using contingency tables, tested with the χ2-test (Fisher's exact test), and expressed with a p value. Multivariate logistic regression models were computed to explore independent associations and expressed as (adjusted) odds ratios (aOR) with 95% confidence intervals (CI). The findings of this study indicate no statistically significant relationship between blood mercury, manganese, or lead levels and stillbirth, preterm birth, low birth weight, and low Apgar score. However, the covariate diabetes mellitus (aOR 5.58, 95% CI (1.38-22.53)) was independently associated with preterm birth and the covariate hypertension (aOR 2.72, 95% CI (1.081-6.86)) with low birth weight. Nevertheless, the observed high proportions of pregnant women with blood levels of mercury, manganese, and lead above the reference levels values of public health concern warrants environmental health research on risk factors for adverse birth outcomes to develop public health policy interventions to protect pregnant Surinamese women and their newborns from potential long-term effects.

A new active learning approach for adsorbate-substrate structural elucidation in silico.

Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, such as finding the optimum adsorption geometry and energy, is challenging, due to the huge number of possibilities of assembling the adsorbate on the surface. In the current work, we have developed an artificial intelligence (AI) approach based on an active learning (AL) method for adsorption optimization on the surface of materials. AL uses machine learning (ML) regression algorithms and their uncertainties to make a decision (based on a policy) for the next unexplored structures to be computed, increasing, though, the probability of finding the global minimum with a small number of calculations. The methodology allows an accurate and automated structural elucidation of the adsorbate on the surface, based on the minimization of the total electronic energy. The new AL method for adsorption optimization was developed and implemented in the quantum machine learning software/agent for material design and discovery (QMLMaterial) program and was applied for C60@TiO2 anatase (101). It marks another software extension with a new feature in addition to the automatic structural elucidation of defects in materials and of nanoparticles as well. SCC-DFTB calculations were used to build the complex search surfaces with a reasonably low computational cost. An artificial neural network (NN) was employed in the AL framework evaluated together with two uncertainty quantification methods: K-fold cross-validation and non-parametric bootstrap (BS) resampling. Also, two different acquisition functions for decision-making were used: expected improvement (EI) and the lower confidence bound (LCB).

Insight into the antioxidant activities of ten Fabaceae plant species that are medicinally used by the Aucan Tribal Peoples from the Republic of Suriname (South America).

Fabaceae are associated with a high antioxidant activity (AA) and a high total phenolic (TPC), total flavonoid (TFC), and selenium content (SeC). In this study, the aqueous extracts from ten Fabaceae species that are medicinally used by the Aucan Tribal Peoples from Suriname (South America), were evaluated for AA using a DPPH and a FRAP assay, and for TPC, TFC, and SeC using Folin-Ciocalteu's, an AlCl3 colorimetric, and an azure B-based method. Associations between pairs of these variables were determined by Pearson correlation coefficient. One-way ANOVA with post-hoc Tukey's test was used to evaluate the data for statistically significant differences (p < 0.05). The I. stipularis (bark), C. guyanensis (bark), A. jupunba (twigs), and M. urens (fruit) extracts had the highest DPPH IC50 values (36 - 70 µg/mL) and FRAP values (346 - 573 µM FeE/100 µg) and the highest TPC (25 - 41 GAEq/100 µg), TFC (21 - 39 REq/100 µg), and SeC (4 -17 µg/g). The values for the T. indica (leaf), P. macroloba (bark), M. pigra (whole plant), S. quinquangulata (leaf), A. sensitiva (whole plant), and L. leucocephala (leaf) extracts were > 10-fold lower. AA, TPC, TFC, and SeC correlated well with each other (correlation coefficient ≥ 0.83, p ≤ 0.0030). Thus, AA, TPC, TFC, and SeC may represent important determinants of the health benefits of the former four samples but not of the others. Future studies should focus on the precise contribution of AA, TPC, TFC, and SeC to the therapeutic value of medicinal Fabaceae.

The distribution of disease in the Republic of Suriname - A pharmacoepidemiological analysis using the claims database of the State Health Foundation of the year 2017.

The patterns of prescription drug use in Suriname in the year 2017 have been determined with the purpose of obtaining indications about the distribution of disease in the country. The claims database of the State Health Foundation (Staatsziekenfonds, SZF) of Suriname was used for calculations of prescription rates of the fifty most prescribed drugs overall and after stratification according to gender, age, and residence of the insured persons. Information in the database had been de-identified, and the prescribed medicines had been coded according to the Anatomic Therapeutic Chemical Classification System. Statistically significant differences among the prescription rates were assessed with the two samples test of proportions using normal theory method and χ2 Goodness of Fit tests (p < 0.05). Additionally, the Bonferroni adjustment was used to adjust for type 1 error inflation resulting from multiple comparisons. Overall, drugs for the cardiovascular, respiratory, and musculo-skeletal systems had the highest prescription rates (p < 0.001). Furthermore, rates were generally higher in females than in males, in the older age groups than in younger individuals, and in the coastal regions compared to the country's interior (p < 0.001). These findings are largely in line with data found in the literature and support the use of this pharmacoepidemiological approach to assess the distribution of disease in Suriname.

Prevalence and safety of prescription medicine use during pregnancy in the Republic of Suriname in the year 2017: a pharmacoepidemiological analysis.

BACKGROUND: Using the claims database of the State Health Foundation from 2017, the prevalence and safety of prescription medicines given to pregnant women in Suriname (South America) have been determined. METHODS: Prescription rates and proportions of the total number of prescriptions were calculated, overall and stratified for subgroups of age, region of residence, major Anatomical Therapeutic Chemical - and safety classification (Australian categorization system). Data were compared with the Σ2-test and the two samples test of proportions using normal theory method; p-values <0.01 were considered statistically significant differences. RESULTS: Average prescription rates (number of prescriptions by number of patients) were 24.0, 29.7, and 32.5 in age groups 15-29, 30-44, and 45+ years, respectively (p<0.001), and 26.4, 23.0, and 14.0 in the urban-coastal, rural-coastal, and rural-interior region, respectively (p<0.001). CONCLUSIONS: The use of prescription medicines was common (rates up to 40.4), ranged from antibiotics to vitamins, and most were safe. However, 3.2% (some antibiotics and antiepileptics) belonged to safety category D, carrying a definite human fetal risk. However, the potential benefits of these drugs warranted their use in pregnant women. These findings are largely in line with literature data, although future studies must verify their generalizability to the total Surinamese population.

Ancestral patterns of recessive dystrophic epidermolysis bullosa mutations in Hispanic populations suggest sephardic ancestry.

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genodermatosis caused by mutations in the gene coding for type VII collagen (COL7A1). More than 800 different pathogenic mutations in COL7A1 have been described to date; however, the ancestral origins of many of these mutations have not been precisely identified. In this study, 32 RDEB patient samples from the Southwestern United States, Mexico, Chile, and Colombia carrying common mutations in the COL7A1 gene were investigated to determine the origins of these mutations and the extent to which shared ancestry contributes to disease prevalence. The results demonstrate both shared European and American origins of RDEB mutations in distinct populations in the Americas and suggest the influence of Sephardic ancestry in at least some RDEB mutations of European origins. Knowledge of ancestry and relatedness among RDEB patient populations will be crucial for the development of future clinical trials and the advancement of novel therapeutics.

An Integrated Model to Accelerate Patient Satisfaction Improvement.

Patient satisfaction is gaining traction in the strategic direction and daily operations of hospital executives. The financial penalty/incentive tied to patient satisfaction scores creates a burning platform to accelerate progress. Previous studies have shown the effectiveness of various improvement strategies including leadership rounding and employee training, among others. There has not been a study utilizing an integrated model that incorporates known best practices into a holistic approach. The integrated model included service excellence training, nursing unit-specific action plans, and weekly leadership rounding. Implementation of the model led to significant and sustainable improvements in patient satisfaction in the community hospital setting. This approach can be leveraged and scaled in other organizations to accelerate the pace of change.

Reliability of semiempirical and DFTB methods for the global optimization of the structures of nanoclusters.

In this work, we explore the possibility of using computationally inexpensive electronic structure methods, such as semiempirical and DFTB calculations, for the search of the global minimum (GM) structure of chemical systems. The basic prerequisite that these inexpensive methods will need to fulfill is that their lowest energy structures can be used as starting point for a subsequent local optimization at a benchmark level that will yield its GM. If this is possible, one could bypass the global optimization at the expensive method, which is currently impossible except for very small molecules. Specifically, we test our methods with clusters of second row elements including systems of several bonding types, such as alkali, metal, and covalent clusters. The results reveal that the DFTB3 method yields reasonable results and is a potential candidate for this type of applications. Even though the DFTB2 approach using standard parameters is proven to yield poor results, we show that a re-parametrization of only its repulsive part is enough to achieve excellent results, even when applied to larger systems outside the training set.

Big Things Come in Bundled Packages: Implications of Bundled Payment Systems in Health Care Reimbursement Reform.

With passage of the Affordable Care Act, the ever-evolving landscape of health care braces for another shift in the reimbursement paradigm. As health care costs continue to rise, providers are pressed to deliver efficient, high-quality care at flat to minimally increasing rates. Inherent systemwide inefficiencies between payers and providers at various clinical settings pose a daunting task for enhancing collaboration and care coordination. A change from Medicare's fee-for-service reimbursement model to bundled payments offers one avenue for resolution. Pilots using such payment models have realized varying degrees of success, leading to the development and upcoming implementation of a bundled payment initiative led by the Center for Medicare and Medicaid Innovation. Delivery integration is critical to ensure high-quality care at affordable costs across the system. Providers and payers able to adapt to the newly proposed models of payment will benefit from achieving cost reductions and improved patient outcomes and realize a competitive advantage.

Sugar and Aldehyde Content in Flavored Electronic Cigarette Liquids.

Introduction: Sugars are major constituents and additives in traditional tobacco products, but little is known about their content or related toxins (formaldehyde, acetaldehyde, and acrolein) in electronic cigarette (e-cigarette) liquids. This study quantified levels of sugars and aldehydes in e-cigarette liquids across brands, flavors, and nicotine concentrations (n = 66). Methods: Unheated e-cigarette liquids were analyzed using liquid chromatography mass spectrometry and enzymatic test kits. Generalized linear models, Fisher's exact test, and Pearson's correlation coefficient assessed sugar, aldehyde, and nicotine concentration associations. Results: Glucose, fructose and sucrose levels exceeded the limits of quantification in 22%, 53% and 53% of the samples. Sucrose levels were significantly higher than glucose [χ2(1) = 85.9, p < .0001] and fructose [χ2(1) = 10.6, p = .001] levels. Formaldehyde, acetaldehyde, and acrolein levels exceeded the limits of quantification in 72%, 84%, and 75% of the samples. Acetaldehyde levels were significantly higher than formaldehyde [χ2(1) = 11.7, p = .0006] and acrolein [χ2(1) = 119.5, p < .0001] levels. Differences between nicotine-based and zero-nicotine labeled e-cigarette liquids were not statistically significant for sugars or aldehydes. We found significant correlations between formaldehyde and fructose (-0.22, p = .004) and sucrose (-0.25, p = .002) and acrolein and fructose (-0.26, p = .0006) and sucrose (-0.21, p = .0006). There were no significant correlations between acetaldehyde and any of the sugars or any of the aldehydes and glucose. Conclusions: Sugars and related aldehydes were identified in unheated e-cigarette liquids and their composition may influence experimentation in naïve users and their potential toxicity. Implications: The data can inform the regulation of specific flavor constituents in tobacco products as a strategy to protect young people from using e-cigarettes, while balancing FDA's interest in how these emerging products could potentially benefit adult smokers who are seeking to safely quit cigarette smoking. The data can also be used to educate consumers about ingredients in products that may contain nicotine and inform future FDA regulatory policies related to product standards and accurate and comprehensible labeling of e-cigarette liquids.

Warning Statements and Safety Practices Among Manufacturers and Distributors of Electronic Cigarette Liquids in the United States.

Introduction: Prior to the US Food and Drug Administration's (FDA) regulation of electronic cigarettes and warning statements related to nicotine addiction, there was no critical examination of manufacturer/distributor voluntary practices that could potentially inform FDA actions aimed to protect consumers. This study examined the content of warning statements and safety characteristics of electronic cigarette liquid bottles using a national sample. Methods: Research staff randomly selected four electronic cigarette liquid manufacturers/distributors from four US geographic regions. Staff documented the characteristics of product packaging and content of warning statements on 147 electronic cigarette liquids (0-30 mg/ml of nicotine) purchased online from 16 manufacturers/distributors in April of 2016. Results: Data showed that 97.9% of the electronic cigarette liquid bottles included a warning statement, most of which focused on nicotine exposure rather than health. Only 22.4% of bottles used a warning statement that indicated the product "contained nicotine." Of bottles that advertised a nicotine-based concentration of 12 mg/ml, 26% had a warning statements stated that the product "contains nicotine." None of the statements that indicated that the product "contained nicotine" stated that nicotine was "addictive." All bottles had a safety cap and 12% were in plastic shrink-wrap. Fifty-six percent of the websites had a minimum age requirement barrier that prevented under-aged persons from entering. Conclusions: Most manufacturers/distributors printed a warning statement on electronic cigarette liquid bottles, but avoided warning consumers about the presence and the addictiveness of nicotine. Studies are needed to examine manufacturer/distributor modifications to product packaging and how packaging affects consumer behaviors. Implications: These data can inform future FDA requirements related to the packaging and advertising of e-cigarette liquids; regulation related to the content of warning statements, including exposure warning statements, which are not currently mandated; and requirements on websites or language on packaging to help manufacturers adhere to the minimum age of purchase regulation. The data can also be used to help FDA develop additional guidance on the framing of statements on packaging that helps consumers make informed decisions about purchasing the product or protecting young people from use or unintentional exposure to the product.

Implementation of a formulary management process.

PURPOSE: The application of lean methodology in an initiative to redesign the formulary maintenance process at an academic medical center is described. SUMMARY: Maintaining a hospital formulary requires clear communication and coordination among multiple members of the pharmacy department. Using principles of lean methodology, pharmacy department personnel within a multihospital health system launched a multifaceted initiative to optimize formulary management systemwide. The ongoing initiative began with creation of a formulary maintenance redesign committee consisting of pharmacy department personnel with expertise in informatics, automation, purchasing, drug information, and clinical pharmacy services. The committee met regularly and used lean methodology to design a standardized process for management of formulary additions and deletions and changes to medications' formulary status. Through value stream analysis, opportunities for process and performance improvement were identified; staff suggestions on process streamlining were gathered during a series of departmental kaizen events. A standardized template for development and dissemination of monographs associated with formulary additions and status changes was created. In addition, a shared Web-based checklist was developed to facilitate information sharing and timely initiation and completion of tasks involved in formulary status changes, and a permanent formulary maintenance committee was established to monitor and refine the formulary management process. CONCLUSION: A clearly defined, standardized process within the pharmacy department was developed for tracking necessary steps in enacting formulary changes to encourage safe and efficient workflow.

Age and Sex Normalization of Intestinal Permeability Measures for the Improved Assessment of Enteropathy in Infancy and Early Childhood: Results From the MAL-ED Study.

OBJECTIVES: The aim of the study was to describe changes in intestinal permeability in early childhood in diverse epidemiologic settings. METHODS: In a birth cohort study, the lactulose:mannitol (L:M) test was administered to 1980 children at 4 time points in the first 24 months of life in 8 countries. Data from the Brazil site with an incidence of diarrhea similar to that seen in the United States and no growth faltering was used as an internal study reference to derive age- and sex-specific z scores for mannitol and lactulose recoveries and the L:M ratio. RESULTS: A total of 6602 tests demonstrated mannitol recovery, lactulose recovery, and the L:M ratio were associated with country, sex, and age. There was heterogeneity in the recovery of both probes between sites with mean mannitol recovery ranging for 1.34% to 5.88%, lactulose recovery of 0.19% to 0.58%, and L:M ratios 0.10 to 0.17 in boys of 3 months of age across different sites. We observed strong sex-specific differences in both mannitol and lactulose recovery, with boys having higher recovery of both probes. Alterations in intestinal barrier function increased in most sites from 3 to 9 months of age and plateaued or diminished from 9 to 15 months of age. CONCLUSIONS: Alterations in recovery of the probes differ markedly in different epidemiologic contexts in children living in the developing world. The rate of change in the L:M-z ratio was most rapid and consistently disparate from the reference standard in the period between 6 and 9 months of age, suggesting that this is a critical period of physiologic impact of enteropathy in these populations.