Ecotoxicological risk assessment of 14 pesticides and corresponding metabolites to groundwater and soil organisms using China-PEARL model and RQ approach

Global use of pesticides brings uncertain risks to human and nontarget species via environmental matrix. Currently, various models for exposure risk assessment are developed and widely used to forecast the impact of pesticides on environmental organisms. In this study, five commonly used insecticides, seven herbicides and three fungicides were chosen to analyze the subsequent risks in groundwater in simulated scenarios using China-PEARL (Pesticide Emission Assessment at Regional and Local Scales) model. In addition, their exposure risks to soil organisms were characterized based on risk quotient (RQ) approach. The results indicated that 23.3% of the total 528 predicted environmental concentrations (PECs) of pesticides and respective metabolites in groundwater from six Chinese simulated locations with ten crops were above 10 μg L^-1. Furthermore, acceptable human risks of pesticides in groundwater were observed for all simulation scenarios (RQ < 1). Based on the derived PECs in soil short-term and long-term exposure simulation scenarios, all compounds were evaluated to be with acceptable risks to soil organisms, except that imidacloprid was estimated to be with unacceptable chronic risk (RQ = 27.5) to earthworms. Overall, the present findings provide an opportunity for a more-comprehensive understanding of exposure toxicity risks of pesticides leaching into groundwater and soil.

Prevention and Mitigation of Rural Higher Education Dropout in Colombia: A Dynamic Performance Management Approach

Background: Dropout in higher education is a socio-educational phenomenon that has the scope to limit the benefits of education as well as to widen social disparities. For this reason, governments have implemented various public policies for its prevention and mitigation. However, in rural populations, such policies have proven to be ineffective. The aim of this paper is to simulate public policy scenarios for the treatment of school dropout in rural higher education in Colombia from a Dynamic Performance Management approach. Methodology: To achieve the aim, a parameterised simulation model was designed with data from Colombian state entities in rural higher education. Five simulations were carried out. The analysis of the results was carried out using descriptive statistics and comparison of means using the Wilcoxon Sign Rank statistic. Results: The adoption of such an approach based on simulations suggests that policies to expand the coverage of educational credits and financial support, as well as the addition of a family income subsidy, allow for a reduction in the number of dropouts. Conclusions: A dynamic, data-driven approach can be effective in preventing and mitigating dropout in these areas. It also highlights the importance of identifying the key factors contributing to dropout. The results also suggest that government policies can have a significant impact on school retention in rural areas.

Testing hypotheses on the calcification in scleractinian corals using a spatio-temporal model that shows a high degree of robustness

Calcification in photosynthetic scleractinian corals is a complicated process that involves many different biological, chemical, and physical sub-processes that happen within and around the coral tissue. Identifying and quantifying the role of separate processes in vivo or in vitro is difficult or not possible. A computational model can facilitate this research by simulating the sub-processes independently. This study presents a spatio-temporal model of the calcification physiology, which is based on processes that are considered essential for calcification: respiration, photosynthesis, Ca^2＋-ATPase, carbonic anhydrase. The model is used to test different hypotheses considering ion transport across the calicoblastic cells and Light Enhanced Calcification (LEC). It is also used to quantify the effect of ocean acidification (OA) on the Extracellular Calcifying Medium (ECM) and ATP-consumption of Ca^2＋-ATPase. It was able to reproduce the experimental data of three separate studies and finds that paracellular transport plays a minor role compared to transcellular transport. In the model, LEC results from increased Ca^2＋-ATPase activity in combination with increased metabolism. Implementing OA increases the concentration of CO₂ throughout the entire tissue, thereby increasing the availability of CO₃^- in the ECM. As a result, the model finds that calcification becomes more energy-demanding and the calcification rate increases.

A review of simulation models for the long-term management of type 2 diabetes in low-and-middle income countries

INTRODUCTION

The burden of type 2 diabetes is steadily increasing in low-and-middle-income countries, thereby posing a major threat from both a treatment, and funding standpoint. Although simulation modelling is generally relied upon for evaluating long-term costs and consequences associated with diabetes interventions, no recent article has reviewed the characteristics and capabilities of available models used in low-and-middle-income countries. We review the use of computer simulation modelling for the management of type 2 diabetes in low-and-middle-income countries.

METHODS

A search for studies reporting computer simulation models of the natural history of individuals with type 2 diabetes and/or decision models to evaluate the impact of treatment strategies on these populations was conducted in PubMed. Data were extracted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and assessed using modelling checklists. Publications before the year 2000, from high-income countries, studies involving animals and analyses that did not use mathematical simulations were excluded. The full text of eligible articles was sourced and information about the intervention and population being modelled, type of modelling approach and the model structure was extracted.

RESULTS

Of the 79 articles suitable for full text review, 44 studies met the inclusion criteria. All were cost-effectiveness/utility studies with the majority being from the East Asia and Pacific region (n = 29). Of the included studies, 34 (77.3%) evaluated the cost-effectiveness of pharmacological interventions and approximately 75% of all included studies used HbA1c as one of the treatment effects of the intervention. 32 (73%) of the publications were microsimulation models, and 29 (66%) were state-transition models. Most of the studies utilised annual cycles (n = 29, 71%), and accounted for costs and outcomes over 20 years or more (n = 38, 86.4%).

CONCLUSIONS

While the use of simulation modelling in the management of type 2 diabetes has been steadily increasing in low-and-middle-income countries, there is an urgent need to invest in evaluating therapeutic and policy interventions related to type 2 diabetes in low-and-middle-income countries through simulation modelling, especially with local research data. Moreover, it is important to improve transparency and credibility in the reporting of input data underlying model-based economic analyses, and studies.

Revealing photon transmission in an ultraviolet reactor: Advanced approaches for measuring fluence rate distribution in water for model validation

Fluence rate (FR) distribution (optical field) is of great significance in the optimal design of ultraviolet (UV) reactors for disinfection or oxidation processes in water treatment. Since the 1970s, various simulation models have been developed, which can be combined with computational fluidic dynamic software to calculate the fluence delivered in a UV reactor. These models strive for experimental validation and further improvement, which is a major challenge for UV technology in water treatment. Herein, a review of the simulation models of the FR distribution in a UV reactor and the applications of the current main experimental measurement approaches including conventional flat-type UV detector, spherical actinometer, and micro-fluorescent silica detector (MFSD), is presented. Moreover, FR distributions in a UV reactor are compared between various simulation models and MFSD measurements. In addition, the main influential factors on the FR distribution, including inner-wall reflection, refraction and shadowing effects of adjacent lamps, and turbidity effect are discussed, which is helpful for improving the accuracy of the simulation models and avoiding dark regions in the reactor design. This paper provides an overview on the simulation models and measurement approaches for the FR distribution, which is helpful for the model selection in fluence calculations and gives high confidence on the optimal design of UV reactors in regard to present methods.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

On-farm assessment of eucalypt yield gaps - a case study for the producing areas of the state of Minas Gerais, Brazil

The concept of yield gaps provides a basis for identifying the main sources of production losses, caused by water or management deficiencies, which may help foresters and forest companies to better plan and make decisions in their areas. Thus, the aim of this study was to identify the magnitude and the major causes of yield gaps of eucalypts, being this the most planted forest genus in Brazil, in different producing regions of the state of Minas Gerais that has the largest planted area. To these ends, potential (Yp) and attainable (Ya) yields were simulated using the agroecological zone model (AEZ-FAO) adapted and calibrated for Brazilian eucalypt clones. Actual yield (Yr) data were obtained from 22 sites located in the state of Minas Gerais from 2009 to 2016, considering an average forest rotation of 6.7 years and plantings occurring between 2002 and 2010. From this, the total yield gap (YGtot), yield gap by water deficit (YGwd), and yield gap by sub-optimal management (YGman) were determined. The YGwd ranged from 37 to 69 m³ ha^-1 year^-1 across the 22 sites assessed, with an average value of 55 m³ ha^-1 year^-1. On the other hand, the YGman ranged from zero (optimal management) to 31 m³ ha^-1 year^-1. The eucalypt yield gap in commercial areas of Minas Gerais state was mainly caused by water deficit, which represented 77% of the total yield gap. On the other hand, the deficiencies in forest management accounted for 23% of the total yield gap.

Utilizing In-Hospital Fabrication to Decrease Simulation Costs

BACKGROUND

Two restrictive factors for surgical training through simulation, are the cost of and accessibility to materials and consoles for simulation models. Commercial surgical simulation models continue to maintain high prices with a wide range of fidelity levels. We believe that by utilizing in-house fabrication, these barriers can be decreased while maintaining and even improving the functionality of surgical simulation models as well as increase their individualization and customization.

METHODS

By using a combination of digital and manual fabrication techniques such as 3D printing and basic mold making methods, we were able to create models equivalent to current commercial products by utilizing the first of its kind MakerHEALTH space and collaborating with our surgical simulation staff. We then compared our research and development, start-up, materials, operational, and labor costs to buying comparable commercial models with the simulation usage rates of our institution.

RESULTS

We were able to decrease the costs of a 6 model simulation sample set (appendectomy, cholecystectomy, common bile duct exploration, ventral hernia, chest tube insertion, and suture pads) at our institution from $99,646.60 to $13,817.21 for a medical student laborer, $14,500.56 for a surgical resident laborer, $15,321.08 for a simulation staff laborer, and $18,984.48 for an attending physician laborer.

CONCLUSION

We describe successful approaches for the creation of cost-effective and modular simulation models with the aim of decreasing the barriers to entry and improving surgical training and skills. These techniques make it financially feasible for learners to train during larger faculty-led workshops and on an individual basis, allowing for access to simulation at any time or place.

Artificial intelligence and computer simulation models in critical illness

Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.

KPDGUI: An interactive application for optimization and management of a virtual kidney paired donation program

BACKGROUND AND OBJECTIVES

The aim in kidney paired donation (KPD) is typically to maximize the number of transplants achieved through the exchange of donors in a pool comprising incompatible donor-candidate pairs and non-directed (or altruistic) donors. With many possible options in a KPD pool at any given time, the most appropriate set of exchanges cannot be determined by simple inspection. In practice, computer algorithms are used to determine the optimal set of exchanges to pursue. Here, we present our software application, KPDGUI (Kidney Paired Donation Graphical User Interface), for management and optimization of KPD programs.

METHODS

While proprietary software platforms for managing KPD programs exist to provide solutions to the standard KPD problem, our application implements newly investigated optimization criteria that account for uncertainty regarding the viability of selected transplants and arrange for fallback options in cases where potential exchanges cannot proceed, with intuitive resources for visualizing alternative optimization solutions.

RESULTS

We illustrate the advantage of accounting for uncertainty and arranging for fallback options in KPD using our application through a case study involving real data from a paired donation program, comparing solutions produced under different optimization criteria and algorithmic priorities.

CONCLUSIONS

KPDGUI is a flexible and powerful tool for offering decision support to clinicians and researchers on possible KPD transplant options to pursue under different user-specified optimization schemes.

Silicone-based simulation models for peripheral nerve microsurgery

BACKGROUND

There is a need for a peripheral nerve model on which surgeons-in-training can simulate the repair of nerve injuries at their own pace. Although practicing on animal models/cadavers is considered the "gold standard" of microsurgical training, the proposed model aims to provide a platform for improving the technical skills of surgical trainees prior to their practice on cadaver/animal models. In addition, this model has the potential to serve as a standardized test medium for assessing the skill sets of surgeons.

METHODS

Several formulations of silicone were utilized for the design and fabrication of a model which realizes the hierarchical structure of peripheral nerves. The mechanical properties were characterized via the Universal Testing Machine; the damage caused by the needle on the entry sites was assessed through scanning electron microscopy (SEM).

RESULTS

Mechanical properties of the formulations of silicone were tested to mimic human peripheral nerves. A formulation with 83.3 wt% silicone oil and 0.1 wt% cotton fiber was chosen to be used as nerve fascicles. Both 83.3 wt% silicone oil with cotton fiber and 66.6 wt% silicone oil without fiber provided a microsuturing response similar to that of epineurium at a wall thickness of 1 mm. SEM also confirmed that the entry of the needle did not introduce significant holes at the microsuturing sites.

CONCLUSIONS

The proposed peripheral nerve model mimicked human tissues mechanically and cosmetically, and a simulation of the repair of a fifth-degree nerve injury was achieved.

Centralising and optimising decentralised stroke care systems: a simulation study on short-term costs and effects

Background

Centralisation of thrombolysis may offer substantial benefits. The aim of this study was to assess short term costs and effects of centralisation of thrombolysis and optimised care in a decentralised system.

Methods

Using simulation modelling, three scenarios to improve decentralised settings in the North of Netherlands were compared from the perspective of the policy maker and compared to current decentralised care: (1) improving stroke care at nine separate hospitals, (2) centralising and improving thrombolysis treatment to four, and (3) two hospitals. Outcomes were annual mean and incremental costs per patient up to the treatment with thrombolysis, incremental cost-effectiveness ratio (iCER) per 1% increase in thrombolysis rate, and the proportion treated with thrombolysis.

Results

Compared to current decentralised care, improving stroke care at individual community hospitals led to mean annual costs per patient of $US 1,834 (95% CI, 1,823–1,843) whereas centralising to four and two hospitals led to $US 1,462 (95% CI, 1,451–1,473) and $US 1,317 (95% CI, 1,306–1,328), respectively (P < 0.001). The iCER of improving community hospitals was $US 113 (95% CI, 91–150) and $US 71 (95% CI, 59–94), $US 56 (95% CI, 44–74) when centralising to four and two hospitals, respectively. Thrombolysis rates decreased from 22.4 to 21.8% and 21.2% (P = 0.120 and P = 0.001) in case of increasing centralisation.

Conclusions

Centralising thrombolysis substantially lowers mean annual costs per patient compared to raising stroke care at community hospitals simultaneously. Small, but negative effects on thrombolysis rates may be expected.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0275-3) contains supplementary material, which is available to authorized users.

Computational pharmacokinetics and in vitro-in vivo correlation of anti-diabetic synergistic phyto-composite blend

Despite tremendous strides in modern medicine stringent control over insulin resistance or restoration of normoglycemia has not yet been achieved. With the growth of molecular biology, omics technologies, docking studies, and in silico pharmacology, modulators of enzymes and receptors affecting the molecular pathogenesis of the disease are being considered as the latest targets for anti-diabetic therapy. Therapeutic molecular targets are now being developed basing on the up or down regulation of different signaling pathways affecting the disease. Phytosynergistic anti-diabetic therapy is in vogue both with classical and non-classical medicinal systems. However its chemo-profiling, structural and pharmacokinetic validation awaits providing recognition to such formulations for international acceptance. Translational health research with its focus on benchside product development and its sequential transition to patient bedside puts the pharma RDs to a challenge to develop bio-waiver protocols. Pharmacokinetic simulation models and establishment of in vitro-in vivo correlation can help to replace in vivo bioavailability studies and provide means of quality control for scale up and post approval modification. This review attempts to bring different shades highlighting phyto-synergy, molecular targeting of antidiabetic agents via different signaling pathways and bio-waiver studies under a single umbrella.

Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones

Previously, Reither et al. (2015) demonstrated that hierarchical age-period-cohort (HAPC) models perform well when basic assumptions are satisfied. To contest this finding, Bell and Jones (2015) invent a data generating process (DGP) that borrows age, period and cohort effects from different equations in Reither et al. (2015). When HAPC models applied to data simulated from this DGP fail to recover the patterning of APC effects, B&J reiterate their view that these models provide "misleading evidence dressed up as science." Despite such strong words, B&J show no curiosity about their own simulated data--and therefore once again misapply HAPC models to data that violate important assumptions. In this response, we illustrate how a careful analyst could have used simple descriptive plots and model selection statistics to verify that (a) period effects are not present in these data, and (b) age and cohort effects are conflated. By accounting for the characteristics of B&J's artificial data structure, we successfully recover the "true" DGP through an appropriately specified model. We conclude that B&Js main contribution to science is to remind analysts that APC models will fail in the presence of exact algebraic effects (i.e., effects with no random/stochastic components), and when collinear temporal dimensions are included without taking special care in the modeling process. The expanded list of coauthors on this commentary represents an emerging consensus among APC scholars that B&J's essential strategy--testing HAPC models with data simulated from contrived DGPs that violate important assumptions--is not a productive way to advance the discussion about innovative APC methods in epidemiology and the social sciences.