Attenuation of withdrawal signs, blood cortisol, and glucose level with various dosage regimens of morphine after precipitated withdrawal syndrome in mice

Morphine withdrawal usually results in unsuccessful outcomes. Despite partial benefits from alternative substances such as methadone, its use may not lead to the desired result due to the lack of mental tranquility during the withdrawal period. In this study, by means of an animal model, morphine itself was used to manage morphine dependence. Forty mice were divided into 5 groups, in which 4 groups became dependent by increasing daily doses of morphine for 7 days (15-45 mg/kg). Afterwards, the animals received morphine for 14 days by either of the following regimens: � Once daily 45 mg/kg (positive controls) � Increasing the interval (each time 6 hours longer than the previous interval) � Irregular interval in every 36, 12 and 24 hours until the 21th day � 12, 24, 36 hours decreasing doses (each time 2.5 mg/kg less than the former dosage). Negative controls received saline solution only. On day 22, total withdrawal index (TWI) was determined by injecting 3 mg/kg of naloxone. Thereafter, blood samples were taken for the measurement of cortisol and glucose levels. TWI significantly decreased in all test groups in comparison with the positive control animals (P<0.001). Cortisol levels significantly decreased when either the dosage or the administration frequencies were decreased on a regular and gradual basis (P<0.005). Blood glucose levels significantly decreased in animals that received decreasing doses of morphine (P<0.005). This study suggests that no other measures may be required in clinical practice except for changing the dosage regimen of morphine for the cessation of self-administration. © 2016, Shiraz University of Medical Sciences. All rights reserved

Fire Promotes Arsenic Mobilization and Rapid Arsenic(III) Formation in Soil via Thermal Alteration of Arsenic-Bearing Iron Oxides

Arsenic in oxic surface soils readily associates with Fe(III) oxide minerals such as ferrihydrite and goethite, predominantly as As(V). Fires are a common feature in many landscapes, creating high-temperature soil conditions which drive thermal transformation of these As(V)-bearing minerals. However, it is unknown whether fire-induced transformation of ferrihydrite and goethite can alter the mobility of As, or alter As(V) speciation (e.g., via pyrolysis induced electron-transfer generating the more mobile and toxic inorganic As(III) species). Here, we subject an organic-rich soil (∼15% organic C) mixed (4:1) with As(V)-bearing ferrihydrite and goethite (total As of 2.8–3.8 μmol g-1), to various temperatures (200–800°C) and heating durations (5–120 min) and examine the consequences for As and Fe via X-ray absorption spectroscopy, X-ray diffraction, 57Fe Mössbauer spectroscopy and selective extracts. We show that heating transformed both ferrihydrite and goethite to mainly maghemite at temperatures &gt;400°C and tended to increase exchangeable surface-complexed As (AsEx) in ferrihydrite yet decrease AsEx in goethite. We demonstrate for the first time that ferrihydrite and goethite-bound As(V) can be rapidly reduced to As(III) during heating of organic-rich soil. Electrons were readily transferred to both Fe(III) and As(V), with reduction of As(V) to As(III) peaking at intermediate temperatures and time periods (maxima of ∼88% for ferrihydrite; ∼80% for goethite). Although As(III) formation was fast (within 5–10 min at temperatures &gt;400°C), it was followed by partial re-oxidation to As(V) at higher temperatures and longer time intervals. Additionally, combusted As-bearing ferrihydrite and goethite soil-mixtures display greatly enhanced (2–3 orders of magnitude) mobilization of inorganic As(III)aq species upon re-wetting with water. Mobilization of As(III)aq was positively correlated with solid-phase As(III) formation and was greater for goethite than ferrihydrite. These findings challenge the current prevailing view that As(V) reduction to As(III) in soil is mainly limited to waterlogged conditions and suggest that moderate-temperature fires of short duration in oxic soils, may generate substantial labile As(III) species and lead to a pulse of As(III)aq mobilization upon initial rainfall and re-wetting. Further investigation is recommended to explore the consequences for arsenic cycling in fire-prone natural landscapes and agricultural systems which involve controlled-burn practices

Purely radiative perfect fluids

We study `purely radiative' (div E = div H = 0) and geodesic perfect fluids with non-constant pressure and show that the Bianchi class A perfect fluids can be uniquely characterized --modulo the class of purely electric and (pseudo-)spherically symmetric universes-- as those models for which the magnetic and electric part of the Weyl tensor and the shear are simultaneously diagonalizable. For the case of constant pressure the same conclusion holds provided one also assumes that the fluid is irrotational.Comment: 12 pages, minor grammatical change

Regulation of connexin 43 and microRNA expression via β2-adrenoceptor signaling in 1321N1 astrocytoma cells

Connexin 43 (Cx43) is the main gap junction protein in astrocytes and exerts the same effects on growth inhibition in astrocytoma and glioma as microRNA-146a (miR-146a) in glioma. β2-adrenergic receptor (AR) signaling modulates Cx43 expression in myocytes via components downstream of protein kinase A (PKA) and exchange protein directly activated by cAMP (Epac). However, it remains to be elucidated how expression of Cx43 is modulated in astrocytes. In the present study, 1321N1 astrocytoma cells were treated with β2-AR signaling agents in order to evaluate the expression of Cx43 and miRNAs. RNA and protein were extracted from the cells for use in reverse transcription-quantitative polymerase chain reaction and western blot analysis, respectively. The results revealed that clenbuterol increased miR-146a level and upregulated Cx43 expression via cAMP/PKA at the mRNA and protein level. Pre-inhibition of adenyl cyclase decreased expression of Cx43 and miR-146a. PKA activation and overexpression of miR-146a in A-1321N1 cells increased the expression of Cx43. β2-AR stimulation and 6Bnz, a PKA activator, suppressed oncomiRs miR-155 and miR-27a, while 8-(4-chlorophenylthio)-2'-O-methyladenosine-3',5'-cyclic monophosphate, an Epac activator, increased their levels. The current findings demonstrated that β2-AR signaling has growth inhibitory effects via modulation of the cAMP/PKA pathway in A-1321N1 cells through increasing the expression level of Cx43 and miR-146a as well as decreasing miR-155 and miR-27a levels. Thus, stimulation of the β2-AR and PKA signaling pathway may be a useful approach for astrocytoma therapy

Hypothermic Oxygenated Machine Perfusion Prevents Arteriolonecrosis of the Peribiliary Plexus in Pig Livers Donated after Circulatory Death

BACKGROUND: Livers derived from donation after circulatory death (DCD) are increasingly accepted for transplantation. However, DCD livers suffer additional donor warm ischemia, leading to biliary injury and more biliary complications after transplantation. It is unknown whether oxygenated machine perfusion results in better preservation of biliary epithelium and the peribiliary vasculature. We compared oxygenated hypothermic machine perfusion (HMP) with static cold storage (SCS) in a porcine DCD model. METHODS: After 30 min of cardiac arrest, livers were perfused in situ with HTK solution (4°C) and preserved for 4 h by either SCS (n = 9) or oxygenated HMP (10°C; n = 9), using pressure-controlled arterial and portal venous perfusion. To simulate transplantation, livers were reperfused ex vivo at 37°C with oxygenated autologous blood. Bile duct injury and function were determined by biochemical and molecular markers, and a systematic histological scoring system. RESULTS: After reperfusion, arterial flow was higher in the HMP group, compared to SCS (251±28 vs 166±28 mL/min, respectively, after 1 hour of reperfusion; p = 0.003). Release of hepatocellular enzymes was significantly higher in the SCS group. Markers of biliary epithelial injury (biliary LDH, gamma-GT) and function (biliary pH and bicarbonate, and biliary transporter expression) were similar in the two groups. However, histology of bile ducts revealed significantly less arteriolonecrosis of the peribiliary vascular plexus in HMP preserved livers (>50% arteriolonecrosis was observed in 7 bile ducts of the SCS preserved livers versus only 1 bile duct of the HMP preserved livers; p = 0.024). CONCLUSIONS: Oxygenated HMP prevents arteriolonecrosis of the peribiliary vascular plexus of the bile ducts of DCD pig livers and results in higher arterial flow after reperfusion. Together this may contribute to better perfusion of the bile ducts, providing a potential advantage in the post-ischemic recovery of bile ducts

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24

Criteria for Viability Assessment of Discarded Human Donor Livers during Ex Vivo Normothermic Machine Perfusion

Although normothermic machine perfusion of donor livers may allow assessment of graft viability prior to transplantation, there are currently no data on what would be a good parameter of graft viability. To determine whether bile production is a suitable biomarker that can be used to discriminate viable from non-viable livers we have studied functional performance as well as biochemical and histological evidence of hepatobiliary injury during ex vivo normothermic machine perfusion of human donor livers. After a median duration of cold storage of 6.5 h, twelve extended criteria human donor livers that were declined for transplantation were ex vivo perfused for 6 h at 37 °C with an oxygenated solution based on red blood cells and plasma, using pressure controlled pulsatile perfusion of the hepatic artery and continuous portal perfusion. During perfusion, two patterns of bile flow were identified: (1) steadily increasing bile production, resulting in a cumulative output of ≥ 30 g after 6 h (high bile output group), and (2) a cumulative bile production <20 g in 6 h (low bile output group). Concentrations of transaminases and potassium in the perfusion fluid were significantly higher in the low bile output group, compared to the high bile output group. Biliary concentrations of bilirubin and bicarbonate were respectively 4 times and 2 times higher in the high bile output group. Livers in the low bile output group displayed more signs of hepatic necrosis and venous congestion, compared to the high bile output group. In conclusion, bile production could be an easily assessable biomarker of hepatic viability during ex vivo machine perfusion of human donor livers. It could potentially be used to identify extended criteria livers that are suitable for transplantation. These ex vivo findings need to be confirmed in a transplant experiment or a clinical trial

Algorithms for enhancing public health utility of national causes-of-death data

<p>Abstract</p> <p>Background</p> <p>Coverage and quality of cause-of-death (CoD) data varies across countries and time. Valid, reliable, and comparable assessments of trends in causes of death from even the best systems are limited by three problems: a) changes in the <it>International Statistical Classification of Diseases and Related Health Problems </it>(ICD) over time; b) the use of tabulation lists where substantial detail on causes of death is lost; and c) many deaths assigned to causes that cannot or should not be considered underlying causes of death, often called garbage codes (GCs). The Global Burden of Disease Study and the World Health Organization have developed various methods to enhance comparability of CoD data. In this study, we attempt to build on these approaches to enhance the utility of national cause-of-death data for public health analysis.</p> <p>Methods</p> <p>Based on careful consideration of 4,434 country-years of CoD data from 145 countries from 1901 to 2008, encompassing 743 million deaths in ICD versions 1 to 10 as well as country-specific cause lists, we have developed a public health-oriented cause-of-death list. These 56 causes are organized hierarchically and encompass all deaths. Each cause has been mapped from ICD-6 to ICD-10 and, where possible, they have also been mapped to the <it>International List of Causes of Death </it>1-5. We developed a typology of different classes of GCs. In each ICD revision, GCs have been identified. Target causes to which these GCs should be redistributed have been identified based on certification practice and/or pathophysiology. Proportionate redistribution, statistical models, and expert algorithms have been developed to redistribute GCs to target codes for each age-sex group.</p> <p>Results</p> <p>The fraction of all deaths assigned to GCs varies tremendously across countries and revisions of the ICD. In general, across all country-years of data available, GCs have declined from more than 43% in ICD-7 to 24% in ICD-10. In some regions, such as Australasia, GCs in 2005 are as low as 11%, while in some developing countries, such as Thailand, they are greater than 50%. Across different age groups, the composition of GCs varies tremendously - three classes of GCs steadily increase with age, but ambiguous codes within a particular disease chapter are also common for injuries at younger ages. The impact of redistribution is to change the number of deaths assigned to particular causes for a given age-sex group. These changes alter ranks across countries for any given year by a number of different causes, change time trends, and alter the rank order of causes within a country.</p> <p>Conclusions</p> <p>By mapping CoD through different ICD versions and redistributing GCs, we believe the public health utility of CoD data can be substantially enhanced, leading to an increased demand for higher quality CoD data from health sector decision-makers.</p