Havsisen i arktiska bassängen : nutid och framtid i ett globalt uppvärmningsperspektiv

Abstract: The Arctic ice sheet has been declining since the end of the 19.th century both with consideration to its Area and to its thickness. Since the 1960.s this development has been accelerating and as recently as 2007 the ex-tent of the ice was smaller than at any time since observations began. Since then the ice has however recovered slightly and is now close to the negative trend line. Since the temperature increases in the arctic are between two and three times the magnitude of the increases in the global mean temperature it is possible to at an early date see the effects of global warming, this further increases the interest in the ice sheet´s development. The purpose of this paper has been to examine the processes that impact the ice and to examine why the ice sheet is decreasing. The paper is a literature review and the source material has primarily consisted of scientific papers. The papers used have been using several different methods to gather information about the ice, primarily modeling, satellite and submarine data has been used. However some articles have also been used different methods such as gathering and analyzing drift wood, observing beach ridges and measuring the surface temperature around the Arctic. The picture that emerges from these papers is that in the short term the ice is mainly impacted by temporary weather patterns, however in the longer term rising temperatures are driving the changes since the temperature is following a very clear trend. While the extent of the ice sheet is gradually declining and it´s thickness is gradually decreasing it is also growing more unstable, this is because thick ice has a stabilizing effect on the ice sheets extent. Models of the ice sheets future points to an ice free arctic ocean in September at some point after 2040. In a historical perspective this won´t be unique however. During the last 10,000 years the ice sheets extent has been both far larger and far smaller than it is todaySammanfattning: Det Arktiska istäcket har sedan slutet av 1800-talet minskat både med avseende på utbredning och på tjocklek. Sedan 1960-talet har denna utveckling accelererat och senast 2007 var isens utbredning mindre än någon gång sedan mätningarna började, det har därefter återhämtat sig och ligger nära den minskande trendlinjen. Då temperaturökningen i de arktiska regionerna är tre gånger större än ökningen av den globala medeltemperaturen är det möjligt att på ett tidigt stadium se effekterna av den globala uppvärmningen något som ytterligare ökar intresset av isens utveckling. Syftet med arbetet har varit att undersöka de faktorer som påverkar isen och undersöka varför minskningen sker. Arbetet har bestått av en litteraturstudie med syftet att undersöka det Arktiska istäckets historia och framtida utveckling. Artiklarna som ingått i studien har använt sig av en lång rad metoder för att undersöka isens utveckling, främst har de dock byggt på modelleringar och satellit och ubåtsmätningar. Andra metoder som använts är insamling och analys av drivved, observationer av strandvallar och temperaturmätningar. Bilden som framträder är att istäckets variation på kort sikt beror på tillfälliga väderfenomen, utvecklingen på lång sikt styrs dock till stor del av temperaturen då denna har en mycket tydlig trend. Samtidigt som isen gradvis blir mindre både ifråga om utbredning och tjocklek kommer den också att bli mer instabil då isens tjocklek har en stabiliserande effekt på dess ytmässiga utbredning. Modelleringar som gjorts pekar på ett i september isfritt ishav någon gång efter 2040. Historiskt är detta dock inte unikt. Under den senaste 10000 års perioden har isens utbredning både varit avsevärt mycket större och mindre än den är idag

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate

Challenge model of TNFα turnover at varying LPS and drug provocations

A mechanism-based biomarker model of TNFα-response, including different external provocations of LPS challenge and test compound intervention, was developed. The model contained system properties (such as\ua0kt, kout), challenge characteristics (such as\ua0ks, kLPS, Km,\ua0LPS, Smax, SC50) and test-compound-related parameters (Imax, IC50). The exposure to test compound was modelled by means of first-order input and Michaelis–Menten type of nonlinear elimination. Test compound potency was estimated to 20\ua0nM with a 70% partial reduction in TNFα-response at the highest dose of 30\ua0mg\ub7kg−1. Future selection of drug candidates may focus the estimation on potency and efficacy by applying the selected structure consisting of TNFα\ua0system and LPS challenge characteristics. A related aim was to demonstrate how an exploratory (graphical) analysis may guide us to a tentative model structure, which enables us to better understand target biology. The analysis demonstrated how to tackle a biomarker with a baseline below the limit of detection. Repeated LPS-challenges may also reveal how the rate and extent of replenishment of TNFα\ua0pools occur. Lack of LPS exposure-time courses was solved by including a biophase model, with the underlying assumption that TNFα-response time courses, as such, contain kinetic information. A transduction type of model with non-linear stimulation of TNFα\ua0release was finally selected. Typical features of a challenge experiment were shown by means of model simulations. Experimental shortcomings of present and published designs are identified and discussed. The final model coupled to suggested guidance rules may serve as a general basis for the collection and analysis of pharmacological challenge data of future studies

Serum metabolite signature predicts the acute onset of diabetes in spontaneously diabetic congenic BB rats

The clinical presentation of type 1 diabetes is preceded by a prodrome of beta cell autoimmunity. We probed the short period of subtle metabolic abnormalities, which precede the acute onset of diabetes in the spontaneously diabetic BB rat, by analyzing the serum metabolite profile detected with combined gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS). We found that the metabolite pattern prior to diabetes included 17 metabolites, which differed between individual diabetes prone (DP) BB rats and their age and sex matched diabetes resistant (DR) littermates. As the metabolite signature at the 40 days of age baseline failed to distinguish DP from DR, there was a brief 10-day period after which the diabetes prediction pattern was observed, that includes fatty acids (e.g. oleamide), phospholipids (e.g. phosphocholines) and amino acids (e.g. isoleucine). It is concluded that distinct changes in the serum metabolite pattern predict type 1 diabetes and precede the appearance of insulitis in spontaneously diabetic BB DP rats. This observation should prove useful to dissect mechanisms of type 1 diabetes

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents

Purpose: Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication. Methods: We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE). Results: The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110\ua0Gy, which is reduced to 80 or 30\ua0Gy with co-administration of 25 or 100\ua0mg\ua0kg\ua0−1\ua0of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans. Conclusions: The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans

Modeling of radiation therapy and radiosensitizing agents in tumor xenografts

III-36\ua0Tim\ua0Cardilin\ua0Modeling of radiation therapy and radiosensitizing agents in tumor xenografts\ua0Tim Cardilin (1,2), Joachim Almquist (1), Mats Jirstrand (1), Astrid Zimmermann (3), Floriane Lignet (4), Samer El Bawab (4), and Johan Gabrielsson (5)(1) Fraunhofer-Chalmers Centre, Gothenburg, Sweden, (2) Department of Mathematical Sciences, Chalmers University of Technology and Gothenburg University, Gothenburg, Sweden, (3) Merck, Translational Innovation Platform Oncology, Darmstadt, Germany, (4) Merck, Global Early Development - Quantitative Pharmacology, Darmstadt, Germany, (5) Division of Pharmacology and Toxicology, Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, SwedenObjectives:\ua0To conceptually and mathematically describe the treatment effects of radiation and radiosensitizing agents on tumor volume in xenografts with respect to short- and long-term effects.Methods:\ua0Data were generated in FaDu xenograft mouse models, where animals were treated with radiation given either as monotherapy (2 Gy per dose) or together with an early-discovery radiosensitizing agent (25 or 100 mg/kg per dose) that interferes with the repair of the DNA damage induced by irradiation. Animals received treatment following a clinically-relevant administration schedule with doses five days a week for six weeks. Tumor diameters were measured by caliper twice a week for up to 140 days. A pharmacodynamic tumor model was adapted from a previously-published model [1,2]. The improved model captures both short- and long-term treatment effects including tumor eradication and tumor regrowth. Short-term radiation effects are described by allowing lethally irradiated cells up to one more cell division before apoptosis. Long-term radiation effects are described by an irreversible decrease in tumor growth rate. The radiosensitizing agent was assumed to stimulate both processes. The model also includes a natural death rate of cancer cells. The model was calibrated to the xenograft data using a mixed-effects approach based on the FOCE method that was implemented in Mathematica [3]. Between-subject variability was accounted for in initial tumor volume, as well as in the short- and long-term radiation effects.Results:\ua0Data across all treatment groups were well-described by the model. All model parameters were estimated with acceptable precision and biologically reasonable values. Vehicle growth was approximately exponential during the observed time period with an estimated tumor doubling time of approximately 5 days. Tumor growth following radiation therapy resulted in significant tumor regression followed by either tumor eradication (2 animals) or slow regrowth (7 animals). The short- and long-term effects incorporated into the tumor model were able to account for both of these scenarios. A simple analysis shows that if the tumor growth rate is decreased below the natural death rate, the tumor will be eradicated. Otherwise, the tumor will regrow but at a slower rate compared to pre-treatment. The model predicts that each fraction of radiation (2 Gy) results in lethal damage in 15 % of viable cells, and that a total dose above 120 Gy will eradicate the tumor. Tumor growth following combination therapy with a lower dose (25 mg/kg) resulted in more cases of tumor eradication (6 animals) and fewer cases of regrowth (3 animals), whereas combination therapy with the higher dose (100 mg/kg) resulted in tumor eradication in all 9 animals. When radiation therapy was complemented by radiosensitizing treatment (100 mg/kg per dose), each fraction of 2 Gy was estimated to kill 25 % of viable cells, and the total radiation dose required for tumor eradication was decreased by a factor four to 30 Gy.Conclusions:\ua0A tumor model has been developed to describe the treatment effects of radiation therapy, as well as combination therapies involving radiation, in tumor xenografts. The model distinguishes between short- and long-term effects of radiation treatment and can describe different tumor dynamics, including tumor eradication and tumor regrowth at different rates. The novel tumor model can be used to predict treatment outcomes for a broad range of treatments including radiation therapy and combination therapies with different radiosensitizing agents.References:\ua0[1] Cardilin T, Almquist J, Jirstrand M, Zimmermann A, El Bawab S, Gabrielsson J. Model-based evaluation of radiation and radiosensitizing agents in oncology. CPT: Pharmacometrics & Syst. Pharmacol.\ua0(2017).[2] Cardilin T, Zimmermann A, Jirstrand M, Almquist J, El Bawab S, Gabrielsson J. Extending the Tumor Static Concentration Curve to average doses – a combination therapy example using radiation therapy. PAGE 25 (2016) Abstr 5975 [www.page-meeting.org/?abstract=5975].[3] Almquist J, Leander J, Jirstrand M. Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood. J Pharmacokinet Pharmacodyn (2015) 42: 191-209

Dose–response-time modelling : second-generation turnover model with integral feedback control

This study presents a dose-response-time (DRT) analysis based on a large preclinical biomarker dataset on the interaction between nicotinic acid (NiAc) and free fatty acids (FFA). Data were collected from studies that examined different rates, routes, and modes of NiAc provocations on the FFA time course. All information regarding the exposure to NiAc was excluded in order to demonstrate the utility of a DRT model. Special emphasis was placed on the selection process of the biophase model. An inhibitory Imax-model, driven by the biophase amount, acted on the turnover rate of FFA. A second generation NiAc/FFA model, which encompasses integral (slow buildup of tolerance - an extension of the previously used NiAc/FFA turnover models) and moderator (rapid and oscillatory) feedback control, was simultaneously fitted to all time courses in normal rats. The integral feedback control managed to capture an observed 90% adaptation (i.e., almost a full return to baseline) when 10 days constant-rate infusion protocols of NiAc were used. The half-life of the adaptation process had a 90% prediction interval between 3.5-12 in the present population. The pharmacodynamic parameter estimates were highly consistent when compared to an exposure-driven analysis, partly validating the DRT modelling approach and suggesting the potential of DRT analysis in areas where exposure data are not attainable. Finally, new numerical algorithms, which rely on sensitivity equations to robustly and efficiently compute the gradients in the parameter optimization, were successfully used for the mixed-effects approach in the parameter estimation

Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions

A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 μmol kg−1 of body weight) or over 300 min (0, 5, 10 or 51 μmol kg−1 of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M1) inhibited the formation of R and the last compartment (MN) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC50, of NiAc was 45 nmol L−1, the fractional turnover rate kout was 0.41 L mmol−1 min−1 and the turnover rate of moderator ktol was 0.027 min−1. A lower physiological limit of NEFA was modeled as a NiAc-independent release (kcap) of NEFA into plasma and was estimated to 0.032 mmol L−1 min−1. This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues

Pulmonary sarcoidosis is associated with exosomal vitamin D-binding protein and inflammatory molecules

BACKGROUND: Sarcoidosis is an inflammatory granulomatous disorder characterized by accumulation of TH1-type CD4+T cells and immune effector cells within affected organs, most frequently the lungs. Exosomes are extracellular vesicles conveying intercellular communication with possible diagnostic and therapeutic applications.OBJECTIVES: Weaimed to provide an understanding of the proinflammatory role of bronchoalveolar lavage fluid (BALF) exosomes in patients with sarcoidosis and to find candidates for disease biomarkers.METHODS: Weperformed a mass spectrometric proteomics characterization of BALF exosomes from 15 patients with sarcoidosis and 5 healthy control subjects and verified the most interesting results with flow cytometry, ELISA, and Western blot analyses in an additional 39 patients and 22 control subjects.RESULTS: Morethan 690 proteins were identified in the BALF exosomes, several of which displayed significant upregulation in patients, including inflammation-associated proteins, such as leukotriene A4 hydrolase. Most of the complement-activating factors were upregulated, whereas the complement regulator CD55 was seen less in patients comparedwith healthy control subjects. In addition, for the first time, we detected vitamin D-binding protein in BALF exosomes, which was more abundant in patients. To evaluate exosome-associated vitamin D-binding protein as a biomarker for sarcoidosis, we investigated plasma exosomes from 23 patients and 11 healthy control subjects and found significantlyhigher expression in patients.CONCLUSION: Together,these data contribute to understanding the role of exosomes in lung disease and provide suggestions for highly warranted sarcoidosis biomarkers. Furthermore, the validation of an exosome-associated biomarker in the blood of patients provides novel, and less invasive, opportunities for disease diagnosis.</h4