Med Biol Eng Comput - Reaction-diffusion modelling for microphysiometry on cellular specimens.

Tópicos

{ model(2220) cell(1177) simul(1124) }
{ imag(1057) registr(996) error(939) }
{ drug(1928) target(777) effect(648) }
{ algorithm(1844) comput(1787) effici(935) }
{ model(2656) set(1616) predict(1553) }
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{ take(945) account(800) differ(722) }
{ model(3480) simul(1196) paramet(876) }
{ group(2977) signific(1463) compar(1072) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2675) segment(2577) method(1081) }
{ problem(2511) optim(1539) algorithm(950) }
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{ monitor(1329) mobil(1314) devic(1160) }
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{ network(2748) neural(1063) input(814) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
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{ studi(1119) effect(1106) posit(819) }
{ state(1844) use(1261) util(961) }
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{ record(1888) medic(1808) patient(1693) }
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{ research(1218) medic(880) student(794) }
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{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
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{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
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{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Using modeling and simulation, we quantify the influence of spatiotemporal dynamics on the accuracy of data obtained from sensors placed in microscaled reaction volumes. The model refers to cellular reaction (i.e. proton extrusion and oxygen consumption) in complex, buffering solutions. Whole cells or viable tissues cultured in such devices are monitored in real time with integrated sensors for pH and dissolved oxygen. A 3D finite element model of diffusion and metabolic reaction was set up. With respect to pH, the effect of buffering species on proton diffusion is analysed in detail. To account for the delayed time response of real sensors, the sensor impulse response time was implemented by linear convolution. A validation of the model has been achieved by an electrochemical approach. The model reveals significant deviations of measured pH and O2, and values of these parameters actually occurring at different sites of the cell culture volume. It is applicable to any setting of (bio-) sensors involving reaction and diffusion of dissolved gases and particularly H(+) ions in buffered solutions.

Resumo Limpo

use model simul quantifi influenc spatiotempor dynam accuraci data obtain sensor place microsc reaction volum model refer cellular reaction ie proton extrus oxygen consumpt complex buffer solut whole cell viabl tissu cultur devic monitor real time integr sensor ph dissolv oxygen d finit element model diffus metabol reaction set respect ph effect buffer speci proton diffus analys detail account delay time respons real sensor sensor impuls respons time implement linear convolut valid model achiev electrochem approach model reveal signific deviat measur ph o valu paramet actual occur differ site cell cultur volum applic set bio sensor involv reaction diffus dissolv gase particular h ion buffer solut

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