Biomedical Engineering – From Theory to Applications

More documents

Recommendations

Info

58 Biomedical Engineering – From Theory to Applications Fig. 8. Simulated behaviour of untreated LTNP HIV-infected patients for ten years with model described in (4). The graph shows viral load (dashed line) and CD4 + cells (solid line) Fig. 9. Typical clinical behaviour of HIV infection for about ten years. Figure shows HIV copies (triangles) and CD4 + cells (squares), in case of untreated HIV-infected human A straightforward application of the control theory to model (7) was proposed in Pannocchia et al., (2010), with the application of a MPC strategy in anti-HIV therapy. MPC algorithms (Mayne et al., 2000) utilize a mathematical model of the system to be controlled, to generate the predicted values of the future response. Predicted values are then
Physiological Cybernetics: An Old-Novel Approach for Students in Biomedical Systems used to compute a control sequence over a finite prediction horizon, in order to optimize the future behaviour of the controlled system. The control sequence is chosen minimizing a suitable cost function, including a measure of the deviation of the future state variables from reference target values and a measure of the control effort, while respecting state and control constraints. In plain words, the core of the control algorithm is an optimization algorithm, keeping the controlled variables close to their targets and within suitable constraints. The first output in the optimal sequence of control actions is then injected into the system, and the computation is repeated at subsequent control intervals. The problem was how to adapt MPC to determine the optimal drug scheduling in anti-HIV therapy. Some examples of MPC applied to biomedical applications like control of the glucose– insulin system in diabetics (Parker et al., 1999), anaesthesia (Ionescu et al., 2008), and HIV (Zurakowski & Teel., 2006) have been presented in literature, but all applications were considered for models admitting a steady-state stable equilibrium. On the other hand, MPC emerged as the more suitable solution for solving the drug optimal administration problem in anti-HIV therapy, even if the model was unstable. MPC algorithm pursued the following logic: a. future outputs of the control algorithm are generated by the HIV model; measurements on individual patient are considered and compared with the predictions of the model. b. the cost function to be minimized keeps the controlled variables e.g., CD4 + cells and free virions concentration close to the targets and respecting suitable soft constraints on the manipulated variables. c. the cost function of the future control movements is minimized using a sequence of future PI and RTI drugs over the chosen control horizon, but only the first element of the suggested control sequence is applied to the system. d. at the successive decision time, the algorithm is solved again if measurements of CD4 + cells and free virions concentration are available and the drug sequence is updated, repeating step c) Some practical issues were considered (see Pannocchia et al., (2010) for a detailed study), because MPC was applied considering the two different cases of continuous applications of drugs, or of a structured interruption of therapy (STI) for patients. STI is a treatment strategy in HIV-infected patients, involves interrupting HAART in controlled clinical settings, for a specified duration of time. The possible explanation of the effectiveness of this clinical protocol was an induced autovaccination in the patients. The use of STI is currently debated between clinical researchers and most studies agree that STI may be successful if therapy is initiated early in HIV infection, but unsuccessful for people who started therapy later. Furthermore, a discrete dosage approach required to modify the control algorithm using a non linear MPC: this was due to the clinical request to maintain a maximum dosage of drugs, as in standard HAART protocol, in order to reduce the risks of virus mutations. Some comments are mandatory to stress the results of this model based on a differential equation deterministic approach. From the viewpoint of a model builder, two different situations have to be usually considered: basal and pathological conditions. In the case of infections, like HIV, the mathematical model have to mirror the natural evolution of HIV infection, and the pathological model must be more accurate, because today it is the only one that can be validated by experimental data, since patients are all maintained under therapy. The impact of therapy into HIV models must be introduced in a way as simple as 59
Page 1 and 2:
BIOMEDICAL ENGINEERING - FROM THEOR
Page 3:
free online editions of InTech Book
Page 6:
VI Contents Chapter 9 Targeted Magn
Page 10:
X Preface stand-alone and readers c
Page 14 and 15:
2 Biomedical Engineering - From The
Page 16 and 17:
4 Fig. 2. Process for retrieval inf
Page 18 and 19:
6 Biomedical search engine Scientif
Page 20 and 21: 8 Biomedical Engineering - From The
Page 22 and 23: 10 Biomedical Engineering - From Th
Page 24 and 25: 12 Bireme http://regional.bv salud.
Page 26 and 27: 14 Semantic Networks analysis Biome
Page 112 and 113: 100 Biomedical Engineering - From T
Page 120 and 121:
108 Method (Detection) CIEF-tITP-CZ
Page 122 and 123:
110 Method (Detection) Analyte Matr
Page 124 and 125:
112 Biomedical Engineering - From T
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
120 6. Conclusion Biomedical Engine
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
150 5. Conclusions Biomedical Engin
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
162 1 st phase : half year 4 2 nd p
Page 176 and 177:
Page 178 and 179:
166 7. Innovative active training B
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
172 12. Maturing projects’ story
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
180 16. Acknowledgments Biomedical
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
190 R* M M M M MR*M O O O O M O R*
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
196 Fig. 9. Chemical structure of 5
Page 210 and 211:
198 Relative Intensity (AU) Biomedi
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
204 2. Preparation of IO nanopartic
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
Page 234 and 235:
Page 236 and 237:
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Page 244 and 245:
Page 246 and 247:
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
Page 268 and 269:
256 3. Deposition techniques Biomed
Page 270 and 271:
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Page 280 and 281:
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 290 and 291:
Page 292 and 293:
Page 294 and 295:
Page 296 and 297:
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Page 306 and 307:
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
300 2. Nanoparticles in biomedical
Page 314 and 315:
Page 316 and 317:
Page 318 and 319:
Page 320 and 321:
Page 322 and 323:
Page 324 and 325:
Page 326 and 327:
Page 328 and 329:
Page 330 and 331:
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
Page 354 and 355:
Page 356 and 357:
Page 358 and 359:
Page 360 and 361:
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Page 380 and 381:
Page 382 and 383:
Page 384 and 385:
Page 386 and 387:
Page 388 and 389:
Page 390 and 391:
Page 392 and 393:
Page 394 and 395:
Page 396 and 397:
Page 398 and 399:
Page 400 and 401:
Page 402 and 403:
Page 404 and 405:
Page 406 and 407:
Page 408 and 409:
Page 410 and 411:
Page 412 and 413:
Page 414 and 415:
Page 416 and 417:
Page 418 and 419:
Page 420 and 421:
Page 422 and 423:
Page 424 and 425:
Page 426 and 427:
Page 428 and 429:
Page 430 and 431:
Page 432 and 433:
Page 434 and 435:
Page 436 and 437:
Page 438 and 439:
Page 440 and 441:
Page 442 and 443:
Page 444 and 445:
Page 446 and 447:
434 3. Three dimensional virtual mo
Page 448 and 449:
Page 450 and 451:
Page 452 and 453:
Page 454 and 455:
Page 456 and 457:
Page 458 and 459:
Page 460 and 461:
Page 462 and 463:
Page 464 and 465:
Page 466 and 467:
454 In this case, the eigenvalues a
Page 468 and 469:
456 where Biomedical Engineering -
Page 470 and 471:
Page 472 and 473:
Page 474 and 475:
Page 476 and 477:
Page 478 and 479:
Page 480 and 481:
Page 482 and 483:
Page 484 and 485:
472 Nb b b l l1 Biomedical Engineer
Page 486 and 487:
Page 488 and 489:
Page 490 and 491:
478 Load F (μN) Parallel-oriented
Page 492 and 493:
Page 494 and 495:
Page 496 and 497:
Page 498:
show all

Biomedical Engineering – From Theory to Applications

Create successful ePaper yourself

Delete template?

Save as template?