Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

Most signal processing methods were developed for continuous signals. Digital devices, such as the computer, process only discrete signals. This dissertation proposes new techniques to accurately define and efficiently implement an important signal processing method---the time--frequency distribution (TFD)---using discrete signals. The TFD represents a signal in the joint time--frequency domain. Because these distributions are a function of both time and frequency they, unlike traditional signal processing methods, can display frequency content that changes over time. TFDs have been used successfully in many signal processing applications as almost all real-world signals have time-varying frequency content. Although TFDs are well defined for continuous signals, defining and computing a TFD for discrete signals is problematic. This work overcomes these problems by making contributions to the definition, computation, and application of discrete TFDs. The first contribution is a new discrete definition of TFDs. A discrete TFD (DTFD) should be free from the sampling-related distortion known as aliasing and satisfy all the important mathematical properties that the continuous TFD satisfies. Many different DTFD definitions exist but none come close to attaining this ideal. I propose three new components which make up the DTFD: 1) a new discrete Wigner--Ville distribution (DWVD) definition which satisfies all properties, 2) a new discrete analytic signal which minimises aliasing in the DWVD, and 3) a new method to define and convolve the discrete kernel with the DWVD to produce the DTFD. The result: a DTFD definition that, relative to the existing definitions, better approximates the ideal DTFD. The second contribution is two sets of computationally efficient algorithms to compute the proposed DTFD. The first set of algorithms computes the DTFD exactly; the second set requires less memory than the first set by computing time- and, or frequency-decimated versions of the DTFD. Both sets of algorithms reduce the computational load by exploiting symmetries in the DTFD and by constructing kernel-specific algorithms for four different kernel types. The third, and final, contribution is a biomedical application for the proposed DTFD and algorithms. This application is to accurately detect seizure events in newborn electroencephalogram (EEG) signals. Existing detection methods do not perform well enough for use in a clinical setting. I propose a new method which is more robust than existing methods and show how using the proposed DTFD, comparative to an existing DTFD, improves detection performance for this method. In summary, this dissertation makes practical contributions to the area of time--frequency signal processing by proposing an improved DTFD definition, efficient DTFD algorithms, and an improved newborn EEG seizure detection method using DTFDs.

A Terapia Fotodinâmica (TFD) é uma modalidade terapêutica promissora que tem mostrado resultados clínicos efetivos, a lém de custo benefício favorável ao sistema de saúde. Embora a TFD esteja associada à indução de morte celular por necrose e, ou apoptose, pesquisas recentes comprovam a ativação da autofagia. Visando entender a relação entre a quantidade de espécies reativas de oxigênio (EROs), produzidas após fotoativação dos fotossensibilizadores (FSs), com a indução de morte autofágica, foram utilizados os FSs fenotiazínicos estruturalmente semelhantes, azul de metileno (MB) e 1,9-dimetil azul de metileno (DMMB); as linhagens celulares HeLa e HaCat, como modelos biológicos e LEDs emitindo em 633 nm, como fonte luminosa. Os ensaios de viabilidade em função da dose de luz e da concentração dos FSs verificaram que o aumento de morte celular está diretamente relacionado ao aumento da concentração e ao aumento da dose de luz, para ambos FSs. Verificou-se que nas condições de IC50 a concentração do DMMB (10 nmol/L) é menor que a do MB (2,0 µmol/L) em duas ordens de grandeza, e essa diferença também se reflete no grau de desbalanço oxidativo gerado após fotossensibilização. Foi verificado que para o MB, a elevada geração de EROs está fortemente correlacionada com a perda de viabilidade, enquanto que para o DMMB essa correlação é fraca, uma vez que há perda de sobrevida sem grandes gerações de EROs. No entanto, a diminuição de sobrevida causada pelo DMMB se correlaciona forte e significativamente ao aumento da autofagia, indicando ocorrência de morte celular autofágica tanto em células HaCaT quanto em células HeLa. As análises de dano em organelas indicaram que ambos FSs, após serem fotoativados, causam danos em lisossomas e em mitocôndrias de células HaCaT. E confirmou-se, por ensaio de localização subcelular, que ambos FSs estão nessas organelas. Uma vez que a localização subcelular do FS influencia no mecanismo de morte celular foto desenvolvido, verificou-se que o MB nas mesmas concentrações nanomolares do DMMB não induz autofagia, pois o mesmo encontra-se fotoquimicamente inativo nas mitocôndrias, devido à redução pelas coenzimas presentes nesta organela. O DMMB possui um potencial de redução menor que o MB, o que impede a redução deste FS nas mitocôndrias, e, mesmo em baixas concentrações, o DMMB é capaz de comprometer a integridade de mitocôndrias e lisossomas, e induz ir autofagia como um mecanismo de morte celular. As condições em que o MB não se encontra totalmente reduzido no ambiente celular são em concentrações mais elevadas, nas quais a geração do nível de estresse oxidativo é maior e não se observa resposta autofágica após fotossensibilização. Esses resultados mostram que a eficiência de morte celular causada por TFD não está necessariamente relacionada ao nível de estresse oxidativo gerado, uma vez que o DMMB induziu estresse oxidativo em menor extensão do que MB e, no entanto, induziu morte celular em maior extensão. Confirmou-se o conceito de que, fotossensibilizadores mais eficazes para a TFD devem resultar da melhoria na especificidade das reações de fotossensibilização nos alvos celulares e não apenas em melhoria na eficiência de geração de ERO. / Photodynamic Therapy (PDT) is a promising therapeutic modality that has shown effective clinical outcomes and benefits in terms of costs to the national health system. Although PDT is associated with induction of cell death by necrosis or apoptosis, recent data suggest the activation of autophagy. In order to understand the relationship between reactive oxygen species (ROS), generated after light activation of photosensitizers (PSs), and the autophagic cell death induction, we have used two phenothiazines with similar structure - methylene blue (MB) and 1,9-dimethyl methylene blue (DMMB); HaCaT and HeLa cells were used as biological models and LEDs emitting at 633 nm were used as light source. Cell viability assays as function of light dose and PS concentration showed that the increase in cell death was directly proportional to the PS concentration and light dose, to the both PSs. At IC50 was verified that DMMB concentration (10 nmol/L) is lower than MB concentration (2,0 µmol/L) in two order of magnitude, and this difference is reflected in degree of oxidative stress promoted by photosensitizers . Only for MB the amount of detected ROS is highly correlated with loss of cell viability, while for DMMB this correlation is weak, because there is loss of viability without large generation of ROS. Nevertheless, the viability decreased for DMMB is highly correlated with the increase of autophagy, indicating occurrence of autophagic cell death in both HaCaT cells and in HeLa cells. The analyses of damaged cell organelles indicated that both PSs, after be photoactivated, induce lysosomal and mithochondrial damage in HaCaT cells. And the subcellular localization assay confirmed that DMMB and MB are localized in these organelles. Because the subcellular localization of PSs influences cell death mechanisms, this research identified that MB, in the same nanomolar concentration of DMMB, does not induce autophagy, because it is photochemically inactive in mitochondria due the reducing coenzymes present in this organelle. DMMB has a lower reduction potential than MB, which hinders PS reduction in mitochondria, and possibly generate a mild oxidative stress that compromise the integrity of mitochondria and lysosomes, and justify autophagy induction as a cell death mechanism. The conditions that MB is not fully reduced in the cellular environment are at higher concentrations, in which was detected high level of oxidative stress and autophagic cell death was not observed after photosensitization. These results show that the efficiency of cell death induced by PDT is not necessarily related with oxidative stress level, since the oxidative stress induced by DMMB was lesser than by MB, however, the cell death was greater. This research confirms the concept that more effective photosensitizers for PDT means greater specificity of photosensitization reactions, and not only improvement of the efficiency of ROS generation.

Made available in DSpace on 2016-08-17T18:39:29Z (GMT). No. of bitstreams: 1 2220.pdf: 1450706 bytes, checksum: 0253030468c7f8632d10be1f594463aa (MD5) Previous issue date: 2008-08-08 / Universidade Federal de Sao Carlos / Photodynamic therapy (PDT) is a modality for treatment of tumors, and uses a combination of a drug (photosensitizer) and light in the presence of the molecular oxygen to selectively damage target tissue. In the absent of one of these components, the cytotoxic effect is not observed. Since 1990, many works in the literature studies the topical application of precursors of protoporphyrin IX (PpIX) in PDT, such 5- aminolevulinic acid (ALA) and methyl aminolevulinate (MAL). The purpose of this work was realized an comparative study in vivo between two commercial and available drugs precursors of PpIX, the ALAsense (5-aminolevulinic acid - ALA) from Russian and Metvix (methyl aminolevulinate MAL) from United Kingdom. Experiments were carried out in animals to analyze the performance and the ALA photodynamic MAL in liver of rats. The fluorescence spectra of the liver were collected at pre-determined time. The time of accumulation of PpIX was observed by 2 hours and 45 minutes for the ALA and MAL for 4 hours after application of drugs in the liver. The formation, accumulation and depth of penetration of PpIX in liver tissue were determined by fluorescence spectroscopy. Using a total of 21 animals were the irradiation of the liver fotossensibilizado with ALA or MAL alone with different doses of light (20, 50, 100 and 200J/cm2) or in a combination MAL + ALA to 8%, 16% and 32 dose of 100J/cm2. Thirty hours after the lighting, the animals were killed and livers removed. The area of necrosis of the liver was assessed macroscopically and the samples were prepared for histological study, considering especially the aspects and depth of necrosis. In histological analysis were carried out many aspects of necrosis and the normal liver. The depths of necrosis were measured and the threshold dose obtained using a mathematical model proposed in the literature. Moreover, the monitoring was carried out of O2 consumption of mitochondria isolated from livers of rats, after topical administration of drugs precursors of PpIX (ALA and MAL) in order to check the influence of these substances in mitochondrial bioenergetics. The results showed a higher penetration of MAL in the tissue, as well as greater depth of necrosis when compared to the ALA. These results suggest that MAL has a tendency to better photodynamic response than ALA to the criteria studied. / Terapia Fotodinâmica (TFD) é uma modalidade terapêutica para tratamento de tumores que provoca a destruição do tecido alvo através da combinação de uma droga (fotossensibilizador) e uma fonte de luz na presença de oxigênio molecular. Na ausência de algum desses componentes, o efeito citotóxico não é observado. Desde 1990, têm-se estudado a aplicação tópica de substâncias precursoras da protoporfirina IX (PpIX) associada à TFD, como o ácido 5-aminolevulínico (ALA) e o metil aminolevulinato (MAL). O objetivo do presente trabalho foi realizar um estudo comparativo in vivo entre duas substâncias precursoras da PpIX , o ALAsense (ácido 5-aminolevulínico - ALA) da Rússia e o Metvix (metil aminolevulinato MAL) do Reino Unido. Foram realizados experimentos em animais para analisar o desempenho fotodinâmico ALA e pelo MAL em fígado de ratos. Os espectros de fluorescência do fígado foram coletados em tempos prédeterminados. O tempo de acúmulo da PpIX observado foi de 2 horas e 45 minutos para o ALA e 4 horas para o MAL após a aplicação da droga no fígado. A formação, acúmulo e a profundidade de penetração da PpIX no tecido hepático foram determinados através da espectroscopia de fluorescência. Utilizando um total de 21 animais foi realizada a irradiação do fígado fotossensibilizado com ALA ou com MAL isoladamente com diferentes doses de luz (20, 50, 100 e 200J/cm2) ou na forma combinada MAL + ALA a 8%, 16 e 32% com dose de 100J/cm2. Trinta horas após a iluminação, os animais foram mortos e os fígados removidos. A área necrosada do fígado foi avaliada macroscopicamente e as amostras foram preparadas para o estudo histológico, considerando, principalmente, os aspectos e a profundidade da necrose. Na análise histológica realizada foram observados vários aspectos da necrose e da região normal do fígado. As profundidades de necrose foram medidas e a dose limiar obtida utilizando-se um modelo matemático proposto na literatura. Além disso, foi realizado o monitoramento do consumo de O2 de mitocôndrias isoladas de fígados de ratos, após administração tópica dos medicamentos precursores da PpIX (ALA e MAL) afim de verificar a influência dessas substâncias na bioenergética mitocondrial. Os resultados obtidos mostraram uma maior penetrabilidade do MAL no tecido, bem como uma maior profundidade de necrose quando comparado ao ALA. Esses resultados sugerem que o MAL possui uma tendência a melhor resposta fotodinâmica que o ALA para os critérios estudados.