Target-specific multiphysics modeling for thermal medicine applications

Dissertation to obtain the degree of Doctor of Philosophy in Biomedical EngineeringThis thesis addresses thermal medicine applications on murine bladder hyperthermia and brain temperature monitoring. The two main objectives are interconnected by the key physics in thermal medicine: heat transfer. The first goal is to develop an analytical solution to characterize the heat transfer in a multi-layer perfused tissue. This analytical solution accounts for important thermoregulation mechanisms and is essential to understand the fundamentals underlying the physical and biological processes associated with heat transfer in living tissues. The second objective is the development of target-specific models that are too complex to be solved by analytical methods. Thus, the software for image segmentation and model simulation is based on numerical methods and is used to optimize non-invasive microwave antennas for specific targets. Two examples are explored using antennas in the passive mode (probe) and active mode (applicator). The passive antenna consists of a microwave radiometric sensor developed for rapid non-invasive feedback of critically important brain temperature. Its design parameters are optimized using a power-based algorithm. To demonstrate performance of the device, we build a realistic model of the human head with separate temperaturecontrolled brain and scalp regions. The sensor is able to track brain temperature with 0.4 °C accuracy in a 4.5 hour long experiment where brain temperature is varied in a 37 °C, 27 °C and 37 °C cycle. In the second study, a microwave applicator with an integrated cooling system is used to develop a new electro-thermo-fluid (multiphysics) model for murine bladder hyperthermia studies. The therapy procedure uses a temperature-based optimization algorithm to maintain the bladder at a desired therapeutic level while sparing remaining tissues from dangerous temperatures. This model shows that temperature dependent biological properties and the effects of anesthesia must be accounted to capture the absolute and transient temperature fields within murine tissues. The good agreement between simulation and experimental results demonstrates that this multiphysics model can be used to predict internal temperatures during murine hyperthermia studies

Marcação CE, optimização e caracterização de uma superfície de redução de pressão para doentes acamados

A presente dissertação tem como objectivo principal o estudo e a elaboração da Marcação CE de um dispositivo médico de classe I. Este dispositivo, com a marca comercial Corkgel, tem como finalidade o alívio de pressão em doentes acamados e a prevenção de úlceras de pressão. O processo para a obtenção da Marcação CE implica a elaboração da documentação técnica, o cumprimento dos requisitos essenciais presentes nas directivas europeias e a realização da análise de risco do dispositivo em estudo. O conceito da Marcação CE é apresentado no Capítulo I. No Capítulo II estão discriminados os passos para a obtenção da marcação CE para qualquer dispositivo médico, sendo também abordadas as diferenças entre dispositivos médicos de classes de risco diferentes (I, IIa, IIb, III). No Capítulo III é apresentada a documentação técnica para o processo de marcação CE do Corkgel regida pela directiva 93/42/CE. A caracterização experimental dos dispositivos Corkgel, essencial para a gestão de risco, está descrita no Capítulo IV. A gestão de risco requer dados analíticos e/ou evidências de conformidade, nesse sentido, foram realizados testes de toxicidade, através de técnicas de espectroscopia e cromatografia gasosa, testes de envelhecimento químico/mecânico, inflamabilidade e microbiologia, que são referentes aos requisitos de saúde. Além disso, foram realizados testes de pressão, transpiração e de compatibilidade com técnicas de imagem médica, no sentido de evidenciar a eficácia do Corkgel em reduzir a incidência das úlceras de pressão. O cumprimento dos requisitos essenciais implica um processo de gestão de risco que tem como finalidade a identificação dos riscos com vista à sua redução/eliminação. Este processo foi accionado na fase de pré mercado, mas terá que continuar na fase pós mercado. Nem todos os testes para avaliar a eficácia do Corkgel foram terminados, além disso os testes de microbiologia e toxicidade não foram conclusivos. Quando o produto estiver em conformidade, deverá ser registado num organismo notificado, por exemplo no INFARMED, em Portugal. Merece referência o facto que, após a aposição da marcação CE, o fabricante terá que estabelecer procedimentos de vigilância de mercado, para prevenir e/ou corrigir não conformidades que venham a ser detectadas

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost