V. H. Magalhães, V. Pinto, P. Sousa, L. Gonçalves, Emilio Fernández, G. Minas
{"title":"A microfluidic device for size-based microplastics and microalgae separation","authors":"V. H. Magalhães, V. Pinto, P. Sousa, L. Gonçalves, Emilio Fernández, G. Minas","doi":"10.1109/ENBENG58165.2023.10175366","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175366","url":null,"abstract":"Phytoplankton are microscopic marine algae that constitute the foundation of the aquatic food web. They are essential to drive life in the oceans but can also be harmful in the form of algae blooms, either by producing phytoplankton toxins or massive biomass proliferation. Microalgae cell size is a relevant morphologic trait that can help identifying species responsible for these blooms at early stages. Sorting microalgae based on cell size reduces the complexity of the sea water samples, making identification easier, and allows enrichment of the target size. This work reports the study and optimization of an inertial microfluidic device for size-based separation and concentration of microparticles/microalgae. It is demonstrated the suitability of the device for sorting and separation of microparticles/microalgae of varying sizes. Furthermore, the enrichment of microalgae was also demonstrated, achieving a 2.5-fold increase fluorescence detection with only one spiral passage. This method is well suited for integration in monitoring devices due to its easy fabrication and integration in miniaturized systems and has potential as a pre-sorting and enrichment step prior to analysis. In addition, it can be used to improve the monitoring performance of early harmful algal blooms, or detection of microplastics in the water.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130198630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cristiana P Von Rekowski, Rúben Araújo, Tiago A H Fonseca, C. Calado, Luís Bento, I. Pinto
{"title":"Laboratory biomarkers associated to death in the first three COVID-19 waves in Portugal","authors":"Cristiana P Von Rekowski, Rúben Araújo, Tiago A H Fonseca, C. Calado, Luís Bento, I. Pinto","doi":"10.1109/ENBENG58165.2023.10175345","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175345","url":null,"abstract":"Besides the pandemic being over, new SARS-CoV-2 lineages, and sub-lineages, still pose risks to global health. Thus, in this preliminary study, to better understand the characteristics of COVID-19 patients and the effect of certain hematologic biomarkers on their outcome, we analyzed data from 337 patients admitted to the ICU of a single-center hospital in Lisbon, Portugal, in the first three waves of the pandemic. Most patients belonged to the second (40.4%) and third (41.2%) waves. The ones from the first wave were significantly older and relied more on respiratory techniques like invasive mechanic ventilation and extracorporeal membrane oxygenation. There were no significant differences between waves regarding mortality in the ICU. In general, non-survivors had worse laboratory results. Biomarkers significantly associated with death changed depending on the waves. Increased high-sensitivity cardiac troponin I results, and lower eosinophil counts were associated to death in all waves. In the second and third waves, the international normalized ratio, lymphocyte counts, and neutrophil counts were also associated to mortality. A higher risk of death was linked to increased myoglobin results in the first two waves, as well as increased creatine kinase results, and lower platelet counts in the third wave.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126447900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Asymmetry Measures of Dermoscopic Images for Automated Melanoma Detection","authors":"Keith Lancaster, G. Zouridakis","doi":"10.1109/ENBENG58165.2023.10175337","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175337","url":null,"abstract":"Melanoma is the deadliest form of skin cancer and early detection is critical for successful treatment. Dermoscopy is an effective tool for assessing the likelihood of a suspicious lesion being malignant. In this study, we focus on improving the detection of lesion asymmetry, one of the key factors of automated melanoma recognition. We employ two approaches: irregularity of the lesion contour assessed with measures that compare lesion quadrants with respect to area, color, and melanin content, and size theory using one-dimensional measuring functions to determine asymmetry. Measuring functions were mapped into size functions and compared using bottleneck distances, which were then used as classification features. Annotated dermoscopic images were used to train and assess classifiers for both methods. Our results show that the combined methods exhibit 95% lesion classification accuracy and suggest that size functions may be suitable for detecting melanoma directly. Our findings confirm that smartphone-based systems can be valuable assistive devices with substantial benefits for both individual healthcare and public health outcomes. This technology has the potential to enhance patient accessibility, particularly in low-resource settings and areas with limited healthcare access.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127038580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"BlanketGen - A Synthetic Blanket Occlusion Augmentation Pipeline for Motion Capture Datasets","authors":"João Carmona, Tam'as Kar'acsony, Joao Paulo Cunha","doi":"10.1109/ENBENG58165.2023.10175320","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175320","url":null,"abstract":"Human motion analysis has seen drastic improvements recently, however, due to the lack of representative datasets, for clinical in-bed scenarios it is still lagging behind. To address this issue, we implemented BlanketGen, a pipeline that augments videos with synthetic blanket occlusions. With this pipeline, we generated an augmented version of the pose estimation dataset 3DPW called BlanketGen-3DPW. We then used this new dataset to fine-tune a Deep Learning model to improve its performance in these scenarios with promising results. Code and further information are available at https://gitlab.inesctec.pt/brain-lab/brain-lab-public/blanket-gen-releases.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126679682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"BlanketSet - A Clinical Real-World In-Bed Action Recognition and Qualitative Semi-Synchronised Motion Capture Dataset","authors":"João Carmona, Tam'as Kar'acsony, Joao Paulo Cunha","doi":"10.1109/ENBENG58165.2023.10175335","DOIUrl":"https://doi.org/10.1109/ENBENG58165.2023.10175335","url":null,"abstract":"Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at rdm.inesctec.pt/dataset/nis-2022-004.","PeriodicalId":125330,"journal":{"name":"2023 IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130435700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}