Three-Dimensional (3D) Printed Microneedles for Microencapsulated Cell Extrusion

Cell-hydrogel based therapies offer great promise for wound healing. The specific aim of this study was to assess the viability of human hepatocellular carcinoma (HepG2) cells immobilized in atomized alginate capsules (3.5% (w/v) alginate, d = 225 µm ± 24.5 µm) post-extrusion through a three-dimensional (3D) printed methacrylate-based custom hollow microneedle assembly (circular array of 13 conical frusta) fabricated using stereolithography. With a jetting reliability of 80%, the solvent-sterilized device with a root mean square roughness of 158 nm at the extrusion nozzle tip (d = 325 μm) was operated at a flowrate of 12 mL/min. There was no significant difference between the viability of the sheared and control samples for extrusion times of 2 h (p = 0.14, α = 0.05) and 24 h (p = 0.5, α = 0.05) post-atomization. Factoring the increase in extrusion yield from 21.2% to 56.4% attributed to hydrogel bioerosion quantifiable by a loss in resilience from 5470 (J/m3) to 3250 (J/m3), there was no significant difference in percentage relative payload (p = 0.2628, α = 0.05) when extrusion occurred 24 h (12.2 ± 4.9%) when compared to 2 h (9.9 ± 2.8%) post-atomization. Results from this paper highlight the feasibility of encapsulated cell extrusion, specifically protection from shear, through a hollow microneedle assembly reported for the first time in literature

Applications of plasma-liquid systems : a review

Plasma-liquid systems have attracted increasing attention in recent years, owing to their high potential in material processing and nanoscience, environmental remediation, sterilization, biomedicine, and food applications. Due to the multidisciplinary character of this scientific field and due to its broad range of established and promising applications, an updated overview is required, addressing the various applications of plasma-liquid systems till now. In the present review, after a brief historical introduction on this important research field, the authors aimed to bring together a wide range of applications of plasma-liquid systems, including nanomaterial processing, water analytical chemistry, water purification, plasma sterilization, plasma medicine, food preservation and agricultural processing, power transformers for high voltage switching, and polymer solution treatment. Although the general understanding of plasma-liquid interactions and their applications has grown significantly in recent decades, it is aimed here to give an updated overview on the possible applications of plasma-liquid systems. This review can be used as a guide for researchers from different fields to gain insight in the history and state-of-the-art of plasma-liquid interactions and to obtain an overview on the acquired knowledge in this field up to now

Imparting 3D representations to artificial intelligence for a full assessment of pressure injuries.

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning methods. The main objective of this dissertation is to prove the efficiency of Deep Learning techniques in tackling one of the important health issues we are facing in our society, through medical imaging. Pressure injuries are a dermatology related health issue associated with increased morbidity and health care costs. Managing pressure injuries appropriately is increasingly important for all the professionals in wound care. Using 2D photographs and 3D meshes of these wounds, collected from collaborating hospitals, our mission is to create intelligent systems for a full non-intrusive assessment of these wounds. Five main tasks have been achieved in this study: a literature review of wound imaging methods using machine learning techniques, the classification and segmentation of the tissue types inside the pressure injury, the segmentation of these wounds and the design of an end-to-end system which measures all the necessary quantitative information from 3D meshes for an efficient assessment of PIs, and the integration of the assessment imaging techniques in a web-based application

PainDroid: An android-based virtual reality application for pain assessment

Earlier studies in the field of pain research suggest that little efficient intervention currently exists in response to the exponential increase in the prevalence of pain. In this paper, we present an Android application (PainDroid) with multimodal functionality that could be enhanced with Virtual Reality (VR) technology, which has been designed for the purpose of improving the assessment of this notoriously difficult medical concern. Pain- Droid has been evaluated for its usability and acceptability with a pilot group of potential users and clinicians, with initial results suggesting that it can be an effective and usable tool for improving the assessment of pain. Participant experiences indicated that the application was easy to use and the potential of the application was similarly appreciated by the clinicians involved in the evaluation. Our findings may be of considerable interest to healthcare providers, policy makers, and other parties that might be actively involved in the area of pain and VR research

Artificial Intelligence-Powered Chronic Wound Management System: Towards Human Digital Twins

Artificial Intelligence (AI) has witnessed increased application and widespread adoption over the past decade. AI applications to medical images have the potential to assist caregivers in deciding on a proper chronic wound treatment plan by helping them to understand wound and tissue classification and border segmentation, as well as visual image synthesis. This dissertation explores chronic wound management using AI methods, such as Generative Adversarial Networks (GAN) and Explainable AI (XAI) techniques. The wound images are collected, grouped, and processed. One primary objective of this research is to develop a series of AI models, not only to present the potential of AI in wound management but also to develop the building blocks of human digital twins. First of all, motivations, contributions, and the dissertation outline are summarized to introduce the aim and scope of the dissertation. The first contribution of this study is to build a chronic wound classification and its explanation utilizing XAI. This model also benefits from a transfer learning methodology to improve performance. Then a novel model is developed that achieves wound border segmentation and tissue classification tasks simultaneously. A Deep Learning (DL) architecture, i.e., the GAN, is proposed to realize these tasks. Another novel model is developed for creating lifelike wounds. The output of the previously proposed model is used as an input for this model, which generates new chronic wound images. Any tissue distribution could be converted to lifelike wounds, preserving the shape of the original wound. The aforementioned research is extended to build a digital twin for chronic wound management. Chronic wounds, enabling technologies for wound care digital twins, are examined, and a general framework for chronic wound management using the digital twin concept is investigated. The last contribution of this dissertation includes a chronic wound healing prediction model using DL techniques. It utilizes the previously developed AI models to build a chronic wound management framework using the digital twin concept. Lastly, the overall conclusions are drawn. Future challenges and further developments in chronic wound management are discussed by utilizing emerging technologies

Desenvolvimento de curativos anti-inflamatórios à base de amido utilizando moléculas derivadas de subprodutos do tomate

Most wound dressings currently used are not ideal for a total, fast, and effective healing process due to their reduced biocompatibility derived from their synthetic nature and lack of bioactive properties. Alternatively, new bio-based dressings with antimicrobial and/or anti-inflammatory activity have been developed. However, the natural polymers currently used come from food, acting as competitors for human nutrition. Agrifood byproducts-derived biomolecules can help overcome this competition. In this work, the feasibility of using tomato byproducts-derived molecules for the development of starch-based dressings with anti-inflammatory properties was studied. For this purpose, the influence of tomato pomace-derived hot-water soluble extract concentration (1%, 5%, and 10% w/w in relation to starch dry weight), rich in polysaccharides (TE) and/or phenolic compounds (PE), on the chromatic, mechanical, physicochemical, and active properties (antimicrobial and anti-inflammatory) of starch-based films obtained by solvent casting was studied. In a circular economy concept, starch was recovered from industrial potato processing slurries. TE and PE extracts showed, respectively, 14.7% and 18.5% of protein, 43.7% and 28.6% of polysaccharides, 2.8% and 15.1% of phenolic compounds, and promising antioxidant activity (IC50 values of 2.5 mg/mL and 0.8 mg/mL, respectively). When incorporated into starch-based formulations, TE and PE allowed to develop transparent films with a yellowish coloration and less rigid and traction resistant films than the control films. TE originated hydrophobic and flexible films, while PE decreased the films’ hydrophobicity and stretchability. Regarding the active properties, after 24h, both films with TE and PE revealed anti-inflammatory activity in the presence of a pro-inflammatory agent, where, for starch-based films with 10% TE and 10% PE, the inflammation decreased by ca. 48% and 100%, respectively. To develop starch-based dressings, in this work the electrospinning of starch-based solutions was also optimized. After adjusting the solvent, starch concentration, and dissolution time, starch-based fibers were obtained, although in small amount for application in the development of dressings. The tomato pomace-derived molecules and starch recovered from potato washing slurries have shown to be promising raw materials for the development of biobased anti-inflammatory dressings, which will allow to promote more biocompatible and functional wound healing devices than the materials derived from inert synthetic polymers.Muitos dos curativos para feridas atualmente utilizados não são os ideais para um processo de cura total, rápido e eficaz devido à sua reduzida biocompatibilidade, oriunda da sua natureza sintética e da ausência de propriedades funcionais. Como alternativa têm sido desenvolvidos novos curativos de origem biológica com atividade antimicrobiana e/ou anti inflamatória. No entanto, os polímeros de origem natural até à data utilizados provêm de alimentos, atuando como competidores para a alimentação humana. As biomoléculas derivadas dos subprodutos da indústria agroalimentar podem ajudar a superar esta competição. Neste trabalho foi estudada a viabilidade da utilização de moléculas derivadas dos subprodutos de tomate para o desenvolvimento de pensos à base de amido com propriedades anti-inflamatórias. Para o efeito foi estudada a influência da concentração (1%, 5% e 10% m/m em relação à massa seca de amido) de extratos solúveis em água quente derivados to repiso de tomate, ricos em polissacarídeos (TE) e/ou compostos fenólicos (PE), nas propriedades cromáticas, mecânicas, físico-químicas e ativas (anti-inflamatórias e antimicrobianas) dos filmes à base de amido obtidos pelo método de evaporação do solvente. Num conceito de economia circular, o amido foi recuperado de lamas provenientes do processamento industrial de batata. Os extratos TE e PE apresentaram, respetivamente, 14,7% e 18,5% de proteína, 43,7% e 28,6% de polissacarídeos, 2,8% e 15,1% de compostos fenólicos e promissora atividade antioxidante (IC50 de 2,5 mg/mL e 0,8 mg/mL, respetivamente). Quando incorporados em formulações à base de amido, os extratos TE e PE permitiram desenvolver filmes transparentes com coloração amarelada e menor rigidez e resistência à tração do que os filmes controlo. TE originou filmes hidrofóbicos e flexíveis, enquanto PE diminuiu a hidrofobicidade e extensibilidade dos filmes. Quanto às propriedades ativas, ambos os filmes com TE e PE demostraram atividade anti-inflamatória quando na presença de um agente pró-inflamatório, observando-se, ao fim de 24 h, uma diminuição da inflamação de cerca de 48% e 100% para os filmes à base de amido com 10% de TE e PE, respetivamente. Com o intuito de desenvolver pensos à base de amido, neste trabalho também se otimizou a eletrofiação de soluções à base de amido. Após o ajuste do solvente, da concentração de amido e do tempo necessário para a sua dissolução foram obtidas fibras à base amido, porém em baixa quantidade para posterior aplicação no desenvolvimento de pensos. As biomoléculas existentes no repiso de tomate e o amido recuperado das lamas de lavagem de batata mostraram-se promissores para o desenvolvimento de curativos anti-inflamatórios de origem biológica, o que permitirá promover dispositivos médicos para cura de feridas mais biocompatíveis e funcionais dos que os materiais derivados de polímeros sintéticos inertes.Mestrado em Biotecnologi

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201