Bioengineering, Volume 8, Issue 2 (February 2021) – 16 articles

Neurodegenerative diseases (NDDs) are incurable and debilitating conditions that result in progressive degeneration and/or death of nerve cells in the central nervous system (CNS). Identification of viable therapeutic targets and new treatments for CNS disorders and in particular, for NDDs is a major challenge in the field of drug discovery. These difficulties can be attributed to the diversity of cells involved, extreme complexity of the neural circuits, the limited capacity for tissue regeneration, and our incomplete understanding of the underlying pathological processes. Drug discovery is a complex and multidisciplinary process. The screening attrition rate in current drug discovery protocols mean that only one viable drug may arise from millions of screened compounds resulting in the need to improve discovery technologies and protocols to address the multiple causes of attrition. This has identified the need to screen larger libraries where the use of efficient high-throughput screening (HTS) becomes key in the discovery process. HTS can investigate hundreds of thousands of compounds per day. However, if fewer compounds could be screened without compromising the probability of success, the cost and time would be largely reduced. To that end, recent advances in computer-aided design, in silico libraries, and molecular docking software combined with the upscaling of cell-based platforms have evolved to improve screening efficiency with higher predictability and clinical applicability. We review, here, the increasing role of HTS in contemporary drug discovery processes, in particular for NDDs, and evaluate the criteria underlying its successful application. We also discuss the requirement of HTS for novel NDD therapies and examine the major current challenges in validating new drug targets and developing new treatments for NDDs. Full article

In recent years, biopolymers have been attracting the attention of researchers and specialists from different fields, including biotechnology, material science, engineering, and medicine. The reason is the possibility of combining sustainability with scientific and technological progress. This is an extremely broad research topic, and a distinction has to be made among different classes and types of biopolymers. Polyhydroxyalkanoate (PHA) is a particular family of polyesters, synthetized by microorganisms under unbalanced growth conditions, making them both bio-based and biodegradable polymers with a thermoplastic behavior. Recently, PHAs were used more intensively in biomedical applications because of their tunable mechanical properties, cytocompatibility, adhesion for cells, and controllable biodegradability. Similarly, the 3D-printing technologies show increasing potential in this particular field of application, due to their advantages in tailor-made design, rapid prototyping, and manufacturing of complex structures. In this review, first, the synthesis and the production of PHAs are described, and different production techniques of medical implants are compared. Then, an overview is given on the most recent and relevant medical applications of PHA for drug delivery, vessel stenting, and tissue engineering. A special focus is reserved for the innovations brought by the introduction of additive manufacturing in this field, as compared to the traditional techniques. All of these advances are expected to have important scientific and commercial applications in the near future. Full article

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between −1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions. Full article

The most prevalent form of bioprinting—extrusion bioprinting—can generate structures from a diverse range of materials and viscosities. It can create personalized tissues that aid in drug testing and cancer research when used in combination with natural bioinks. This paper reviews natural bioinks and their properties and functions in hard and soft tissue engineering applications. It discusses agarose, alginate, cellulose, chitosan, collagen, decellularized extracellular matrix, dextran, fibrin, gelatin, gellan gum, hyaluronic acid, Matrigel, and silk. Multi-component bioinks are considered as a way to address the shortfalls of individual biomaterials. The mechanical, rheological, and cross-linking properties along with the cytocompatibility, cell viability, and printability of the bioinks are detailed as well. Future avenues for research into natural bioinks are then presented. Full article

The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic. Full article

This paper deals with a brief review of the recent developments in computational modelling applied to innovative treatments of spine diseases. Additionally, it provides a perspective on the research directions expected for the forthcoming years. The spine is composed of distinct and complex tissues that require specific modelling approaches. With the advent of additive manufacturing and increasing computational power, patient-specific treatments have moved from being a research trend to a reality in clinical practice, but there are many issues to be addressed before such approaches become universal. Here, it is identified that the major setback resides in validation of these computational techniques prior to approval by regulatory agencies. Nevertheless, there are very promising indicators in terms of optimised scaffold modelling for both disc arthroplasty and vertebroplasty, powered by a decisive contribution from imaging methods. Full article

Conductive polymers (CPs) have recently been applied in the development of scaffolds for tissue engineering applications in attempt to induce additional cues able to enhance tissue growth. Polyaniline (PANI) is one of the most widely studied CPs, but it requires to be blended with other polymers in order to be processed through conventional technologies. Here, we propose the fabrication of nanofibers based on a polycaprolactone (PCL)-PANI blend obtained using electrospinning technology. An extracellular matrix-like fibrous substrate was obtained showing a good stability in the physiological environment (37 °C in PBS solution up 7 days). However, since the high hydrophobicity of the PCL-PANI mats (133.5 ± 2.2°) could negatively affect the biological response, a treatment with atmospheric plasma was applied on the nanofibrous mats, obtaining a hydrophilic surface (67.1 ± 2°). In vitro tests were performed to confirm the viability and the physiological-like morphology of human foreskin fibroblast (HFF-1) cells cultured on the plasma treated PCL-PANI nanofibrous scaffolds. Full article

Clustered regularly interspaced short palindromic repeat (CRISPR/Cas) proteins can be designed to bind specified DNA and RNA sequences and hold great promise for the accurate detection of nucleic acids for diagnostics. We integrated commercially available reagents into a CRISPR/Cas9-based lateral flow assay that can detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences with single-base specificity. This approach requires minimal equipment and represents a simplified platform for field-based deployment. We also developed a rapid, multiplex fluorescence CRISPR/Cas9 nuclease cleavage assay capable of detecting and differentiating SARS-CoV-2, influenza A and B, and respiratory syncytial virus in a single reaction. Our findings provide proof-of-principle for CRISPR/Cas9 point-of-care diagnosis as well as a scalable fluorescent platform for identifying respiratory viral pathogens with overlapping symptomology. Full article

Current prognostic risk scores in cardiac surgery do not benefit yet from machine learning (ML). This research aims to create a machine learning model to predict one-year mortality of a patient after transcatheter aortic valve implantation (TAVI). We adopt a modern gradient boosting on decision trees classifier (GBDTs), specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling the identification of the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 consecutive TAVI cases, reaching a C-statistic of 0.83 with CI [0.82, 0.84]. The model has achieved a positive predictive value ranging from 57% to 64%, suggesting that the patient selection made by the heart team of professionals can be further improved by taking into consideration the clinical data we identified as important and by exploiting ML approaches in the development of clinical risk scores. Our approach has shown promising predictive potential also with respect to widespread prognostic risk scores, such as logistic European system for cardiac operative risk evaluation (EuroSCORE II) and the society of thoracic surgeons (STS) risk score, which are broadly adopted by cardiologists worldwide. Full article

Decoding motor intentions from non-invasive brain activity monitoring is one of the most challenging aspects in the Brain Computer Interface (BCI) field. This is especially true in online settings, where classification must be performed in real-time, contextually with the user’s movements. In this work, we use a topology-preserving input representation, which is fed to a novel combination of 3D-convolutional and recurrent deep neural networks, capable of performing multi-class continual classification of subjects’ movement intentions. Our model is able to achieve a higher accuracy than a related state-of-the-art model from literature, despite being trained in a much more restrictive setting and using only a simple form of input signal preprocessing. The results suggest that deep learning models are well suited for deployment in challenging real-time BCI applications such as movement intention recognition. Full article

Short bowel syndrome is a pathological condition resulting from extensive resection of the intestine, generally performed due to congenital abnormalities, Crohn’s disease, mesenteric ischemia, or neoplasms. The main consequence of this syndrome is a reduction of intestinal absorption, which causes malnutrition and dehydration. In the most severe cases, specific and complex surgical procedures are requested to manage the syndrome. Such procedures consist of the intestinal lengthening, with lead to an increase of absorptive mucosal surface and intestinal transit time and an overall enhancement of intestinal absorption. One of the most promising surgical procedures is spiral intestinal lengthening and tailoring, which consists of a spiral incision of the intestinal wall and in the elongation longitudinally of the intestine by sliding one flap over the other. The final intestinal lengthening is strictly dependent on a series of parameters, some of which are defined by the surgeon. The present paper proposes a mathematical model, based on patient specific anatomical data, which aims to help the surgeon in defining the optimal parameters for the intervention and in foreseeing its outcomes from the preoperative planning phase. Such a tool can assist the physician in the surgery room by improving the procedure and reducing surgical times. Full article

Organs On-a-Chip represent novel platforms for modelling human physiology and disease. The lymph node (LN) is a relevant immune organ in which B and T lymphocytes are spatially organized in a complex architecture, and it is the place where the immune response initiates. The present study addresses the utility of a recently designed LN-on-a-chip to dissect and understand the effect of drugs delivered to cells in a fluidic multicellular 3D setting that mimics the human LN. To do so, we analyzed the motility and viability of human B and T cells exposed to hydroxychloroquine (HCQ). We show that the innovative LN platform, which operates at a microscale level, allows real-time monitoring of co-cultured B and T cells by imaging, and supports cellular random movement. HCQ delivered to cells through a constant and continuous flow induces a reduction in T cell velocity while promotes persistent rotational motion. We also find that HCQ increases the production of reactive oxygen species in T cells. Taken together, these results highlight the potential of the LN-on-a-chip to be applied in drug screening and development, and in cellular dynamics studies. Full article

Age-related macular degeneration (AMD) is the leading cause of vision loss in the elderly worldwide. So far, the etiology and the progression of AMD are not well known. Animal models have been developed to study the mechanisms involved in AMD; however, according to the “Three Rs” principle, alternative methods have been investigated. Here we present a strategy to develop a “Three Rs” compliant retinal three-dimensional (3D) in vitro model, including a Bruch’s membrane model and retina pigment epithelium (RPE) layer. First, tensile testing was performed on porcine retina to set a reference for the in vitro model. The results of tensile testing showed a short linear region followed by a plastic region with peaks. Then, Bruch’s membrane (BrM) was fabricated via electrospinning by using Bombyx mori silk fibroin (BMSF) and polycaprolactone (PCL). The BrM properties and ARPE-19 cell responses to BrM substrates were investigated. The BrM model displayed a thickness of 44 µm, with a high porosity and an average fiber diameter of 1217 ± 101 nm. ARPE-19 cells adhered and spread on the BMSF/PCL electrospun membranes. In conclusion, we are developing a novel 3D in vitro retinal model towards the replacement of animal models in AMD studies. Full article

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows. Full article

The administration of gene-editing tools has been proposed as a promising therapeutic approach for correcting mutations that cause diseases. Gene-editing tools, composed of relatively large plasmid DNA constructs that often need to be co-delivered with a guiding protein, are unable to spontaneously penetrate mammalian cells. Although viral vectors facilitate DNA delivery, they are restricted by the size of the plasmid to carry. In this work, we describe a strategy for the stable encapsulation of the gene-editing tool piggyBac transposon into Poly (β-amino ester) nanoparticles (NPs). We propose a non-covalent and a covalent strategy for stabilization of the nanoformulation to slow down release kinetics and enhance intracellular delivery. We found that the formulation prepared by covalently crosslinking Poly (β-amino ester) NPs are capable to translocate into the cytoplasm and nuclei of human glioblastoma (U87MG) cells within 1 h of co-culturing, without the need of a targeting moiety. Once internalized, the nanoformulation dissociates, delivering the plasmid presumably as a response to the intracellular acidic pH. Transfection efficiency is confirmed by green fluorescence protein (GFP) expression in U87MG cells. Covalently stabilized Poly (β-amino ester) NPs are able to transfect ~55% of cells causing non-cytotoxic effects. The strategy described in this work may serve for the efficient non-viral delivery of other gene-editing tools. Full article