Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining.

The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches.

Biomaterials text mining: A hands-on comparative study of methods on polydioxanone biocompatibility.

Scientific information extraction is fundamental for research and innovation, but is currently mostly a manual, time-consuming process. Text Mining tools (TMTs) enable automated, accurate and quick information extraction from text, but there is little precedent of their use in the biomaterials field. Here, we compare the ability of various TMTs to extract useful information from biomaterials abstracts. Focusing on the biocompatibility of polydioxanone, a biodegradable polymer for which there are relatively few scientific publications, we tested several tools ranging from machine learning approaches and statistical text analysis to MeSH indexing and domain-specific semantic tools for Named Entity Recognition. We also evaluated their output alongside a manual review of systematic reviews and meta-analyses. The findings show that TMTs can be highly efficient and powerful for mapping biomaterials texts and rapidly yield up-to-date information. Here, TMTs enable one to identify dominating themes, see the evolution of specific terms and topics, and learn about key medical applications in biomaterials literature over the years. The analysis also shows that ambiguity around biomaterials nomenclature is a significant challenge in mining biomedical literature that is yet to be tackled. This research showcases the potential value of using Natural Language Processing and domain-specific tools to extract and organize biomaterials data.

DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline.

Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload. Among them, text mining systems present an attractive option for the automated extraction of information from text documents into structured datasets. This work presents the first automated system for biomaterial related information extraction from the National Library of Medicine's premier bibliographic database (MEDLINE) research abstracts into a searchable database. The system is a text mining pipeline that periodically retrieves abstracts from PubMed and identifies research and clinical studies of biomaterials. Thereafter, the pipeline identifies sixteen concept types of interest in the abstract using the Biomaterials Annotator, a tool for biomaterials Named Entity Recognition (NER). These concepts of interest, along with the abstract and relevant metadata are then deposited in DEBBIE, the Database of Experimental Biomaterials and their Biological Effect. DEBBIE is accessible through a web application that provides keyword searches and displays results in an intuitive and meaningful manner, aiming to facilitate an efficient mapping and organization of biomaterials information.

Development and Optimisation of Hydrogel Scaffolds for Microvascular Network Formation.

Traumatic injuries are a major cause of morbidity and mortality worldwide; however, there is limited research on microvascular traumatic injuries. To address this gap, this research aims to develop and optimise an in vitro construct for traumatic injury research at the microvascular level. Tissue engineering constructs were created using a range of polymers (collagen, fibrin, and gelatine), solvents (PBS, serum-free endothelial media, and MES/NaCl buffer), and concentrations (1-5% w/v). Constructs created from these hydrogels and HUVECs were evaluated to identify the optimal composition in terms of cell proliferation, adhesion, migration rate, viability, hydrogel consistency and shape retention, and tube formation. Gelatine hydrogels were associated with a lower cell adhesion, whereas fibrin and collagen ones displayed similar or better results than the control, and collagen hydrogels exhibited poor shape retention; fibrin scaffolds, particularly at high concentrations, displayed good hydrogel consistency. Based on the multipronged evaluation, fibrin hydrogels in serum-free media at 3 and 5% w/v were selected for further experimental work and enabled the formation of interconnected capillary-like networks. The networks formed in both hydrogels displayed a similar architecture in terms of the number of segments (10.3 ± 3.21 vs. 9.6 ± 3.51) and diameter (8.6446 ± 3.0792 µm vs. 7.8599 ± 2.3794 µm).

OxVent: Design and evaluation of a rapidly-manufactured Covid-19 ventilator.

BACKGROUND: The manufacturing of any standard mechanical ventilator cannot rapidly be upscaled to several thousand units per week, largely due to supply chain limitations. The aim of this study was to design, verify and perform a pre-clinical evaluation of a mechanical ventilator based on components not required for standard ventilators, and that met the specifications provided by the Medicines and Healthcare Products Regulatory Agency (MHRA) for rapidly-manufactured ventilator systems (RMVS). METHODS: The design utilises closed-loop negative feedback control, with real-time monitoring and alarms. Using a standard test lung, we determined the difference between delivered and target tidal volume (VT) at respiratory rates between 20 and 29 breaths per minute, and the ventilator's ability to deliver consistent VT during continuous operation for >14 days (RMVS specification). Additionally, four anaesthetised domestic pigs (3 male-1 female) were studied before and after lung injury to provide evidence of the ventilator's functionality, and ability to support spontaneous breathing. FINDINGS: Continuous operation lasted 23 days, when the greatest difference between delivered and target VT was 10% at inspiratory flow rates >825 mL/s. In the pre-clinical evaluation, the VT difference was -1 (-90 to 88) mL [mean (LoA)], and positive end-expiratory pressure (PEEP) difference was -2 (-8 to 4) cmH2O. VT delivery being triggered by pressures below PEEP demonstrated spontaneous ventilation support. INTERPRETATION: The mechanical ventilator presented meets the MHRA therapy standards for RMVS and, being based on largely available components, can be manufactured at scale. FUNDING: Work supported by Wellcome/EPSRC Centre for Medical Engineering,King's Together Fund and Oxford University.

Electrospun Fibres with Hyaluronic Acid-Chitosan Nanoparticles Produced by a Portable Device.

Electrospinning is a versatile technique to produce nano/microscale fibrous scaffolds for tissue engineering and drug delivery applications. This research aims to demonstrate that hyaluronic acid-chitosan (HA-CS) nanoparticles can be electrospun together with polycaprolactone (PCL) and gelatine (Ge) fibres using a portable device to create scaffolds for tissue repair. A range of polymer solutions of PCL-gelatine at different weight/volume concentrations and ratios were electrospun and characterised. Fibre-cell interaction (F11 cells) was evaluated based on cell viability and proliferation and, from here, a few polymer blends were electrospun into random or aligned fibre arrangements. HA-CS nanoparticles were synthesised, characterised, and used to functionalise electrospun fibres (8% w/v at 70 PCL:30 Ge), which were chosen based on cell viability. Different concentrations of HA-CS nanoparticles were tested to determine cytotoxicity. A single dosage (1 × 10-2 mg/mL) was associated with higher cell proliferation compared with the cell-only control. This nanoparticle concentration was embedded into the electrospun fibres as either surface modification or blend. Fibres with blended NPs delivered a higher cell viability than unmodified fibres, while NP-coated fibres resulted in a higher cell proliferation (72 h) than the NP-blended ones. These biocompatible scaffolds allow cell attachment, maintain fibre arrangement, promote directional growth and yield higher cell viability.