Publications
7 results found
Kechagias S, Theodoridis K, Broomfield J, et al., 2023, The effect of nodal connectivity and strut density within stochastic titanium scaffolds on osteogenesis, Frontiers in Bioengineering and Biotechnology, Vol: 11, ISSN: 2296-4185
Modern orthopaedic implants use lattice structures that act as 3D scaffolds to enhance bone growth into and around implants. Stochastic scaffolds are of particular interest as they mimic the architecture of trabecular bone and can combine isotropic properties and adjustable structure. The existing research mainly concentrates on controlling the mechanical and biological performance of periodic lattices by adjusting pore size and shape. Still, less is known on how we can control the performance of stochastic lattices through their design parameters: nodal connectivity, strut density and strut thickness. To elucidate this, four lattice structures were evaluated with varied strut densities and connectivity, hence different local geometry and mechanical properties: low apparent modulus, high apparent modulus, and two with near-identical modulus. Pre-osteoblast murine cells were seeded on scaffolds and cultured in vitro for 28 days. Cell adhesion, proliferation and differentiation were evaluated. Additionally, the expression levels of key osteogenic biomarkers were used to assess the effect of each design parameter on the quality of newly formed tissue. The main finding was that increasing connectivity increased the rate of osteoblast maturation, tissue formation and mineralisation. In detail, doubling the connectivity, over fixed strut density, increased collagen type-I by 140%, increased osteopontin by 130% and osteocalcin by 110%. This was attributed to the increased number of acute angles formed by the numerous connected struts, which facilitated the organization of cells and accelerated the cell cycle. Overall, increasing connectivity and adjusting strut density is a novel technique to design stochastic structures which combine a broad range of biomimetic properties and rapid ossification.
Broomfield J, Kalofonou M, Franklin S, et al., 2023, Handheld ISFET Lab-on-Chip detection of TMPRSS2-ERG and AR mRNA for prostate cancer prognostics, IEEE Sensors Letters, Vol: 7, Pages: 1-4, ISSN: 2475-1472
Ion-sensitive field-effect transistors (ISFETs) in combination with unmodified complementary metal oxide semiconductors present a point-of-care platform for clinical diagnostics and prognostics. This work illustrates the sensitive and specific detection of two circulating mRNA markers for prostate cancer, the androgen receptor and the TMPRSS2-ERG fusion using a target-specific loop-mediated isothermal amplification method. TMPRSS2-ERG and androgen receptor RNA were detected down to 3x10 1 and 5x10 1 copies respectively in under 30 minutes. Administration of these assays onto the ISFET Lab-on-chip device was successful and the specificity of each marker was corroborated with mRNA extracted from prostate cancer cell lines.
Tripathi P, Gulli C, Broomfield J, et al., 2023, Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks., Computers in Biology and Medicine, Vol: 161, Pages: 1-11, ISSN: 0010-4825
The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to spectrograms is beneficial as it makes the data compatible with 2D convolutional neural networks and helps gain significant performance improvement over neural networks trained on the time domain data. The trained network achieves an accuracy of 84% with a size of 30kB making it suitable for deployment on edge devices. This facilitates a new wave of intelligent lab-on-chip platforms that combine microfluidics, CMOS-based chemical sensing arrays and AI-based edge solutions for more intelligent and rapid molecular diagnostics.
Broomfield J, Kalofonou M, Pataillot-Meakin T, et al., 2022, Detection of YAP1 and AR-V7 mRNA for prostate cancer prognosis using an ISFET lab-on-chip platform, ACS Sensors, Vol: 7, Pages: 3389-3398, ISSN: 2379-3694
Prostate cancer (PCa) is the second most common cause of male cancer-related death worldwide. The gold standard of treatment for advanced PCa is androgen deprivation therapy (ADT). However, eventual failure of ADT is common and leads to lethal metastatic castration-resistant PCa. As such, the detection of relevant biomarkers in the blood for drug resistance in metastatic castration-resistant PCa patients could lead to personalized treatment options. mRNA detection is often limited by the low specificity of qPCR assays which are restricted to specialized laboratories. Here, we present a novel reverse-transcription loop-mediated isothermal amplification assay and have demonstrated its capability for sensitive detection of AR-V7 and YAP1 RNA (3 × 101 RNA copies per reaction). This work presents a foundation for the detection of circulating mRNA in PCa on a non-invasive lab-on-chip device for use at the point-of-care. This technique was implemented onto a lab-on-chip platform integrating an array of chemical sensors (ion-sensitive field-effect transistors) for real-time detection of RNA. Detection of RNA presence was achieved through the translation of chemical signals into electrical readouts. Validation of this technique was conducted with rapid detection (<15 min) of extracted RNA from prostate cancer cell lines 22Rv1s and DU145s.
Broomfield J, Kalofonou M, Pataillot-Meakin T, et al., 2022, Detection of YAP1 and AR-V7 mRNA for Prostate Cancer prognosis using an ISFET Lab-On-Chip platform, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>Prostate cancer (PCa) is the second most common cause of male cancer-related death worldwide. The gold standard of treatment for advanced PCa is androgen deprivation therapy (ADT). However, eventual failure of ADT is common and leads to lethal metastatic castration resistant PCa (mCRPC). As such, the detection of relevant biomarkers in the blood for drug resistance in mCRPC patients could lead to personalized treatment options. mRNA detection is often limited by the low specificity of qPCR assays which are restricted to specialised laboratories. Here, we present a novel reversetranscription loop-mediated isothermal amplification (RT-LAMP) assay and have demonstrated its capability for sensitive detection of AR-V7 and YAP1 RNA (3×10<jats:sup>1</jats:sup> RNA copies per reaction). This work presents a foundation for the detection of circulating mRNA in PCa on a non-invasive Lab-on-chip (LoC) device for use at point-of-care. This technique was implemented onto a Lab-on-Chip platform integrating an array of chemical sensors (ion-sensitive field-effect transistors - ISFETs) for real-time detection of RNA. Detection of RNA presence was achieved through the translation of chemical signals into electrical readouts. Validation of this technique was conducted with rapid detection (<jats:italic><</jats:italic>15 min) of extracted RNA from prostate cancer cell lines 22Rv1s and DU145s.</jats:p>
Roddan R, Subrizi F, Broomfield J, et al., 2021, Chemoenzymatic Cascades toward Methylated Tetrahydroprotoberberine and Protoberberine Alkaloids, ORGANIC LETTERS, Vol: 23, Pages: 6342-6347, ISSN: 1523-7060
Roddan R, Sula A, Mendez-Sanchez D, et al., 2020, Single step syntheses of (1<i>S</i>)-aryl-tetrahydroisoquinolines by norcoclaurine synthases, COMMUNICATIONS CHEMISTRY, Vol: 3, ISSN: 2399-3669
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