scholarly journals Automated Parallel Pattern Search Optimisation of Microfluidic Geometry for Extracellular Vesicle Liquid Biopsies

2021 ◽  
Author(s):  
Colin L Hisey ◽  
AJ Tyler ◽  
Arvin Lim ◽  
Lawrence W Chamley ◽  
Cherie Blenkiron ◽  
...  
Keyword(s):  
High Performance ◽  
Disease Diagnosis ◽  
Extracellular Vesicle ◽  
Pattern Search ◽  
Microfluidic Channels ◽  
Liquid Biopsies ◽  
Pillar Arrays ◽  
Unit Cells ◽  
Parallel Pattern ◽  
Elution Method

Microfluidic liquid biopsies using affinity-based capture of extracellular vesicles (EVs) have demonstrated great potential for providing rapid disease diagnosis and monitoring. However, little effort has been devoted to optimising the geometry of the microfluidic channels for maximum EV capture due to the inherent challenges of physically testing many geometric designs. To address this, we developed an automated parallel pattern search (PPS) optimiser by combining a Python optimiser, COMSOL Multiphysics, and high performance computing. This unique approach was applied to a triangular micropillar array geometry by parameterising repeating unit cells, making several assumptions, and optimising for maximum particle capture efficiency. We successfully optimised the triangular pillar arrays and surprisingly found that simply maximising the total number of pillars and effective surface area did not result in maximum EV capture, as devices with slightly larger pillars and more spacing between pillars allowed contact with slower moving EVs that followed the pillar contours more closely. We then experimentally validated this finding using bioreactor-produced EVs in the best and worst channel designs that were functionalised with an antibody against CD63. Captured EVs were quantified using a fluorescent plate reader, followed by an established elution method and nanoparticle tracking analysis. These results demonstrate the power of automated microfluidic geometry optimisations for EV liquid biopsies and will support further development of this rapidly growing field.

Analysis of organic impurities of besifloxacin hydrochloride by high performance liquid chromatography with isocratic and gradient elution.

2019 ◽  
Vol 16 ◽  
Author(s):  
Joanna Wittckind Manoel ◽  
Camila Ferrazza Alves Giordani ◽  
Livia Maronesi Bueno ◽  
Sarah Chagas Campanharo ◽  
Elfrides Eva Sherman Schapoval ◽  
...  
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Mobile Phase ◽  
High Performance ◽  
Gradient Elution ◽  
Pharmaceutical Product ◽  
Raw Material ◽  
Isocratic Elution ◽  
Organic Impurities ◽  
Elution Method

Introduction: Impurity analysis is an important step in the quality control of pharmaceutical ingredients and final product. Impurities can arise from drug synthesis or excipients and even at small concentrations may affect product efficacy and safety. In this work two methods using high performance liquid chromatography (HPLC) were developed and validated for the evaluation of besifloxacin and its impurity synthesis, with isocratic elution and another with gradient elution. Method: The analysis by HPLC in isocratic elution mode was performed using a cyano column maintained at 25 °C. The mobile phase was composed by 0.5% triethylamine (pH 3.0): acetonitrile (88:12 v/v) eluted at a flow rate of 1.0 ml/min with detection at 330 nm. The gradient elution method was carried out with the same column and mobile phase components only modifying the rate between organic and aqueous phase during analysis. The procedures have been validated according to internationally accepted guidelines, observing results within acceptable limits. Results: The methods presented were found to be linear in the 140 to 260 µg/ml range for besifloxacin and 0.3 to 2.3 µg/ml for an impurity named A. The limits of detection and quantification were respectively 0.07 and 0.3 µg/ml for impurity A, with a 20 µL injection volume. The precision achieved for all analyses performed provided RSD inter-day equal to 6.47 and 6.36% for impurity A with isocratic elution and gradient, respectively. The accuracy was higher than 99% and robustness exhibited satisfactory results. In the isocratic method an analysis time of 25 min and 15 min was obtained for gradient. For impurity A, the number of theoretical plates in the isocratic mode was about 5000 while in the gradient mode it was about 45000, hence, it made the column more efficient by changing the mobile phase composition during elution. In besifloxacin raw material and in pharmaceutical product used in this study, other related impurities were present but but impurity A was searched for and not detected Conclusion: The proposed methods can be applied for quantitative determination of impurities in the analysis of the besifloxacin raw material, as well as in ophthalmic suspension of the drug, considering the quantitation limit.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

Protein biomarkers in breast cancer-derived extracellular vesicles for use in liquid biopsies

2021 ◽  
Author(s):  
Dan Li ◽  
Wenjia Lai ◽  
Di Fan ◽  
Qiaojun Fang
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Extracellular Vesicles ◽  
Liquid Biopsy ◽  
Breast Cancer Diagnosis ◽  
Extracellular Vesicle ◽  
Detection Methods ◽  
Protein Markers ◽  
Liquid Biopsies ◽  
Diagnosis And Prognosis

Breast cancer is the most common malignant disease in women worldwide. Early diagnosis and treatment can greatly improve the management of breast cancer. Liquid biopsies are becoming convenient detection methods for diagnosing and monitoring breast cancer due to their non-invasiveness and ability to provide real-time feedback. A range of liquid biopsy markers, including circulating tumor proteins, circulating tumor cells, and circulating tumor nucleic acids, have been implemented for breast cancer diagnosis and prognosis, with each having its own advantages and limitations. Circulating extracellular vesicles are messengers of intercellular communication that are packed with information from mother cells and are found in a wide variety of bodily fluids; thus, they are emerging as ideal candidates for liquid biopsy biomarkers. In this review, we summarize extracellular vesicle protein markers that can be potentially used for the early diagnosis and prognosis of breast cancer or determining its specific subtypes.

Effects of embedded sub-micron pillar arrays in microfluidic channels on large DNA electrophoresis

2009 ◽  
Vol 30 (18) ◽  
pp. 3242-3249 ◽  
Author(s):  
Yick Chuen Chan ◽  
Yitshak Zohar ◽  
Yi-Kuen Lee
Keyword(s):  
Microfluidic Channels ◽  
Dna Electrophoresis ◽  
Pillar Arrays

scholarly journals Small extracellular vesicle-encapsulated miR-181b-5p, miR-222-3p and let-7a-5p: Next generation plasma biopsy-based diagnostic biomarkers for inflammatory breast cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250642
Author(s):  
Sarah Hamdy Ahmed ◽  
Nancy A. Espinoza-Sánchez ◽  
Ahmed El-Damen ◽  
Sarah Atef Fahim ◽  
Mohamed A. Badawy ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Inflammatory Breast Cancer ◽  
Roc Curves ◽  
Cost Effective ◽  
Extracellular Vesicle ◽  
Lymphatic Invasion ◽  
Precipitation Method ◽  
Liquid Biopsies ◽  
Diagnostic Biomarkers

Inflammatory breast cancer (IBC) is a rare, but aggressive entity of breast carcinoma with rapid dermal lymphatic invasion in young females. It is either poorly or misdiagnosed as mastitis because of the absence of a distinct lump. Small extracellular vesicles (sEVs) circulating in liquid biopsies are a novel class of minimally invasive diagnostic alternative to invasive tissue biopsies. They modulate cancer progression via shuttling their encapsulated cargo including microRNAs (miRNAs) into recipient cells to either trigger signaling or induce malignant transformation of targeted cells. Plasma sEVs < 200 nm were isolated using a modified cost-effective polyethylene glycol (PEG)-based precipitation method and compared to standard methods, namely ultracentrifugation and a commercial kit, where the successful isolation was verified by different approaches. We evaluated the expression levels of selected sEV-derived miR-181b-5p, miR-222-3p and let-7a-5p using quantitative real PCR (qPCR). Relative to non-IBC, our qPCR data showed that sEV-derived miR-181b-5p and miR-222-3p were significantly upregulated, whereas let-7a-5p was downregulated in IBC patients. Interestingly, receiver operating characteristic (ROC) curves analysis revealed that diagnostic accuracy of let-7a-5p alone was the highest for IBC with an area under curve (AUC) value of 0.9188, and when combined with miR-222-3p the AUC was improved to 0.973. Further, 38 hub genes were identified using bioinformatics analysis. Together, circulating sEV-derived miR-181b-5p, miR-222-3p and let-7a-5p serve as promising non-invasive diagnostic biomarkers for IBC.

scholarly journals Wnt Activity and Cell Proliferation Are Coupled to Extracellular Vesicle Release in Multiple Organoid Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Gyöngyvér Orsolya Sándor ◽  
András Áron Soós ◽  
Péter Lörincz ◽  
Lívia Rojkó ◽  
Tünde Harkó ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Disease Diagnosis ◽  
Extracellular Vesicle ◽  
Cellular Heterogeneity ◽  
Ductal Adenocarcinoma ◽  
Diagnostic Tools ◽  
Proliferating Cells ◽  
Pathway Activation ◽  
A Cell ◽  
Potential Tool

Extracellular vesicles (EV) are considered as a potential tool for early disease diagnosis; however, factors modifying EV release remain partially unknown. By using patient-derived organoids that capture the cellular heterogeneity of epithelial tissues, here we studied the connection between the Wnt-producing microniche and EV secretion in multiple tissues. Although nearly all cells in pancreatic ductal (PD) and pancreatic ductal adenocarcinoma (PDAC) samples expressed porcupine (PORCN), an enzyme critical for Wnt secretion, only a subpopulation of lung bronchiolar (NL) and lung adenocarcinoma (LUAD) organoid cells produced active Wnt. The microniche for proliferating cells was shaped not only by PORCN + cells in NL and LUAD organoids but also by fibroblast-derived EVs. This effect could be blocked by using Wnt secretion inhibitors. Whereas inhibiting Wnt secretion in PD NL or LUAD organoids critically changed both cell proliferation and EV release, these were uncoupled from each other in PDAC. Sorting for CD133 identified a cell population in the LUAD microniche that produced organoids with a high percentage of PORCN + and proliferating cells and an elevated EV secretion, which may explain that CD133 marks LUAD cells with malignant behavior. Collectively, we show here that high cell proliferation rate, induced by Wnt pathway activation, is coupled to a higher EV release, a critical finding that may be considered when developing EV-based diagnostic tools.

scholarly journals Meta-neural-network for real-time and passive deep-learning-based object recognition

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingkai Weng ◽  
Yujiang Ding ◽  
Chengbo Hu ◽  
Xue-Feng Zhu ◽  
Bin Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Acoustic Scattering ◽  
Scattered Wave ◽  
Disease Diagnosis ◽  
Great Success ◽  
Destructive Testing ◽  
Unit Cells ◽  
Handwritten Digit

AbstractAnalyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.

scholarly journals An miRNA fingerprint using neural-enriched extracellular vesicles from blood plasma: towards a biomarker for amyotrophic lateral sclerosis/motor neuron disease

Open Biology ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 200116 ◽  
Author(s):  
Sandra Anne Banack ◽  
Rachael Anne Dunlop ◽  
Paul Alan Cox
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Blood Plasma ◽  
Motor Neuron ◽  
Motor Neuron Disease ◽  
Extracellular Vesicles ◽  
Disease Diagnosis ◽  
Extracellular Vesicle ◽  
Improve Patient ◽  
Lateral Sclerosis

Biomarkers for amyotrophic lateral sclerosis/motor neuron disease (ALS/MND) are currently not clinically available for disease diagnosis or analysis of disease progression. If identified, biomarkers could improve patient outcomes by enabling early intervention and assist in the determination of treatment efficacy. We hypothesized that neural-enriched extracellular vesicles could provide microRNA (miRNA) fingerprints with unequivocal signatures of neurodegeneration. Using blood plasma from ALS/MND patients and controls, we extracted neural-enriched extracellular vesicle fractions and conducted next-generation sequencing and qPCR of miRNA components of the transcriptome. We here report eight miRNA sequences which significantly distinguish ALS/MND patients from controls in a replicated experiment using a second cohort of patients and controls. miRNA sequences from patient blood samples using neural-enriched extracellular vesicles may yield unique insights into mechanisms of neurodegeneration and assist in early diagnosis of ALS/MND.

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