Detection, prevention and treatment of COVID‐19 and opportunities for nanobiotechnology

Bao, Qing; Yang, Tao; Yang, Mingying; Mao, Chuanbin

doi:10.1002/viw.20200181

Cited by 14 publications

(6 citation statements)

References 133 publications

(269 reference statements)

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“…The limit of detection (LOD) with PdAu@Au CNCs was measured to be 10-50 µmol L −1 (Figure S11, Supporting Information), which is almost a tenth of the physiological concentration in bio-fluids (200-500 µmol L −1 ). [17] These results concluded that the PdAu@Au CNCs can induce notable signal enhancement in both SERS and LDI-MS detection compared to the monomeric counterparts.…”

Section: Designed Alloy Substrate Materials For Sers/ldi-ms Co-detectionmentioning

confidence: 84%

An Alloy Platform of Dual‐Fingerprints for High‐Performance Stroke Screening

Shu

Zhang

et al. 2022

Adv Funct Materials

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A multi‐modal serum profiling platform holds promise for precision diagnosis of diseases. Still, advanced tools are in demand to deliver the multi‐modal serum profiling. Herein, a bimodal spectrometric protocol is designed for stoke serum profiling using an alloy platform, by integrating label‐free surface‐enhanced Raman spectroscopy (SERS) and laser desorption/ionization mass spectrometry (LDI‐MS). The PdAu@Au concave cube with a wide localized surface plasmonic resonance (LSPR) range simultaneously enhances the signals from SERS and LDI‐MS, enabling high‐throughput co‐detection of vibrational and metabolic fingerprints of 0.1 µL serum in 2 min with simple pretreatment. Further, a dual‐fingerprints screening model of stroke is constructed, by adaptive machine learning with a programming nonlinear fitting model. The area under the curve are 0.949 (0.917–0.977, 95% confidence interval (CI)) and 0.911 (0.812–0.984, 95% CI), in the discovery and validation cohorts, respectively. Finally, five metabolites are identified that correlated to SERS signals and mapped the relevant pathways. This study features high performance in terms of throughput, speed, sample volume, and accuracy, providing new insight into the construction of multiplexed characterization platforms for precision diagnostic.

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Section: Designed Alloy Substrate Materials For Sers/ldi-ms Co-detectionmentioning

confidence: 84%

An Alloy Platform of Dual‐Fingerprints for High‐Performance Stroke Screening

Shu

Zhang

et al. 2022

Adv Funct Materials

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“…In this regard, NPs can be used as antiviral drug to treat various viral infections. The use of NPs provides an opportunity for the development of new antiviral therapies, and the possibility of drug resistance is very low compared with traditional chemical antiviral therapies (Bao et al, 2022; Gurunathan et al, 2020). These characteristics make them interesting candidate materials for therapeutic drugs or surface coatings to neutralize viruses (N. Lin et al, 2021).…”

Section: Application Of Zno Nmts To Viruses and Their Mechanisms Of A...mentioning

confidence: 99%

Biological mechanism of ZnO nanomaterials

et al. 2023

J of Applied Toxicology

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Modern nanotechnology has made zinc oxide nanomaterials (ZnO NMts) multifunctional, stable, and low cost, enabling them to be widely used in commercial and biomedical fields. With its wide application, the risk of human direct contact and their release into the environment also increases. This review aims to summarize the toxicity studies of ZnO NMts in vivo, including neurotoxicity, inhalation toxicity, and reproductive toxicity. The antibacterial and antiviral mechanisms of ZnO NMts in vitro and the toxicity to eukaryotic cells were summarized. The summary found that it was mainly related to reactive oxygen species (ROS) produced by oxidative stress. It also discusses the potential harm to body and the favorable prospects of the widespread use of antibacterial and antiviral in the future medical field. The review also emphasizes that the dosage and use method of ZnO NMts will be the focus of future biomedical research.

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“…PCR is the gold standard for clinical COVID-19 diagnosis but suffers from a high false-negative rate (9.3-16.7%) and time-consuming procedures (approximately hours), [38] which makes it essential to develop more accurate and faster diagnostic techniques for epidemic prevention purpose. [39] LDI MS based molecular approaches have been approved as a potential solution for COVID-19 diagnostics according to previous literature (accuracy of 90-99%), [9c,40] owing to the balance between analytical throughput, speed, and result performance. However, LDI MS based downstream metabolites analysis for COVID-19 diagnosis and metabolic-biomarker selection has not been reported yet.…”

Section: Sars-cov-2 Diagnosis and Metabolic-biomarker Panel Discoverymentioning

confidence: 99%

Deep Learning of Dual Plasma Fingerprints for High‐Performance Infection Classification

Cao

Xiao

Zhang

et al. 2022

Small

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Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host‐derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints‐based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550–0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs‐DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs‐DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID‐2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID‐19 management (AUC of 0.677–0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

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Detection, prevention and treatment of COVID‐19 and opportunities for nanobiotechnology

Cited by 14 publications

References 133 publications

An Alloy Platform of Dual‐Fingerprints for High‐Performance Stroke Screening

An Alloy Platform of Dual‐Fingerprints for High‐Performance Stroke Screening

Biological mechanism of ZnO nanomaterials

Deep Learning of Dual Plasma Fingerprints for High‐Performance Infection Classification

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