The ongoing global pandemic (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a huge public health issue. Hence, we devised a multiplex reverse transcription loop-mediated isothermal amplification (mRT-LAMP) coupled with a nanoparticle-based lateral flow biosensor (LFB) assay (mRT-LAMP-LFB) for diagnosing COVID-19. Using two LAMP primer sets, the ORF1ab (opening reading frame 1a/b) and N (nucleoprotein) genes of SARS-CoV-2 were simultaneously amplified in a single-tube reaction, and detected with the diagnosis results easily interpreted by LFB. In presence of FITC (fluorescein)-/digoxin- and biotin-labeled primers, mRT-LAMP produced numerous FITC-/digoxin- and biotin-attached duplex amplicons, which were determined by LFB through immunoreactions (FITC/digoxin on the duplex and anti-FITC/digoxin on the test line of LFB) and biotin/treptavidin interaction (biotin on the duplex and strptavidin on the polymerase nanoparticle). The accumulation of nanoparticles leaded a characteristic crimson band, enabling multiplex analysis of ORF1ab and N gene without instrumentation. The limit of detection (LoD) of COVID-19 mRT-LAMP-LFB was 12 copies (for each detection target) per reaction, and no cross-reactivity was generated from non-SARS-CoV-2 templates. The analytical sensitivity of SARS-CoV-2 was 100% (33/33 oropharynx swab samples collected from COVID-19 patients), and the assay's specificity was also 100% (96/96 oropharynx swab samples collected from non-COVID-19 patients). The total diagnostic test can be completed within 1 h from sample collection to result interpretation. In sum, the COVID-19 mRT-LAMP-LFB assay is a promising tool for diagnosing SARS-CoV-2 infections in frontline public health field and clinical laboratories, especially from resource-poor regions.

Enzymatic catalysis in living cells enables the in-situ detection of cellular metabolites in single cells, which could contribute to early diagnosis of diseases. In this study, enzyme is packaged in amorphous metal-organic frameworks (MOFs) via a one-pot co-precipitation process under ambient conditions, exhibiting 5–20 times higher apparent activity than when the enzyme is encapsulated in corresponding crystalline MOFs. Molecular simulation and cryo-electron tomography (Cryo-ET) combined with other techniques demonstrate that the mesopores generated in this disordered and fuzzy structure endow the packaged enzyme with high enzyme activity. The highly active glucose oxidase delivered by the amorphous MOF nanoparticles allows the noninvasive and facile measurement of glucose in single living cells, which can be used to distinguish between cancerous and normal cells.

Mimicking the cellular environment, metal-organic frameworks (MOFs) are promising for encapsulating enzymes for general applications in environments often unfavorable for native enzymes. Markedly different from previous researches based on bulk solution synthesis, here, we report the synthesis of enzyme-embedded MOFs in a microfluidic laminar flow. The continuously changed concentrations of MOF precursors in the gradient mixing on-chip resulted in structural defects in products. This defect-generating phenomenon enables multimodal pore size distribution in MOFs and therefore allows improved access of substrates to encapsulated enzymes while maintaining the protection to the enzymes. Thus, the as-produced enzyme-MOF composites showed much higher (~one order of magnitude) biological activity than those from conventional bulk solution synthesis. This work suggests that while microfluidic flow synthesis is currently underexplored, it is a promising strategy in producing highly active enzyme-MOF composites.

No abstract

Currently, blockchain technology, which is decentralized and may provide tamper-resistance to recorded data, is experiencing exponential growth in industry and research. In this paper, we propose the MIStore, a blockchain-based medical insurance storage system. Due to blockchain’s the property of tamper-resistance, MIStore may provide a high-credibility to users. In a basic instance of the system, there are a hospital, patient, insurance company and n servers. Specifically, the hospital performs a (t, n)-threshold MIStore protocol among the n servers. For the protocol, any node of the blockchain may join the protocol to be a server if the node and the hospital wish. Patient’s spending data is stored by the hospital in the blockchain and is protected by the n servers. Any t servers may help the insurance company to obtain a sum of a part of the patient’s spending data, which servers can perform homomorphic computations on. However, the n servers cannot learn anything from the patient’s spending data, which recorded in the blockchain, forever as long as more than n − t servers are honest. Besides, because most of verifications are performed by record-nodes and all related data is stored at the blockchain, thus the insurance company, servers and the hospital only need small memory and CPU. Finally, we deploy the MIStore on the Ethererum blockchain and give the corresponding performance evaluation.