A review of current effective COVID-19 testing methods and quality control.

COVID-19 is a highly infectious disease caused by the SARS-CoV-2 virus, which primarily affects the respiratory system and can lead to severe illness. The virus is extremely contagious, early and accurate diagnosis of SARS-CoV-2 is crucial to contain its spread, to provide prompt treatment, and to prevent complications. Currently, the reverse transcriptase polymerase chain reaction (RT-PCR) is considered to be the gold standard for detecting COVID-19 in its early stages. In addition, loop-mediated isothermal amplification (LMAP), clustering rule interval short palindromic repeats (CRISPR), colloidal gold immunochromatographic assay (GICA), computed tomography (CT), and electrochemical sensors are also common tests. However, these different methods vary greatly in terms of their detection efficiency, specificity, accuracy, sensitivity, cost, and throughput. Besides, most of the current detection methods are conducted in central hospitals and laboratories, which is a great challenge for remote and underdeveloped areas. Therefore, it is essential to review the advantages and disadvantages of different COVID-19 detection methods, as well as the technology that can enhance detection efficiency and improve detection quality in greater details.

Vaccination with Span, an antigen guided by SARS-CoV-2 S protein evolution, protects against challenge with viral variants in mice.

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (Span). Span was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined Span protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (Swt) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, Span vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous Span booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

Knowledge ecosystem of financial markets: a new perspective on alleviating the supply-demand contradiction of the rural finance in the post-COVID-19 era

Purpose This paper aims to establish a systematic cognition to alleviate the supply-demand contradiction in rural financial markets from an integrated perspective of knowledge management and proposes the concept of rural financial knowledge ecosystem (RFKE) to encourage multifaceted solutions. Design/methodology/approach The authors qualitatively describe the process that the knowledge management dilemmas cause the supply-demand contradiction in the rural finance and further summarize a systematic methodology from three dimensions: the knowledge subject, the knowledge environment and the knowledge ecology. Findings The authors list four types of knowledge management dilemmas leading to the supply-demand contradiction in the rural finance, i.e. the weak knowledge sharing, the poor knowledge flow, the slow knowledge updating and the imperfect knowledge environment. Meanwhile, the RFKE model consisting of the ecological subject, the ecological environment and the ecological regulation is also presented. Research limitations/implications The role of knowledge management in improving the allocation of financial resources to various rural financial market participants (government, rural financial institutions, farmers, agricultural enterprises, etc.). Originality/value The authors creatively give the RFKE model, which complements and enriches the theory of knowledge management. Meanwhile, relevant management practices are urgently needed under the macro circumstance of the COVID-19 pandemic and the rural revitalization in China.

Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD);(2) EHG recording time to delivery (TTD) ≤ 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD ≤ 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.

Screening of potent neutralizing antibodies against SARS-CoV-2 using convalescent patients-derived phage-display libraries.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to threaten public health worldwide, the development of effective interventions is urgently needed. Neutralizing antibodies (nAbs) have great potential for the prevention and treatment of SARS-CoV-2 infection. In this study, ten nAbs were isolated from two phage-display immune libraries constructed from the pooled PBMCs of eight COVID-19 convalescent patients. Eight of them, consisting of heavy chains encoded by the immunoglobulin heavy-chain gene-variable region (IGHV)3-66 or IGHV3-53 genes, recognized the same epitope on the receptor-binding domain (RBD), while the remaining two bound to different epitopes. Among the ten antibodies, 2B11 exhibited the highest affinity and neutralization potency against the original wild-type (WT) SARS-CoV-2 virus (KD = 4.76 nM for the S1 protein, IC50 = 6 ng/mL for pseudoviruses, and IC50 = 1 ng/mL for authentic viruses), and potent neutralizing ability against B.1.1.7 pseudoviruses. Furthermore, 1E10, targeting a distinct epitope on RBD, exhibited different neutralization efficiency against WT SARS-CoV-2 and its variants B.1.1.7, B.1.351, and P.1. The crystal structure of the 2B11-RBD complexes revealed that the epitope of 2B11 highly overlaps with the ACE2-binding site. The in vivo experiment of 2B11 using AdV5-hACE2-transduced mice showed encouraging therapeutic and prophylactic efficacy against SARS-CoV-2. Taken together, our results suggest that the highly potent SARS-CoV-2-neutralizing antibody, 2B11, could be used against the WT SARS-CoV-2 and B.1.1.7 variant, or in combination with a different epitope-targeted neutralizing antibody, such as 1E10, against SARS-CoV-2 variants.

MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection.

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.