Progress in COVID research and developments during pandemic

Label-Free Mapping of Multivalent Binding Pathways with Ligand-Receptor-Anchored Nanopores

Understanding single-molecule multivalent ligand-receptor interactions is crucial for comprehending molecular recognition at biological interfaces. However, label-free identifications of these transient interactions during multistep binding processes remains challenging. Herein, we introduce a ligand-receptor-anchored nanopore that allows the protein to maintain structural flexibility and favorable orientations in native states, mapping dynamic multivalent interactions. Using a four-state Markov chain model, we clarify two concentration-dependent binding pathways for the Omicron spike protein (Omicron S) and soluble angiotensin-converting enzyme 2 (sACE2): sequential and concurrent. Real-time kinetic analysis at the single-monomeric subunit level reveals that three S1 monomers of Omicron S exhibit a consistent and robust binding affinity toward sACE2 (-13.1 ± 0.2 kcal/mol). These results highlight the enhanced infectivity of Omicron S compared to other homologous spike proteins (WT S and Delta S). Notably, the preceding binding of sACE2 to Omicron S facilitates the subsequent binding steps, which was previously obscured in bulk measurements. Our single-molecule studies resolve the controversy over the disparity between the measured spike protein binding affinity with sACE2 and the viral infectivity, offering valuable insights for drug design and therapies.

Decoding Biomechanical Cues Based on DNA Sensors

Biological systems perceive and respond to mechanical forces, generating mechanical cues to regulate life processes. Analyzing biomechanical forces has profound significance for understanding biological functions. Therefore, a series of molecular mechanical techniques have been developed, mainly including single-molecule force spectroscopy, traction force microscopy, and molecular tension sensor systems, which provide indispensable tools for advancing the field of mechanobiology. DNA molecules with a programmable structure and well-defined mechanical characteristics have attached much attention to molecular tension sensors as sensing elements, and are designed for the study of biomechanical forces to present biomechanical information with high sensitivity and resolution. In this work, a comprehensive overview of molecular mechanical technology is presented, with a particular focus on molecular tension sensor systems, specifically those based on DNA. Finally, the future development and challenges of DNA-based molecular tension sensor systems are looked upon.

Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods

Background and Aims

Due to the COVID-19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID-19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID-19.

Methods

We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID-19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.

Results

A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge-based CDSS (n = 52) and knowledge-based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.

Conclusion

CDSS for COVID-19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision-making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge-based and knowledge-based CDSS in COVID-19 diagnosis.

Rapid and simultaneous multiple detection of a tripledemic using a dual-gate oxide semiconductor thin-film transistor-based immunosensor

The simultaneous infection with a tripledemic-simultaneous infection with influenza A pH1N1 virus (Flu), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV)-necessitates the development of accurate and fast multiplex diagnostic tests. The coronavirus disease 2019 (COVID-19) pandemic has emphasized the importance of virus detection. Field-effect transistor (FET)-based immuno-biosensors have a short detection time and do not require labeling or polymerase chain reaction. This study demonstrates the rapid, sensitive detection of influenza A pH1N1, SARS-CoV-2, and RSV using a multiplex immunosensor based on a dual-gate oxide semiconductor thin-film transistor (TFT), a type of FET. The dual-gate oxide TFT was modified by adjusting both top and bottom gate insulators to improve capacitive coupling to approximately 120-fold amplification, exhibiting a high pH sensitivity of about 10 V/pH. The dual-gate oxide TFT-based immunosensor detected the target proteins (hemagglutinin (HA) protein of Flu, spike 1 (S1) protein of SARS-CoV-2, and fusion protein of RSV) of each virus, with a limit of detection of approximately 1 fg/mL. Cultured viruses in phosphate-buffered saline or artificial saliva and clinical nasopharynx samples were detected in 1-μL sample volumes within 60 s. This promising diagnosis could be potentially as point-of-care tests to facilitate a prompt response to future pandemics with high sensitivity and multiplexed detection without pretreatment.

Chemical profile of anti-epidemic sachet based on multiple sample preparation coupled with gas chromatography-mass spectrometry analysis combined with an embedded peaks resolution method and their action mechanisms

The anti-epidemic sachet (Fang Yi Xiang Nang, FYXN) in traditional Chinese medicine (TCM) can prevent COVID-19 through volatile compounds that can play the role of fragrant and dampness, heat-clearing and detoxifying, warding off filth and pathogenic factors. Nevertheless, the anti-(mutant) SARS-CoV-2 compounds and the compounds related to the mechanism in vivo, and the mechanism of FYXN are still vague. In this study, the volatile compound set of FYXN was constructed by gas chromatography-mass spectrometry (GC-MS) based on multiple sample preparation methods, which include headspace (HS), headspace solid phase microextraction (HS-SPME) and pressurized liquid extraction (PLE). In addition, selective ion analysis (SIA) was used to resolve embedded chromatographic peaks present in HS-SPME results. Preliminary analysis of active compounds and mechanism of FYXN by network pharmacology combined with disease pathway information based on GC-MS results. A total of 96 volatile compounds in FYXN were collected by GC-MS analysis. 39 potential anti-viral compounds were screened by molecular docking. 13 key pathways were obtained by KEGG pathway analysis (PI3K-Akt signaling pathway, HIF-1 signaling pathway, etc.) for FYXN to prevent COVID-19. 16 anti-viral compounds (C95, C91, etc.), 10 core targets (RELA, MAPK1, etc.), and 16 key compounds related to the mechanism in vivo (C56, C30, etc.) were obtained by network analysis. The relevant pharmacological effects of key pathways and key compounds were verified by the literature. Finally, molecular docking was used to verify the relationship between core targets and key compounds, which are related to the mechanism in vivo. A variety of sample preparation methods coupled with GC-MS analysis combined with an embedded peaks resolution method and integrated with network pharmacology can not only comprehensively characterize the volatile compounds in FYXN, but also expand the network pharmacology research ideas, and help to discover the active compounds and mechanisms in FYXN.