ABSTRACT
Due to the COVID-19 pandemic, majority of the Biomedical Science students were not able to undergo their clinical internship at diagnostic laboratories and this has created an impact on students’ skills and the future of the Malaysian healthcare system. Hence, our objective was to implement arevolutionized Biomedical Science practicum completely in a virtual environment, without compromising the learning outcomes during the pandemic in 2021. To achieve the intended learning outcomes, various online teaching-learning and assessment activities were carefully curated in accordance to standard program guidelines, learning outcomes, student learning time and thorough analysis of actual student logbooks. Learning materials were reinforced with various initiatives such as actual engagements with real-life scenarios via synchronous meetings with external panelists from hospitals. Online video-log (Vlog) and a logbook of daily activities were used as part of the assessment to ensure that students were able to learn and reflect on the activities performed. The study showed that all students displayed increased confidence levels in medical laboratory skills. They were also able to apply them in real-life situations due to the clear instructions and realistic experience via the virtual learning activities. Therefore, students who participated in the virtual practicum demonstrated almost similar levels of performance when compared to the students who went for physical practicums in the year 2020. Our virtual practicum has achieved its intended outcomes of empowering students with similar skills as those who underwent physical clinical placements in diagnostic laboratories. Those skills include successful acquisition of discipline-specific knowledge, collaborative and communication skills, as well as solid experimental methods and good laboratory practices. © 2022 by the authors.
ABSTRACT
In this article, we introduce a new community-contributed command called xtbunitroot, which implements the panel-data unit-root tests developed by Karavias and Tzavalis (2014, Computational Statistics and Data Analysis 76: 391-407). These tests allow for one or two structural breaks in deterministic components of the series and can be seen as panel-data counterparts of the tests by Zivot and Andrews (1992, Journal of Business and Economic Statistics 10: 251-270) and Lumsdaine and Papell (1997, Review of Economics and Statistics 79: 212-218). The dates of the breaks can be known or unknown. The tests allow for intercepts and linear trends, nonnormal errors, and cross-section heteroskedasticity and dependence. They have power against homogeneous and heterogeneous alternatives and can be applied to panels with small or large time-series dimensions.
ABSTRACT
Machine learning-based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery. Here we propose QMO, a generic query-based molecule optimization framework that exploits latent embeddings from a molecule autoencoder. QMO improves the desired properties of an input molecule based on efficient queries, guided by a set of molecular property predictions and evaluation metrics. We show that QMO outperforms existing methods in the benchmark tasks of optimizing small organic molecules for drug-likeness and solubility under similarity constraints. We also demonstrate substantial property improvement using QMO on two new and challenging tasks that are also important in real-world discovery problems: (1) optimizing existing potential SARS-CoV-2 main protease inhibitors towards higher binding affinity and (2) improving known antimicrobial peptides towards lower toxicity. Results from QMO show high consistency with external validations, suggesting an effective means to facilitate material optimization problems with design constraints. Zeroth-order optimization is used on problems where no explicit gradient function is accessible, but single points can be queried. Hoffman et al. present here a molecular design method that uses zeroth-order optimization to deal with the discreteness of molecule sequences and to incorporate external guidance from property evaluations and design constraints.
ABSTRACT
Purpose: To investigate the bio-active components and the potential mechanism of the prescription remedy, Han-Shi blocking lung, with network pharmacology with a view to expanding its application. Methods: Chemical components were first collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Pharmmapper database and GeneCards were used to predict the targets related to active components and COVID-19. Using DAVIDE and KOBAS 3.0 databases, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched. A "components-targets-pathways" (C-T-P) network was conducted by Cytoscape 3.7.1 software. With the aid of Discovery Studio 2016 software, bio-active components were selected to dock with SARS-COV-2 3CL and ACE2. Results: From the prescription, 47 bio-active components, 83 targets and 103 signaling pathways were obtained in total (p < 0.05). 126 GO entries (p < 0.05) were screened by GO enrichment analysis. Molecular docking results showed that procyanidin B1 eriodictyol, (4E, 6E)-1, 7-bis(4-hydroxyphenyl)hepta-4, 6-dien-3-one, and quercetin had higher docking scores with SARS-COV-2 3CL and ACE2. Conclusion: With network pharmacology and molecular docking, the bio-active components and targets of this prescription, Han-Shi blocking lung, against COVID-19 were identified. Taken together, this study provided a basis for the treatment of COVID-19 and further promotion of this prescription.
ABSTRACT
Introduction: Angong Niuhuang Pills (AGNH), a Chinese patent medicine recommended in the “Diagnosis and Treatment Plan for COVID-19 (8th Edition),” may be clinically effective in treating COVID-19. The active components and signal pathways of AGNH through network pharmacology have been examined, and its potential mechanisms determined. Methods: We screened the components in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) via Drug-like properties (DL) and Oral bioavailability (OB);PharmMapper and GeneCards databases were used to collect components and COVID-19 related targets;KEGG pathway annotation and GO bioinformatics analysis were based on KOBAS3.0 database;“herb-components-targets-pathways” (H-C-T-P) network and protein-protein interaction network (PPI) were constructed by Cytoscape 3.6.1 software and STRING 10.5 database;we utilized virtual molecular docking to predict the binding ability of the active components and key proteins. Results: A total of 87 components and 40 targets were screened in AGNH. The molecular docking results showed that the docking scores of the top 3 active components and the targets were all greater than 90. Conclusion: Through network pharmacology research, we found that moslosooflavone, oroxylin A, and salvigenin in AGNH can combine with ACE2 and 3CL, and then are involved in the MAPK and JAK-STAT signaling pathways. Finally, it is suggested that AGNH may have a role in the treatment of COVID-19.