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In this work, two exercises are described that are designed to teach students about the evolution and behavior of the electronic bands of graphene and bilayer graphene. These exercises involve performing extended Hückel molecular orbital theory calculations on polyacenes and polycyclic aromatic hydrocarbons. In the first exercise, students investigate how the molecular orbitals of polyacenes converge into bands as polyacene size increases. Further, students learn that long-range interactions cause frontier-orbital crossing as the size of the polyacene increases. In the second exercise, the concepts of band structures, band crossing, and k-space are explored using the results of frontier orbital calculations on [pi]-stacked dimers of polycyclic aromatic hydrocarbons, which represent molecular analogues of layered 2D materials. The results of these calculations show how the geometry and layer-layer offset of the dimer system can affect its electronic structure, and the results can be extrapolated to provide a framework for understanding why subtle changes in the relative orientations of the layers in bilayer graphene can produce qualitative changes in the electronic properties. These calculations are easily implemented with Python or with widely available algebraic manipulation software such as Maple or Mathematica. These exercises are accessible to students who have experience with extended Hückel molecular orbital theory and are suitable for inclusion in the physical chemistry curriculum at the upper-level undergraduate or introductory graduate level.

BACKGROUND. SARS-CoV-2 plasma viremia has been associated with severe disease and death in COVID-19 in small-scale cohort studies. The mechanisms behind this association remain elusive. METHODS. We evaluated the relationship between SARS-CoV-2 viremia, disease outcome, and inflammatory and proteomic profiles in a cohort of COVID-19 emergency department participants. SARS-CoV-2 viral load was measured using a quantitative reverse transcription PCR-based platform. Proteomic data were generated with Proximity Extension Assay using the Olink platform. RESULTS. This study included 300 participants with nucleic acid test-confirmed COVID-19. Plasma SARS-CoV-2 viremia levels at the time of presentation predicted adverse disease outcomes, with an adjusted OR of 10.6 (95% CI 4.4-25.5, P < 0.001) for severe disease (mechanical ventilation and/or 28-day mortality) and 3.9 (95% CI 1.5-10.1, P = 0.006) for 28-day mortality. Proteomic analyses revealed prominent proteomic pathways associated with SARS- CoV-2 viremia, including upregulation of SARS-CoV-2 entry factors (ACE2, CTSL, FURIN), heightened markers of tissue damage to the lungs, gastrointestinal tract, and endothelium/vasculature, and alterations in coagulation pathways. CONCLUSION. These results highlight the cascade of vascular and tissue damage associated with SARS-CoV-2 plasma viremia that underlies its ability to predict COVID-19 disease outcomes. FUNDING. Mark and Lisa Schwartz; the National Institutes of Health (U19AI082630); the American Lung Association; the Executive Committee on Research at Massachusetts General Hospital; the Chan Zuckerberg Initiative; Arthur, Sandra, and Sarah Irving for the David P. Ryan, MD, Endowed Chair in Cancer Research; an EMBO Long-Term Fellowship (ALTF 486-2018); a Cancer Research Institute/Bristol Myers Squibb Fellowship (CRI2993); the Harvard Catalyst/Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, NIH awards UL1TR001102 and UL1TR002541-01); and by the Harvard University Center for AIDS Research (National Institute of Allergy and Infectious Diseases, 5P30AI060354)., Introduction With coronavirus disease 2019 (COVID-19) causing over 2 million deaths globally by early 2021 (1), there remains an urgent need to elucidate disease pathogenesis to improve clinical management and [...]

Macrophages use an array of innate immune sensors to detect intracellular pathogens and to tailor effective antimicrobial responses. In addition, extrinsic activation with the cytokine IFN‐γ is often required as well to tip the scales of the host‐pathogen balance toward pathogen restriction. However, little is known about how host‐pathogen sensing impacts the antimicrobial IFN‐γ‐activated state. It was observed that in the absence of IRF3, a key downstream component of pathogen sensing pathways, IFN‐γ‐primed macrophages more efficiently restricted the intracellular bacterium Legionella pneumophila and the intracellular protozoan parasite Trypanosoma cruzi. This effect did not require IFNAR, the receptor for Type I IFNs known to be induced by IRF3, nor the sensing adaptors MyD88/TRIF, MAVS, or STING. This effect also did not involve differential activation of STAT1, the major signaling protein downstream of both Type 1 and Type 2 IFN receptors. IRF3‐deficient macrophages displayed a significantly altered IFN‐γ‐induced gene expression program, with up‐regulation of microbial restriction factors such as Nos2. Finally, we found that IFN‐γ‐primed but not unprimed macrophages largely excluded the activated form of IRF3 from the nucleus following bacterial infection. These data are consistent with a relationship of mutual inhibition between IRF3 and IFN‐γ‐activated programs, possibly as a component of a partially reversible mechanism for modulating the activity of potent innate immune effectors (such as Nos2) in the context of intracellular infection. [ABSTRACT FROM AUTHOR]

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) plasma viremia has been associated with severe disease and death in coronavirus disease 2019 (COVID-19) in small-scale cohort studies. The mechanisms behind this association remain elusive., Methods: We evaluated the relationship between SARS-CoV-2 viremia, disease outcome, inflammatory and proteomic profiles in a cohort of COVID-19 emergency department participants. SARS-CoV-2 viral load was measured using qRT-PCR based platform. Proteomic data were generated with Proximity Extension Assay (PEA) using the Olink platform., Results: Three hundred participants with nucleic acid test-confirmed COVID-19 were included in this study. Levels of plasma SARS-CoV-2 viremia at the time of presentation predicted adverse disease outcomes, with an adjusted odds ratio (aOR) of 10.6 (95% confidence interval [CI] 4.4, 25.5, P<0.001) for severe disease (mechanical ventilation and/or 28-day mortality) and aOR of 3.9 (95%CI 1.5, 10.1, P=0.006) for 28-day mortality. Proteomic analyses revealed prominent proteomic pathways associated with SARS-CoV-2 viremia, including upregulation of SARS-CoV-2 entry factors (ACE2, CTSL, FURIN), heightened markers of tissue damage to the lungs, gastrointestinal tract, endothelium/vasculature and alterations in coagulation pathways., Conclusions: These results highlight the cascade of vascular and tissue damage associated with SARS-CoV-2 plasma viremia that underlies its ability to predict COVID-19 disease outcomes.

COVID-19 has caused over 1 million deaths globally, yet the cellular mechanisms underlying severe disease remain poorly understood. By analyzing several thousand plasma proteins in 306 COVID-19 patients and 78 symptomatic controls over serial timepoints using two complementary approaches, we uncover COVID-19 host immune and non-immune proteins not previously linked to this disease. Integration of plasma proteomics with nine published scRNAseq datasets shows that SARS-CoV-2 infection upregulates monocyte/macrophage, plasmablast, and T cell effector proteins. By comparing patients who died to severely ill patients who survived, we identify dynamic immunomodulatory and tissue-associated proteins associated with survival, providing insights into which host responses are beneficial and which are detrimental to survival. We identify intracellular death signatures from specific tissues and cell types, and by associating these with angiotensin converting enzyme 2 (ACE2) expression, we map tissue damage associated with severe disease and propose which damage results from direct viral infection rather than from indirect effects of illness. We find that disease severity in lung tissue is driven by myeloid cell phenotypes and cell-cell interactions with lung epithelial cells and T cells. Based on these results, we propose a model of immune and epithelial cell interactions that drive cell-type specific and tissue-specific damage in severe COVID-19.