The politics of COVID-19: Differences between U.S. red and blue states in COVID-19 regulations and deaths.

The study investigated infection variables and control strategies in 2020 and 2021 and their influence on COVID-19 deaths in the United States, with a particular focus on comparing red (Republican) and blue (Democratic) states. The analysis reviewed cumulative COVID-19 deaths per 100,000 by year, state political affiliation, and a priori latent factor groupings of mitigation strategies (lockdown days in 2020, mask mandate days, vaccination rates), social demographic variables (ethnicity, poverty rate), and biological variables (median age, obesity). Analyses first identified possible relationships between all assessed variables using K-means clustering for red, blue, and purple states. Then, a series of regression models were fit to assess the effects of mitigation strategies, social, and biological factors specifically on COVID-19 deaths in red and blue states. Results showed distinct differences in responding to COVID infections between red states to blue states, particularly the red states lessor adoption of mitigation factors leaving more sway on biological factors in predicting deaths. Whereas in blue states, where mitigation factors were more readily implemented, vaccinations had a more significant influence in reducing the probability of infections ending in death. Overall, study findings suggest politicalization of COVID-19 mitigation strategies played a role in death rates across the United States.

Assessment of range uncertainty in lung-like tissue using a porcine lung phantom and proton radiography.

Thoracic tumours are increasingly considered indications for pencil beam scanned proton therapy (PBS-PT) treatments. Conservative robustness settings have been suggested due to potential range straggling effects caused by the lung micro-structure. Using proton radiography (PR) and a 4D porcine lung phantom, we experimentally assess range errors to be considered in robust treatment planning for thoracic indications. A human-chest-size 4D phantom hosting inflatable porcine lungs and corresponding 4D computed tomography (4DCT) were used. Five PR frames were planned to intersect the phantom at various positions. Integral depth-dose curves (IDDs) per proton spot were measured using a multi-layer ionisation chamber (MLIC). Each PR frame consisted of 81 spots with an assigned energy of 210 MeV (full width at half maximum (FWHM) 8.2 mm). Each frame was delivered five times while simultaneously acquiring the breathing signal of the 4D phantom, using an ANZAI load cell. The synchronised ANZAI and delivery log file information was used to retrospectively sort spots into their corresponding breathing phase. Based on this information, IDDs were simulated by the treatment planning system (TPS) Monte Carlo dose engine on a dose grid of 1 mm. In addition to the time-resolved TPS calculations on the 4DCT phases, IDDs were calculated on the average CT. Measured IDDs were compared with simulated ones, calculating the range error for each individual spot. In total, 2025 proton spots were individually measured and analysed. The range error of a specific spot is reported relative to its water equivalent path length (WEPL). The mean relative range error was 1.2% (1.5 SD 2.3 %) for the comparison with the time-resolved TPS calculations, and 1.0% (1.5 SD 2.2 %) when comparing to TPS calculations on the average CT. The determined mean relative range errors justify the use of 3% range uncertainty for robust treatment planning in a clinical setting for thoracic indications.

Validation of the proton range accuracy and optimization of CT calibration curves utilizing range probing.

Proton therapy is affected by range uncertainty, which is partly caused by an ambiguous conversion from x-ray attenuation to proton stopping power. CT calibration curves, or Hounsfield look-up tables (HLUTs), are institution-specific and may be a source of systematic errors in treatment planning. A range probing method to verify, optimize and validate HLUTs for proton treatment is proposed. An initial HLUT was determined according to the stoichiometric approach. For HLUT validation, three types of animal tissue phantoms were prepared: a pig's head, 'thorax' and femur. CT scans of the phantoms were taken and a structure, simulating a water slab, was added on the scan distal to the phantoms to mimic the detector used for integral depth-dose measurements. The CT scans were imported into the TPS to calculate individual pencil beams directed through the phantoms. The phantoms were positioned at the therapy system isocenter using x-ray imaging. Shoot-through pencil beams were delivered, and depth-dose profiles were measured using a multi-layer ionization chamber. Measured depth-dose curves were compared to the calculated curves and the range error per spot was determined. Based on the water equivalent path length (WEPL) of individual spot, a range error margin was defined. Ratios between measured error and theoretical margin were calculated per spot. The HLUT optimization was performed by identifying systematic shifts of the mean range error per phantom and minimizing the ratios between range errors and uncertainty margins. After optimization, the ratios of the actual range error and the uncertainty margin over the complete data set did not exceed 0.75 (1.5 SD), indicating that the actual errors are covered by the theoretical uncertainty recipe. The feasibility of using range probing to assess range errors was demonstrated. The theoretical uncertainty margins in the institution-specific setting potentially may be reduced by ~25%.

Immunoglobulin heavy chain locus of the rat: striking homology to mouse antibody genes.

DNA encoding the rat diversity segment (D), joining segment (JH), and constant (C) region mu, gamma 2a, gamma 1, gamma 2b, epsilon and alpha of the Ig heavy chain has been isolated from a cosmid library. Restriction mapping allowed us to identify two gene clusters: D-JH-C mu and C gamma 1-C gamma 2b-C epsilon-C alpha in addition to a single C gamma 2a gene. Analysis of genomic DNA by Southern blotting permitted identification of the C gamma 2c gene and led to the proposal of the following gene order for the rat Ig heavy chain locus: D-JH-C mu-C delta-(C gamma 2c, C gamma 2a)-C gamma 1-C gamma 2b-C epsilon-C alpha. There is striking homology between the rat and mouse Ig heavy chain loci as regards gene order and distance between CH genes. Partial DNA sequencing confirms this homology and shows that exon sequences are more conserved than are intron sequences. One of the most conserved intron regions between rat and mouse is that spanning the Ig heavy chain enhancer (91% homology). However, the relationship between the different C gamma subclasses in rat differs from that in mouse. Comparison of the C gamma CH3 domains shows that the rat C gamma 2b gene is most homologous to mouse C gamma 2a/b, whereas the rat C gamma 1 and C gamma 2a genes, both very similar to each other, are most homologous to the mouse C gamma 1 gene.

Role of sulphydryl groups in T lymphocyte-mediated cytotoxicity.

The role of thiols in T cell-mediated cytotoxicity was investigated by studying the thiols of the target cell and cytotoxic cell separately. Agents which protect the reduced thiols of the target cell inhibit their lysis by the cytotoxic T cells; thiol reactive reagents may be directly toxic to the target cell. The thiol groups of the effector cell are also important, since pre-treatment with thiol reactive reagents inhibits killing.

The mechanism of Fc-mediated interaction of eosinophils with immobilized immune complexes. II. Identification of two membrane proteins, modified by the interaction.

Human peripheral blood eosinophils attach to and flatten down onto antibody-coated surfaces and subsequently degranulate. An antibody-coated surface was prepared by treating a layer of agar, containing tetanus toxoid antigen and eosinophil chemotactic factor (ECF), with human anti-tetanus immunoglobin. Changes in eosinophil surface proteins during attachment to the antibody-coated agar layer were detected by lactoperoxidase catalysed iodination. Purified eosinophils were pre-treated with unlabelled iodide, lactoperoxidase and hydrogen peroxide to block pre-existing accessible tyrosine residues on the cell surface. They were then allowed to interact with the agar layer, and subsequently treated with lactoperoxidase and 125I-labelled iodide to label newly accessible surface proteins. Separation of the radioactive proteins by sodium dodecyl sulphate/polyacrylamide gel electrophoresis revealed that, while incubation of the cells in suspension restored the major proteins to the cell surface, interaction with the antibody-coated agar layer caused the appearance of additional proteins of apparent molecular weight 55K, 30K, 28K and 18K. The 55K, 28K and 18K proteins were greatly reduced when antibody was absent, but the 55K protein was distinguishable from immunoglobulin G (IgG) heavy chain, since it could be detected in low amounts even in the absence of antibody. It was found in purified plasma membranes and it could be separated from IgG heavy chain by iso-electric focusing. The possibility is discussed that this protein is either linked to the receptor for the Fc portion of IgG, or that it is itself the receptor. The 18K protein required both antibody and ECF for maximum expression, but was seen in limited amounts with ECF alone. Possibly it is concerned with an ECF-mediated recognition of IgG. Unlike the 55K protein, it binds concanavalin A. Plasma membranes were prepared from eosinophils by lysis in borate, followed by purification on a glass-bead column. Both the 55K and the 18K proteins were found to be major components of the eosinophil membrane.