Biological/Biomedical Accelerator Mass Spectrometry Targets. 1. Optimizing the CO2 Reduction Step Using Zinc Dust

Biological and biomedical applications of accelerator mass spectrometry (AMS) use isotope ratio mass spectrometry to quantify minute amounts of long-lived radioisotopes such as 14C. AMS target preparation involves first the oxidation of carbon (in sample of interest) to CO2 and second the reduction of CO2 to filamentous, fluffy, fuzzy, or firm graphite-like substances that coat a −400-mesh spherical iron powder (−400MSIP) catalyst. Until now, the quality of AMS targets has been variable; consequently, they often failed to produce robust ion currents that are required for reliable, accurate, precise, and high-throughput AMS for biological/biomedical applications. Therefore, we described our optimized method for reduction of CO2 to high-quality uniform AMS targets whose morphology we visualized using scanning electron microscope pictures. Key features of our optimized method were to reduce CO2 (from a sample of interest that provided 1 mg of C) using 100 ± 1.3 mg of Zn dust, 5 ± 0.4 mg of −400MSIP, and a reduction temperature of 500 °C for 3 h. The thermodynamics of our optimized method were more favorable for production of graphite-coated iron powders (GCIP) than those of previous methods. All AMS targets from our optimized method were of 100% GCIP, the graphitization yield exceeded 90%, and δ13C was −17.9 ± 0.3‰. The GCIP reliably produced strong 12C− currents and accurate and precise Fm values. The observed Fm value for oxalic acid II NIST SRM deviated from its accepted Fm value of 1.3407 by only 0.0003 ± 0.0027 (mean ± SE, n = 32), limit of detection of 14C was 0.04 amol, and limit of quantification was 0.07 amol, and a skilled analyst can prepare as many as 270 AMS targets per day. More information on the physical (hardness/color), morphological (SEMs), and structural (FT-IR, Raman, XRD spectra) characteristics of our AMS targets that determine accurate, precise, and high-hroughput AMS measurement are in the companion paper

Greater cardiac response of colloid than saline fluid loading in septic and non-septic critically ill patients with clinical hypovolaemia

Background and objective: The haemodynamics of crystalloid and colloid fluid loading may depend on underlying disease, i.e. sepsis versus non-sepsis. Design and setting: A single-centre, single-blinded, randomized clinical trial was carried out on 24 critically ill sepsis and 24 non-sepsis patients with clinical hypovolaemia, assigned to loading with normal saline, gelatin 4%, hydroxyethyl starch 6% or albumin 5% in a 90-min (delta) central venous pressure (CVP)-guided fluid loading protocol. Transpulmonary thermodilution was done each 30 min, yielding, among others, global end-diastolic volume and cardiac indices (GEDVI, CI). Results: Sepsis patients had hyperdynamic hypotension in spite of myocardial depression and dilatation, and greater inotropic/vasopressor requirements than non-sepsis patients. Independent of underlying disease, CVP and GEDVI increased more after colloid than saline loading (P < 0.018), so that CI increased by about 2% after saline and 12% after colloid loading (P = 0.029). The increase in preload-recruitable stroke work was also greater with colloids and did not differ among conditions. Conclusion: Fluid loading with colloids results in a greater linear increase in cardiac filling, output and stroke work than does saline loading, in both septic and non-septic clinical hypovolaemia, in spite of myocardial depression and presumably increased vasopermeability potentially decreasing the effects of colloid fluid loading in the former. © The Author(s) 2010

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome

Active Inference, Novelty and Neglect

In this chapter, we provide an overview of the principles of active inference. We illustrate how different forms of short-term memory are expressed formally (mathematically) through appealing to beliefs about the causes of our sensations and about the actions we pursue. This is used to motivate an approach to active vision that depends upon inferences about the causes of 'what I have seen' and learning about 'what I would see if I were to look there'. The former could manifest as persistent 'delay-period' activity - of the sort associated with working memory, while the latter is better suited to changes in synaptic efficacy - of the sort that underlies short-term learning and adaptation. We review formulations of these ideas in terms of active inference, their role in directing visual exploration and the consequences - for active vision - of their failures. To illustrate the latter, we draw upon some of our recent work on the computational anatomy of visual neglect

Association of Toll-like receptor 7 variants with life-threatening COVID-19 disease in males: findings from a nested case-control study

Background: Recently, loss-of-function variants in TLR7 were identified in two families in which COVID-19 segregates like an X-linked recessive disorder environmentally conditioned by SARS-CoV-2. We investigated whether the two families represent the tip of the iceberg of a subset of COVID-19 male patients.Methods: This is a nested case-control study in which we compared male participants with extreme phenotype selected from the Italian GEN-COVID cohort of SARS-CoV-2-infected participants (&lt;60y, 79 severe cases versus 77 control cases). We applied the LASSO Logistic Regression analysis, considering only rare variants on young male subsets with extreme phenotype, picking up TLR7 as the most important susceptibility gene.Results: Overall, we found TLR7 deleterious variants in 2.1% of severely affected males and in none of the asymptomatic participants. The functional gene expression profile analysis demonstrated a reduction in TLR7-related gene expression in patients compared with controls demonstrating an impairment in type I and II IFN responses.Conclusion: Young males with TLR7 loss-of-function variants and severe COVID-19 represent a subset of male patients contributing to disease susceptibility in up to 2% of severe COVID-19

Quality of Graphite Target for Biological/Biomedical/Environmental Applications of 14C-Accelerator Mass Spectrometry

Catalytic graphitization for 14C-accelerator mass spectrometry (14C-AMS) produced various forms of elemental carbon. Our high-throughput Zn reduction method (C/Fe = 1:5, 500 °C, 3 h) produced the AMS target of graphite-coated iron powder (GCIP), a mix of nongraphitic carbon and Fe3C. Crystallinity of the AMS targets of GCIP (nongraphitic carbon) was increased to turbostratic carbon by raising the C/Fe ratio from 1:5 to 1:1 and the graphitization temperature from 500 to 585 °C. The AMS target of GCIP containing turbostratic carbon had a large isotopic fractionation and a low AMS ion current. The AMS target of GCIP containing turbostratic carbon also yielded less accurate/precise 14C-AMS measurements because of the lower graphitization yield and lower thermal conductivity that were caused by the higher C/Fe ratio of 1:1. On the other hand, the AMS target of GCIP containing nongraphitic carbon had higher graphitization yield and better thermal conductivity over the AMS target of GCIP containing turbostratic carbon due to optimal surface area provided by the iron powder. Finally, graphitization yield and thermal conductivity were stronger determinants (over graphite crystallinity) for accurate/precise/high-throughput biological, biomedical, and environmental14C-AMS applications such as absorption, distribution, metabolism, elimination (ADME), and physiologically based pharmacokinetics (PBPK) of nutrients, drugs, phytochemicals, and environmental chemicals

Ultra-rare RTEL1 gene variants associate with acute severity of COVID-19 and evolution to pulmonary fibrosis as a specific long COVID disorder

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused an ongoing pandemic of a pathology termed Coronavirus Disease 19 (COVID-19). Several studies reported that both COVID-19 and RTEL1 variants are associated with shorter telomere length, but a direct association between the two is not generally acknowledged. Here we demonstrate that up to 8.6% of severe COVID-19 patients bear RTEL1 ultra-rare variants, and show how this subgroup can be recognized. Methods: A cohort of 2246 SARS-CoV-2-positive subjects, collected within the GEN-COVID Multicenter study, was used in this work. Whole exome sequencing analysis was performed using the NovaSeq6000 System, and machine learning methods were used for candidate gene selection of severity. A nested study, comparing severely affected patients bearing or not variants in the selected gene, was used for the characterisation of specific clinical features connected to variants in both acute and post-acute phases. Results: Our GEN-COVID cohort revealed a total of 151 patients carrying at least one RTEL1 ultra-rare variant, which was selected as a specific acute severity feature. From a clinical point of view, these patients showed higher liver function indices, as well as increased CRP and inflammatory markers, such as IL-6. Moreover, compared to control subjects, they present autoimmune disorders more frequently. Finally, their decreased diffusion lung capacity for carbon monoxide after six months of COVID-19 suggests that RTEL1 variants can contribute to the development of SARS-CoV-2-elicited lung fibrosis. Conclusion: RTEL1 ultra-rare variants can be considered as a predictive marker of COVID-19 severity, as well as a marker of pathological evolution in pulmonary fibrosis in the post-COVID phase. This notion can be used for a rapid screening in hospitalized infected people, for vaccine prioritization, and appropriate follow-up assessment for subjects at risk. Trial Registration NCT04549831 (www.clinicaltrial.org