21st international Biometals Webinars
Bibliographic record
Abstract
The 21st International Biometals Webinar featured two distinct yet complementary perspectives on the role of metals in biology, addressing both contemporary bioinorganic chemistry and the origins of life. The first presentation detailed the development and application of liquid chromatography (LC)-based metallomics tools to investigate toxic metal species in mammalian blood plasma, focusing on exposure–disease relationships at the blood–organ interface. Using size-exclusion and reversed-phase chromatography coupled with inductively coupled plasma atomic emission spectrometry (ICP-AES), the study elucidated mechanisms of methylmercury transport from blood to brain, identifying homocysteine as a chaperone facilitating methylmercury translocation across the blood–brain barrier. Additional findings revealed that toxic metals such as mercury and cadmium bind to hemoglobin and carbonic anhydrase released upon red blood cell rupture, implicating these metalloproteins in disease processes like neurotoxicity and atherosclerosis. The second presentation challenged the prevailing organic-centric paradigm of life’s emergence by emphasizing the fundamental and catalytic roles of transition metals in bioenergetics and enzyme function. It argued that life is as much “metallic” as organic, with transition metal ions mediating redox reactions essential for free energy conversion and low-entropy maintenance. This metallic perspective suggests that the origin of life was driven by metal-containing minerals harnessing environmental redox gradients, providing a thermodynamically plausible pathway distinct from the classical “primordial soup” hypothesis. Together, these contributions underscore the critical importance of metals in both understanding disease mechanisms and re-evaluating life’s biochemical and evolutionary foundations. Introduction to the 21st international Biometals Webinars Application of LC-based metallomics tools to probe the exposure-disease relationship of toxic metal species at the blood-organ nexus There are two types of metal species that can enter the human bloodstream but for which the outcome at the organ level is not well understood: toxic metal species (Cd2+, Hg2+, CH3Hg+, thimerosal, phenylmercuric acetate) and gold-nanoparticles, which offer considerable potential to selectively deliver immobilized drugs to target tissues after attaching a targeting sequence. We employ liquid chromatography-based metallomics approaches to better understand these processes in conjunction with electrospray ionization mass spectrometry, X-ray absorption spectroscopy and/or transmission electron microscopy. While different LC-separation modes in conjunction with different mobile phase compositions allow to probe bioinorganic processes that unfold in blood plasma, red blood cells cytosol and/or protein-free hepatocyte cytosol,1 the utilization of an inductively coupled plasma atomic emission spectrometer as a metal-specific detector provides the unique capability to simultaneously detect a large variety of metals.2 This presentation will highlight how the integration of the results that are obtained with this analytical approach into the biochemistry of the whole organism3 provides a powerful means to effectively address pertinent health relevant bioinorganic chemistry problems which have a strong toxicological chemistry and/or pharmacological flavor to significantly advance human health in the 21st century. [1] N. Pourzadi, J. Gailer, J. Chromatogr. A 2024, 1736, 465409 [2] N. Pourzadi, et al., Nanomedicine 2025, 20, 1127-1138 [3] M. Degorge, J. Gailer, Toxics, 2025, 13, 636 Is Life organic or metallic? ... and why does that matter in trying to deduce its emergence? Is Life organic or metallic? … and why that matters with respect to its emergence The notion that life is (basically) all about organic molecules, and that consequently it must have emerged out of a mixture of organic molecules, is generally considered an obvious truism. I will try to trace this mindset back to its origins more than 200 years ago and confront its development during the 19th and early 20th century with the major advances in the physical sciences and in particular in thermodynamics. If all goes according to plan, your certainties about the primacy of organics in life and in its emergence will be somewhat shaken after you have listened to this webinar ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.360 | 0.246 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".