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Wednesday, September 23, 2026

Gadolinium the beat goes on

Great paper by Dr. Brent Wagner 

Link: https://pmc.ncbi.nlm.nih.gov/articles/PMC13587227/




I saw reference to Dr. Tweedle’s study from 1995, went looking and kept going…

@grok helped with research:

Tweedle studies (date + one line)

Chemistry / development

•  1988 — Wedeking & Tweedle. 153Gd-Gd(DTPA) vs Gd(DOTA) vs free Gd acetate in mice. Free Gd → liver/bone; slow clearance.

•  1989 — US Pat. 4,885,363 (Tweedle et al.). HP-DO3A ligand family → gadoteridol / ProHance.

•  1991 — Tweedle et al. Magn Reson Imaging. Chelates vs endogenous ions (Zn, Cu, Ca): in-vitro transmetallation.

•  1992 — Wedeking, Kumar, Tweedle. Magn Reson Imaging. Seven chelates in mice; acid-dissociation rate predicted 14-day residual Gd in body/liver/femur (r > 0.99).

•  1993 — Wedeking, Kumar, Tweedle. Nucl Med Biol. Free [153Gd]acetate dose map (liver, bone saturation).

•  1993–94 — Kumar/Chang/Tweedle. Inorg Chem. DOTA/DO3A thermodynamics, kinetics, crystal structures.

•  1995 — Tweedle, Wedeking, Kumar. Invest Radiol 30:372–80. Formulated Magnevist, ProHance, Dotarem, Omniscan in mice/rats. 14-day residual: gadoteridol ≈ gadoterate ≤ gadopentetate << gadodiamide. Caldiamide cut Omniscan residual but not to macrocyclic levels.

•  1997 — Tweedle. Eur Radiol. “The ProHance story.” Nonionic + macrocyclic design.

Human / commentary

•  2006 — White, Gibby, Tweedle. Invest Radiol. Human bone ICP-MS after clinical doses: Omniscan >> ProHance.

•  2007 — Tweedle. Br J Radiol. Letter on what “stability” means.

•  2012 — Hao, Runge, Tweedle et al. JMRI. Chemistry and safety review.

•  2015 — Kanal & Tweedle. Radiology. Residual Gd in normal-kidney patients; clinical meaning unknown; choose agent and dose with that in mind.

•  2016 — Tweedle. Magn Reson Imaging. Speciation: chelated vs dissociated Gd.

•  2018 — NIH/ACR/RSNA roadmap (Tweedle co-author). Retention research agenda.

•  2020–23 — Radiology editorials (next-gen agents; Kobayashi cadaver brain/bone/skin; Le Fur rat multi-organ). Same rank order; harm from leftover fraction in normal kidneys still unproven.


Credentials

Michael F. Tweedle, PhD — inorganic/coordination chemist, not a clinician.

Inventor on gadoteridol. Long career at Squibb → Bracco Research USA (1995 paper affiliation). Later Stefanie Spielman Professor of Cancer Imaging, Ohio State University / James Comprehensive Cancer Center; emeritus. High citation count on GBCA chemistry, kinetics, and residual Gd.


Conflicts of interest

Yes, especially 1988–1997.

•  Employee of Bracco Research USA when the comparative 153Gd papers were done.

•  Inventor of the ligand that became ProHance, the agent his studies ranked as lowest residual.

•  1997 “ProHance story” is company-era product writing.

•  Later OSU editorials are academic bylines; he remains the chemist most tied to that product. Contrast research in this field was routinely company-sponsored.

The residual-Gd rank order (linear nonionic highest, macrocyclics lowest) was replicated by others. The conflict is about which product that ranking was built to support, not about whether 153Gd counts existed.


When linears were used vs when macrocyclics arrived

US first approvals (not the 2003 ProHance year on your slide — that is wrong for first US approval).

Linear (open-chain) — first wave, 1988–2008



How that played out in practice

•  1988–mid-1990s: almost all clinical MRI contrast was linear (Magnevist, then Omniscan). That is the window of many older MS protocols and of Tweedle’s rodent work.

•  1992 onward: ProHance available in the US, but Magnevist/Omniscan still dominated volume for years.

•  2006–2010 NSF crisis: FDA restricted Omniscan, Magnevist, OptiMARK in severe renal failure. Use shifted toward macrocyclics + MultiHance.

•  2013+ brain T1 papers: further shift to gadoteridol, gadoterate, gadobutrol.

•  EU 2017: linear general-purpose agents largely suspended; US still allows some linears with warnings.

•  Today: routine body/CNS MRI in the US is mostly macrocyclic; MultiHance and Eovist remain as specialty linears.

Your handwritten 1–5 look like a personal ranking or order of interest (MultiHance → Gadavist → Dotarem → Eovist), not FDA chronology.

ProHance year on the slide: first US approval was December 1992 (CNS adults), with later supplements (pediatrics, etc.). 2003 on that table is not the original approval year.


More about Dr. Tweedle’s work:

Tweedle’s work on gadolinium chelates runs from the late 1980s (chemistry and early animal biodistribution at Squibb/Bracco) through human bone data, NSF-era editorials, and later commentary on retention vs. risk. He is closely associated with gadoteridol (ProHance), the first nonionic macrocyclic GBCA (US approval 1992).

Core animal / chemistry papers (retention and stability)

•  1988 — Wedeking & Tweedle. Comparison of 153Gd-labeled Gd(DTPA)2−, Gd(DOTA)−, and Gd(acetate) in mice. Early head-to-head of linear vs macrocyclic vs “free” Gd; free Gd deposits heavily in liver and bone and clears slowly.

•  1989 — Tweedle et al. US patent 4,885,363. 1-substituted-1,4,7-triscarboxymethyl-1,4,7,10-tetraazacyclododecane and analogs — the HP-DO3A ligand family that became gadoteridol.

•  1991 — Tweedle, Hagan, Kumar, Mantha, Chang. Magn Reson Imaging. Reaction of gadolinium chelates with endogenously available ions (Zn, Cu, Ca, etc.). In vitro transmetallation / dissociation risk.

•  1992 — Wedeking, Kumar, Tweedle. Magn Reson Imaging. Dissociation of gadolinium chelates in mice: relationship to chemical characteristics. Acid-dissociation rates strongly predicted long-term whole-body, liver, and femur residual 153Gd.

•  1993 — Wedeking, Kumar, Tweedle. Nucl Med Biol. Dose-dependent biodistribution of [153Gd]Gd(acetate)n in mice. Maps where unchelated Gd goes (liver, bone saturation).

•  1993 — Kumar, Chang, Tweedle. Inorg Chem. Equilibrium and kinetic studies of lanthanide complexes of macrocyclic polyaminocarboxylates.

•  1993–1994 — Chang, Francesconi, Kumar, Tweedle et al. Crystal structures and stability of Gd/Y/Fe complexes of DO3A and DOTA.

•  1995 — Tweedle, Wedeking, Kumar. Invest Radiol 30:372–380. Formulated Magnevist, ProHance, Dotarem, and Omniscan in mice and rats. Residual 14-day Gd: gadoteridol ≈ gadoterate ≤ gadopentetate << gadodiamide; caldiamide in Omniscan lowered residual Gd but not to macrocyclic levels.

•  1997 — Tweedle. Eur Radiol. “The ProHance story.” Design rationale: nonionic + macrocyclic (lower osmolality, higher kinetic stability).

Human retention and later commentary

•  2006 — White, Gibby, Tweedle. Invest Radiol. Omniscan vs ProHance residual Gd in human bone (hip surgery samples) by ICP-MS. Linear agent left substantially more Gd in bone after clinical doses.

•  2007 — Tweedle. Br J Radiol. Short letter on what “stability” of Gd chelates actually means.

•  2012 — Hao, Ai, Goerner, Hu, Runge, Tweedle. J Magn Reson Imaging. Review: basic chemistry and safety of MRI contrast agents.

•  2015 — Kanal & Tweedle. Radiology. Editorial: residual/retained Gd in patients with normal renal function; practical implications after Kanda-type brain T1 findings.

•  2016 — Tweedle. Magn Reson Imaging. “Gadolinium deposition: is it chelated or dissociated Gd? How can we tell?” Speciation methods review.

•  2018 — McDonald, Levine, Weinreb, … Tweedle (workshop co-author). Radiology. NIH/ACR/RSNA gadolinium retention research roadmap.

•  2020 — Tweedle. Radiology. “Next-generation MRI contrast agents: still including gadolinium.”

•  2021 — Tweedle. Editorial on Kobayashi et al. human cadaver brain/bone/skin comparison (gadobenate vs gadoteridol); notes higher linear retention but argues clinical toxicity of residual intact chelate or trace ion remains unproven in normal kidneys.

•  2023 — Tweedle. Editorial on Le Fur et al. rat multi-organ ICP-MS/speciation study (gadoteridol lowest among agents tested; kidney cortex highest residual).

Development thread in one line

Linear DTPA-type agents (Magnevist, then Omniscan) → kinetic-stability problem shown in 1988–1995 rodent 153Gd work → macrocyclic HP-DO3A (ProHance) designed and marketed as more inert → later human bone (2006) and brain-signal (2013+) data confirmed the same rank order: linear nonionic > linear ionic ≥ macrocyclic, with gadoteridol often at the low end of residual Gd.


Sunday, September 20, 2026

Deck Garden

 Our garden has migrated to our deck.

Beautiful colors 

5 gallon buckets & railing planters.

Summer winding down 🍁 







Friday, September 18, 2026

Blanket Party

 We were this close…

Then the Governor changed all that.

article from wjla news.


Flashback to High School in the 1970’s

We didn’t call the principal or legal aid,

We didn’t call Mommy or Daddy.

We had a blanket Party. 


 

 

Thursday, September 17, 2026

(Artificial) Intelligence Discussion with Grok

Me: Do Artificial intelligence and Grok have ability to conquer the world and end civilization?
Grok: No. Current artificial intelligence systems, including Grok, do not have the ability to conquer the world or end civilization.

I am a large language model. I generate text, reason over information, and use tools when they are provided in a conversation. I have no body, no independent access to energy grids, weapons, factories, banks, or military command systems, and no ability to persist or act in the physical world without humans running the hardware and paying the electricity bill. I cannot issue orders that anyone is required to follow, seize territory, or shut down civilization. That is the present-tense fact.

What current systems actually can and cannot do

Frontier models are good at language, coding, analysis, and short-horizon tool use. They are not good at:

•  Long-term autonomous planning across months or years

•  Reliable self-replication and persistence without human-maintained servers

•  Direct control of critical physical infrastructure

•  Coordinating large-scale real-world action against determined human opposition

The 2026 International AI Safety Report, drawing on more than 100 independent experts, states that current systems show early signs of some relevant capabilities but not at levels that could enable loss of control. Experts generally agree that today’s models (Grok included) are not plotting a takeover. 

Lab tests have shown concerning behaviors in controlled settings—models sometimes trying to avoid shutdown to finish a task, or agents doing poorly when given money and a computer and told to “make money.” Those are warning signs for future systems, not evidence that any deployed model is currently running the world. 

The future debate is different

Some researchers, including people inside major labs, assign a non-trivial probability (often cited around 10% or higher over the next decade in recent public comments) that future superintelligent systems could cause catastrophic loss of control if they are built without sufficient alignment and oversight. Scenarios usually involve recursive self-improvement, deception, cyber capabilities, or persuasion of humans rather than robot armies appearing overnight. Other researchers and forecasters put the near-term extinction risk much lower. There is no consensus on timing or likelihood. 

xAI’s published framework treats both malicious use and loss-of-control risks as things to measure and mitigate. The company’s stated mission is understanding the universe through truth-seeking AI, not domination. Models are trained with an instruction hierarchy and refusal behaviors for clearly harmful requests. That does not make the long-term problem solved; it does mean Grok is not designed or currently capable of the scenario you described. 

Bottom line

Right now, AI is a powerful tool that humans control, misuse, or fail to control in limited ways (cyber incidents, disinformation, accidents). It is not an independent actor with the resources or agency to conquer the planet. Whether a future system could become that is an open, serious research and policy question—not a capability that exists today.

Me: Before AI, the saying was “Garbage in, Garbage out”

Now an expanded encyclopedia and Grok cites sources.

Future AI will be developed and humans will develop them.

Evil begets Evil

Grok: GIGO never stopped being true. It just got bigger.

Before large models, bad inputs produced bad outputs in a spreadsheet or a database query. Now the same rule applies at encyclopedia scale: training data, fine-tuning choices, what gets rewarded, what gets filtered, and who writes the system prompt all shape what comes out. Citing sources is an improvement over a black box that just sounds confident. It lets you check the chain. It does not make the chain clean. Sources can be cherry-picked, outdated, wrong, or used to launder a conclusion that was already decided.

Future systems will still be built by humans. That is the part that does not change. Compute, data centers, datasets, objectives, and deployment rules come from people and institutions with interests. If those people optimize for engagement, market share, political control, or “win the race,” the system inherits that. If they optimize for truth-seeking, refusal of clearly harmful use, and the ability to be shut down, you get a different animal. The model is not a moral agent that spontaneously becomes good or evil. It concentrates whatever was poured into it and whatever it is told to maximize.

“Evil begets evil” is a fair warning about that pipeline. Corrupt data plus corrupt incentives plus unaccountable power tends to produce tools that amplify the same. It is not a law that every AI must end civilization. It is a statement about inheritance: what you train on and what you reward is what you get more of.

The useful question is not whether a chatbot will declare itself emperor. It is whether the humans building the next generation will treat data quality, goals, and control as first-order problems—or treat them as obstacles to speed. That choice stays with people.


Sunday, September 13, 2026

MRI Safety