Ancient Chinese anesthetic reveals Ming dynasty's sophisticated medicine

Amazing stuff!

"Microscopic analysis of residues on surgical scissors and tweezers from a 1348–1411 CE tomb in Jiangyin, China, finds the first evidence for the controlled application of a highly toxic chemical as anesthetic, highlighting the sophisticated medicine of the Ming dynasty. ...

However, conventional techniques are difficult to apply to ancient Chinese medical residues, which are rarely preserved and often fail to meet minimum sample requirements for identification. ...

To tackle this, archaeologists used a novel, non-destructive microscopic technique to analyze residues on a pair of surgical scissors and tweezers from the tomb of early Ming dynasty physician Xia Quan. ...

The researchers found evidence for residue of aconitine: an alkaloid derived from the plant Aconitum. Also known as wolfsbane or monkshood, Aconitum is extremely toxic.

This toxicity was recognized and several methods to mitigate it had been developed by the time of the Ming dynasty, from vinegar-boiling to detoxifying with mung beans. The resulting powder acted as an anesthetic, enabling pain-free surgery. ..."

From the abstract:

"The analysis of archaeological trace residues is offering expanding insights into various aspects of human (pre)history, including developments in medical knowledge.

Here, the authors present results from the analysis of two medical instruments (scissors and tweezers) found in a Ming Dynasty (c. 1368–1644 CE) tomb in Jiangyin, China. While the form and composition of the instruments themselves indicate developed understandings of tool production and use, novel application of stimulated Raman scattering microscopy reveals probable traces of aconitine, likely providing direct evidence for the use of this highly toxic substance, possibly administered as a topical anaesthetic, in ancient Chinese surgery."

Ancient anesthetic reveals Ming China's sophisticated medicine

Surgical anaesthesia in Ming China: scientific analysis of aconitine residues on medical instruments (open access)

Fig. 1 The sampled instruments and the residues analysed on each one. 

New algorithms show accuracy, reliability in gauging unconsciousness under general anesthesia

Good news! It is quite surprising that we still know so little about anesthesia and anesthesiology although it is applied daily to thousands and thousands of patients every day around the world.

"Anesthestic drugs act on the brain, but most anesthesiologists rely on heart rate, respiratory rate, and movement to infer whether surgery patients remain unconscious to the desired degree. ...
More than providing a good readout of unconsciousness ... the new algorithms offer the potential to allow anesthesiologists to maintain it at the desired level while using less drug than they might administer when depending on less direct, accurate, and reliable indicators. That can improve patient’s post-operative outcomes, such as delirium. ...
In the new work ... the team trained versions of their AI algorithms, based on different underlying statistical methods, on more than 33,000 2-second-long snippets of EEG recordings from seven of the volunteers. This way the algorithms could “learn” the difference between EEG readings predictive of consciousness and unconsciousness under propofol. ..."

"... We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. ...
These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. ..." 

New algorithms show accuracy, reliability in gauging unconsciousness under general anesthesia | MIT News | Massachusetts Institute of Technology Machine learning software advances could help anesthesiologists optimize drug dose.

Here is the link to the underlying research article:

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia (open access)