Race Against Time

Laura rubbed her eyes, trying to shake off the sleep as quickly as possible.

The message she had just received left no doubt: she was in a race against time.

She needed to identify the toxin that had been injected into Isabelle, a 12-year-old child, by an unknown venomous snake, apparently not yet cataloged, during an innocent weekend excursion to Pico Jaraguá, in S?o Paulo. The doctors had administered a polyvalent anti-venom serum, but the initial results were not promising. Luckily, only one of the snake's fangs had hit Isabelle, injecting a smaller amount of venom and ensuring a slower progression of the effects.

Even so, Laura knew she had at most 6 hours to identify the generating toxin and suggest a substance in time to prevent sequelae and save the life of that poor child.

She immediately accessed the hospital's robust cloud network and started the liquid chromatography process, called LC-MS, which would detect traces of peptides and proteins from the venom in the blood. In the best-case scenario, by comparing with a database of known venoms, the system itself could indicate the most appropriate serum. Despite the base being quite large, her powerful cloud network, coupled with a cutting-edge LLM AI model, could bring an answer in a few minutes.

When Laura chose to pursue a career as a biotechnology data scientist, she never imagined she would have to do shifts in the same way as doctors. But now, on the eve of the 22nd century, the speed at which problems could be solved brought hope for cases that were previously insoluble, especially in emergency situations.

The computer emitted a beep; the process had finished. “Ah, crap!” - Laura exclaimed. Apparently, it was a new venom that wasn't in the database. The analysis of the available anti-venom serums also didn't bring any option with a probability of efficacy above 50%. “Isn’t it ironic?” – Laura thought, “That in a world where we extinguish species at an unprecedented speed, others are still unknown? And right in the largest urban center in South America???” But there was no time for lamentations. If she couldn't solve the problem through the expressway, then it was time to use her trump cards.

She requested access to the special network, available only for emergency cases, which allowed a computational power 100 times greater than her normal network. She would need every GPU available.

Using her password, she gained access to the network and immediately transferred the protein profile found to the application that would synthesize antibodies that would perfectly match the toxin's profile. In parallel, she prepared her latest generation biological 3D printer.

It's true that it was still a very expensive process, but with it, she could produce the antibody molecules in sufficient quantity for Isabelle, in a matter of 1 or 2 hours, eliminating the two weeks necessary for this process, which normally occurred in laboratories from bacterial cultures and cloning of antibodies.

It had taken several decades, but the refinement of this process, coupled with a highly positive track record, allowed approval by the Health Agency (Anvisa) for authorization to eliminate pre-clinical and clinical tests of serums created in AI laboratories. Of course, the urgency of the situation and the lack of options were strong stimuli to obtain such permission.

With everything ready, now Laura had to wait. As soon as she received the correct structure, the printer would start printing the antibody, already in a clean solution suitable for inoculation.

And so medicine progressed. For every virus, for every toxin, a molecule of its own, free of side effects in humans, specifically designed for the aggressor agent.

And most importantly, Isabelle would smile again. Of that, Laura had no doubt.