The Hidden Hand - Long Gaming Pathogen Design
Time Delayed Bioweapons
While I do have a significant amount of highly complex or long-form topics to write about some days fate rewards you for your perseverance, and today was one of those days. So while I shift my writing schedule (sometimes I am also a vibe writer…lol) to focus on this cryptic topic to publish at some point in the next few days, here we are with another topic of interest to me.
Among my many long-term interests here, tracking “odd outbreaks”, as signals, thus referring to their frequency in a certain period, allied with interdisciplinary analysis pf information allows me to forecast what could be of importance before others realize it. The most significant of those has been Avian Flu.
In the most recent articles, I have stated I do not think the current Avian Flu is a significant problem for humans yet, because it is not entirely adapted to mammalian receptors, it doesn’t cross the barrier species between other animals, and even other animals to humans that effectively without direct contact (literally touching a dead bird…) and we do have a level of cross-immunity. Overall, if healthy with a decent portion of your immune cells intact you are safe from infection itself, and even after infection, safe from severe disease.
So what changed ? Nothing much, or drastically. But always remember, Language, signals, frequency (topology approach to each of the aforementioned, etc). A few days ago a Canadian teenager found itself in critical condition with a presumed Avian flu infection. It was later confirmed to be Avian Flu the clade that comes from poultry and on rare occasions jumps and infects humans. No contact, no source, nothing was found that could be traced as a source of infection.
So far, most Avian flu infections are caused by direct contact. This means the person needs to be around a significant amount of sick animals (that create a literal cloud of viral particles) or touch a severely sick or dead animal (and somehow have a cut on the hands or put the hands around the face…). This is concerning because severe infection in adolescents and younger people is a peculiar trait of highly pathogenic strains.
The US itself has a recent total of 46 cases. Scratch that, a new case in Oregon was just announced. But I much prefer, from both a threat analysis and a personal thing perspective to look into China, there are always interesting things occurring in China. H9N2 avian flu infects 7 more in China has infected 7 people recently all without known sources which replicated the Canadian case, and 6 of the patients were young (7, 6, 5, 3, 1 year old and a 10-month-old boy) with old elderly patient at 67.
Time is peculiar in all cases because the WHO just has a virtual meeting about Preparing for containment and mitigation of pandemic H5N1 influenza, Uses of statistical and mathematical modeling. As someone who kept a close eye on Avian Flu before almost everyone else, kept track of it, significant notes, I still don’t think the current clades/strains are a problem, but I suspect some seeding is starting to take place or something else…
Time-Delay “Engineered Threats”. Engineered Threats are vulgarly known as Bioweapons. Time-Delay here doesn’t mean latency, or enabling a pathogen to bypass immunological defenses to create a reservoir inside the host, so when enough stress or a specific, secondary trigger is introduced it enables that bio-threat to get off the “latency” and start a productive infection.
Time-Delay here means using incredibly sophisticated means, precisely designed to obfuscate origin, intent, and direction, taking years for the threat to actually surface because it is “mostly” following “Nature’s design”. You later can introduce favorable variables to your “time-delayed threat” to shift it towards your intended goal, let’s say, a failed vaccine in a Middle Eastern country leading to the surface of a Highly Pathogenic virus that comes to dominate the world later on (this is a direct reference to the current Avian Flu).
Why do I mention this event? Not just for contextual framing or to give you an analytical starting point, but I now firmly believe many “odd” outbreaks were not merely laboratory accidents (lab leaks); some of these were caused by groups accruing a significant amount of data on the “right” hosts.
This sophisticated data-gathering process can be achieved using a few approaches. Designing the pathogen to begin its lifecycle at a lower fitness point in the evolutionary landscape, means starting at a point it is not an immediate threat to the targeted population, as the virus remains in the wild or confined to specific conditions. However, this is a designer pathogen and thus will follow a pre-determined evolutionary pathway, climbing the ladder towards fitness peak, leading to high transmissibility, higher pathogenicity, or high yield impact.
In this context (called fitness landscape) the concept of an evolutionary valley is necessary. This means a pathogen replicates less efficiently and spreads less effectively compared to other strains or competing pathogens. Epidemiological models tend to focus on pathogens causing high morbidity or mortality, leaving "valley-phase" pathogens largely unnoticed.
Engineering a designer pathogen to inhabit the valley requires the addition of specific traits, to reduce its fitness while preserving its ability to replicate and persist in minimal conditions. Altering its polymerase function to possess minor inefficient can reduce the replication rate without inducing failure, effectively limiting the spread. Weakening the virus binding affinity to host receptors ensures less contagion in the short term.
As the last simplified step, altering the immune response towards the pathogen by simply assuring it has a suboptimal immune evasive capability. It stays under the radar of the immune system and persists, without causing severe symptoms or drawing attention.
This can be done by guided evolution. Similar to how potentially SARS-CoV-2 came to be, you passaged a pathogen through lab models that imitate your desired target population, as the virus adapts and mutations that increase are identified, you are able to forecast how the virus can potentially behave under specific conditions. Serial passaging pathogens in lab animals are the bedrock of Gain of Function research.
These can be achieved using a myriad of tools, one being “Mutational Scaffolding” (here using my own interpretation) a method where early mutations create a genetic foundation for later, more impactful changes. These scaffolds ensure that the virus follows a predictable evolutionary path rather than taking random, unpredictable directions. This technique requires extensive knowledge of viral epistasis, where certain genetic changes depend on or interact with others.
Allied to the approach above is Epistatic control. Epistasis is the interdependence of genetic mutations—acts as the unseen architecture of a pathogen's evolution. Imagine a virus with a mutation that increases infectivity at the cost of structural integrity. On its own, this change could cripple the pathogen’s survival.
However, introduce a secondary mutation that reinforces structural stability, and the two alterations together transform the pathogen into a highly infectious and resilient entity. This interplay is not mere chance; it is the blueprint for engineering pathogens that evolve in predictable, calculated steps. By mapping these relationships, you can sculpt the evolutionary trajectory, ensuring that beneficial mutations emerge in a specific, controlled sequence
This synergy between mutations, where the combination creates a result far greater than the sum of its parts, exemplifies the power of epistatic interactions. What I just described here will sound theoretical, but it is quite literally how the RBD and the NTD in SARS-CoV-2 act.
By mapping epistatic relationships, how one mutation influences the viability or effects of other mutations, you essentially can gather enough data to forecast the pathogen’s evolutionary trajectory, giving the ability to predict which mutations will arise in which sequence. A pathogen designed to initially infect, then thrive in animals, but with silent mutations that will only occur under specific conditions, creating a predictable road towards efficient human infection.
Doing so by leveraging natural hosts, and manipulating wildlife reservoirs. You introduce a pathogen into animal populations that naturally act as reservoirs (bats, rodents, livestock… remind you of something ?) and the pathogen evolves over time. You can achieve this by introducing a pre-adapted virus to the desired wild species, overtime with selection pressure, and numerous replication cycles, it drives the virus towards survival, add the techniques above and you can “induce a species jump”.
You can achieve that by using Trigger-Point mechanisms, meaning the activation of certain genes in the pathogen only occurs under specific conditions. Temperature variation (host or habitat), humidity thresholds, biological trigger-points such as changes in populations immune, co-infection with specific pathogens, or even the sudden lack of nutrients in an ecosystem. Let’s say systemic thiamine deficiency in aquatic wildlife directly affecting the epidemiology of avian influenza in wild aquatic birds.
Ages ago I “warned” on how “easy” it is for someone to use a Machine Learning approach, and AI to effectively catapult many of the most complex steps of creating “novel” pathogens, and toxins.
What if I told you a team of scientists created a virtual lab, using AI agents to design new SARS-CoV-2 nanobodies, that were experimentally validated in the real world ? An AI agent functions autonomously. Unlike a Language Model which only responds to commands (prompts) such as asking for the model to do X (like writing a Python program, then asking to use that program to achieve your task) the AI agents can identify problems, coordinate with other agents, and iteratively refine solutions without direct human input.
Imagine the same approach applied to designing mutational scaffolds or mapping epistatic interactions. An AI agent could analyze vast datasets on viral genetics, predict the most effective evolutionary trajectories, and design a series of mutations to achieve a desired outcome. In the past 12 months alone the number of tools in regards to understanding biology has grown exponentially… I have more to say about these matters but I will leave it for another day.
As a joke to lighten your mood this weekend, someone recorded my cells talking. Watch the original source too he deserves all the views.
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🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂🤣😂💯Pure gold!! "...his organs LAUGHED at us, on the way in, man!"
Most excellent, and an example of why we worth controlling, saving, evolving, whatever your benevolent/malevolent bent may be😉
Ta for sharing, and keep up the excellent work, it matters.🙏😉
#follownone #mistakeswereNOTmade #getlocalised
So this is what Baric and the bat lady were working on before the PLA were caught with their pants down. You can’t put this stuff into a fiction novel because nobody would believe you.