Alex Bilzerian
RT @TenreiroDaniel: you never hear people describe technologies as 'disruptive' anymore, but it was once the adjective of choice
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RT @TenreiroDaniel: you never hear people describe technologies as 'disruptive' anymore, but it was once the adjective of choice
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Alex Bilzerian
RT @alexbilz: Luis Raúl Pericchi: "The language of science is the language of probability, and not of p-values.”
https://t.co/OSYIsfMTdg https://t.co/gXeu6nvqi9
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RT @alexbilz: Luis Raúl Pericchi: "The language of science is the language of probability, and not of p-values.”
https://t.co/OSYIsfMTdg https://t.co/gXeu6nvqi9
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Alex Bilzerian
RT @alexbilz: Taleb's note on p-hacking is worth a close read if you haven't already: https://t.co/xXhGz1QZRT https://t.co/jNONBKSpRr
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RT @alexbilz: Taleb's note on p-hacking is worth a close read if you haven't already: https://t.co/xXhGz1QZRT https://t.co/jNONBKSpRr
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Alex Bilzerian
'The Nobel Prize in Economic Sciences is a dubious thing at best.'
https://t.co/b1uRPlEt8d via @mises
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'The Nobel Prize in Economic Sciences is a dubious thing at best.'
https://t.co/b1uRPlEt8d via @mises
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Alex Bilzerian
RT @DeryaTR_: I absolutely love working with o1-preview! I haven’t felt this intellectually stimulated in a long time. Not only am I brainstorming ideas, but now I’m designing experiments with o1 and asking it to virtually run them, predict potential outcomes, and then refine and iterate.
Below is just one example. While this is a highly specific and complex topic that may be hard to grasp without a deep immunology background, I wanted to share the thought process. It might be useful for others to apply similar scenarios in their own fields.
The question below is about developing engineered cells to fight cancer, the idea was also from o1-preview with some guidance and brainstorming from me😀:
Prompt: "You have now completed all the experiments you proposed. Determine three potential outcomes for each experiment, whether they succeed or fail. Then, propose solutions or reflect on what you have learned, and ask new questions. Based on that, suggest what you would do next as an experiment. Be critical of your own approach."
Engineering CAR-T Cells with ‘Reserve Energy Packs’ via Metabolic Modulation
Experimental Approach:
1. Overexpression of Metabolic Enzymes:
• Target Genes: Enzymes involved in energy storage and release, such as glycogen synthase or creatine kinase.
• Vector Construction: Clone target genes into lentiviral vectors.
2. Transduction of T Cells:
• Activation: Isolate and activate CD8+ T cells.
• Transduction: Introduce metabolic enhancement constructs and CAR vector.
3. Metabolic Assessments:
• Energy Storage Measurement: Assess glycogen levels or creatine phosphate stores in CAR-T cells.
• Energy Utilization: Measure ATP levels and energy expenditure during activation.
4. Functional Assays:
• Exhaustion Induction: Subject cells to prolonged activation.
• Assessment: Evaluate cytotoxicity, cytokine production, proliferation, and survival.
Potential Outcomes of experiments:
Outcome 1: Enhanced Energy Reserves Improve CAR-T Cell Function
• Observations:
• Engineered CAR-T cells have increased energy stores.
• Display sustained function under prolonged activation.
• Reduced exhaustion markers compared to controls.
• Implications:
• Supports the idea that bolstering energy reserves can mitigate exhaustion.
• Provides a metabolic strategy to enhance CAR-T cell endurance.
• Next Steps:
• Optimize Energy Utilization:
• Ensure that enhanced energy stores are efficiently mobilized during activation.
• Combine with Other Metabolic Enhancements:
• Integrate with mitochondrial biogenesis or metabolic flexibility strategies.
• Long-Term Monitoring:
• Assess the stability of metabolic enhancements over time.
• Critical Reflection:
• Positive results suggest a viable approach. However, metabolic interventions require careful calibration.
Outcome 2: No Significant Impact on CAR-T Cell Function
• Observations:
• Despite increased energy stores, CAR-T cell function remains unchanged.
• Exhaustion markers and functional assays are similar to controls.
• Implications:
• Additional energy reserves may not be the limiting factor in exhaustion.
• Energy utilization pathways may not be effectively engaged.
• Next Steps:
• Enhance Mobilization Mechanisms:
• Overexpress enzymes involved in energy release during activation.
• Assess Metabolic Integration:
• Examine whether energy stores are accessible during high-demand periods.
• Explore Alternative Pathways:
• Focus on improving energy efficiency or reducing energy consumption.
• Critical Reflection:
• Suggests that simply increasing energy stores is insufficient. The entire metabolic network must be considered.
Outcome 3: Metabolic Modifications Cause Cellular Stress
• Observations:
• Altered energy metabolism l[...]
RT @DeryaTR_: I absolutely love working with o1-preview! I haven’t felt this intellectually stimulated in a long time. Not only am I brainstorming ideas, but now I’m designing experiments with o1 and asking it to virtually run them, predict potential outcomes, and then refine and iterate.
Below is just one example. While this is a highly specific and complex topic that may be hard to grasp without a deep immunology background, I wanted to share the thought process. It might be useful for others to apply similar scenarios in their own fields.
The question below is about developing engineered cells to fight cancer, the idea was also from o1-preview with some guidance and brainstorming from me😀:
Prompt: "You have now completed all the experiments you proposed. Determine three potential outcomes for each experiment, whether they succeed or fail. Then, propose solutions or reflect on what you have learned, and ask new questions. Based on that, suggest what you would do next as an experiment. Be critical of your own approach."
Engineering CAR-T Cells with ‘Reserve Energy Packs’ via Metabolic Modulation
Experimental Approach:
1. Overexpression of Metabolic Enzymes:
• Target Genes: Enzymes involved in energy storage and release, such as glycogen synthase or creatine kinase.
• Vector Construction: Clone target genes into lentiviral vectors.
2. Transduction of T Cells:
• Activation: Isolate and activate CD8+ T cells.
• Transduction: Introduce metabolic enhancement constructs and CAR vector.
3. Metabolic Assessments:
• Energy Storage Measurement: Assess glycogen levels or creatine phosphate stores in CAR-T cells.
• Energy Utilization: Measure ATP levels and energy expenditure during activation.
4. Functional Assays:
• Exhaustion Induction: Subject cells to prolonged activation.
• Assessment: Evaluate cytotoxicity, cytokine production, proliferation, and survival.
Potential Outcomes of experiments:
Outcome 1: Enhanced Energy Reserves Improve CAR-T Cell Function
• Observations:
• Engineered CAR-T cells have increased energy stores.
• Display sustained function under prolonged activation.
• Reduced exhaustion markers compared to controls.
• Implications:
• Supports the idea that bolstering energy reserves can mitigate exhaustion.
• Provides a metabolic strategy to enhance CAR-T cell endurance.
• Next Steps:
• Optimize Energy Utilization:
• Ensure that enhanced energy stores are efficiently mobilized during activation.
• Combine with Other Metabolic Enhancements:
• Integrate with mitochondrial biogenesis or metabolic flexibility strategies.
• Long-Term Monitoring:
• Assess the stability of metabolic enhancements over time.
• Critical Reflection:
• Positive results suggest a viable approach. However, metabolic interventions require careful calibration.
Outcome 2: No Significant Impact on CAR-T Cell Function
• Observations:
• Despite increased energy stores, CAR-T cell function remains unchanged.
• Exhaustion markers and functional assays are similar to controls.
• Implications:
• Additional energy reserves may not be the limiting factor in exhaustion.
• Energy utilization pathways may not be effectively engaged.
• Next Steps:
• Enhance Mobilization Mechanisms:
• Overexpress enzymes involved in energy release during activation.
• Assess Metabolic Integration:
• Examine whether energy stores are accessible during high-demand periods.
• Explore Alternative Pathways:
• Focus on improving energy efficiency or reducing energy consumption.
• Critical Reflection:
• Suggests that simply increasing energy stores is insufficient. The entire metabolic network must be considered.
Outcome 3: Metabolic Modifications Cause Cellular Stress
• Observations:
• Altered energy metabolism l[...]
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Alex Bilzerian RT @DeryaTR_: I absolutely love working with o1-preview! I haven’t felt this intellectually stimulated in a long time. Not only am I brainstorming ideas, but now I’m designing experiments with o1 and asking it to virtually run them, predict…
eads to accumulation of metabolic intermediates.
• Increased cellular stress or toxicity.
• Reduced viability and function of CAR-T cells.
• Implications:
• Metabolic pathways are tightly regulated; perturbations can have adverse effects.
• Imbalance between energy storage and utilization may harm cells.
• Next Steps:
• Fine-Tune Gene Expression:
• Adjust expression levels to prevent accumulation of intermediates.
• Monitor Metabolic Health:
• Use assays to detect signs of metabolic stress early.
• Alternative Strategies:
• Focus on enhancing metabolic regulation rather than increasing reserves.
• Critical Reflection:
• Highlights the risks associated with metabolic engineering. Must ensure interventions do not disrupt cellular homeostasis.
Outcome 4: Metabolic Enhancement Leads to Immunosuppressive Phenotype
• Observations:
• Engineered CAR-T cells begin to express immunosuppressive molecules such as IL-10 or TGF-β.
• Increased expression of checkpoint molecules like PD-L1, contributing to an immunosuppressive microenvironment.
• Reduced cytotoxicity against target cells and inhibition of bystander T cell responses.
• Implications:
• Metabolic alterations may push CAR-T cells toward a regulatory or suppressive phenotype.
• This shift could undermine the therapeutic efficacy and potentially promote tumor growth by suppressing anti-tumor immunity.
• Next Steps:
• Reassess Metabolic Pathways:
• Identify which metabolic changes are linked to the immunosuppressive phenotype and adjust accordingly.
• Implement Gene Editing:
• Use CRISPR/Cas9 to knock out genes responsible for immunosuppressive cytokine production.
• Monitor Immune Function:
• Regularly assess the expression of immunosuppressive markers and cytokines during experiments.
• Critical Reflection:
• Underlines the potential for unintended immunomodulatory effects when manipulating metabolism.
• Necessitates comprehensive monitoring of immune functions beyond cytotoxicity.
• Highlights the importance of balancing metabolic enhancements to avoid promoting undesired phenotypes.
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• Increased cellular stress or toxicity.
• Reduced viability and function of CAR-T cells.
• Implications:
• Metabolic pathways are tightly regulated; perturbations can have adverse effects.
• Imbalance between energy storage and utilization may harm cells.
• Next Steps:
• Fine-Tune Gene Expression:
• Adjust expression levels to prevent accumulation of intermediates.
• Monitor Metabolic Health:
• Use assays to detect signs of metabolic stress early.
• Alternative Strategies:
• Focus on enhancing metabolic regulation rather than increasing reserves.
• Critical Reflection:
• Highlights the risks associated with metabolic engineering. Must ensure interventions do not disrupt cellular homeostasis.
Outcome 4: Metabolic Enhancement Leads to Immunosuppressive Phenotype
• Observations:
• Engineered CAR-T cells begin to express immunosuppressive molecules such as IL-10 or TGF-β.
• Increased expression of checkpoint molecules like PD-L1, contributing to an immunosuppressive microenvironment.
• Reduced cytotoxicity against target cells and inhibition of bystander T cell responses.
• Implications:
• Metabolic alterations may push CAR-T cells toward a regulatory or suppressive phenotype.
• This shift could undermine the therapeutic efficacy and potentially promote tumor growth by suppressing anti-tumor immunity.
• Next Steps:
• Reassess Metabolic Pathways:
• Identify which metabolic changes are linked to the immunosuppressive phenotype and adjust accordingly.
• Implement Gene Editing:
• Use CRISPR/Cas9 to knock out genes responsible for immunosuppressive cytokine production.
• Monitor Immune Function:
• Regularly assess the expression of immunosuppressive markers and cytokines during experiments.
• Critical Reflection:
• Underlines the potential for unintended immunomodulatory effects when manipulating metabolism.
• Necessitates comprehensive monitoring of immune functions beyond cytotoxicity.
• Highlights the importance of balancing metabolic enhancements to avoid promoting undesired phenotypes.
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Alex Bilzerian
Alfréd Rényi had a notorious coffee addiction.
He's often credited with quipping: "A mathematician is a device for turning coffee into theorems."
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Alfréd Rényi had a notorious coffee addiction.
He's often credited with quipping: "A mathematician is a device for turning coffee into theorems."
The paradox of poetry and information theory https://t.co/YvLQOSq0yQ tweet
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Alex Bilzerian
'Sexpionage: The Exploitation of Sex by Soviet Intelligence' - Lewis (1976, PDF):
https://t.co/TnsWLkSJBh
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'Sexpionage: The Exploitation of Sex by Soviet Intelligence' - Lewis (1976, PDF):
https://t.co/TnsWLkSJBh
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