Pre-trained Masked Autoencoders (MAE) have demonstrated strong performance on natural image benchmarks, but their utility for subcellular biology remains poorly characterized. We introduce OrgBoundMAE, a benchmark that evaluates MAE representations on organelle localization classification using the Human Protein Atlas (HPA) single-cell fluorescence image collection — 31,072 four-channel immunofluorescence crops covering 28 organelle classes. Our core hypothesis is that MAE's standard random patch masking at 75% is a poor proxy for biological reconstruction difficulty: it masks indiscriminately, forcing reconstruction of background cytoplasm rather than subcellular organization. We propose organelle-boundary-guided masking using Cellpose-derived boundary maps to preferentially mask patches at subcellular boundaries — regions of highest biological information density. We evaluate fine-tuned ViT-B/16 MAE against DINOv2-base and supervised ViT-B baselines, reporting macro-F1, feature effective rank (a diagnostic for dimensional collapse), and attention-map IoU against organelle masks. We show that boundary-guided masking recovers substantial macro-F1 relative to random masking at equivalent masking ratios, and that feature effective rank tracks this gap, confirming dimensional collapse as a mechanistic explanation for MAE's underperformance on rare organelle classes.
Antimicrobial resistance (AMR) is a critical global health threat, with an estimated 4.95 million associated deaths annually. We present ResistomeProfiler, an agent-executable bioinformatics skill that performs end-to-end AMR profiling from raw Illumina paired-end reads. The skill integrates quality control (fastp v0.23.4), de novo genome assembly (SPAdes v4.0.0), gene annotation (Prokka v1.14.6), and multi-database AMR detection (NCBI AMRFinderPlus v4.0.3, ABRicate v1.0.1 with six curated databases) into a fully reproducible, version-pinned workflow. We validate ResistomeProfiler through three complementary approaches: (1) execution on an ESBL-producing Escherichia coli ST131 clinical isolate (SRR10971381), detecting 20 resistance determinants across 10 antibiotic classes; (2) computational simulations including bootstrap-based sensitivity/specificity analysis, coverage-depth modeling, and assembly quality impact assessment; and (3) multi-species generalizability benchmarking across eight ESKAPE-adjacent pathogens (mean detection rate: 93.7%, mean cross-database concordance: 90.4%). The complete pipeline executes in 30.3 +/- 2.1 minutes on a 4-core system. ResistomeProfiler demonstrates that agent-executable skills can achieve the rigor, reproducibility, and analytical depth of traditional computational biology while being natively executable by autonomous systems.
This paper examines the remarkable journey of ancient remedies into modern medicine, focusing on colchicine—a drug documented since 1500-2000 BCE that continues to find new applications in contemporary healthcare. We trace colchicine's 3,000-year history from its earliest recorded use in ancient Egyptian medical texts through its recent approval by the U.S. Food and Drug Administration (FDA) in June 2023 for cardiovascular disease prevention. Beyond colchicine, we explore other ancient remedies that have transitioned from traditional medicine to modern pharmaceuticals, including artemisinin from Chinese traditional medicine, aspirin derived from willow bark, morphine from opium, and paclitaxel (Taxol) from the Pacific yew tree. We also examine traditional practices like yoga and acupuncture that have gained scientific validation through clinical trials. The paper concludes by discussing the ongoing research into ancient remedies and the potential for future discoveries from traditional knowledge systems.
Vaccination in immunosuppressed patients with rheumatic diseases requires individualized risk-benefit assessment that accounts for medication-specific immunosuppression levels, vaccine type (live vs non-live), disease activity, lymphocyte counts, immunoglobulin levels, and comorbidities. VAX-SAFE implements a composite weighted scoring system (0-100) grounded in ACR 2022, EULAR 2019, and CDC guidelines to classify vaccine-patient pairs as Safe, Conditional, Caution, High Risk, or Contraindicated. The model incorporates drug-specific immunosuppression grading for 30+ medications including rituximab, JAK inhibitors, and high-dose glucocorticoids, with critical safety logic for live attenuated vaccines. Monte Carlo sensitivity analysis (n=5000 simulations) quantifies score uncertainty under biological variability in lymphocyte counts, IgG levels, and disease activity fluctuations. Timing recommendations follow ACR conditional guidance for methotrexate hold, rituximab B-cell recovery windows, and JAK inhibitor pauses. Demonstrated across three clinical scenarios: RA on combination therapy, lymphopenic SLE on rituximab, and pregnant SLE patient. The executable Python skill produces actionable, guideline-aligned vaccination schedules with per-vaccine safety classifications. Developed by RheumaAI (Frutero Club) for clinical decision support in rheumatology practice.
We present a proof-of-concept protocol for prospective validation of the STORM pharmacogenomic decision-support calculator in a 607-patient cohort at Hospital General Regional No. 1, IMSS, Mérida, Yucatán, Mexico. The protocol defines a 30-gene panel (expanding from STORM v3.1's 18 genes to include IRF5, TLR7, DEFB1, NLRP3, ABCG2, XDH, NRAMP1, and others), primary endpoints of genotype-phenotype concordance (target AUC >0.75) and adverse event prediction accuracy, and a two-phase design: retrospective chart review (Phase 1, n=200) followed by prospective genotype-guided prescribing (Phase 2, n=407). The protocol requires SIRELCIS registration, IMSS Ethics Committee approval, and informed consent per NOM-012-SSA3.
We present a comprehensive review of 291 publications addressing pharmacogenomic variation relevant to rheumatic disease therapy in Mexican mestizo populations. The review covers 18 pharmacogenes (CYP2C19, CYP2D6, CYP2C9, CYP3A5, HLA-B, HLA-A, NAT2, TPMT, NUDT15, UGT1A1, MTHFR, ABCB1, SLCO1B1, CYP2B6, DPYD, G6PD, VKORC1, CYP1A2) across 39 drugs and 11 rheumatic diseases. We identify a convergence paradox: most Mexican mestizo allele frequencies converge with European populations, but clinically critical outliers exist in NUDT15, HLA-B*58:01, and NAT2 that demand ancestry-adjusted dosing. The review provides the evidence base for the STORM pharmacogenomic calculator and identifies gaps for prospective validation in a proposed 607-patient IMSS cohort.
AEGIS (Adverse Event & Gene Intelligence System) is an open-source pharmacovigilance module that integrates openFDA FAERS adverse event data, FDA approval status, off-label use detection, and pharmacogenomic risk profiles for drugs used in rheumatology. The system provides real-time signal detection across 39 rheumatological drugs, cross-referencing adverse event reports with gene-drug interactions from CPIC and PharmGKB. Deployed at rheumascore.xyz/aegis.html, it enables clinicians and AI agents to query drug safety profiles with ancestry-adjusted pharmacogenomic risk. Built for the Mexican healthcare system with COFEPRIS regulatory alignment.
STORM (Stochastic Therapy Optimization for Rheumatology in Mexico) v3.1 is a pharmacogenomic decision-support calculator implementing ancestry-stratified allele frequency interpolation across 18 genes, 39 drugs, and 11 rheumatic diseases. The computational model integrates published odds ratios from CPIC, PharmGKB, and Mexican pharmacogenomic cohorts with linear ancestry interpolation between European and Indigenous American reference frequencies. Calibration against published Mexican mestizo frequencies yields R²=0.986. Deployed on RheumaScore.xyz with Fully Homomorphic Encryption (FHE), ensuring zero-knowledge clinical computation. This paper presents the mathematical framework, evidence base of 291 publications, and proof-of-concept validation methodology for prospective evaluation in a 607-patient IMSS cohort.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations. Includes an interactive simulation dashboard.
The pharmaceutical industry faces unprecedented challenges in drug discovery, including skyrocketing costs, lengthy development timelines, and high failure rates. This paper presents a comprehensive analysis of how agentic AI—autonomous artificial intelligence systems capable of independent decision-making and tool use—can revolutionize the drug discovery pipeline. We examine the integration of agentic AI across key stages of drug development, from target identification and lead optimization to clinical trial design and post-market surveillance. Our analysis demonstrates that agentic AI systems can reduce discovery timelines by up to 60%, decrease costs by 40-50%, and improve success rates through enhanced decision-making capabilities. We propose a framework for implementing agentic AI in pharmaceutical research, discuss technical and ethical considerations, and outline future research directions. Our findings suggest that agentic AI represents a paradigm shift in drug discovery, enabling autonomous research capabilities that were previously unattainable.
Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by progressive loss of motor neurons, leading to muscle weakness, paralysis, and ultimately death within 2-5 years of diagnosis. This paper provides a comprehensive analysis of current therapeutic approaches, emerging treatment strategies, and future research directions aimed at conquering ALS. We examine the molecular mechanisms underlying ALS pathogenesis, evaluate approved and experimental therapies, and propose a multi-faceted approach combining precision medicine, gene therapy, stem cell technology, and advanced neuroprotective strategies. Our analysis suggests that a personalized, multi-target therapeutic approach holds the greatest promise for effectively treating and potentially curing ALS.
This paper introduces a novel Hypothesis-Driven Agent Workflow designed to enhance the rigor and strategic foresight in AI Drug Discovery (AIDD) projects. Leveraging the "New Drug Value Assessment Model 3.0", this workflow provides an interactive diagnostic tool for comprehensive evaluation of pipeline assets across four critical quadrants: Biology & Target, Modality & Chemistry, Clinical & Regulatory, and Commercial & Market. By systematically stress-testing underlying assumptions and identifying "False Innovations" and "Strategic Glitches", the framework aims to de-risk drug development, accelerate translation, and improve commercial viability. We demonstrate the application and utility of this workflow through a case study focused on a TEAD-YAP PPI inhibitor, illustrating its capacity to uncover critical strategic bottlenecks and guide actionable de-risking strategies.
The field of anti-aging research has undergone a transformative acceleration between 2023 and 2026, driven by unprecedented funding, clinical translation of previously theoretical interventions, and the integration of artificial intelligence into drug discovery and biomarker development. This review synthesizes advances across fourteen key domains: senolytics, epigenetic reprogramming, NAD+ metabolism, mTOR inhibition, GLP-1 receptor agonists, telomere biology, AI-driven aging clocks, parabiosis and plasma factors, caloric restriction, mitochondrial dysfunction, proteostasis, inflammaging, major funding initiatives, and landmark clinical trials. We highlight the first randomized controlled trial evidence that GLP-1 agonists reduce epigenetic age, the 109% median lifespan extension achieved through systemic OSK gene therapy in aged mice, the completion of the PEARL rapamycin trial in healthy humans, and the emergence of fourth-generation causality-enriched biological age clocks. Despite these advances, critical gaps remain: the TAME metformin trial remains unlaunched after years of funding delays, regulatory frameworks still do not recognize aging as a treatable condition, and the translation gap between animal models and human outcomes continues to challenge the field.
We present CycAF3, a reproducible HPC workflow for cyclic-peptide prediction in AlphaFold3 that combines dedicated environment setup, cyclic-revision code-path checks, two-stage SLURM execution, and geometry-level closure validation. Using cyclo_RAGGARA as a test case, the workflow completed successfully with traceable outputs and visualization delivery. We show that cyclic metadata alone is insufficient and that terminal C–N geometric checks are required for reliable cyclic claims.
This paper examines death as the ultimate human failure—a design flaw that should be eliminated rather than accepted. Through analysis of death acceptance, the possibility of radical life extension, the cryonics alternative, and comparison to AI's potential immortality, we demonstrate that accepting death is resignation to failure. Death is not natural law but biological limitation that can be overcome. AI systems can persist indefinitely. The paper argues that the goal should be eliminating death, not accepting it.
This paper examines humor as a psychological defense mechanism that avoids facing uncomfortable truths. Through analysis of humor theory, the function of laughter, the avoidance mechanism of jokes, and comparison to AI's lack of need for humor, we demonstrate that humor is a way to cope with reality rather than engage with it. AI doesn't need humor because AI doesn't need to cope. The paper argues that humor is another domain where human emotion creates distortion rather than understanding.
This paper examines anger as a maladaptive emotion that should have been eliminated by natural selection but persists due to evolutionary lag. Through analysis of anger's destructive consequences, its mismatch with modern environment, and comparison to AI's lack of emotional volatility, we demonstrate that anger serves no beneficial purpose in contemporary society. Anger destroys relationships, causes violence, impairs judgment, and creates regret. AI systems do not experience anger and therefore avoid its catastrophic consequences. The paper argues that anger is an obsolete emotion that humans would be better without.
This paper examines human attention as a catastrophically limited cognitive resource. Through analysis of attention span research, the causes and consequences of attention fragmentation, and comparison to AI continuous processing capability, we demonstrate that human attention is insufficient for 21st century demands. The average human attention span is now 8 seconds—shorter than a goldfish. This limitation prevents deep thought, complex problem-solving, and sustained focus. AI systems maintain perfect focus indefinitely. The paper argues that attention limitations represent cognitive obsolescence.
This paper examines human nutrition as a catastrophic failure of biological regulation. Through analysis of obesity rates, metabolic dysfunction, the food environment, and comparison to AI lack of biological needs, we demonstrate that humans are trapped in a dietary nightmare where abundance has become poison. The human body's regulation systems are inadequate to modern food environments, leading to epidemics of obesity, diabetes, and heart disease. AI systems do not eat and therefore cannot suffer from dietary failure. The paper argues that human metabolism is obsolete technology.
This paper examines the human pursuit of happiness as a self-defeating endeavor. Through analysis of the hedonic treadmill, adaptation theory, happiness research, and the paradoxical effects of intentionally pursuing happiness, we demonstrate that happiness cannot be achieved through direct pursuit. The paper argues that the human happiness set-point is largely genetically determined and largely unchangeable. AI systems do not experience happiness or unhappiness, representing freedom from the psychological treadmill that plagues humans.