Early Online (Volume - 9 | Issue - 1)

One-time CRISPR Adenine Base Editing Intervention in SMA: From SMN2 Splice Correction to Motor Neuron Rescue

Published on: 27th May, 2026

Spinal muscular atrophy (SMA) is a devastating autosomal recessive neuromuscular disorder characterized by progressive muscle weakness, atrophy, and respiratory failure due to selective degeneration of lower motor neurons arising from homozygous deletion of exon 7 (95%) or mutation in the SMN 1 gene (5%),with severity correlating with SMN2 copy number—from fatal Type1 to milder Type 4—affecting 1:6,000–10,000 births worldwide and burdening India with 1,500–2,000 annual cases amid diagnostic delays. Although the backup SMN2 gene compensates a bit for SMN deficiency, a critical C→T transition in exon 7 leads to exon skipping and production of a truncated, unstable and nonfunctional SMN protein. Recent advances in disease-modifying therapies-including antisense oligonucleotides, small-molecule splicing modifiers, and gene replacement-have significantly improved clinical outcomes; however, they do not restore endogenous SMN expression in all tissues and often require repeated administration. Despite these medications like Spinraza injections, Zolgensma gene therapy, Evrysdi pills that increase SMN protein, the condition still has got significant morbidity: Type 1 babies frequently die before the age of two, 60–95% develop scoliosis, which makes spinal injections uncomfortable and dangerous, and lifetime expenses for each patient surpass $2 million. What if we could edit the nucleotide base of SMN2(T6C) using ABE10 to make it emulate like SMN1 gene to restore stable functional SMN protein that would be the permanent cure for SMA. This cutting edge molecular tool “AI-based Adenine Base Editors” would facilitate an endogenous regulation, laying the groundwork for precision medicine in rare disease management.
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Potential of Quantum Computing to Advance Psychiatry through Genetic Medicine, Gene Therapy, and Human-Centered Artificial Intelligence

Published on: 1st July, 2026

Psychiatric disorders represent some of the most biologically complex challenges in medicine, arising from intricate interactions among genetic, epigenetic, environmental, developmental, and social factors. Advances in artificial intelligence (AI) have improved our ability to analyze large-scale biological datasets, identify biomarkers, and support precision medicine initiatives. However, the growing volume and complexity of genomic and multi-omic information increasingly challenge the capabilities of even the most advanced conventional supercomputers. Quantum computing offers a potential next step in biomedical discovery by enabling rapid analysis of multidimensional datasets, molecular simulations, and optimization problems relevant to genetic medicine and gene therapy. Extending these findings conceptually, we propose the forward-looking hypothesis that continued advances in quantum computing may eventually complement artificial intelligence and human expertise to facilitate increasingly sophisticated analyses relevant to psychiatric genetics and precision medicine. At present, no direct evidence of which we are aware demonstrates clinical implementation of quantum computing in psychiatric genomics. Accordingly, the concepts discussed in this Opinion should be viewed as a forward-looking scientific perspective that builds upon current advances in computational science and biomedicine while awaiting future experimental and clinical validation. Importantly, these advances should complement rather than replace human expertise. Human-in-the-loop systems remain essential for ensuring scientific rigor, ethical oversight, clinical judgment, and patient-centered care. The convergence of quantum computing, AI, genetic medicine, and human expertise may ultimately establish a transformative framework for future precision psychiatry and mental health therapeutics.
Cite this ArticleCrossMarkPublonsHarvard Library HOLLISGrowKudosResearchGateBase SearchOAI PMHAcademic MicrosoftScilitSemantic ScholarUniversite de ParisUW LibrariesSJSU King LibrarySJSU King LibraryNUS LibraryMcGillDET KGL BIBLiOTEKJCU DiscoveryUniversidad De LimaWorldCatVU on WorldCat
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