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

Traffic-informed Pavement Performance Prediction and Maintenance Prioritization Using the Pavement Condition Index: A Case Study from Afghanistan

Published on: 27th August, 2026

Road infrastructure serves as the primary medium of economic and agricultural transportation in developing countries. Afghanistan’s district roads play a critical role in connecting rural production centers to markets; however, systematic maintenance is often hindered by inadequate funding, lack of structural monitoring, and climatic deterioration. This research develops pavement condition assessment and deterioration prediction models using Pavement Condition Index (PCI), pavement age, characteristic rebound deflection (Dc), and Average Annual Daily Traffic (AADT). Multiple regression models were generated based on recorded field data across six district road sections in Herat Province. Regression coefficients demonstrated high goodness of fit (R² = 0.964 – 0.992), indicating reliable predictive capability. Based on PCI-based prioritization, maintenance strategies were ranked and scheduled under budget constraints. Results indicate that Sections D01 and D02 urgently require rehabilitation due to structural fatigue and distress propagation, while other sections can be preserved through preventive maintenance. This study provides a scientifically supported PMS framework for Afghan district roads, enabling optimized resource allocation, deterioration forecasting, and long-term serviceability planning.
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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