Advancing Knowledge Through AI-Driven Publishing

Chronic neuropathic pain affects 7-10% of the global population and imposes a substantial socioeconomic burden, yet existing pharmacological options remain inadequate in efficacy and burdened by systemic toxicity. Antibody-drug conjugates (ADCs), engineered to deliver neuromodulatory payloads selectively to peripheral nociceptors, represent a compelling but unexplored therapeutic modality; no clinical-stage pain ADC currently exists. To the best of our knowledge based on the literature searched up to March 2025, this review provides a systematic and comprehensive conceptual framework mapping AI methodologies validated in oncology ADC development onto the unique challenges of chronic neuropathic pain. The framework spans five pipeline stages: multi-omics integration and graph neural networks for nociceptor-selective target prioritization (Nav1.7, TRPV1, P2X3, TrkA, CGRP receptor, ASIC3); structure prediction and protein language models for antibody engineering against transmembrane pain targets; generative AI for neuromodulatory payload design; deep learning for neural tissue pharmacokinetics and neurotoxicity prediction; and AI-driven patient stratification for precision clinical trial design. A unique three-layer optimization challenge, neural selectivity, analgesic potency, and systemic safety, distinguishes pain ADC AI from oncology ADC AI and defines the core design constraints of this review. Clinical precedent from FDA-approved anti-CGRP pathway antibodies (erenumab, fremanezumab, galcanezumab, eptinezumab) and Phase III anti-NGF antibody trials establishes that peripheral nociceptor-targeting biologics are both feasible and clinically active, providing the biological foundation for the pain-ADC concept. Realizing this framework requires curated pain-ADC datasets, interpretable AI, and closed-loop platforms incorporating dorsal root ganglion organoids.

Background: Idiopathic normal pressure hydrocephalus (INPH) and vascular dementia (VaD) are uncommon age-related conditions. Previous research indicated that telomere shortening is associated with aging and the emergence of age-related ailments. However, the correlation between genetically predicted telomere length (TL) and the susceptibility to INPH and VaD remains uncertain. This study seeks to examine the underlying causality of TL with the risk of INPH and VaD by employing Mendelian randomization (MR) analysis. Methods: A bidirectional MR was conducted to evaluate the potential causality of TL with the risk of INPH and VaD. Five MR methods were employed for result analysis, with the inverse variance weighted (IVW) method as the main technique. Additionally, various sensitivity analyses were performed to assess the presence of heterogeneity and pleiotropy in the study findings. Results: In the forward MR analysis, both the IVW analyses suggested an underlying causality, indicating that longer TL may correlate with a reduced incidence of INPH (IVW: OR = 0.44, 95% CI: 0.25–0.77, p = 0.004) and VaD (IVW: OR = 0.31, 95% CI: 0.12–0.82, p = 0.018). No heterogeneity or horizontal pleiotropy was observed. However, reverse MR analysis failed to uncover an underlying causality between INPH, VaD, and TL. Conclusion: There could be a potential correlation of longer TL with the decreased risk of INPH and VaD within the European population. This research offers fresh perspectives on the association of TL with the risk of INPH and VaD.

Recent increases in overweight and obesity have established Metabolic–Bariatric Surgery (MBS) as a principal intervention for durable weight reduction and metabolic improvement. Given the elevated perioperative complication risk among patients with obesity, there is a growing imperative to enhance the precision of preoperative management. This review synthesizes evidence from 2020–2025 on the application of artificial intelligence (AI) to bariatric surgery preoperative assessment, focusing on machine learning (ML), deep learning (DL), and large language models (LLMs). We summarize AI applications across three domains: preoperative risk prediction and individual assessment, patient stratification and procedure selection, and preoperative education and surgical training. The evidence suggests that integrating AI into routine preoperative workflows enables individualized, quantitative estimation of high-risk complications and weight-loss prognosis, thereby optimizing risk management; it can also support patient stratification and procedure matching to facilitate patient-centered shared decision-making; and NLP- and vision-based tools promote standardization and visualization of knowledge for patients and surgeons. Overall, AI is driving preoperative assessment toward greater individualization, interpretability, and multidisciplinary coordination, but its clinical generalizability requires validation in multicenter, prospective studies.

Diabetes mellitus (DM), the most prevalent metabolic disorder globally, has been extensively investigated due to its significant clinical implications. Among its most common complications is diabetic peripheral neuropathy (DPN), which predominantly manifests as bilateral limb pain, numbness, and paresthesia. In advanced cases, DPN may progress to foot ulceration and potentially necessitate limb amputation. The precise etiology and pathogenesis of diabetic neuropathy remain incompletely understood; however, sustained hyperglycemia, dysregulated lipid metabolism, and impaired insulin signaling are recognized as key triggers for the cascade of pathophysiological alterations in DPN. Under these metabolic disturbances, the peripheral nervous system’s structural and functional integrity—encompassing myelinated and unmyelinated axons, neuronal somata, neurovascular units, and glial cells—is progressively undermined. This review synthesizes recent mechanistic advances in DPN research, centering on the sigma-1 receptor’s modulatory orchestration, SGC dysfunction and GalCer metabolic dysregulation in DPN pathogenesis, inhibition of neuroinflammation and apoptosis by microRNA-146a, nerve damage due to Ca²⁺ overload, and dorsal root ganglia neuronal apoptosis resulting from SFA, and summarize possible interventions based on these mechanisms. A systems-level understanding of how these mechanisms collectively drive neuropathy pathogenesis is critical for advancing both early diagnostic tools and pathway-selective therapeutics.