Peripheral nerve injury (PNI) remains a major cause of long-term sensory and motor dysfunction, imposing a substantial clinical and socioeconomic burden worldwide. Although peripheral nerves possess intrinsic regenerative capacity, functional recovery is often incomplete because of the complex interplay among axonal degeneration, Schwann cell plasticity, neuroimmune responses, extracellular matrix remodeling, metabolic reprogramming, remyelination, and target reinnervation. Recent advances in transcriptomics, proteomics, epigenomics, metabolomics, and single-cell technologies have expanded molecular characterization of nerve degeneration and regeneration, revealing numerous candidate biomarkers and therapeutic targets. However, biological heterogeneity, fragmented datasets, and the lack of robust predictive frameworks still limit translation into clinical practice. In this review, we provide a comprehensive overview of the cellular and molecular mechanisms governing peripheral nerve repair and examine emerging biomarker classes associated with regenerative competence, neuroinflammation, remyelination, and functional recovery. We also discuss therapeutic strategies targeting Schwann cell reprogramming, axonal growth, immune modulation, metabolic adaptation, and remodeling of the regenerative microenvironment. We further examine how artificial intelligence (AI) and machine learning (ML) may support multi-omics integration, biomarker prioritization, outcome prediction, and therapeutic target discovery, while emphasizing that PNI-specific clinical validation remains limited. We propose a systems-level framework that links molecular signatures with clinical outcomes and highlights the potential of AI-enabled multimodal analytics to support translational research. Age-related molecular variation should be incorporated as an additional analytical dimension, allowing comparison of regenerative and non-regenerative signatures across younger and older tissues. Together, these approaches provide a framework for testing age-aware molecular markers and computational models in future translational studies.
Background With the aging population, the demand for palliative care is expected to increase. Pain management is a critical component of palliative care with unique considerations for older patients. The aim of this scoping review was to map the nature and extent of the evidence on pain management strategies in this population. Methods We searched databases for peer-reviewed studies and grey literature on pain management in older adults receiving palliative care. Strategies were classified as opioid analgesics, non-opioid medications, procedural, non-pharmacological, educational, and multifaceted interventions. Frequencies and proportions were used to describe the strategies, reported improvements, and levels of evidence. Results We included 117 studies. Most focused on cancer populations (82/117, 71%), and the majority of participants (98/114, 86%) were aged 65–74 years on average. Overall, 68% of the studies yielded moderate to low level of evidence. Opioids and multifaceted interventions were the most frequently evaluated strategies, each examined in 32% (37/117) of studies. Across both high- and moderate/low-evidence studies, pain reduction was most consistently reported for procedural interventions (3/3, 100%; 3/3, 100%), opioids (3/5, 60%; 25/32, 78%), and multifaceted interventions (4/9, 44%; 20/23, 87%). Favourable findings were most often reported for combinations of opioids, opioids for breakthrough pain, nerve blocks or local anesthesia, cannabis, music or massage therapy, exercise, and biopsychosocial or care organization interventions. Conclusion This review mapped pain management strategies for older adults in palliative care, identified key evidence gaps, particularly among oldest-old individuals and those with non-cancer conditions, and highlighted the need for stronger research.
How cellular and microenvironmental changes are translated into tissue-level decline remains incompletely understood. Here, we view tissue maintenance through the lens of multicellular coordination, defined as the endogenous management of dependencies among component activities. These dependencies are embodied in a tissue’s relational state across signalling networks, epigenetic states, cell composition, and extracellular matrix. The different turnover timescales of these substrates may permit system memory and path dependence. We analyse two constraints that may limit coordination and distributed maintenance: self-reference, wherein the maintenance machinery is itself vulnerable to decline, and a sensing bottleneck, which limits the extent to which cells can perceive and represent age-related change. We explore how tissue architecture and renewal shape the consequences of these constraints for coordination, relational state and tissue function. In some cases, coordination may stabilise a pathological state even after removal of the initiating driver, creating a coordination trap. This view may help explain why tissues exhibit different age-related trajectories and suggests that interventions may also need to restore functional tissue coordination.
