Microbiome-targeted Pain Modulation via Multi-omics Profiling and Vagus Nerve Stimulation in Enhanced Recovery after Cholecystectomy Protocols
Year: 2025
Type: article
Abstract: Introduction: The research designed a microbiome-specific pain control model to augment Enhanced Recovery After Surgery (ERAS) protocols for cholecystectomy surgeries, which targets the shortcomings of the traditional medication-centric approach to pain management. Methodology: The system incorporates longitudinal multi-omics profiling, microbial control of gut–brain axis therapy and vagus nerve stimulation (VNS) to manage pain at the neural periphery. It classifies patients into responder and non-responder groups with high-throughput sequencing and metabolomics. Metabolic responders are administered probiotics to bio-transform polysaccharides into fatty acids while non-responders are treated with individualised faecal microbiota transplantation. Transcutaneous auricular VNS is programmed at minimal baseline settings unique to each person allowing for real-time pain score monitoring alongside stimulation control. Results: We have built a deep learning neural network that can sequence and analyse the complex interactions of taxa and metabolites within the span of a neuroimaging session <48 h. This approach not only enhances ecological pain resilience but also reduces opioid consumption by over 4%. In addition, this model improves post-operative outcomes such as reduced duration of hospitalisation and increased time to resumption of normal activities. Conclusion: It is possible to integrate microbial population dynamics as a unique therapeutic target into ERAS protocols, providing inter-patient optimisation for pain and treatment response variability and enhancing personalisation. The closed-loop nature of the system guarantees flexibility, which can be further developed for other uses in surgical recovery.
Authors: Roaa Jawad Aljawaed, Ayat Abdullah Alessa, Fatemah Abdulhadi Al Turaifi, Fatema Abbas Al-Dandan, Sarah Yousef Alhassan, Faisal Hammad A. Aladhyani, Meshari Mohammed H. Alotaibi, Khalid Abdulrahman Alotaibi, Mohammad Al Mohaini
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4413280477