Background: Laparoscopic cholecystectomy is the gold standard surgical procedure for treating gallstone disease. Despite it being minimally invasive, various medications and methods are used to alleviate postoperative pain, and some patients still experience moderate-to-severe pain. This is a crucial problem that must be solved to avoid chronic pain. As part of postoperative multimodal analgesia, regional block is being increasingly applied in surgery under ultrasound guidance. We aimed to evaluate the analgesic effect of erector spinae plane block in adult patients undergoing laparoscopic cholecystectomy. Methods: PubMed, Cochrane Library, EMBASE, and Web of Science were searched for randomized controlled trials investigating the efficacy of erector spinae plane block on postoperative pain after laparoscopic cholecystectomy. The primary outcome was the postoperative pain score. The secondary outcomes were the cumulative intraoperative and postoperative opioid consumption at 24 h, incidence of postoperative nausea and vomiting, and shoulder pain after surgery. The results were pooled using the fixed- or random-effects model with Review Manager 5.3. Results: Fifteen randomized controlled trials involving 947 patients were included in the analysis. Postoperative pain score in the erector spinae plane block group was lower than that in the control group at postoperative 12 h (MD − 0.81, 95% CI − 1.1 to − 0.51, p < 0.00001) and 24 h (MD − 0.41, 95% CI − 0.62 to − 0.19, p = 0.0002). Cumulative opioid consumption was lower in the erector spinae plane block group than in the control group at postoperative 24 h (MD − 7.88, 95% CI − 10.17 to − 5.58, p < 0.00001). The erector spinae plane block group also experienced a lower incidence of postoperative nausea and vomiting than the control group. Opioid consumption and the incidence of postoperative nausea and vomiting were similar between the erector spinae plane block group and other block groups, including the oblique subcostal transversus abdominis plane block and quadratus lumborum block groups. Conclusions: Ultrasound-guided erector spinae plane block provides effective postoperative analgesia in adults undergoing laparoscopic cholecystectomy.

Obesity is characterized by excessive fat accumulation that triggers chronic low-grade inflammation and systemic immune dysregulation, significantly increasing the risk of metabolic disorders including insulin resistance, type 2 diabetes, and cardiovascular disease. This review examines the bidirectional relationship between obesity and immune dysfunction, focusing on how immune cell infiltration in adipose tissue drives inflammatory processes. We highlight the phenotypic shifts in key immune populations—macrophages polarized toward proinflammatory M1 phenotypes, T cell exhaustion occurrs, and alterations appear in B cells, natural killer (NK) cells, and dendritic cells—that collectively contribute to metabolic deterioration. The gut microbiome emerged as a critical mediator in this relationship, influencing both immune responses and metabolic regulation through gut–liver and gut–brain axes. We explore emerging immunomodulatory therapeutic strategies, including anti-inflammatory agents, microbiota interventions, and targeted immune therapies such as innovative nanomedicine approaches. The review also addresses the challenges of immunotherapy in obesity, particularly the paradoxical effects observed in cancer immunotherapy outcomes and the need for personalized treatment approaches. Artificial intelligence is highlighted as a potential tool to enhance patient stratification and treatment optimization in future immunomodulatory interventions. Understanding these immunometabolic interactions provides a foundation for developing more effective therapeutic strategies that could transform obesity management and reduce the burden of obesity-related metabolic diseases.

Objective This study aimed to explore orthopaedic patients’ and families’ experiences with artificial intelligence (AI)-driven chatbots for perioperative health information, focusing on usability, effectiveness and perceptions. Design A descriptive qualitative design was employed. Setting This study was conducted at a regional care centre for orthopaedics. Participants We recruited 13 participants (patients undergoing orthopaedic surgeries and family members) through purposive sampling. Face-to-face semistructured interviews were conducted to capture participants’ experiences and insights. Data collection was concluded when data saturation was achieved. All interviews were audio recorded and transcribed verbatim within 24 hours. Transcripts were verified and analysed using the Colaizzi’s data analysis method. Results Four themes emerged from interviews, including: (1) preference of AI chatbots over search engines; (2) improved accessibility and quality of information; (3) preference of AI over human interactions and (4) importance of effective prompting. Conclusions AI-driven chatbots offer a promising adjunct to perioperative patient education by delivering immediate, tailored guidance that overcomes the limitations of conventional search engines and busy clinical settings. Study participants valued chatbots’ efficient, context-sensitive retrieval, professional-level advice and non-judgmental interactions, which fostered trust and reduced anxiety. Effective prompting emerged as a key user skill, directly shaping response relevance and accuracy. Chatbot-generated health information should be regularly reviewed for accuracy. Structured tutorials may be offered for user capacity building.

The rapid development of artificial intelligence (AI) technology, in particular AlphaFold, has greatly improved protein structure prediction and design, reshaped protein biology and expanded research directions in anesthesiology and perioperative medicine. AI relies on deep learning to accurately model key proteins, such as G protein-coupled receptors, to aid drug development. In perioperative medicine, AI improves individualized treatment, patient safety and postoperative recovery through biomarker identification and anesthetic protocol optimization. In addition, AI accelerates anesthetic drug discovery, optimizes drug screening, toxicity prediction and clinical trials to improve the efficiency of research and development. Whilst data interpretation and diversity remain challenges, the continued advancement of AI in the fields of precision medicine and perioperative management will promote the development of individualized anesthesia and precision medicine.