Millions worldwide require Renal Replacement Therapy (RRT) as a treatment essential for survival. However, optimizing RRT strategies via AI is challenging due to heterogeneous patient dynamics, missing data, and the absence of an AI-oriented health assessment criterion. We propose an AI-Oriented Comprehensive Normalized Assessment (CNA) for healthy status and apply it to optimize RRT strategies by using offline reinforcement learning (RL). The key idea of CNA is transforming vital-sign distributions into a standard normal space, enabling a unified, data-driven health-status score defined by deviations from referent intervals, which also provides an AI-oriented criterion to assess strategy quality and supports RL termination. We further design a structured 23-dimensional state representation that integrates 19 indicators with 4 RRT descriptors, and employ matrix decomposition to reconstruct missing vital signs, improving data completeness for learning. These components are incorporated into multiple offline RL algorithms and validated via systematic ablation studies on RRT feature subsets. Compared with physicians’ observed treatments, the best learned strategy reduces mortality from 13.2% to 5.0% (reducing 62.24%) and shortens average in-hospital stay from 308.5 to 250.1 h (reducing 18.93%), demonstrating both methodological innovation and the potential of CNA-guided RL to improve RRT outcomes in nephrology.

Objective Idiopathic pulmonary fibrosis (IPF) carries a poor prognosis, and existing treatments merely delay disease progression, leaving a significant unmet need for safe and effective therapeutic agents. This study aims to explore the anti-IPF effect of dexmedetomidine (Dex) and its underlying mechanism. Methods A total of 50 male C57BL/6J mice (8 weeks old, 20u00B12 g) were used. In the first cohort, 30 mice were randomly assigned to Control, bleomycin (BLM), and BLM+Dex groups (n=10). The second cohort of 20 mice was randomized to BLM+Dex and BLM+Atip (atipamezole) groups (n=10). Pulmonary fibrosis was induced by a single intratracheal instillation of BLM (1.2 mg/kg in 50 u00B5L saline) in all groups except the Control group, which received an equal volume of saline. One hour after modeling, mice in the Control and BLM groups received intraperitoneal injections of 100 u00B5L saline; the BLM+Dex group received Dex (25 u00B5g/kg in 100 u00B5L); and the BLM+Atip group received Atip (250 u00B5g/kg) combined with Dex at the same dose. All treatments were administered once daily for 7 consecutive days. Both cohorts were followed for 4 weeks, during which survival was recorded daily. At the end of the experiment, noninvasive pulmonary function tests were performed, and lung tissues were collected for HE and Massonu2019s staining, hydroxyproline quantification, and immunofluorescence staining to assess the extent of fibrosis. Results Compared with the BLM group, Dex increased the survival rate from 50% to 80% and prevented body weight loss (P=0.0030), reduced the area fraction of collagen fibers (P=0.0014), decreased hydroxyproline content (P=0.0161), and downregulated the expression of transforming growth factor-u03B21 (TGF-u03B21) and u03B1-smooth muscle actin (u03B1-SMA) in lung tissue (P=0.0004, Pu0026lt;0.0001). Nevertheless, all the above improvements of Dex were reversed following Atip pretreatment. Conclusion Early intervention with Dex can ameliorate BLM-induced pulmonary fibrosis in mice, and its protective mechanism may be related to the activation of u03B12AR.

Objective To investigate the impact of different sedation strategies on short-term outcomes in intensive care unit(ICU)patients receiving invasive mechanical ventilation(IMV),and to assess whether these effects differ in special populations such as the elderly,those with malignancy,or hepatic impairment.Methods This retrospective observational cohort study utilized data from 2 large critical care databases,MIMIC-IV(v2.2)and eICU-CRD(v2.0).Inclusion criteria were:age≥18 years;endotracheal intubation and IMV within 48 h after ICU admission;ICU length of stay(LOS)≥48 h;initiation of a single-agent sedation strategy(dexmedetomidine,propofol,or benzodiazepines)from 2 h before to 48 h after intubation.Patients receiving combined sedatives,undergoing re-intubation,or with single-drug sample sizes<100 were excluded.The primary outcome was time to extubation;secondary outcomes included in-hospital mortality,ICU LOS,incidence of delirium,and major adverse cardiovascular events(MACEs).Inverse probability of treatment weighting(IPTW)based on gradient boosting machine algorithms was employed to control confounding bias,followed by augmented inverse probability weighting(AIPW)doubly robust models to estimate average treatment effects(ATE),odds ratios(ORs),and 95%confidence intervals(CIs).Prespecified subgroup analyses were applied to examine interactions with age,malignancy,and hepatic status.Results A total of 12 561 patients were enrolled,comprising 1 111 in the dexmedetomidine group,8 021 in the propofol group,and 3 429 in the benzodiazepine group.After IPTW weighting,baseline variables achieved overall balance across groups.In the AIPW doubly robust models,compared with the dexmedetomidine group,the propofol group demonstrated prolonged time to extubation by 0.782 d(ATE=0.782 d,95%CI:0.769 to 0.796,P<0.001),and the benzodiazepine group by 1.791 d(ATE=1.791 d,95%CI:1.779 to 1.804,P<0.001);compared with benzodiazepines,the propofol group showed shortened extubation time by 1.009 d(ATE=-1.009 d,95%CI:-1.018 to-1.000,P<0.001).For secondary outcomes,compared with dexmedetomidine,both propofol and benzodiazepines were associated with increased risk for in-hospital mortality(propofol:OR=1.905,95%CI:1.647 to 2.201,P<0.001;benzodiazepines:OR=2.768,95%CI:2.262 to 3.388,P<0.001)and for delirium(propofol:OR=1.905,95%CI:1.428 to 2.541,P<0.001;benzodiazepines:OR=2.382,95%CI:1.681 to 3.376,P<0.001).Additionally,ICU LOS was prolonged by 0.367 d and 1.012 d in the propofol and benzodiazepine groups,respectively(propofol:ATE=0.367 d,95%CI:0.351 to 0.382,P<0.001;benzodiazepines:ATE=1.012 d,95%CI:0.996 to 1.029,P<0.001),and the total incidence of MACEs increased by 6.527 and 15.199 events(propofol:ATE=6.527 events,95%CI:6.112 to 6.942,P<0.001;benzodiazepines:ATE=15.199 events,95%CI:14.727 to 15.671,P<0.001).Regarding ventilator-associated pneumonia(VAP),propofol was associated with a lower risk than dexmedetomidine(OR=0.722,95%CI:0.648 to 0.805,P<0.001),whereas no significant difference was observed between benzodiazepines and dexmedetomidine(OR=1.082,95%CI:0.934～1.252,P=1.000).Subgroup and interaction analyses revealed significant interactions between sedation strategy and malignancy(P-for-interaction=0.001)and moderate-to-severe liver disease(P-for-interaction=0.048).Among patients with malignancy,propofol showed a trend toward shorter time to extubation than dexmedetomidine(ATE=-1.326 d,95%CI:-2.732 to-0.080,P=0.064);similarly,in patients with moderate-to-severe liver disease,propofol also demonstrated a trend toward shorter extubation time than dexmedetomidine(ATE=-1.232 d,95%CI:-2.939 to-0.475,P=0.157),suggesting attenuated or even reversed benefits of dexmedetomidine in these special populations.Conclusion In ICU patients receiving invasive mechanical ventilation,dexmedetomidine,is associated with shorter time to extubation and lower risks of in-hospital mortality,delirium,and cardiovascular adverse events compared with propofol and benzodiazepines.However,these advantage of dexmedetomidine are diminished among patients with malignancy or moderate-to-severe liver disease.

Background The JAK/STAT pathway plays a pivotal role in hepatic ischaemia/reperfusion (I/R) injury, a serious perioperative complication. Although signal transducers and activators of transcription 1 (STAT1) activation is known to drive I/R-induced injury, the specific post-translational modifications (PTMs) governing its activity in hepatic I/R remain poorly understood.Objective This study identifies SMYD2, SET and MYND domain Containing 2 (SMYD2) as a critical regulator of STAT1 and investigates the mechanistic basis of SMYD2-mediated PTMs in modulating STAT1 function during hepatic I/R.Design Using an integrated transcriptomic-proteomic approach and functional screening, we identified SMYD2 as a critical regulator of STAT1 activation in hepatic I/R injury. Clinical correlations linked SMYD2 expression to postoperative liver function, while loss-of-function and gain-of-function studies in vitro and in vivo validated its mechanistic role.Results Our findings demonstrate that SMYD2 modulates hepatic I/R injury through the JAK-STAT1 pathway. Clinically, elevated SMYD2 expression correlated with improved liver function and better surgical outcomes following hepatectomy. Mechanistic studies revealed that SMYD2 physically interacts with STAT1 and mediates its methylation at lysine 175 (K175), thereby inhibiting STAT1 phosphorylation and nuclear translocation. Both in vitro and in vivo studies demonstrated that SMYD2 overexpression alleviated hepatic I/R injury, whereas its genetic depletion or pharmacological inhibition exacerbated the damage.Conclusion This study establishes SMYD2 as a novel negative regulator of STAT1 activity through K175 methylation, providing new insights into the epigenetic control of STAT1 during hepatic I/R injury. Our findings reveal a previously unrecognised mechanism for fine-tuning STAT1 signalling in hepatic I/R injury, and targeting the SMYD2-STAT1 axis may present a promising therapeutic strategy for mitigating I/R-associated liver damage.

Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis. Federated learning (FL) has attracted growing attention as a promising framework for collaborative model development, as it allows multiple institutions to jointly train predictive models without directly sharing or centralizing raw data. Nevertheless, its practical performance, robustness, and privacy-preserving benefits remain insufficiently evaluated using real-world clinical datasets. To bridge this gap, this study systematically examines the application of federated learning to multi-center sepsis prediction. The experimental dataset consists of 648 clinically screened samples collected from three tertiary hospitals in China, with rigorous inclusion and exclusion criteria. We establish a centralized training paradigm as the performance baseline, and then implement a horizontal federated learning framework for distributed collaborative modeling. Extensive experimental results demonstrate that the federated learning-based model achieves highly comparable prediction accuracy to the centralized counterpart, while fundamentally avoiding privacy leakage. Further privacy security analysis verifies that malicious attackers cannot reconstruct the original patient data from the transmitted model parameters, indicating strong resistance against data reconstruction attacks. This work not only validates the practicality and security of federated learning in clinical sepsis prediction, but also provides a reliable and feasible solution for privacy-preserving multi-center medical collaboration.

The conventional common bile duct ligation (CBDL) model suffers from high postoperative mortality (frequently exceeding 50%), which severely limits longitudinal monitoring of disease progression and complications such as hepatopulmonary syndrome (HPS). We developed an improved CBDL (MCBDL) to preserve the continuity of bile duct, and compared MCBDL (n=40) with traditional CBDL (n=40) and sham operation (n=10) within 42 days. MCBDL captures cirrhosis and extrahepatic organ injury while significantly improving survival (72.5% vs. 45%, P=0.003) and attenuating systemic inflammation (IL-6: 117 ± 11 vs. 153 ± 26 pg/mL, P=0.047). Scanning electron microscopy revealed persistent secretory activity in ligated epithelia of both groups, but only CBDL permitted leakage from isolated stumps, creating a pathogenic cycle driving mortality. By preserving ductal continuity, MCBDL is associated with significantly reduced mortality while maintaining the hallmark features of cholestatic cirrhosis (METAVIR F4) and HPS-related extrahepatic complications, providing a stable platform for mechanistic studies.

Radiation-induced lung injury (RILI) is a common complication of thoracic radiotherapy and can be critically exacerbated by pre-existing pulmonary inflammation, yet the synergistic mechanisms driving this pathology remain elusive. Using a murine model of combined lung injury induced by lipopolysaccharide (LPS) and thoracic irradiation (IR), we identified macrophage extracellular traps (METs), a type of web-like chromatin structure released by macrophages, rather than neutrophil extracellular traps (NETs), as a prominent pathogenic process. Mechanistically, the combination of LPS and irradiation induced an early endothelial senescence-associated phenotype and a CXC chemokine-enriched secretory profile. These signals engaged macrophage CXCR2, leading to p38/ERK pathway activation and reactive oxygen species (ROS) production that contributed to METs formation (METosis). Functionally, METs serve as potential dual-phase mediators, contributing to epithelial barrier disruption during acute injury and promoting epithelial-mesenchymal transition (EMT)-like epithelial remodeling, thereby potentially linking early inflammatory damage to subsequent fibrotic progression. Furthermore, we demonstrate that the bioactive compound Cordycepin exerts protective effects by suppressing p38/ERK pathway activation and attenuating METosis. Collectively, these findings support a potential endothelial senescence-associated secretory signaling-METosis axis, providing a novel mechanistic framework and candidate therapeutic strategies for managing high-risk radiation-induced lung injury.

In the intensive care setting, sepsis continues to be a major contributor to patient illness and death; however, its timely detection is hindered by the complex, sparse, and heterogeneous nature of electronic health record (EHR) data. We propose Triplet-GCN, a single-branch graph convolutional model that represents each encounter as patient–feature–value triplets, constructs a bipartite EHR graph, and learns patient embeddings via a Graph Convolutional Network (GCN) followed by a lightweight multilayer perceptron (MLP). The pipeline applies type-specific preprocessing – median imputation and standardization for numeric variables, effect coding for binary features, and mode imputation with low-dimensional embeddings for rare categorical attributes – and initializes patient nodes with summary statistics, while retaining measurement values on edges to preserve "who measured what and by how much". In a retrospective, multi-center Chinese cohort (N = 648; 70/30 train–test split) drawn from three tertiary hospitals, Triplet-GCN consistently outperforms strong tabular baselines (KNN, SVM, XGBoost, Random Forest) across discrimination and balanced error metrics, yielding a more favorable sensitivity–specificity trade-off and improved overall utility for early warning. These findings indicate that encoding EHR as triplets and propagating information over a patient–feature graph produce more informative patient representations than feature-independent models, offering a simple, end-to-end blueprint for deployable sepsis risk stratification.

Hepatopulmonary syndrome (HPS) is a condition characterized by pulmonary angiogenesis and refractory hypoxemia, often seen in patients with chronic liver disease. Its unclear mechanism means that liver transplantation is the only effective therapy. Agrimoniin, a compound from Pilosa ledeb, shows potential in protecting against liver cirrhosis via anti-angiogenic and anti-glycolytic effects. This study investigates agrimoniin as a potential integrated therapy for HPS-related liver and lung dysfunction. Using transcriptome data and an ICU cohort, we analyzed the role of glycolysis in chronic liver disease progression. HPS rats were established via common bile duct ligation, and serum metabolites were measured. The oxygen consumption rate and extracellular acidification rate were also detected. Rats were treated with agrimoniin (3 mg/kg/day or 8 mg/kg/day) at the early stage of HPS. Our results showed that imbalanced oxidative phosphorylation and glycolysis correlated with chronic liver disease progression and poorer outcomes. Decreased oxygen consumption rate and increased extracellular acidification rate, as well as increased glycolysis, were observed in the HPS group. Agrimoniin treatment improved liver and lung function by inhibiting pathological angiogenesis and glycolysis. Through TCM suite analysis, molecular docking, and dynamics simulations, PGC-1α was identified as a potential target of agrimoniin. Inhibiting PGC-1α blocked agrimoniin's benefits on angiogenesis and glycolysis flux. Thus, agrimoniin may be a potential integrated therapy for HPS by activating PGC-1α to inhibit glycolysis and angiogenesis.

Hepatopulmonary syndrome (HPS) is a condition characterized by pulmonary angiogenesis and refractory hypoxemia, often seen in patients with chronic liver disease. Its unclear mechanism means that liver transplantation is the only effective therapy. Agrimoniin, a compound from Pilosa ledeb, shows potential in protecting against liver cirrhosis via anti-angiogenic and anti-glycolytic effects. This study investigates agrimoniin as a potential integrated therapy for HPS-related liver and lung dysfunction. Using transcriptome data and an ICU cohort, we analyzed the role of glycolysis in chronic liver disease progression. HPS rats were established via common bile duct ligation, and serum metabolites were measured. The oxygen consumption rate and extracellular acidification rate were also detected. Rats were treated with agrimoniin (3 mg/kg/day or 8 mg/kg/day) at the early stage of HPS. Our results showed that imbalanced oxidative phosphorylation and glycolysis correlated with chronic liver disease progression and poorer outcomes. Decreased oxygen consumption rate and increased extracellular acidification rate, as well as increased glycolysis, were observed in the HPS group. Agrimoniin treatment improved liver and lung function by inhibiting pathological angiogenesis and glycolysis. Through TCM suite analysis, molecular docking, and dynamics simulations, PGC-1u03B1 was identified as a potential target of agrimoniin. Inhibiting PGC-1u03B1 blocked agrimoniinu2019s benefits on angiogenesis and glycolysis flux. Thus, agrimoniin may be a potential integrated therapy for HPS by activating PGC-1u03B1 to inhibit glycolysis and angiogenesis.

With the continuous advancement of medical technologies, perioperative anesthesia management decisions are confronted with challenges of complexity and dynamicity, rendering traditional machine learning models insufficient for clinical needs. As one of the breakthrough applications in artificial intelligence, large language models (LLMs) may offer better help for intelligent anesthesia management. LLMs are capable of processing multidimensional and multi-source data, enabling more comprehensive prediction and intervention suggestions, thereby optimizing anesthesia management processes. This review summarizes the current applications of LLMs in anesthesiology and proposes methods for building specialized LLMs to tackle challenges in application. This work albeit mainly in the proposed stage aims to provide references for future development in this field and to promote in-depth research of LLMs in anesthesiology. Graphical Abstract

Objective: Sepsis is a life-threatening condition caused by severe infection leading to acute organ dysfunction. This study proposes a data-driven metric and a continuous reward function to optimize personalized heparin therapy in surgical sepsis patients. Methods: Data from the MIMIC-IV v1.0 and eICU v2.0 databases were used for model development and evaluation. The training cohort consisted of abdominal surgery patients receiving unfractionated heparin (UFH) after postoperative sepsis onset. We introduce a new RL-based framework: converting the discrete SOFA score to a continuous cxSOFA for more nuanced state and reward functions; Second, defining "good" or "bad" strategies based on cxSOFA by a stepwise manner; Third, proposing a Treatment Effect Comparison Matrix (TECM), analogous to a confusion matrix for classification tasks, to evaluate the treatment strategies. We applied different RL algorithms, Q-Learning, DQN, DDQN, BCQ and CQL to optimize the treatment and comprehensively evaluated the framework. Results: Among the AI-derived strategies, the cxSOFA-CQL model achieved the best performance, reducing mortality from 1.83

The present study aimed to explore the anti-inflammatory mechanism of dexmedetomidine (Dex), an α2-adrenoceptor (α2-AR) agonist, in renal ischemia-reperfusion (RIR)-induced acute lung injury (ALI). RIR was induced in C57BL/6J mice by bilateral renal pedicles occlusion for 60 min followed by 24 h of reperfusion. Mice were pretreated with Dex alone or in combination with atipamezole (Atip), an α2-AR antagonist. Pulmonary histopathological assessment, arterial blood gas analysis, cell count and multiple cytokine examination in bronchoalveolar lavage fluid (BALF), evaluation of the global inflammation status in lung tissue, and investigation of alveolar macrophage phenotypes were carried out. In vitro, the polarization of mouse alveolar macrophages (MH-S) treated with serum from normal or RIR mice was indirectly detected by quantitative polymerase chain reaction (qPCR). The findings demonstrated that, in comparison to RIR animals, dexmedetomidine mitigated lung injury and remarkably promoted macrophage polarization towards an anti-inflammatory M2 phenotype in the pulmonary tissue. Concurrently, a reduction in inflammatory cell infiltration and levels of pro-inflammatory cytokines was observed. In vitro studies verified that dexmedetomidine directed MH-S towards the M2 phenotype after stimulation with RIR serum. However, these effects were mostly reversed following administration of atipamezole. Dexmedetomidine alleviates renal ischemia-reperfusion-induced ALI by activating α2-adrenoceptor, thereby inducing macrophage polarization towards an anti-inflammatory phenotype and reducing pulmonary global inflammation.

Background There was no standardized clinical guideline of the application of preoperative anti- Hepatitis B Virus therapy in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients receiving hepatectomy. This study explored HBV infectious impact on postoperative outcomes of HCC patients. Methods This retrospective study included 962 HCC patients who received hepatectomy from the Chronic Liver Diseases Perioperative Database (n = 360,767). Primary outcome was liver failure post-hepatectomy. Unsupervised consensus clustering classified HBV-related HCC patients into two clusters. A LightGBM model identified 15 factors linked to worse outcomes. Then, restricted cubic splines and logistic regression revealed that HBV DNA load was highly associated with liver failure. Results HBV-related HCC patients showed significantly higher liver failure (24.1% vs. 14.8%, p = 0.035) and severe complications (29.6% vs.18.5%, p = 0.019) compared to no HBV infection HCC patients after propensity score matching. The high HBV DNA load (≥ 3.679 log10 IU/mL) had 1.63-fold increased live failure risk (95% CI: 1.14–2.34). Preoperative antiviral therapy decreased live failure (OR: 0.30, 95% CI: 0.18–0.49) and severe complications (OR: 0.33, 95% CI: 0.22–0.51). The high HBV DNA load was associated with greater live failure risk among patients with either significant fibrosis [FIB-4 > 1.45 (OR: 2.01), APRI > 0.4 (OR: 1.87)] or impaired liver function [ALP > 147 U/L (OR: 3.89)]. Conclusion HBV positive with high DNA load is a risk factor of liver failure and severe postoperative complications in HCC patients undergoing hepatectomy. Antiviral therapy suppressing HBV DNA load below 3.679 log10 IU/ml should be recommended to hepatitis B virus infected patients before receiving liver surgery.

Objective To investigate the influencing factors for post-hepatectomy liver failure(PHLF)in hepatocellular carcinoma(HCC)patients with HBV infection at low viral load,and then construct a risk prediction model.Methods A total of 403 HCC patients who underwent initial hepatectomy in the First Affiliated Hospital of Army Medical University between January 1,2015 and March 1,2023 were recruited,and randomly assigned into a training set and a verification set in a ratio of 7:3.Lasso regression and multivariate logistic regression analyses were applied to screen the risk factors for occurrence of PHLF,and based on these identified factors,a nomogram prediction model was constructed.Receiver operating characteristic(ROC)curve analysis(area under the curve,AUC),calibration curve analysis,decision curve analysis,and clinical impact curve analysis were preformed to assess the predictive efficacy of the model.Results History of anti-viral therapy,history of drinking,logHBsAg,and international normalized ratio(INR)were independent influencing factors for the occurrence of PHLF in HCC patients with HBV infection at low viral load.The model established based on these indicators demonstrated excellent discriminative capabilities in both the training and validation sets,with an AUC value of 0.744 and 0.737,respectively.Calibration curve analysis indicated our model of high accuracy(training:P=0.995;validation:P=0.701),and decision curve analysis and clinical impact curve analysis displayed that our model provided greater clinical benefit.Conclusion Our model can effectively evaluate the risk of PHLF in HCC patients with HBV infection at low viral load,and shows good predictive performance,which has certain guiding significance for timely identification of high-risk populations.

IntroductionThough the importance raised attention, the clinical applications of methods for screening high-risk patients of sepsis after abdominal surgery were restricted. Therefore, we aimed to develop and validate models to screening high-risk patients of sepsis after abdominal surgery based on machine learning with routine variables.Material and methodsThe whole dataset was composed of three representative academic hospitals in China and Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Routine clinical variables were implemented for model development. Boruta was applied for feature selection. Afterwards, ensemble learning and other eight conventional algorithms were used for model fitting and validation based on all features and selected features. The area under curves of the receiver operating characteristic curves (ROCAUCs), sensitivity, specificity, F1 score, accuracy, Net reclassification index (NRI), integrated discrimination improvement (IDI), Decision Curve Analysis (DCA), and calibration curves were used for model evaluation.ResultsA total of 955 patients undergoing abdominal surgery were finally analyzed (sepsis:285, non-sepsis:670). After feature selection, the ensemble learning model constructed by integrating k-Nearest Neighbor (KNN) and Support Vector Machine (SVM), yielded the ROCAUC of 0.892(0.841-0.944), the accuracy of 85.0% on the test data, and the ROCAUC of 0.782(0.727-0.838), the accuracy of 68.1% on the validation data, which performed best. Albumin, ASA score, Neutrophil-lymphocyte ratio, age, and glucose were the top features associated with postoperative sepsis by KNN and SVM.ConclusionsWe developed a new and potential generalizable model to preoperatively screening the high-risk patients of sepsis after abdominal surgery with the advantages of a representative training cohort and routine variables.

Modern anesthesiology has expanded beyond intraoperative care. It now integrates pain management, critical care, and emergency resuscitation. However, it still faces challenges like biological variability in drug responses, unpredictable intraoperative crises, and complex perioperative complications. Artificial intelligence (AI) emerges as a transformative force, can effectively enhance clinical quality and operational efficiency by extracting critical insights from vast amounts of healthcare data including electronic health records, vital sign waveforms, and imaging databases. AI applications in clinical anesthesia span the entire perioperative period, encompassing preoperative risk assessment, intraoperative physiological monitoring with adverse event prediction and visualized procedural guidance, as well as postoperative outcome forecasting and dynamic adaptive individualized treatment to enhance recovery after surgery. Beyond direct patient care, AI enhances operating room efficiency and revolutionizes anesthesia education. Despite progress, challenges persist in algorithm generalizability, data interoperability, and clinical validation. This review synthesizes the transformative role of AI across anesthesiology subspecialties, analyzes the barriers to implementation, and proposes strategic directions to bridge technological innovation with clinical optimization.

Background: With the growing demand for Total Hip Arthroplasty (THA), there is an urgent need to devise individualized perioperative management strategies to expedite patient recovery, particularly in scenarios involving multiple disciplines and concurrent decision-making. Objective:This study aims to construct a decision network and provide decision recommendations based on Bayesian Networks (BNs) for stratified risk groups. Methods: The MIMIC-IV database was employed to establish a developing dataset by extracting patients who underwent THA, followed by K-means clustering for patient phenotyping. A BN model for THA decisions were developed, and support degree was calculated according to conditional probability for decision recommendation. An external validation dataset from the multiple center big data platform was then utilized to validate the BN model's recommendations by calculating the conformity of risk-recommended protocols after phenotype classification. An online tool was developed for convenient application in clinical practice. Results: The developing dataset comprised 1701 admissions, while the validation dataset included 418 admissions. Patients in the developing dataset were divided into three phenotypes: a low-risk group (n=722), a moderate-risk group (n=673), and a high-risk group (n=306). Tailored recommendation protocols were formulated for each phenotype, covering preoperative and postoperative periods, based on support degree. Consistency in the distribution of differences among clustered indicators was observed in both datasets. Adherence to the recommended decisions was associated with reduced postoperative length of stay (PLOS) across all three decision protocols. Conclusions: Decisions recommended by Bayesian Networks for patients of different risk levels can decrease the PLOS and hasten recovery. This systematic approach, integrating risk stratification through clustering with an indicator network constructed by the BN model for decision recommendations, is adept at handling multi-decision tasks and supports clinical physicians in making informed choices regarding perioperative risk assessment and treatment plan selection. The registration has been completed on the website of the China Clinical Trial Registry Center (ChiCTR1900023927).

Background: Dexmedetomidine (Dex) may have anti-inflammatory properties and potentially reduce the incidence of postoperative organ injury. Objective: To investigate whether Dex protects pulmonary and renal function via its anti-inflammatory effects in elderly patients undergoing prolonged major hepatobiliary and pancreatic surgery. Design and Setting: Between October 2019 and December 2020, this randomized controlled trial was carried out at a tertiary hospital in Chongqing, China. Patients: 86 patients aged 60- 75 who underwent long-duration (> 4 hrs) hepatobiliary and pancreatic surgery without significant comorbidities were enrolled and randomly assigned into two groups at a 1:1 ratio. Interventions: Patients were given either Dex or an equivalent volume of 0.9% saline (Placebo) with a loading dose of 1 mu g kg(- 1) for 10 min, followed by 0.5 mu g kg(- 1) hr(- 1) for maintenance until the end of surgery. Main Outcome Measures: The changes in serum concentrations of interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-alpha) were primary outcomes. Results: At one hour postoperatively, serum IL-6 displayed a nine-fold increase (P< 0.05) in the Placebo group. Administration of Dex decreased IL-6 to 278.09 +/- 45.43 pg/mL (95% CI: 187.75 to 368.43) compared to the Placebo group (P=0.019; 432.16 +/- 45.43 pg/mL, 95% CI: 341.82 to 522.50). However, no significant differences in TNF-alpha were observed between the two groups. The incidence of postoperative acute kidney injury was twice as high in the Placebo group (9.30%) compared to the Dex group (4.65%), and the incidence of postoperative acute lung injury was 23.26% in the Dex group, lower than that in the Placebo group (30.23%), although there was no statistical significance between the two groups. Conclusion: Dex administration in elderly patients undergoing major hepatobiliary and pancreatic surgery reduces inflammation and potentially protects kidneys and lungs.