With the rapid advancement of hemodynamic indices and monitoring technologies, their classification methods and application processes have become increasingly complex. Currently, no unified standard hasbeen established, making it difficult to fully meet the clinical requirements for hemodynamic management. To assist in hemodynamic monitoring assessment and therapeutic decision-making in critically ill patients, the Critical Hemodynamic Therapy Collaborative Group, in conjunction with the Critical Ultrasound Study Group, has jointly developed the Standard for the Application of Hemodynamic Monitoring Techniques in Critical Care. The first part of this standard systematically categorizes hemodynamic indicators into flow indicators, pressure and its derivative indicators, and tissue perfusion indicators, while elaborating on the clinical application of each. The second part establishes a standardized clinical implementation pathway for hemodynamic monitoring. It proposes a tiered monitoring strategy-comprising basic, advanced, indication-specific, and special scenario monitoring-tailored to different clinical settings. It emphasizes the central role of critical care ultrasound across all levels of monitoring and establishes hemodynamic assessment standards for organs such as the brain, kidneys, and gastrointestinal tract. This standard aims to provide a unified framework for clinical practice, teaching, training, and research in critical care medicine, thereby promoting standardized development within the discipline.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is the primary cause of mortality in sepsis, with its core pathophysiological mechanism being severe ischemia and hypoxia in critical units—composed of microcirculation and the mitochondria of functional cells—resulting from disruptions in blood flow and oxygen flow following a dysregulated host response. Due to the systemically convergent yet clinically heterogeneous nature of the host response, current understanding and management strategies for hemodynamics remain inconsistent, often leading to inadequate resuscitation or overtreatment. To improve the quality of care, based on a systematic review of the "blood flow-oxygen flow" theory, an expert panel emphasizes reevaluating septic shock from an integrated perspective of blood flow and oxygen flow, and has formulated the Expert Consensus on Blood Flow and Oxygen Delivery Phenotyping and Clinical Management of Septic Shock (2025). The consensus proposes that clinical typing of blood flow-oxygen flow should comprehensively consider cardiac function, vascular tone, oxygen flow utilization status in critical units, and disease trajectory, while optimizing subtype identification by integrating host response phenotypes and artificial intelligence technologies. It advocates establishing a continuous assessment system through multi-site oxygen flow monitoring for organ perfusion, peripheral perfusion monitoring, and critical care ultrasound. Under the framework of the "critical care triangle", the consensus promotes the implementation of individualized, bundled management strategies, providing guidance for multiple management points to restore blood flow-oxygen flow matching, reduce the risk of organ failure, and decrease patient mortality.

Sepsis is a clinically heterogeneous disease with high mortality. It is crucial to develop relevant therapeutic strategies for different sepsis phenotypes, but the impact of phenotypes on patients' clinical outcomes is unclear. This study aimed to identify potential sepsis phenotypes using readily available clinical parameters and assess their predictive value for 28-day clinical outcomes by logistic regression analysis. In this retrospective analysis, researchers extracted clinical data from adult patients admitted to the First Affiliated Hospital of Anhui Medical University between April and August 2022 and from the 2014-2015 eICU Collaborative Study database. K-Means clustering was utilized to identify and refine sepsis phenotypes, and their predictive performance was subsequently evaluated. Logistic regression models were trained independently for each phenotype and five-fold cross-validation was used to predict clinical outcomes. Predictive accuracy was then compared to traditional non-clustered prediction methods using model assessment scores. The study cohort consisted of 250 patients from the First Affiliated Hospital of Anhui Medical University, allocated in a 7:3 ratio for training and testing, respectively, and an external validation cohort of 3100 patients from the eICU Cooperative Research Database. The results of the phenotype-based prediction model demonstrated an improvement in F1 score from 0.74 to 0.82 and AUC from 0.74(95%CI 0.71-0.80) to 0.84(95%CI 0.82-0.87), and these results also highlight the superiority of clinical outcome prediction with the help of sepsis phenotypes over traditional prediction methods. Phenotype-based prediction of 28-day clinical outcomes in sepsis demonstrated significant advantages over traditional models, highlighting the impact of phenotype-driven modeling on clinical outcomes in sepsis.

GPR43, a receptor for short-chain fatty acids (SCFAs), is broadly expressed in intestinal epithelial and immune cells and is essential for preserving barrier integrity and immune homeostasis. Nevertheless, how GPR43 influences gut microbiota composition and intestinal barrier integrity while also regulating macrophage immunometabolism in the context of sepsis remains poorly understood. A cecal ligation and puncture model was used to induce sepsis in mice. Survival, histopathology, and immune responses were compared between Gpr43−/− and wild-type mice; 16S ribosomal RNA (rRNA) sequencing and untargeted metabolomics were performed to evaluate gut microbiota composition and metabolic profiles. Antibiotic-mediated microbiota depletion and fecal microbiota transplantation were used to assess functional impacts. Bone marrow-derived macrophages were employed to investigate the effects of GPR43 deficiency on macrophage polarization. RNA sequencing, metabolic flux analysis, and Western blotting were conducted to explore the molecular mechanisms involved. Peripheral blood mononuclear cell samples from patients with sepsis were analyzed for clinical correlation. Gpr43−/− mice exhibited significantly reduced survival following CLP, along with impaired intestinal barrier function and elevated proinflammatory cytokine levels. Microbiota diversity and SCFA-producing bacteria were markedly decreased, accompanied by reduced SCFA levels in fecal metabolites. Fecal microbiota transplantation (FMT) partially restored gut function and survival in Gpr43−/− mice. GPR43-deficient macrophages displayed a strong M1-polarized phenotype with the upregulation of the glycolytic enzyme ENO1 and its upstream regulator HIF-1α. The inhibition of either ENO1 or HIF-1α reversed the proinflammatory phenotype. A clinical data analysis revealed that GPR43 expression was negatively correlated with IL-6, ENO1, and lactate levels. GPR43 exerts a dual protective role in sepsis by maintaining gut microbiota homeostasis and barrier integrity and by modulating macrophage metabolism and polarization via the HIF-1α–ENO1 axis. This study provides novel insights into the GPR43 in pathogenesis of sepsis and suggests potential therapeutic targets for intervention.

Sepsis is a severe infectious disease with high incidence and mortality rates globally. Early diagnosis of sepsis is crucial for improving patient outcomes. Previous diagnostic methods heavily relied on subjective clinical experience, while the machine learning-based methods can only learn knowledge from a specific dataset. Recently, the rapid development of Large Language Models (LLMs) has significantly enhanced various downstream dialogue tasks by leveraging prior semantic knowledge. Therefore, it is of great interest to explore the potential of LLMs in sepsis diagnosis. This study proposed an early sepsis diagnosis method based on the Chain of Thought (CoT) reasoning using LLMs. First, the clinical data of a patients were transformed into a textual representation to form the prompt. Subsequently, a CoT was created to simulate the reasoning process of human medical experts and utilized the prior semantic knowledge in LLMs to achieve sepsis diagnosis. The proposed method was validated using real clinical data, demonstrating high classification performance with an accuracy of 0.87, recall of 0.98, and F1 score of 0.88. These metrics showed an improvement in F1 score by 7 to 8 percentage points compared to commonly used machine learning classifiers. The experimental results indicated that the proposed method can enhance the performance of early sepsis diagnosis, and the introduction of CoT enhanced the interpretability of diagnostic results, contributing to the application of LLMs in clinical diagnosis.

Background Early and accurate diagnoses of sepsis patients are essential to reduce the mortality. However, the sepsis is still diagnosed in a traditional way in China despite the increasing number of related studies, which may to some extent lead to delays in the treatment. Methods The study included 2,385 patients, including 364 with sepsis, collected from the First Affiliated Hospital of Anhui Medical University and partner hospitals from April to July 2022. External validation was conducted using the MIMIC-III database (over 60,000 patients from 2001 to 2012) and the eICU Collaborative Research Database (139,000 patients from 2014 to 2015). Multiple algorithm models, along with the SHapley Additive exPlanations (SHAP) analysis, are applied to explore the main risk factors for the accurate prediction of the sepsis. Multiple Imputations for filling missing data and the Synthetic Minority Oversampling (SMOTE) balancing method for balancing data are used for the data processing. Result Eighteen diagnostic features are used in the predictive model for early sepsis. The Random Forest model has the best performance among all the models, with an Area Under the Curve (AUC) of 87% and an F1-score (F1) of 77%. Moreover, the interpretation from the SHAP analysis is generally consistent with the current clinical situation. Conclusion The study revealed the relationship between these 18 clinical features and diagnostic outcomes. The results indicate that patients with laboratory values of Systolic Blood Pressure, Albumin, and Heart Rate exceeding certain thresholds are at a high likelihood of developing sepsis.

Background: This study aimed to evaluated whether using norepinephrine during the management of patients with septic shock impact perfusion index(PI) and patients outcomes. Methods: We performed a retrospective study among patients with septic shock from January 2014 to December 2018 who had undergone PICCO-plus cardiac output mornitoring and using norepinephrine during the management.We collected basic clinical characteristics.Hemodynamic parameters including lactate,PI and dose of norepinephrine at T0 and 24h after PICCO catheterization (T24) were obtained.We analyzed the effect of perfusion index and norepinephrine on prognosis of patients with septic shock and the correlation between perfusion index and the dose of norepinephrine. Results: There were 184 patients with septic shock who received during this period, and of these, 44 patients died during their ICU treatment.The PI of the nonsurvivors group was significantly lower than survivors group at T24 (0.5 ± 0.4 vs. 1.5 ± 1.3, P ＜0.001),and the Lac of the nonsurvivors group was significantly higher than the survivors group.In the dose of norepinephrine indicators, we found significantly statistical differences between the two groups at T0 and T24.Dose of norepinephrine and perfusion index were the most independent risk and protective factors for patient ICU mortality. The areas under the curve for a poor prognosis PI were 0.847 (95% CI: 0.782-0.912). The optimal cutoff value of the PI at T24 to predict ICU mortality was 0.6, with a sensitivity of 77.1% and specificity of 80%. Based upon the optimal cutoff value of the PI at T24, we divided patients into groups of PI≥0.6 (n = 125) and PI＜0.6 (n = 59).The Lac of the PI＜0.6 group was higher than the PI≥0.6 group at T24.In the sublingual dose of norepinephrine indicators, the PI＜0.6 group was significantly higher than the PI≥0.6 group.The PI was strong negative correlated with dose of norepinephrine (r = -0.344,P<0.001) and lactate (r = -0.291, P<0.001). Conclusions: A higher PI is a protective factor and using of higher dose of norepinephrine is a risk factor for the prognosis of critically ill patients with septic shock. Lower PI was associated with the higher dose of norepinephrine.

Sepsis is a severe infectious disease with high incidence and mortality rates worldwide. Early diagnosis of sepsis in newly admitted intensive care unit patients is crucial to reduce mortality and improve patient outcomes. The manual diagnostic methods heavily rely on subjective clinical experience, while traditional machine learning methods require time-consuming feature engineering and the performance is limited by the knowledge acquired from scarce datasets. Therefore, to address the aforementioned issues, this study proposes a novel textual representation method for clinical numerical data, leveraging pre-trained language models from the field of natural language processing for sepsis prediction. Specifically, this study innovatively transforms structured clinical numerical data of patients into unstructured textual descriptions. This transformation reframes sepsis prediction into a text classification task, leveraging the rich prior semantic knowledge embedded in pre-trained language models to enhance prediction performance. The proposed method is validated using real ICU clinical data. When employing RoBERTa-base, it achieved an F1 score of 79.03%, which represents an improvement of five percentage points compared with commonly used machine learning classifiers. The experiments confirmed that the proposed method enhances the performance of early sepsis diagnosis and introduces new insights for clinical diagnosis of sepsis.

Intracranial infections are among the most common complications of neurosurgery, with their incidence remaining high despite advancements in current neurosurgical techniques and aseptic technology. While the role of mucosal-associated invariant T (MAIT) cells, a subset of innate-like T lymphocytes, in bacterial defense is well-established, their involvement in intracranial infections remains unclear. In this study, we utilized flow cytometry to assess the phenotype and function of circulating and CSF MAIT cells. Our findings revealed that MAIT cells were higher in the CSF compared to blood. Notably, a higher percentage of IL-17A + MAIT cells was detected in the CSF of patients with intracranial infections. Moreover, markers indicating activation and exhaustion were significantly upregulated in CSF MAIT cells. Furthermore, elevated levels of pro-inflammatory cytokines, including IL-1β, IL-12, and IL-18, were detected in the CSF supernatants. We hypothesized that the elevated levels of IL-1β, IL-12, and IL-18 in the inflammatory milieu synergistically activate MAIT cells in the CSF. In particular, CD25 and Tim-3 expression of MAIT cells was increased by stimulation with IL-1β, IL-12, and IL-18 or CSF supernatants of intracranial infection patients. Collectively, these findings provide important information underlying the innate immune response of patients with intracranial infections.

Selective breeding of mice displaying high and low swim-induced analgesia led to the development of two animal lines divergent in the magnitude of analgesic response to swimming. In this study, animals belonging to the seventh generation of both lines were exposed to two temporally different forms of footshock, one of which produced opioid and the other non-opioid analgesia. We found that selective breeding for high and low swim-induced analgesia exerted a striking influence on the magnitude of the opioid-mediated type of footshock analgesia, but was ineffective on that of the non-opioid type.

We present the case of a 37-year-old male who was admitted to our hospital with fever, weakness, limb pain for six days and dyspnea for 14 hours. The patient had no immune related diseases and was rapidly diagnosed with fulminant myocarditis, which progressed to severe cardiogenic shock during the early stage. Subsequently, he was treated with V-A extracorporeal membrane oxygenation (ECMO). It is worth mentioning that the patient's peripheral blood was taken for metagenomic next-generation sequencing (mNGS) upon admission and the results did not find any pathogenic bacteria. However, there was no further examination (such as coronary angiography and myocardial biopsy) to determine the etiology of myocarditis.

OBJECTIVE:To evaluate whether septic shock patients with pulmonary infection and life-threatening hypoxemia can benefit from V-V ECMO.METHODS:Retrospective clinical data analysis on patients who suffered septic shock with pulmonary infection, categorized into V-V ECMO and control groups. The propensity score matching (PSM) method was used to screen patients matched for age, gender, and disease severity. The primary outcome was 30- and 90-day mortality after diagnosis of septic shock.RESULTS:After PSM, 31 pairs of patients were enrolled in this study, and there were no significant differences between the two groups in terms of gender, age, chronic disease, acute physiological and chronic health evaluation II (APACHE II) score, and sequential organ failure assessment (SOFA) score. Within 28 days after the diagnosis of septic shock, the median time of renal replacement therapy-free days was longer in the V-V ECMO group than in the control group (27 days vs. 9 days; p = 0.044). Kaplan-Meier analysis showed that 30-day mortality was lower in the V-V ECMO group than in the control group (38.7% vs. 61.3%; HR 0.488; 95% CI 0.240-0.992; p = 0.043, by log-rank test); 90-day mortality was not significantly different between the two groups (51.6% vs. 67.7%, p = 0.097).CONCLUSION:Patients receiving V-V ECMO support had lower 30-day mortality and faster recovery of renal function within 28 days compared with those receiving conventional therapy. However, V-V ECMO did not improve 90-day survival in septic shock patients with pulmonary infection.

Coagulopathy is a common and serious problem in patients who received extracorporeal membrane oxygenation (ECMO), and this study evaluated whether the 2018 diffuse intravascular coagulation (DIC) score established by the International Society on Thrombosis and Hemostasis (ISTH) is associated with 90-day mortality in adult ECMO patients. A retrospective study analyzed data from adult patients receiving ECMO in our hospital from September 2018 to April 2021. Pre-ECMO DIC score and other variables were assessed and compared to predict 90-day mortality. Among 103 eligible patients, 55.3% received V-V ECMO and 44.7% received V-A ECMO. The overall 90-day mortality for study patients was 54.4%, including 45.6% in the V-V group and 65.2% in the V-A group. Multiple logistic regression analysis showed that after adjusting for sex, sepsis, and APACHE II score, pre-ECMO DIC scores in the total and V-V group predicted 90-day mortality (odds ratio(OR): 1.419, 95% confidence interval (CI): 1.101–1.828; OR: 2.562; 95% CI: 1.452–4.520). Receiver operating characteristic (ROC) curves displayed that pre-ECMO DIC score of 4 in the total and V-V group was a good predictor of 90-day mortality (area under the curve [AUC] = 0.706, 95% CI: 0.606–0.806; AUC = 0.737, 95% CI: 0.604–0.870). Kaplan–Meier curves demonstrated the 90-day mortality of patients with pre-ECMO DIC score ≥ 4 in the total and V-V group was higher than that of patients with DIC score < 4 (hazard ratio [HR]: 2.821, 95% CI: 1.632–4.879; HR: 3.864, 95% CI: 1.660–8.992). The pre-ECMO ISTH DIC score was associated with 90-day mortality in adult patients undergoing ECMO, particularly in the V-V ECMO group.