OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To investigate the mechanism of human embryonic stem cell-derived mesenchymal stem cells (hESC-MSC) in alleviating brain injury after resuscitation in swine with cardiac arrest (CA). METHODS: Twenty-nine healthy male large white swine were randomly divided into Sham group (n = 9), cardiopulmonary resuscitation (CPR) group (n = 10) and hESC-MSC group (n = 10). The Sham group only completed animal preparation. In CPR group and hESC-MSC group, the swine model of CA-CPR was established by inducing ventricular fibrillation for 10 minutes with electrical stimulation and CPR for 6 minutes. At 5 minutes after successful resuscitation, hESC-MSC 2.5×10⁶/kg was injected via intravenous micropump within 1 hour in hESC-MSC group. Venous blood samples were collected before resuscitation and at 4, 8, 24, 48 and 72 hours of resuscitation. The levels of neuron specific enolase (NSE) and S100B protein (S100B) were detected by enzyme linked immunosorbent assay (ELISA). At 24, 48 and 72 hours of resuscitation, neurological deficit score (NDS) and cerebral performance category (CPC) were used to evaluate the neurological function of the animals. Three animals from each group were randomly selected and euthanized at 24, 48, and 72 hours of resuscitation, and the hippocampus tissues were quickly obtained. Immunofluorescence staining was used to detect the distribution of hESC-MSC in hippocampus. Immunohistochemical staining was used to detect the activation of astrocytes and microglia and the survival of neurons in the hippocampus. The degree of apoptosis was detected by TdT-mediated dUTP nick end labeling (TUNEL). RESULTS: The serum NSE and S100B levels of brain injury markers in CPR group and hESC-MSC group were significantly higher than those in Sham group at 24 hours of resuscitation, and then gradually increased. The levels of NSE and S100B in serum at each time of resuscitation in hESC-MSC group were significantly lower than those in CPR group [NSE (μg/L): 20.69±3.62 vs. 28.95±3.48 at 4 hours, 27.04±5.56 vs. 48.59±9.22 at 72 hours; S100B (μg/L): 2.29±0.39 vs. 3.60±0.73 at 4 hours, 2.38±0.15 vs. 3.92±0.50 at 72 hours, all P < 0.05]. In terms of neurological function, compared with the Sham group, the NDS score and CPC score in the CPR group and hESC-MSC group increased significantly at 24 hours of resuscitation, and then gradually decreased. The NDS and CPC scores of hESC-MSC group were significantly lower than those of CPR group at 24 hours of resuscitation (NDS: 111.67±20.21 vs. 170.00±21.79, CPC: 2.33±0.29 vs. 3.00±0.00, both P < 0.05). The expression of hESC-MSC positive markers CD73, CD90 and CD105 in the hippocampus of hESC-MSC group at 24, 48 and 72 hours of resuscitation was observed under fluorescence microscope, indicating that hESC-MSC could homing to the damaged hippocampus. In addition, compared with Sham group, the proportion of astrocytes, microglia and apoptotic index in hippocampus of CPR group were significantly increased, and the proportion of neurons was significantly decreased at 24, 48 and 72 hours of resuscitation. Compared with CPR group, the proportion of astrocytes, microglia and apoptotic index in hippocampus of hESC-MSC group decreased and the proportion of neurons increased significantly at 24 hours of resuscitation [proportion of astrocytes: (14.33±1.00)% vs. (30.78±2.69)%, proportion of microglia: (12.00±0.88)% vs. (27.89±5.68)%, apoptotic index: (12.89±3.86)% vs. (52.33±7.77)%, proportion of neurons: (39.44±3.72)% vs. (28.33±1.53)%, all P < 0.05]. CONCLUSIONS Application of hESC-MSC at the early stage of resuscitation can reduce the brain injury and neurological dysfunction after resuscitation in swine with CA. The mechanism may be related to the inhibition of immune cell activation, reduction of cell apoptosis and promotion of neuronal survival.
Animals ; Heart Arrest/therapy* ; Cardiopulmonary Resuscitation ; Swine ; Humans ; Male ; Human Embryonic Stem Cells/cytology* ; Mesenchymal Stem Cell Transplantation ; Mesenchymal Stem Cells/cytology* ; Phosphopyruvate Hydratase/blood* ; Brain Injuries/therapy* ; S100 Calcium Binding Protein beta Subunit ; Apoptosis ; Disease Models, Animal

Sepsis is a lethal condition resulting from the host's dysregulated response, involving complex pathophysiological mechanisms, including the host's biphasic immune response and metabolic disturbances. Diagnosing and treating sepsis remain formidable challenges, with the absence of definitive biomarkers and effective therapeutic interventions to date. Animal models of sepsis are pivotal in unraveling the disease's pathogenesis and identifying potential treatments, playing a crucial role in enhancing our comprehension of its intrinsic nature. However, there is no animal model that can comprehensively and accurately simulate the complex pathophysiological process of human sepsis. This review discusses the widely used sepsis animal models, exploring their advantages and limitations in terms of pathogenesis, inflammatory response, pathophysiological changes, and organ dysfunction. It summarizes the application scenarios and latest research advancements of these models and provides an outlook on potential future improvements.
Sepsis/physiopathology* ; Animals ; Disease Models, Animal ; Humans

Exertional heatstroke is defined as a serious clinical syndrome typically characterized by impaired thermoregulation in high-temperature and high-humidity environments, resulting in heat production exceeding heat dissipation, causing core body temperature to exceed 40 centigrade, accompanied by central nervous system dysfunction and multi-organ failure. At present, the commonly used exertional heatstroke animal model is to put mice on a treadmill to run under high temperature and humidity conditions, but additional electrical stimulation is required to maintain the continuous running state of mice. However, additional electrical stimulation may lead to a further increase in mouse body temperature, which adversely affects the stability of the model. Therefore, medical staff from the intensive care unit of Xijing Hospital, Air Force Medical University, specially designed an intelligent experimental device for the exertional heatstroke model in mice, and obtained the national invention Patent of China (ZL 2022 1 1101721.2). The device integrates climate chamber, LCD touch screen and multiple sets of forced running wheel. Experimenters can observe and control the temperature, humidity, and wheel rotation parameters in the climate chamber in real time through a LCD touch screen. Each set of forced running wheel is equipped with a driving device that can be independently controlled. The device makes the mice run continuously without additional stimulation and enables the experimental personnel to observe and control the conditions in the climate chamber. The device successfully solves the problem of instability of the exertional heatstroke animal model and is convenient for the experimental personnel to control flexibly.
Animals ; Heat Stroke ; Mice ; Disease Models, Animal ; Hot Temperature ; Equipment Design ; Humidity ; Body Temperature

OBJECTIVE: To investigate the distribution characteristics of polymorphonuclear neutrophil (PMN) in the lungs during the early stage of severe burns and the mechanism of neutrophil elastase (NE) promoting lung injury. METHODS: 6-8-week-old male C57BL/6J mice were selected for the experiments. A 30% total body surface area (TBSA) III degree burn mouse model was established (severe burn group); the Sham-injury group was treated with 37 centigrade water. In the sodium sivelestat intervention group (SV intervention group), NE competitive inhibitor, sivelestat, 100 mg/kg, was injected via tail vein immediately after injury, while other groups received an equal volume of saline. Ten mice were harvested from each group to observe survival for 72 hours. Respiratory function tests were tested at 0 (immediate), 3, 6, 12, and 24 hours after molding. hematoxylin-eosin (HE) and immunohistochemical staining were used to observe lung tissue structure, inflammatory changes and PMN infiltration. The PMN absolute count in mice lung tissue was detected buy flow cytometry. At 6, 12, and 24 hours after molding, PMN counts and the concentration of NE [enzyme linked immunosorbent assay (ELISA)] in peripheral blood plasma, lung tissue, and bronchoalveolar lavage fluid (BALF) were detected. RESULTS: (1) HE staining results showed that compared with the Sham-injury group, the lungs of mice in the severe burn group showed inflammatory changes and PMN infiltration, with more significant changes at 6 hours. Immunohistochemistry results also confirmed that the expression of NE protein released from PMN significantly increased after 6 hours of severe burn injury [(3.79±0.62)% vs. (0.18±0.05)%, t = 11.56, P < 0.01]. (2) Compared with the Sham-injury group, the number of PMN and the concentration of NE in the peripheral blood and lung tissues in the severe burn group were significantly increased (F values were 13.709, 55.350 and 29.890, 13.286, respectively, all P < 0.01), peaking at 6 hours [plasma PMN count (×10⁹/L): 2.92±1.01 vs. 0.92±0.29, lung tissue PMN absolute count (cells): 48 788.03±11 833.91 vs. 1 516.72±415.35, plasma NE (ng/L): 24 522.71±3 842.92 vs. 7 009.34±4 067.86, lung tissue NE (ng/L): 262 189.04±9 695.13 vs. 65 026.03± 16 016.31, all P < 0.01]. The number of PMN in the lung of severely burned mice was highly correlated with NE concentration (r = 0.892, P < 0.001). There was no significantly difference in the PMN absolute count in the BALF of mice between the Sham-injury group and severe burn group (F = 1.403, P > 0.05). The Sham-injury group and severe burn group contained a small amount of NE in the BALF, and the concentration of NE in the BALF of the severely burned 6 hours and 12 hours groups were significantly higher than those of the Sham-injury group (ng/L: 328.58±158.10, 415.30±240.89 vs. 61.95±15.80, both P < 0.05). (3) Kaplan-Meier survival curve showed that the 72-hour survival rate of mice in the SV intervention group was significantly higher than that in the severe burn group (100% vs. 10%, Log-Rank test: χ² = 19.12, P < 0.001). (4) Compared with the Sham-injury group, all lung function indices of the severe burn group decreased significantly. All lung function indices of SV intervention group improved gradually over time, which were significantly better than those of the severe burn group. (5) Compared with the Sham-injury group, the PMN absolute count in lung tissue and the concentration of NE in plasma and lung tissue were significantly higher in the SV intervention group (F values were 46.709, 3.535, 32.701, respectively, all P < 0.05), with a peak at 6 hours. Compared with the severe burn group, the SV intervention group had a higher PMN absolute count in lung tissue (cells: 8 870.80±7 013.89 vs. 25 974.92±22 240.8, P < 0.05), and higher plasma and lung tissue NE concentrations (ng/L: 14 955.94±3 944.41 vs. 21 972.75±4 573.05, 81 956.87±38 658.35 vs. 168 182.30±83 513.91, both P < 0.01) were significantly decreased. CONCLUSIONS In the early stage of severe burns, there is a significant infiltration of PMN into the lungs. The NE promotes lung injury in the early stage of severe burn, and improve lung injury by inhibiting the action of NE.
Animals ; Burns/metabolism* ; Leukocyte Elastase/metabolism* ; Male ; Mice, Inbred C57BL ; Mice ; Neutrophils/metabolism* ; Lung/metabolism* ; Disease Models, Animal ; Neutrophil Infiltration ; Lung Injury/metabolism* ; Glycine/analogs & derivatives* ; Sulfonamides

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

OBJECTIVE: To develop and compare risk prediction models for in-hospital post-cardiac arrest brain injury (PCABI) in critically ill patients using nomograms and random forest algorithms, aiming to identify the optimal model for early identification of high-risk PCABI patients and providing evidence for precise treatment. METHODS: A retrospective cohort study was used to collect the first-time in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU) from 2008 to 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) as the study population, and the patients' age, gender, body mass, health insurance utilization, first vital signs and laboratory tests within 24 hours of ICU admission, mechanical ventilation, and critical care scores were extracted. Independent influencing factors of PCABI were identified through univariate and multivariate Logistic regression analyses. The included patients were randomly divided into a training cohort and an internal validation cohort in a 7:3 ratio, and the PCABI risk prediction model was constructed by the nomogram and random forest algorithm, respectively, and the model was evaluated by receiver operator characteristic curve (ROC curve), the calibration curve, and the decision curve analysis (DCA), and after the better model was selected, 179 patients admitted to Tianjin Medical University General Hospital as the external validation cohort for external evaluation were collected by using the same inclusion and exclusion criteria. RESULTS: A total of 1 419 patients with without traumatic brain injury who had their first-time IHCA were enrolled, including 995 in the training cohort (including 176 PCABI and 819 non-PCABI) and 424 in the internal validation cohort (including 74 PCABI and 350 non-PCABI). Univariate and multivariate analysis showed that age, potassium, urea nitrogen, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation III (APACHE III), and mechanical ventilation were independent influences on the occurrence of PCABI in patients with IHCA (all P < 0.05). Combining the above variables, we constructed a nomogram model and a random forest model for comparison, and the results show that the nomogram model has better predictive efficacy than the random forest model [nomogram model: area under the ROC curve (AUC) of the training cohort = 0.776, with a 95% credible interval (95%CI) of 0.741-0.811; internal validation cohort AUC = 0.776, with a 95%CI of 0.718-0.833; random forest model: AUC = 0.720, with a 95%CI of 0.653-0.787], and they performed similarly in terms of calibration curves, but the nomogram performed better in terms of decision curve analysis (DCA); at the same time, the nomogram model was robust in terms of external validation cohort (external validation cohort AUC = 0.784, 95%CI was 0.692-0.876). CONCLUSIONS A nomogram risk prediction model for the occurrence of PCABI in critically ill patients was successfully constructed, which performs better than the random forest model, helps clinicians to identify the risk of PCABI in critically ill patients at an early stage and provides a theoretical basis for early intervention.
Humans ; Critical Illness ; Retrospective Studies ; Heart Arrest/complications* ; Nomograms ; Brain Injuries/etiology* ; Intensive Care Units ; Algorithms ; Male ; Female ; Middle Aged ; ROC Curve ; Risk Factors ; Risk Assessment ; Logistic Models ; Aged

OBJECTIVE: To observe the neuroprotective effect of 6-shogaol (6-SH) in global cerebral ischemia/reperfusion injury (CIRI) following cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) in rats. METHODS: Computer-aided molecular docking was used to determine whether 6-SH could spontaneously bind to death-associated protein kinase 1 (DAPK1). SPF-grade male SD rats were randomly divided into a sham group (n = 5), a CPR group (n = 7), and a CPR+6-SH group (n = 7). The CPR group and CPR+6-SH group were further divided into 12-, 24-, and 48-hour subgroups based on observation time points. A rat model of global CIRI after CA-CPR was established by asphyxiation. In the sham group, only tracheal and vascular intubation was performed without asphyxia and CPR induction. The CPR group was intraperitoneally injected with 1 mL of normal saline immediately after successful modeling. The CPR+6-SH group received an intraperitoneal injection of 20 mg/kg 6-SH (1 mL) immediately after successful modeling, followed by administration every 12 hours until the endpoint. Neurological Deficit Score (NDS) was recorded at each time point after modeling. After completion of observation at each time point, rats were anesthetized and sacrificed, and brain tissue specimens were collected. Histopathological changes of neurons were observed under light microscopy after hematoxylin-eosin (HE) staining. Ultrastructural changes of hippocampal neurons and autophagy were observed by transmission electron microscopy (TEM). Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect mRNA expression levels of DAPK1, vacuolar protein sorting 34 (VPS34), Beclin1, and microtubule-associated protein 1 light chain 3 (LC3) in brain tissues. Western blotting was used to detect protein expression levels of DAPK1, phosphorylated DAPK1 at serine 308 (p-DAPK1 ser308), VPS34, Beclin1, and LC3. Immunofluorescence was used to observe Beclin1 and LC3 expression in brain tissues under a fluorescence microscope. RESULTS: Molecular docking results indicated that 6-SH could spontaneously bind to DAPK1. Compared with the sham group, the NDS scores of the CPR group rats were significantly increased at all modeling time points; under light microscopy, disordered cell arrangement, widened intercellular spaces, and edema were observed in brain tissues, with pyknotic and necrotic nuclei in some areas; under TEM, mitochondria were markedly swollen with intact membranes, dissolved matrix, reduced or disappeared cristae, vacuolization, and increased autophagosomes. Compared with the CPR group, the NDS scores of the CPR+6-SH group rats were significantly decreased at all modeling time points; under light microscopy, local neuronal edema and widened perinuclear space were observed; under TEM, mitochondria were mostly mildly swollen with intact membranes, fewer autophagosomes, and alleviated injury. RT-qPCR results showed that compared with the sham group, mRNA expression levels of DAPK1, VPS34, Beclin1, and LC3 in brain tissues were significantly upregulated in all CPR subgroups, with the most pronounced changes at 24 hours. Compared with the CPR group, the CPR+6-SH group showed significantly lower mRNA expression of the above indicators at each time point [24 hours post-modeling (relative expression): DAPK1 mRNA: 3.41±0.68 vs. 4.48±0.62; VPS34 mRNA: 3.63±0.49 vs. 4.66±1.18; Beclin1 mRNA: 3.08±0.49 vs. 4.04±0.22; LC3 mRNA: 2.60±0.36 vs. 3.67±0.62; all P < 0.05]. Western blotting results showed that compared with the sham group, the protein expression levels of DAPK1, VPS34, Beclin1, and LC3 in all CPR subgroups were significantly increased, while the expression of p-DAPK1 ser308 was significantly decreased, with the most pronounced changes observed in the CPR 24-hour subgroup. Compared with the CPR group, the CPR+6-SH subgroups exhibited significantly reduced protein expression of DAPK1, VPS34, Beclin1, and LC3 [24-hour post-modeling: DAPK1/β-actin: 1.88±0.22 vs. 2.47±0.22; VPS34/β-actin: 2.55±0.06 vs. 3.46±0.05; Beclin1/β-actin: 2.12±0.03 vs. 2.87±0.03; LC3/β-actin: 2.03±0.24 vs. 3.17±0.23; all P < 0.05]. Conversely, the expression of p-DAPK1 ser308 was significantly upregulated in the CPR+6-SH group compared to the CPR group [24-hour post-modeling: p-DAPK1 ser308/β-actin: 0.40±0.02 vs. 0.20±0.07, P < 0.05]. Under the fluorescence microscope, fluorescence intensities of Beclin1 and LC3 in the CPR 24-hour group were significantly higher than those in the sham 24-hour group; compared with the CPR 24-hour group, the CPR+6-SH 24-hour group showed significantly reduced fluorescence intensities of Beclin1 and LC3. CONCLUSION 6-SH inhibited the expression of DAPK1, alleviated excessive autophagy after global CIRI following CA-CPR in rats, and exerted neuroprotective effects. The mechanism may be related to phosphorylation at the DAPK1 ser308 site.
Animals ; Rats, Sprague-Dawley ; Male ; Rats ; Cardiopulmonary Resuscitation ; Autophagy/drug effects* ; Heart Arrest/therapy* ; Death-Associated Protein Kinases/metabolism* ; Reperfusion Injury/metabolism* ; Disease Models, Animal ; Neuroprotective Agents/pharmacology* ; Brain Ischemia/metabolism*

OBJECTIVE: To investigate the role and mechanism of the cyclic guanosine monophosphate-adenosine monophosphate synthase/stimulator of interferon gene (cGAS/STING) pathway in oleic acid-induced acute lung injury (ALI) in mice. METHODS: Male wild-type C57BL/6J mice were randomly divided into five groups (each n = 10): normal control group, ALI model group, and 5, 50, 500 μg/kg inhibitor pretreatment groups. The ALI model was established by tail vein injection of oleic acid (7 mL/kg), while the normal control group received no intervention. The inhibitor pretreatment groups were intraperitoneally injected with the corresponding doses of cGAS inhibitor RU.521 respectively 1 hour before modeling. At 24 hours post-modeling, blood was collected, and mice were sacrificed. Lung tissue pathological changes were observed under light microscopy after hematoxylin-eosin (HE) staining, and pathological scores were assessed. Western blotting was used to detect the protein expressions of cGAS, STING, phosphorylated TANK-binding kinase 1 (p-TBK1), phosphorylated interferon regulatory factor 3 (p-IRF3), and phosphorylated nuclear factor-κB p65 (p-NF-κB p65) in lung tissue. Immunohistochemistry was performed to observe STING and p-NF-κB positive expressions in lung tissue. Serum interferon-β (IFN-β) levels were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: Compared with the normal control group, the ALI model group exhibited significant focal alveolar thickening, intra-alveolar hemorrhage, pulmonary capillary congestion, and neutrophil infiltration in the pulmonary interstitium and alveoli, along with markedly increased pathological scores (10.33±0.58 vs. 1.33±0.58, P < 0.05). Protein expressions of cGAS, STING, p-TBK1, p-IRF3, and p-NF-κB p65 in lung tissue significantly increased [cGAS protein (cGAS/β-actin): 1.24±0.02 vs. 0.56±0.02, STING protein (STING/β-actin): 1.27±0.01 vs. 0.55±0.01, p-TBK1 protin (p-TBK1/β-actin): 1.34±0.03 vs. 0.22±0.01, p-IRF3 protein (p-IRF3/β-actin): 1.23±0.02 vs. 0.36±0.01, p-NF-κB p65 protein (p-NF-κB p65/β-actin): 1.30±0.02 vs. 0.53±0.02, all P < 0.05], positive expressions of STING and p-NF-κB in lung tissue were significantly elevated [STING (A value): 0.51±0.03 vs. 0.30±0.07, p-NF-κB (A value): 0.57±0.05 vs. 0.31±0.03, both P < 0.05], and serum IFN-β levels were also significantly higher (ng/L: 256.02±3.84 vs. 64.15±1.17, P < 0.05). The cGAS inhibitor pretreatment groups showed restored alveolar structural integrity, reduced inflammatory cell infiltration, and decreased hemorrhage area, along with dose-dependent lower pathological scores as well as the protein expressions of cGAS, STING, p-TBK1, p-IRF3 and p-NF-κB p65 in lung tissue, with significant differences between the 500 μg/kg inhibitor group and ALI model group [pathological score: 2.67±0.58 vs. 10.33±0.58, cGAS protein (cGAS/β-actin): 0.56±0.03 vs. 1.24±0.02, STING protein (STING/β-actin): 0.67±0.03 vs. 1.27±0.01, p-TBK1 protein (p-TBK1/β-actin): 0.28±0.01 vs. 1.34±0.03, p-IRF3 protein (p-IRF3/β-actin): 0.32±0.01 vs. 1.23±0.02, p-NF-κB p65 protein (p-NF-κB p65/β-actin): 0.63±0.01 vs. 1.30±0.02, all P < 0.05]. Compared with the ALI model group, positive expressions of STING and p-NF-κB in lung tissue were significantly reduced in the 500 μg/kg inhibitor group [STING (A value): 0.40±0.01 vs. 0.51±0.03, p-NF-κB (A value): 0.43±0.02 vs. 0.57±0.05, both P < 0.05], and serum IFN-β levels were also markedly reduced (ng/L: 150.03±6.19 vs. 256.02±3.84, P < 0.05). CONCLUSIONS The cGAS/STING pathway is activated in oleic acid-induced ALI, leading to exacerbated inflammatory responses and increased lung damage. RU.521 can inhibit cGAS, thereby down-regulating the expression of pathway proteins and cytokines, and providing protection to lung tissue.
Animals ; Acute Lung Injury/chemically induced* ; Male ; Nucleotidyltransferases/metabolism* ; Mice ; Signal Transduction ; Mice, Inbred C57BL ; Membrane Proteins/metabolism* ; Oleic Acid/adverse effects* ; Transcription Factor RelA/metabolism* ; Lung/pathology* ; Interferon Regulatory Factor-3/metabolism* ; Disease Models, Animal