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 relationship between serum sodium level and the risk of delirium in patients with sepsis. METHODS: Based on the Medical Information Mart for Intensive Care-IV (MIMIC-IV), adult patients with sepsis in the intensive care unit (ICU) were enrolled. The serum sodium level prior to the onset of sepsis during hospitalization was used as the exposure variable. Delirium was assessed using the ICU-confusion assessment method (ICU-CAM) as the primary outcome. Patients were divided into delirium and non-delirium groups based on the occurrence of delirium. The relationship between serum sodium level and delirium risk was described using restricted cubic spline (RCS) to determine the optimal reference range for serum sodium. Logistic regression analysis was used to evaluate the effect of blood sodium levels on delirium in sepsis patients. Subgroup analyses were performed to explore potential interactions and further validate the robustness of the results. Receiver operator characteristic curve (ROC curve) analysis was performed to assess the predictive value of serum sodium level for delirium occurrence in patients with sepsis. RESULTS: A total of 13 889 patients with sepsis were included, of which 4 831 experienced delirium. The maximum and mean serum sodium values were significantly higher in the delirium group compared to the non-delirium group, while there were no statistically significant differences in terms of initial and minimum serum sodium values between the two groups. Compared with the non-delirium group, the delirium group had a higher mortality and longer hospital stay. The RCS curve showed that a "U"-shaped relationship between serum sodium level and delirium risk in patients with sepsis, with the optimal reference range for average serum sodium was 135.3-141.3 mmol/L. Group based on this reference range, compared to the group with 135.3 mmol/L ≤ serum sodium ≤ 141.3 mmol/L, the delirium incidence and mortality were significantly higher, and the hospital stay was longer in the groups with serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L [delirium incidence: 36.92%, 40.88% vs. 31.22%; 28-day mortality: 23.08%, 20.15% vs. 13.39%; 90-day mortality: 30.75%, 24.81% vs. 18.26%; in-hospital mortality: 19.53%, 17.48% vs. 11.61%; ICU mortality: 14.35%, 14.05% vs. 9.00%; hospital length of stay (days): 10.1 (6.1, 17.7), 9.4 (5.4, 17.0) vs. 8.9 (5.5, 15.4), length of ICU stay (days): 3.7 (2.1, 7.1), 4.0 (2.1, 8.9) vs. 3.2 (1.9, 6.8); all P < 0.01]. Logistic regression analysis showed that, in the initial model and each factor-adjusted models, compared to the reference group with 135.3 mmol/L ≤ serum sodium < 141.3 mmol/L, serum sodium < 135.3 mmol/L increased the risk of delirium in septic patients by 21% to 29% [odds ratio (OR) was 1.21-1.29, all P < 0.01], while serum sodium ≥ 141.3 mmol/L increased the delirium risk by 28%-52% (OR was 1.28-1.52, all P < 0.01). Subgroup analyses based on gender, age, race, diuretic use, and sequential organ failure assessment (SOFA) score revealed there was no significant interactions between subgroup variables and serum sodium, and the results supported that both serum sodium < 135.3 mmol/L and serum sodium ≥ 141.3 mmol/L were risk factors for delirium in septic patients. ROC curve analysis showed that the area under the curve (AUC) for predicting delirium in septic patients based on serum sodium was 0.614, with a cut-off value of 139.5 mmol/L yielding a specificity of 67.5% and sensitivity of 50.9%. CONCLUSIONS The risk of delirium in patients with sepsis is associated with serum sodium level in a "U"-shaped manner. Both high and low serum sodium levels are associated with increased risk of delirium, higher all-cause mortality, and prolonged hospital stays in patients with sepsis. Abnormal serum sodium levels may have predictive value for sepsis-associated delirium and could serve as an early biomarker for identifying delirium in septic patients, although further validation is needed.
Humans ; Delirium/etiology* ; Sepsis/complications* ; Sodium/blood* ; Intensive Care Units ; Risk Factors ; Male ; Middle Aged ; Female ; Aged ; Logistic Models ; Adult

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 analyze the risk factors for ventilator-associated pneumonia (VAP) and its prognosis in patients with severe craniocerebral injury. METHODS: A prospective observational study was conducted. Patients with severe craniocerebral injury admitted to the Second Affiliated Hospital of Xingtai Medical College from January 2020 to December 2022 were enrolled as the study subjects. Patients were divided into VAP group and non-VAP group based on the occurrence of VAP. VAP patients were further stratified into low-risk group [sequential organ failure assessment (SOFA) score 0-5], moderate-risk group (SOFA score 6-8), and high-risk group (SOFA score ≥ 9). General data, serological indicators [interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and signal transducer and activator of transcription 3 (STAT3)], and 28-day prognosis (with mortality as the endpoint event) were compared. Multivariate Logistic regression was used to identify risk factors for VAP and 28-day mortality. Linear regression was applied to analyze the correlations between risk factors and outcomes. RESULTS: A total of 140 patients with severe craniocerebral injury were enrolled, including 49 in the VAP group and 91 in the non-VAP group. The primary cause of injury was traffic accidents, followed by falls and heavy object impacts. Among VAP patients, 38 survived and 11 died within 28 days; 112 were classified as low-risk, 25 as moderate-risk, and 12 as high-risk. Significant differences were observed in age, body mass index (BMI), smoking history, hypertension, diabetes, hyperlipidemia, length of hospital stay, duration of mechanical ventilation, serum albumin levels, and frequency of sputum suction among different subgroups. Serologically, IL-1β, TNF-α, IL-6, and STAT3 mRNA expression levels in the VAP group were significantly higher than those in the non-VAP group. Deceased VAP patients exhibited higher IL-1β, TNF-α, IL-6, and STAT3 mRNA levels compared to survivors. These biomarkers progressively increased from low-risk to high-risk subgroups. Multivariate Logistic regression identified age [odds ratio (OR) were 0.328 and 0.318], BMI (OR were 0.340 and 0.268), hypertension (OR were 0.275 and 0.245), diabetes (OR were 0.319 and 0.307), hyperlipidemia (OR were 0.228 and 0.235), smoking history (OR were 0.255 and 0.240), length of hospital stay (OR were 0.306 and 0.230), duration of mechanical ventilation (OR were 0.247 and 0.219), frequency of sputum suction (OR were 0.325 and 0.228), IL-1β (OR were 0.231 and 0.259), TNF-α (OR were 0.308 and 0.235), IL-6 (OR were 0.298 and 0.277), and STAT3 (OR were 0.259 and 0.265) as independent risk factors for both VAP occurrence and 28-day mortality (all P < 0.05). Correlation analysis revealed that serum albumin levels were negatively correlated with VAP occurrence and mortality (all P < 0.01), while other factors showed positive correlations (all P < 0.01). CONCLUSIONS Age, BMI, length of hospital stay, duration of mechanical ventilation, frequency of sputum suction, hypertension, diabetes, hyperlipidemia, smoking history, IL-1β, TNF-α, and IL-6/STAT3 signaling pathway activation are significantly associated with VAP development and poor prognosis in patients with severe craniocerebral injury, providing a scientific basis for targeted clinical interventions.
Humans ; Risk Factors ; Pneumonia, Ventilator-Associated ; Prognosis ; Prospective Studies ; Craniocerebral Trauma/complications* ; Interleukin-6/blood* ; Male ; Female ; STAT3 Transcription Factor/blood* ; Interleukin-1beta/blood* ; Tumor Necrosis Factor-alpha/blood* ; Middle Aged ; Adult ; Logistic Models

OBJECTIVE: To investigate the frequency and related factors of ineffective triggering (IT) and double triggering (DT) in patients with acute brain injury undergoing invasive mechanical ventilation. METHODS: A retrospective cohort study was conducted using data from a single-center observational trial. Patients with acute brain injury [traumatic brain injury, stroke, and post-craniotomy for brain tumors] undergoing mechanical ventilation in the intensive care unit (ICU) of Beijing Tiantan Hospital, Capital Medical University between June 2017 and July 2019 were retrospectively analyzed. Demographic and clinical data were collected. Respiratory parameters and waveforms during the first 3 days of mechanical ventilation were recorded, with 15-minute waveform segments collected 4 times daily. Airway occlusion pressure (P_0.1) was measured via end-expiratory hold at the end of each recording. IT and DT were identified based on airway pressure, flow, and esophageal pressure waveforms, and the ineffective triggering index (ITI) and DT incidence were calculated. Multivariate Logistic regression was used to identify factors associated with IT and DT. RESULTS: A total of 94 patients with acute brain injury were ultimately enrolled, including 19 cases of traumatic brain injury (20.2%), 39 cases of stroke (41.5%), and 36 cases of post-craniotomy for brain tumor (38.3%). Supratentorial injury was observed in 49 patients (52.1%), while infratentorial injury was identified in 45 patients (47.9%). A total of 94 patients with 1 018 datasets were analyzed; 684 (67.2%) datasets were on pressure support ventilation (PSV), and 334 (32.8%) were on mandatory ventilation. IT was detected in 810 (79.6%) datasets, with a median incidence of 2.1% (0.3%, 12.0%). Datasets demonstrating IT were characterized by lower P_0.1, higher tidal volume (VT), reduced respiratory rate (RR), and decreased minute ventilation (MV) compared to those without IT. The proportion of datasets exhibiting IT was higher during PSV than in mandatory ventilation [83.8% (573/684) vs. 71.0% (237/334), P < 0.05], while, the prevalence of ITI ≥ 10% was lower [23.8% (163/684) vs. 33.5% (112/334), P < 0.05]. DT was detected in 305 datasets (30%), with a median incidence of 0.6% (0.4%, 1.3%). Datasets exhibiting DT were characterized by higher VT, reduced RR, and lower pressure support levels. The incidence of DT was lower in PSV compared to mandatory ventilation modes [0% (0%, 0.3%) vs. 0% (0%, 0.5%), P < 0.05]. The post-craniotomy for brain tumors group exhibited higher ITI, lower RR, reduced MV, and a greater proportion of infratentorial lesions, compared to the TBI group. The infratentorial lesion group demonstrated higher ITI and incidence of DT compared to the supratentorial lesion group [ITI: 3.1% (0.7%, 17.8%) vs. 1.5% (0%, 8.3%), incidence of DT: 0% (0%, 0.5%) vs. 0% (0%, 0%), both P < 0.05]. After adjusting for confounding factors through multivariate logistic regression analysis, infratentorial lesion [odds ratio (OR) = 2.029, 95% confidence interval (95%CI) was 1.465-2.811, P < 0.001], lower P_0.1 (OR = 0.714, 95%CI was 0.616-0.827, P < 0.001), and mandatory ventilation (OR = 1.613, 95%CI was 1.164-2.236, P = 0.004) were independently associated with IT. Additionally, infratentorial lesion (OR = 1.618, 95%CI was 1.213-2.157, P = 0.001), large tidal volume (OR = 1.222, 95%CI was 1.137-1.314, P < 0.001), lower pressure support levels (OR = 0.876, 95%CI was 0.829-0.925, P < 0.001), and mandatory ventilation (OR = 2.750, 95%CI was 1.983-3.814, P < 0.001) were independently associated with DT. CONCLUSION IT and DT were common in patients with acute brain injury. Infratentorial lesions and mandatory ventilation were independently associated with both IT and DT.
Humans ; Respiration, Artificial/methods* ; Retrospective Studies ; Brain Injuries/therapy* ; Intensive Care Units ; Male ; Female ; Middle Aged ; Brain Injuries, Traumatic/therapy* ; Logistic Models ; Aged ; Adult

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 evaluate the prognosis of septic right ventricular dysfunction (SRVD) based on bedside ultrasound and explore its risk factors. METHODS: A prospective observational study was conducted involving septic and septic shock patients admitted to the intensive care unit (ICU) of the General Hospital of Ningxia Medical University from February 2021 to January 2022. Tricuspid annular plane systolic excursion (TAPSE) was measured by M-mode ultrasound within 24 hours after ICU admission. According to the results of TAPSE, the subjects were divided into SRVD group (TAPSE < 16 mm) and non-SRVD group (TAPSE ≥ 16 mm). The gender, age, occurrence of septic shock, underlying diseases, source of patients, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, maximal body temperature within 24 hours after ICU admission, location and number of infections, duration of mechanical ventilation, and 28-day mortality were collected. Hemodynamic parameters, organ function indexes, oxygen therapy parameters and arterial blood gas analysis indexes were recorded within 24 hours after ICU admission. The differences of the above indexes between the two groups were compared. Binary multivariate Logistic regression analysis was used to screen out the independent risk factors for SRVD, and a nomogram of SRVD risk factors was drawn. RESULTS: 116 patients with sepsis and septic shock were enrolled, of which 24 (20.7%) had SRVD and 92 (79.3%) had no SRVD. Compared with the non-SRVD group, the patients in the SRVD group had higher emergency transfer and infection site ≥ 2 ratio, APACHE II score, SOFA score, higher cardiac troponin I (cTnI), myoglobin (Mb), MB isoenzyme of creatine kinase (CK-MB), N-terminal pro-brain natriuretic peptide (NT-proBNP), serum creatinine (SCr), arterial blood lactic acid (Lac) and lower left ventricular ejection fraction (LVEF), platelet count (PLT) within 24 hours after ICU admission, and higher proportion of norepinephrine application and continuous renal replacement therapy (CRRT). Binary multivariate Logistic regression analysis showed that LVEF [odds ratio (OR) = 0.918, 95% confidence interval (95%CI) was 0.851-0.991, P = 0.028], PLT (OR = 0.990, 95%CI was 0.981-0.999, P = 0.035), SCr (OR = 1.008, 95%CI was 1.001-1.016, P = 0.025), and the usage of norepinephrine (OR = 15.198, 95%CI was 1.541-149.907, P = 0.020) were independent risk factors for SRVD in patients with sepsis and septic shock. Based on the above four independent risk factors, a nomogram of SRVD risk factors was drawn. The results showed that the score was 64 when LVEF was 0.50, 18 when SCr was 100 μmol/L, 85 when PLT was 100×10⁹/L, and 39 when norepinephrine was used. When the total score reached 253, the risk of SRVD was 88%. Compared with non-SRVD group, the duration of mechanical ventilation in SRVD group was slightly longer [hours: 80.0 (28.5, 170.0) vs. 47.0 (10.0, 135.0), P > 0.05], and the 28-day mortality was significantly higher [41.7% (10/24) vs. 21.7% (20/92), P < 0.05]. CONCLUSIONS Patients with sepsis may have right ventricular dysfunction, impaired renal function and increased mortality in the early stage. The decrease in LVEF and PLT, the increase in SCr and the application of norepinephrine are independent risk factors for SRVD in patients with sepsis.
Humans ; Prognosis ; Ventricular Dysfunction, Right/diagnostic imaging* ; Risk Factors ; Prospective Studies ; Intensive Care Units ; Shock, Septic ; Male ; Ultrasonography ; Female ; Sepsis/complications* ; Middle Aged ; Point-of-Care Systems ; Aged ; Logistic Models ; APACHE

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