OBJECTIVE To study the ameliorative effect and mechanism of Forsythia suspensa-Lonicera japonica herb pair on acute lung injury （ALI） based on serum exosomal microRNA （miRNA）. METHODS The rats were randomly divided into a blank group （normal saline）， model group （nomal saline）， and F. suspensa-L. japonica herb pair group （2.55 g/kg）， with 10 rats in each group. Except for the blank group， the other groups were used to establish an ALI model by intratracheal dripping of 5 mg/ mL lipopolysaccharides. After modeling， each group was given relevant medicine/normal saline intragastrically， once a day， for 3 consecutive days. After the last medication， the pathological status of lung tissue was observed； lung wet-to-dry weight ratio and leukocyte counts in bronchoalveolar lavage fluid （BALF） were determined. The levels of inflammatory factors [tumor necrosis factor-α（TNF-α）， interleukin-1β （IL-1β）， IL-10] in BALF were determined. Exosomes were isolated from rat serum， and high- throughput sequencing technology was employed to screen differentially expressed miRNA within the exosomes， followed by Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis. Based on the screened differentially expressed miRNA and the enriched KEGG pathways， in vitro cellular experiments were conducted for validation. RESULTS The animal experimental results demonstrated that after intervention with the F. suspensa-L. japonica herb pair， the wet-to-dry weight ratio， the number of leukocytes in BALF， as well as the levels of TNF-α and IL-1β in BALF of ALI rats were all significantly reduced （P＜0.01）， while the level of IL-10 was significantly increased （P＜0.01）. The results of high-throughput sequencing experiments revealed that the F. suspensa-L. japonica herb pair could significantly up-regulate the expressions of miR-345-3p， miR-194-5p， miR-653-5p， and others in exosomes. Among them， the KEGG pathways involved in the target genes of differentially expressed miRNA included the hypoxia-inducible factor-1（HIF-1） signaling pathway， among others. The results of cellular E-mail：huang.haiying@126.com validation experiments showed that overexpressed miR-345-3p could significantly elevate the level of IL-10 in the cell supernatant （P＜0.01）， while significantly reducing the levels of TNF-α and IL-1β in the cell supernatant， as well as the mRNA and protein expression levels of protein kinase B1， phosphatidylinositol 3- kinase， and HIF-1α （P＜0.01）. CONCLUSIONS F. suspensa-L. japonica herb pair can alleviate inflammatory responses and thereby exert a therapeutic effect in improving ALI by up-regulating the expression of miR-345-3p in serum exosomes and inhibiting the activity of the HIF-1 signaling pathway.

OBJECTIVE To investigate the effects of galangin on rheumatoid arthritis （RA） in rats by regulating the Janus kinase 3 （JAK3）/signal transducer and activator of transcription 3 （STAT3） pathway. METHODS Fifty male SD rats were taken， and an emulsion composed of bovine type Ⅱ collagen and Freund’s complete adjuvant was injected subcutaneously to establish an induced arthritis model. The rats that were successfully modeled were randomly divided into model group， low， medium and high dose groups of galangin （1， 5， 15 mg/kg）， and methotrexate group （positive control， 2 mg/kg）， with 10 rats in each group. Another 10 normal rats were taken as the normal group. Starting from the 15th day of modeling， each group of rats was gavaged with the corresponding drug solution or normal saline containing 0.5% Tween 80 once a day for 28 consecutive days. The arthritis index （AI） scores and paw volume of rats were compared before and after gavage administration. Twenty-four hours after the last administration， the serum levels of interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， IL-4 and IL-10 were determined， the pathological changes in ankle joint synovial tissue were observed， and the protein expressions of UNC-51 like kinase 1 （ULK1）， Beclin-1， microtubule-associated protein 1 light chain 3 （LC3）， B cell lymphoma 2 （Bcl-2）， Bcl-2-associated X protein （Bax）， caspase-3， JAK3， phosphorylated JAK3 （p-JAK3）， STAT3 and phosphorylated STAT3 （p-STAT3） in the synovial tissue of the ankle joint were detected， as well as the fluorescence intensity of LC3-positive areas. RESULTS Compared with the model group， the pathological changes such as cellular proliferation of ankle joint synovial tissue and infiltration of inflammatory cells in rats of each administration group showed improvement. Moreover， their AI scores and paw pad volumes （on day 28 after gavage）， the levels of IL-6 and TNF-α， the protein expression of Bcl-2， and the phosphorylation levels of JAK3 and STAT3 were all significantly reduced （ P ＜0.05）. The levels of IL-4 and IL-10， the protein expressions of ULK1， Beclin-1， Bax， caspase-3 and LC3， as well as the fluorescence intensity of LC3-positive areas， were all significantly increased （ P ＜0.05）. Moreover， the effect of galangin was in a dose-dependent manner （ P ＜0.05）. CONCLUSIONS Galangin can induce sustained autophagy in synovial tissue cells of RA rats， promote cell apoptosis， inhibit synovial cell proliferation， and alleviate persistent inflammatory responses. The above anti-RA effects may be related to the inhibition of the JAK3/STAT3 pathway.

ObjectiveTo investigate the mechanism of Forsythiae Fructus-Lonicerae Japonicae Flos（FF） in the treatment of acute lung injury（ALI） by investigating the effects of FF on serum metabolomics of rats with ALI. MethodsThirty male SD rats were acclimated for 1 week， and 6 rats were randomly selected as the blank group. The other 24 rats were injected with lipopolysaccharide（LPS） solution by tracheal drip to establish an ALI model. After successful model establishment， the rats were randomly divided into the model group， the FF low-dose group（3.0 g·kg^-1）， the FF high-dose group（6.0 g·kg^-1）， and the dexamethasone group（5 mg·kg^-1）， with six rats in each group. The FF low- and high-dose groups and the dexamethasone group were received daily oral administration of the corresponding drug solution， and the blank group and the model group were gavaged with an equal amount of saline， treatment was administered continuously for 3 d. The pathological conditions of rat lung tissues were evaluated by hematoxylin-eosin（HE） staining， wet/dry mass ratio（W/D） of the lung tissues， and protein concentration in rat bronchoalveolar lavage fluid（BALF）. Metabolomic analysis of rat serum was performed by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS）， combined with multivariate statistical analysis， the potential biomarkers of FF in treating ALI were screened by variable importance in the projection（VIP） value>1， P<0.05 from t-test， and log₂fold change（FC）>1 or log₂FC<-1. Kyoto Encyclopedia of Genes and Genomes（KEGG） database combined with MetaboAnalyst were used for pathway analysis of the screened differential metabolites. The protein expression levels of sphingosine-1-phosphate（S1P）， phosphatidylinositol 3-kinase（PI3K）， protein kinase B1（Akt1）， and phosphorylated Akt1（p-Akt1） were examined by Western bolt. The expression levels of interleukin（IL）-6， IL-1β， and tumor necrosis factor（TNF）-α in BALF were detected by enzyme-linked immunosorbent assay（ELISA）. ResultsCompared with the blank group， rats in the model group showed ALI pathological features such as alveolar lumen dilatation， interstitial hemorrhage and massive inflammatory cell infiltration， and the protein concentration in BALF and W/D of the lung tissues were significantly elevated（P<0.01）. Compared with the model group， the low- and high-dose groups of FF as well as the dexamethasone group exhibited reduced pulmonary bronchial hemorrhage in rats， and the protein concentration in BALF and W/D were significantly decreased（P<0.05）， and the lung injury was significantly alleviated. Analysis of rat serum metabolomics revealed that FF downregulated 38 biomarkers. Pathway enrichment analysis showed that FF primarily exerted therapeutic effects through 7 key metabolic pathways， including arginine biosynthesis， sphingomyelin metabolism， alanine， aspartate and glutamate metabolism， taurine and hypotaurine metabolism， α-linolenic acid metabolism， niacin and nicotinamide metabolism， and retinol metabolism. The results of Western bolt and ELISA showed that， compared with the blank group， the model group exhibited significantly elevated expression levels of S1P， PI3K， Akt1 and p-Akt1 proteins in the lung tissues， as well as increased expression levels of IL-6， IL-1β and TNF-α in BALF（P<0.01）. Compared with the model group， the expression levels of the aforementioned indicators were significantly downregulated in the low- and high-dose FF groups as well as the dexamethasone group（P<0.05， P<0.01）. ConclusionFF may play a role in ALI by regulating amino acid metabolism and lipid metabolism， and its mechanism may be related to the inhibition of S1P/PI3K/Akt1 signaling pathway to attenuate the inflammatory response caused by ALI.

Normal heart function depends on complex regulation by the brain, and abnormalities in the brain‒heart axis affect various diseases, such as myocardial infarction and anxiety disorders. However, systematic tracking of the brain regions associated with the input and output of the heart is lacking. In this study, we injected retrograde transsynaptic pseudorabies virus (PRV) and anterograde transsynaptic herpes simplex virus (HSV) into the left ventricular wall of mice to identify the whole-brain regions associated with the input to and output from the heart. We successfully detected PRV and HSV expression in at least 170 brain subregions in both male and female mice. Sex differences were discovered mainly in the hypothalamus and medulla, with male mice exhibiting greater correlation and hierarchical clustering than female mice, indicating reduced similarity and increased modularity of virus expression patterns in male mice. Further graph theory and multiple linear regression analysis of different injection timelines revealed that hub regions of PRV had highly similar clusters, with different brain levels, suggesting a top-down, hierarchically transmitted neural control pattern of the heart. Hub regions of HSV had scattered clusters, with brain regions gathered in the cortex and brainstem, suggesting a bottom-up, leapfrog, multipoint neural sensing pattern of the heart. Both patterns contain many hub brain regions that have been previously overlooked in brain‒heart axis studies. These results provide brain targets for future research and will lead to deeper insight into the brain mechanisms involved in specific heart conditions.
Animals ; Male ; Female ; Heart/physiology* ; Mice ; Herpesvirus 1, Suid ; Brain/physiology* ; Mice, Inbred C57BL ; Brain Mapping ; Efferent Pathways/physiology* ; Afferent Pathways/physiology* ; Simplexvirus ; Sex Characteristics

The invasive arterial blood pressure(ABP)monitoring technique is widely utilized in the management of critically ill patients,providing medical professionals with real-time,continuous,and dynamic blood pressure data that plays a crucial role in guiding patient treatment.However,current traditional methods for arterial catheter blood collection and zero setting and measurement technology for ABP and central venous pressure(CVP)present certain issues such as complex operation procedures,inaccurate zero setting,and the inability to simultaneously monitor both ABP and CVP.Additionally,these methods may increase the risk of bloodstream infection and occupational exposure.Therefore,the department of critical care medicine at Tianjin Medical University General Hospital has developed a vascular access convenient blood collection device which has been granted a National Utility Model Patent(patent number:ZL 202322581247.4).This device includes a connecting tube,a specimen collection structure,a blood suction structure,a multifunctional pressure measurement device with laser emission structure,a pressure measurement conversion structure,and an efficient vascular access blood collection device.By optimizing the blood collection procedure and simplifying the manometry process,this design reduces the risk of bloodstream infection for patients and minimizes occupational exposure for medical staff.The device ensures accurate zero point adjustment of pressure measurements while guaranteeing the authenticity and reliability of collected data.Additionally,it supports synchronous monitoring of ABP and CVP,thereby saving medical consumables and reducing workload for healthcare professionals.This simple yet safe and efficient device meets clinical requirements effectively and is highly recommended for widespread use.

Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Objective:To investigate the predictive value of refeeding syndrome (RFS) and its influencing factors for 30-day intensive care unit (ICU) readmission in critically ill patients.Methods:A prospective cohort study was conducted. Critically ill patients admitted to the department of critical care medicine, department of respiratory and critical care medicine, and department of neurology at Tianjin Medical University General Hospital from January to April in 2025 were enrolled. Patients were assessed for RFS according to the American Society for Parenteral and Enteral Nutrition (ASPEN) criteria. General information within 24 hours of ICU admission was collected via the electronic medical record system. Treatment details and 30-day ICU readmission status were dynamically recorded. Participants were divided into readmission and non-readmission groups based on whether ICU readmission occurred within 30 days. Intergroup comparisons were performed to identify differences. Multivariate Logistic regression was used to analyze the relationship between RFS and its influencing factors with 30-day ICU readmission. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of risk factors.Results:A total of 196 critically ill patients were enrolled, among whom 25 (12.76%) were readmitted to ICU within 30 days and 171 (87.24%) were not. Significant differences were observed in the readmission group compared with the non-readmission group, including significantly higher rates of nasogastric decompression, higher acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score, a higher incidence of RFS, and a longer duration of nasogastric decompression. Multivariate Logistic regression analysis showed that RFS was an independent risk factor for 30-day ICU readmission [odds ratio ( OR) = 5.756, 95% confidence interval (95% CI) was 1.603-20.670, P = 0.007]. APACHEⅡ score showed a positive correlation trend with 30-day ICU readmission ( OR = 1.057, 95% CI was 0.991-1.127, P = 0.092). ROC curve analysis showed that the combined prediction model incorporating RFS and APACHEⅡ score had an area under the ROC curve (AUC) of 0.766 (95% CI was 0.668-0.864), with a sensitivity of 88.0% and a specificity of 62.0%, which was significantly superior to a single indicator (the AUC of RFS and APACHEⅡ score was 0.639 and 0.624, respectively). Conclusions:RFS significantly increases the risk of 30-day ICU readmission in critically ill patients. A combined model incorporating RFS and APACHEⅡ score demonstrates good predictive efficacy for 30-day ICU readmission in critically ill patients.

The invasive arterial blood pressure(ABP)monitoring technique is widely utilized in the management of critically ill patients,providing medical professionals with real-time,continuous,and dynamic blood pressure data that plays a crucial role in guiding patient treatment.However,current traditional methods for arterial catheter blood collection and zero setting and measurement technology for ABP and central venous pressure(CVP)present certain issues such as complex operation procedures,inaccurate zero setting,and the inability to simultaneously monitor both ABP and CVP.Additionally,these methods may increase the risk of bloodstream infection and occupational exposure.Therefore,the department of critical care medicine at Tianjin Medical University General Hospital has developed a vascular access convenient blood collection device which has been granted a National Utility Model Patent(patent number:ZL 202322581247.4).This device includes a connecting tube,a specimen collection structure,a blood suction structure,a multifunctional pressure measurement device with laser emission structure,a pressure measurement conversion structure,and an efficient vascular access blood collection device.By optimizing the blood collection procedure and simplifying the manometry process,this design reduces the risk of bloodstream infection for patients and minimizes occupational exposure for medical staff.The device ensures accurate zero point adjustment of pressure measurements while guaranteeing the authenticity and reliability of collected data.Additionally,it supports synchronous monitoring of ABP and CVP,thereby saving medical consumables and reducing workload for healthcare professionals.This simple yet safe and efficient device meets clinical requirements effectively and is highly recommended for widespread use.

Objective:To investigate the predictive value of refeeding syndrome (RFS) and its influencing factors for 30-day intensive care unit (ICU) readmission in critically ill patients.Methods:A prospective cohort study was conducted. Critically ill patients admitted to the department of critical care medicine, department of respiratory and critical care medicine, and department of neurology at Tianjin Medical University General Hospital from January to April in 2025 were enrolled. Patients were assessed for RFS according to the American Society for Parenteral and Enteral Nutrition (ASPEN) criteria. General information within 24 hours of ICU admission was collected via the electronic medical record system. Treatment details and 30-day ICU readmission status were dynamically recorded. Participants were divided into readmission and non-readmission groups based on whether ICU readmission occurred within 30 days. Intergroup comparisons were performed to identify differences. Multivariate Logistic regression was used to analyze the relationship between RFS and its influencing factors with 30-day ICU readmission. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of risk factors.Results:A total of 196 critically ill patients were enrolled, among whom 25 (12.76%) were readmitted to ICU within 30 days and 171 (87.24%) were not. Significant differences were observed in the readmission group compared with the non-readmission group, including significantly higher rates of nasogastric decompression, higher acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score, a higher incidence of RFS, and a longer duration of nasogastric decompression. Multivariate Logistic regression analysis showed that RFS was an independent risk factor for 30-day ICU readmission [odds ratio ( OR) = 5.756, 95% confidence interval (95% CI) was 1.603-20.670, P = 0.007]. APACHEⅡ score showed a positive correlation trend with 30-day ICU readmission ( OR = 1.057, 95% CI was 0.991-1.127, P = 0.092). ROC curve analysis showed that the combined prediction model incorporating RFS and APACHEⅡ score had an area under the ROC curve (AUC) of 0.766 (95% CI was 0.668-0.864), with a sensitivity of 88.0% and a specificity of 62.0%, which was significantly superior to a single indicator (the AUC of RFS and APACHEⅡ score was 0.639 and 0.624, respectively). Conclusions:RFS significantly increases the risk of 30-day ICU readmission in critically ill patients. A combined model incorporating RFS and APACHEⅡ score demonstrates good predictive efficacy for 30-day ICU readmission in critically ill patients.

Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.