ObjectiveTo evaluate the effects of Tongnaoyin on the blood-brain barrier status and neurological impairment in acute ischemic stroke （AIS） patients with the syndrome of phlegm-stasis blocking collaterals by dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI）. MethodsA total of 63 patients diagnosed with AIS in the Jiangsu Province Hospital of Chinese Medicine from October 2022 to December 2023 were enrolled in this study. According to random number table method，the patients were assigned into a control group （32 cases） and an observation group （31 cases）. The control group received conventional Western medical treatment，and the observation group took 200 mL Tongnaoyin after meals，twice a day from day 2 of admission on the basis of the treatment in the control group. After 7 days of treatment，the patients were examined by DCE-MRI. The baseline data for two groups of patients before treatment were compared. The National Institute of Health Stroke Scale （NIHSS） score and modified Rankin Scale （mRS） score were recorded before treatment and after 90 days of treatment for both groups. The rKtrans，rKep，and rVe values were obtained from the region of interest （ROI） of the infarct zone/mirror area and compared between the two groups. ResultsThere was no significant difference in the NIHSS or mRS score between the two groups before treatment. After 90 days of treatment，the NIHSS and mRS scores declined in both groups，and the observation group had lower scores than the control group （P<0.05）. After treatment，the rKtrans and rVe in the observation group were lower than those in the control group （P<0.01）. ConclusionCompared with conventional Western medical treatment alone，conventional Western medical treatment combined with Tongnaoyin accelerates the repair of the blood-brain barrier in AIS patients，thereby ameliorating neurological impairment after AIS to improve the prognosis.

OBJECTIVE: Myocardial inflammation during myocardial infarction (MI) could be inhibited by regulating arachidonic acid (AA) metabolism. Recent studies demonstrated that Sini Decoction (SND) was identified to be an effective prescription for treating heart failure (HF) caused by MI. But the anti-inflammatory mechanism of SND remained unclear. The work was designed to investigate the anti-inflammatory mechanism of SND through the AA metabolism pathway in vitro and in vivo experiments. METHODS: An inflammatory injury model of H9c2 cells was established by lipopolysaccharide (LPS)-stimulated macrophage-conditioned medium (CM). The MI model was built by the ligation of left anterior descending (LAD) branch of coronary artery in rat. Meanwhile, the rats were divided into five groups: sham group, MI group, MI + Celecoxib group, MI + low-dose SND group (SND-L) and MI + high-dose SND group (SND-H). Cardiac function, histopathological changes and serum cytokines were examined four weeks later. Western blot analysis was conducted to verify the key enzymes levels in the AA metabolic pathway, including phospholipase A2 (PLA2), cyclooxygenases (COXs) and lipoxygenases (LOXs). RESULTS: These in vivo results demonstrated that SND could improve the cardiac function and pathological changes of rats with MI, and regulate the key inflammatory molecules in the AA metabolism pathway, including sPLA2, COX-1, COX-2, 5-LOX and 15-LOX. In vitro, SND could decrease the release of pro-inflammatory cytokines including TNF-α and IL-6 and inhibit cell apoptosis in CM-induced H9c2 cells. Moreover, SND could protect H9c2 cells from the damage of CM by regulating nuclear factor kappa-B (NF-κB) signal pathway and the expression of COX-2. CONCLUSION SND may be a drug candidate for anti-inflammatory treatment during MI by regulating the multiple targets in the AA metabolism pathway.

Objective:To explore the effectiveness of find-organize-clarify-understand- select-plan-do-check-act (FOCUS-PDCA) cycle management in improving the accuracy of Nutritional Risk Screening 2002 (NRS 2002) scoring in inpatients.Methods:This study was a retrospective study. Data from a continuous quality improvement project, namely Improving the Accuracy of Nutritional Risk Screening for Inpatients from People's Hospital of Longhua in 2021 were selected. The NRS 2002 scores of newly admitted patients from 8 departments with relatively high nutritional risk (departments of gstroenterology, neurology, nephrology, respiratory medicine, oncology and hematology, neurosurgery, gastrointestinal surgery, and critical care medicine) from April 10, 2021 to April 29, 2021 (before intervention) were collected. The NRS 2002 scores were evaluated by a multidisciplinary nutrition support team in terms of accuracy and error types. Possible issues in the process of nutritional screening were analyzed and mitigated using the FOCUS-PDCA cycle management method. The NRS 2002 scores from July 18, 2021 to August 8, 2021 (after intervention) were collected in real time to summary the data on accuracy and error types and to evaluate the effect of FOCUS-PDCA.Results:The accuracy of NRS 2002 score in the 8 departments was increased from 52.97% (294/555) to 81.13% (473/583) after intervention, and the difference was statistically significant ( χ2=102.606, P<0.001). The accuracy of nutritional status impairment score was 64.14% before intervention, compared with 90.57% after intervention ( χ2=114.484, P<0.001). The accuracy of disease severity score was 78.56%, compared with 89.54% after intervention( χ2=25.736, P<0.001). The false-negative rate was 68.02% before intervention and 31.87% after intervention, and the difference was statistically significant ( χ2=31.501, P<0.001). Conclusion:FOCUS-PDCA can improve the accuracy of NRS 2002 scoring in inpatients, reduce the risk of failing to identify patients at nutritional risk, and contribute to further nutritional diagnosis and treatment.

Objective:To explore the effectiveness of find-organize-clarify-understand- select-plan-do-check-act (FOCUS-PDCA) cycle management in improving the accuracy of Nutritional Risk Screening 2002 (NRS 2002) scoring in inpatients.Methods:This study was a retrospective study. Data from a continuous quality improvement project, namely Improving the Accuracy of Nutritional Risk Screening for Inpatients from People's Hospital of Longhua in 2021 were selected. The NRS 2002 scores of newly admitted patients from 8 departments with relatively high nutritional risk (departments of gstroenterology, neurology, nephrology, respiratory medicine, oncology and hematology, neurosurgery, gastrointestinal surgery, and critical care medicine) from April 10, 2021 to April 29, 2021 (before intervention) were collected. The NRS 2002 scores were evaluated by a multidisciplinary nutrition support team in terms of accuracy and error types. Possible issues in the process of nutritional screening were analyzed and mitigated using the FOCUS-PDCA cycle management method. The NRS 2002 scores from July 18, 2021 to August 8, 2021 (after intervention) were collected in real time to summary the data on accuracy and error types and to evaluate the effect of FOCUS-PDCA.Results:The accuracy of NRS 2002 score in the 8 departments was increased from 52.97% (294/555) to 81.13% (473/583) after intervention, and the difference was statistically significant ( χ2=102.606, P<0.001). The accuracy of nutritional status impairment score was 64.14% before intervention, compared with 90.57% after intervention ( χ2=114.484, P<0.001). The accuracy of disease severity score was 78.56%, compared with 89.54% after intervention( χ2=25.736, P<0.001). The false-negative rate was 68.02% before intervention and 31.87% after intervention, and the difference was statistically significant ( χ2=31.501, P<0.001). Conclusion:FOCUS-PDCA can improve the accuracy of NRS 2002 scoring in inpatients, reduce the risk of failing to identify patients at nutritional risk, and contribute to further nutritional diagnosis and treatment.

ObjectiveTo investigate the changes in cerebral blood perfusion in patients with acute cerebral infarction after taking Tongnaoyin， a traditional Chinese medicine， based on head and neck computed tomography （CT） angiography （CTA） combined with brain CT perfusion imaging （CTP）. MethodA total of 240 patients with cerebral infarction of phlegm and blood stasis syndrome treated in Jiangsu Province Hospital of Traditional Chinese Medicine from March 2018 to September 2023 were randomly divided into a control group （99 cases） and a Tongnaoyin group （141 cases）. Based on the guidelines， the control group was treated with conventional treatment such as anti-aggregation， anticoagulation， lipid-lowering and plaque stabilization， brain protection， and supportive treatment. The Tongnaoyin group was treated with Tongnaoyin of 200 mL in warm conditions in the morning and evening on the basis of the control group. Both groups underwent CTA combined with CTP within 24 hours after admission， and they were reexamined by CTA and CTP in the sixth month after admission. The degree of intracranial artery stenosis was determined according to the North American Symptomatic Carotid Endarterectomy Trial （NASCET） method. The relative cerebral blood volume （rCBV）， relative cerebral blood flow （rCBF）， mean transit time （MTT）， and time to peak （TTP） of the lesion area before and after treatment were compared. The adverse outcomes of the two groups within six months after discharge were compared. ResultCompared with the group before treatment， the degree of vascular stenosis in the Tongnaoyin group was significantly reduced， and the difference was statistically significant （Z=105.369，P<0.05）. Compared with the control group after treatment， the improvement rate of vascular stenosis in the Tongnaoyin group was higher （χ²=84.179，P<0.01）， and the curative effect was better.After treatment， the rCBV and rCBF of patients in the Tongnaoyin group were significantly increased， and the difference was statistically significant （P<0.01）. MTT and TTP showed a trend of shortening， but the difference was not statistically significant. There was no statistically significant difference in rCBV， rCBF， MTT， and TTP in the control group. Compared with those in the control group after treatment， the rCBV and rCBF in the Tongnaoyin group were significantly increased， while MTT and TTP were significantly reduced （P<0.01）. After six months of discharge， the risk of poor prognosis in the Tongnaoyin group was significantly reduced compared with the control group （P<0.05）. ConclusionTongnaoyin has a good effect on improving cerebral blood perfusion in patients with acute cerebral infarction. It can be used as an effective supplement for the conventional treatment of ischemic stroke to improve clinical efficacy.

OBJECTIVES: This study aims to determine the effects of low-level laser (LLL) on the expression of interleukin-6 (IL-6), tumor necrosis factor (TNF)-α, osteoprotegerin (OPG), and receptor activator of nuclear factor-κB ligand (RANKL) in human periodontal ligament cells (HPDLCs) stimulated by high glucose; and identify the molecular mechanism of LLL therapy in the regulation of periodontal inflammation and bone remodeling during orthodontic treatment in diabetic patients. METHODS: HPDLCs were cultured in vitro to simulate orthodontic after loading and irradiated with LLL therapy. The cultured cells were randomly divided into four groups: low glucose Dulbecco's modification of Eagle's medium (DMEM)+stress stimulation (group A), high glucose DMEM+stress stimulation (group B), hypoglycemic DMEM+LLL therapy+stress stimulation (group C), and hyperglycemic DMEM+LLL therapy+stress stimulation (group D). Groups C and D were further divided into C1 and D1 (energy density: 3.75 J/cm²) and C2 and D2 (energy density: 5.625 J/cm²). Cells in groups A, B, C, and D were irradiated by LLL before irradiation. At 0, 12, 24, 48, and 72 h, the supernatants of the cell cultures were extracted at regular intervals, and the protein expression levels of IL-6, TNF-α, OPG, and RANKL were detected by enzyme-linked immunosorbent assay. RESULTS: 1) The levels of IL-6 and TNF-α secreted by HPDLCs increased gradually with time under static pressure stimulation. After 12 h, the levels of IL-6 and TNF-α secreted by HPDLCs in group A were significantly higher than those in groups B, C1, and C2 (P<0.05), which in group B were significantly higher than those in groups D1, and D2 (P<0.01). 2) The OPG protein concentration showed an upward trend before 24 h and a downward trend thereafter. The RANKL protein concentration increased, whereas the OPG/RANKL ratio decreased with time. Significant differen-ces in OPG, RANKL, and OPG/RANKL ratio were found among group A and groups B, C1, C2 as well as group B and groups D1, D2 (P<0.05). CONCLUSIONS 1) In the high glucose+stress stimulation environment, the concentrations of IL-6 and TNF-α secreted by HPDLCs increased with time, the expression of OPG decreased, the expression of RANKL increased, and the ratio of OPG/RANKL decreased. As such, high glucose environment can promote bone resorption. After LLL therapy, the levels of IL-6 and TNF-α decreased, indicating that LLL therapy could antagonize the increase in the levels of inflammatory factors induced by high glucose environment and upregulate the expression of OPG in human HPDLCs, downregulation of RANKL expression in HPDLCs resulted in the upregulation of the ratio of OPG/RANKL and reversed the imbalance of bone metabolism induced by high glucose levels. 2) The decrease in inflammatory factors and the regulation of bone metabolism in HPDLCs were enhanced with increasing laser energy density within 3.75-5.625 J/cm². Hence, the ability of LLL therapy to modulate bone remodeling increases with increasing dose.
Humans ; Osteoprotegerin ; Tumor Necrosis Factor-alpha/metabolism* ; Interleukin-6/pharmacology* ; RANK Ligand/pharmacology* ; Periodontal Ligament/metabolism* ; Lasers ; Glucose/pharmacology*

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is characterized by diffuse alveolar injury primarily caused by an excessive inflammatory response. Regrettably, the lack of effective pharmacotherapy currently available contributes to the high mortality rate in patients with this condition. Xuebijing (XBJ), a traditional Chinese medicine recognized for its potent anti-inflammatory properties, exhibits promise as a potential therapeutic agent for ALI/ARDS. This study aimed to explore the preventive effects of XBJ on ALI and its underlying mechanism. To this end, we established an LPS-induced ALI model and treated ALI mice with XBJ. Our results demonstrated that pre-treatment with XBJ significantly alleviated lung inflammation and increased the survival rate of ALI mice by 37.5%. Moreover, XBJ substantially suppressed the production of TNF-α, IL-6, and IL-1β in the lung tissue. Subsequently, we performed a network pharmacology analysis and identified identified 109 potential target genes of XBJ that were mainly involved in multiple signaling pathways related to programmed cell death and anti-inflammatory responses. Furthermore, we found that XBJ exerted its inhibitory effect on gasdermin-E-mediated pyroptosis of lung cells by suppressing TNF-α production. Therefore, this study not only establishes the preventive efficacy of XBJ in ALI but also reveals its role in protecting alveolar epithelial cells against gasdermin-E-mediated pyroptosis by reducing TNF-α release.
Animals ; Mice ; Alveolar Epithelial Cells ; Pyroptosis ; Gasdermins ; Lipopolysaccharides/adverse effects* ; Tumor Necrosis Factor-alpha ; Acute Lung Injury/drug therapy* ; Respiratory Distress Syndrome

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To analyze the risk factors of acute kidney injury (AKI) in patients with septic shock in intensive care unit (ICU), construct a predictive model, and explore the predictive value of the predictive model.Methods:The clinical data of patients with septic shock who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical College from April 2015 to June 2019 were retrospectively analyzed. According to whether the patients had AKI within 7 days of admission to the ICU, they were divided into AKI group and non-AKI group. 70% of the cases were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. XGBoost model was used to integrate relevant parameters to predict the risk of AKI in patients with septic shock. The predictive ability was assessed through receiver operator characteristic curve (ROC curve), and was correlated with acute physiology and chronic health evaluationⅡ(APACHEⅡ), sequential organ failure assessment (SOFA), procalcitonin (PCT) and other comparative verification models to verify the predictive value.Results:A total of 303 patients with septic shock were enrolled, including 153 patients with AKI and 150 patients without AKI. The incidence of AKI was 50.50%. Compared with the non-AKI group, the AKI group had higher APACHEⅡscore, SOFA score and blood lactate (Lac), higher dose of norepinephrine (NE), higher proportion of mechanical ventilation, and tachycardiac. In the XGBoost prediction model of AKI risk in septic shock patients, the top 10 features were serum creatinine (SCr) level at ICU admission, NE use, drinking history, albumin, serum sodium, C-reactive protein (CRP), Lac, body mass index (BMI), platelet count (PLT), and blood urea nitrogen (BUN) levels. Area under the ROC curve (AUC) of the XGBoost model for predicting the risk of AKI in patients with septic shock was 0.816, with a sensitivity of 73.3%, a specificity of 71.7%, and an accuracy of 72.5%. Compared with the APACHEⅡscore, SOFA score and PCT, the performance of the model improved significantly. The calibration curve of the model showed that the goodness of fit of the XGBoost model was higher than the other scores (the calibration curve had the lowest score, with a score of 0.205).Conclusion:Compared with the commonly used clinical scores, the XGBoost model can more accurately predict the risk of AKI in patients with septic shock, which helps to make appropriate diagnosis, treatment and follow-up strategies while predicting the prognosis of patients.

Objective:To investigate the incidence, risk factors and early prognosis of acute kidney injury (AKI) after heart valve surgery in adults, and to provide evidence for the early prevention and treatment of AKI after heart valve surgery.Methods:Clinical data of patients undergoing heart valve surgery from January 2016 to March 2017 were collected retrospectively. Early postoperative AKI was diagnosed and staged according to Kidney Disease Improving Global Outcomes (KDIGO) clinical guidelines for AKI. Patients were divided into AKI group and non-AKI group according to whether AKI occurred within 48 hours after surgery, and the differences in clinical indexes between the two groups were compared and analyzed. Influencing factors for early postoperative AKI were screened by stepwise multivariate logistic regression.Results:A total of 600 patients were enrolled, including 197 patients in the AKI group and 403 patients in the non-AKI group. The incidence of AKI was 32.83%. In the non-AKI and AKI groups, proportion of renal replacement therapy (RRT) during hospitalization, mechanical ventilation time, intensive care unit (ICU) time and proportion of hospital mortality were different (all P<0.001). In the AKI group, 160 patients (81.22%) were in stage I, 33 patients (16.75%) were in stage Ⅱ, and 4 patients (2.03%) were in stage Ⅲ. The proportion of patients receiving RRT, mechanical ventilation time, ICU time, hospital mortality were also different in patients with AKI of different stages (all P<0.05). Logistic regression analysis results showed that males, body mass index≥24.0 kg/m 2, previous history of cardiac surgery, baseline serum creatinine >115 mmol/L, albumin<35 g/L, aortic occlusion time (AOT)>90 min, blood glucose in ICU after operation>11.1 mmol/L, the difference between the highest blood glucose within 48 hours after the operation and the blood glucose at 0 h after the operation≥2 mmol/L, blood lactic acid in ICU after operation>4 mmol/L and maximum vasoactive drug score within 48 hours after surgery>9 were independent influencing factors for early AKI (all P<0.05). Conclusions:The incidence of AKI within 48 hours after heart valve surgery is over 30%. The independent influencing factors include male, overweight/obesity, previous cardiac surgery, preoperative renal insufficiency, hypoproteinemia, long duration of AOT, post-operative stress hyperglycemia, fluctuating blood glucose levels wildly, hyperlactacidemia, and using more vasoactive drugs. AKI after cardiac surgery affects the early prognosis of patients, the later of the stage leads to the worse prognosis.