ObjectiveTo investigate how Xiaoyao Shukun decoction （XYSKD） regulates the formation and release of neutrophil extracellular traps （NETs） via the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway， thereby reducing inflammation， inhibiting the excessive proliferation of fibroblasts in pelvic adhesion tissue， decreasing adhesion and fibrosis， and repairing the tissue damage in sequelae of pelvic inflammatory disease （SPID）. MethodsA total of 84 Wistar rats were randomly allocated into seven groups： blank， model， XYSKD （8 mg·kg^-1）， mTOR agonist （10 mg·kg^-1）， mTOR agonist + XYSKD （10 mg·kg^-1+8 mg·kg^-1）， mTOR inhibitor （2 mg·kg^-1）， and mTOR inhibitor + XYSKD （2 mg·kg^-1+8 mg·kg^-1）. The rat model of SPID was constructed by starvation， fatigue， and ascending Escherichia coli infection. After 14 days of drug intervention， the ultrastructure of fibroblasts in the pelvic adhesion tissue was observed by transmission electron microscopy. The general morphology of the uterus， fallopian tube， and ovary was observed by laparotomy. The levels of interleukin-1β （IL-1β）， interleukin-17 （IL-17）， and tumor necrosis factor-α （TNF-α） in the peritoneal flushing fluid were determined by enzyme-linked immunosorbent assay （ELISA）. The expression of myeloperoxidase （MPO） and citrullinated histone 3 （H3） in the fallopian tube was detected by immunofluorescence. Western blot and Real-time quantitative polymerase chain reaction （Real-time PCR） were employed to determine the relative protein and mRNA levels， respectively， of neutrophil elastase （NE）， intercellular adhesion molecule-1 （CD54）， α-smooth muscle actin （α-SMA）， H3， PI3K， and Akt. ResultsCompared with the blank group， the model group presented a large number of collagen fibers in bundles， numerous cytoplasmic folds of fibroblasts， reduced or absent mitochondrial cristae， and disordered and expanded endoplasmic reticulum. By laparotomy， extensive pelvic congestion， connective tissue hyperplasia， thickening and hardening of the tubal end near the uterus， and tubal and ovarian adhesion or cyst were observed in the model group. In addition， the model group showed raised levels of IL-1β， IL-17， and TNF-α in the peritoneal flushing fluid （P<0.01）， increased average fluorescence intensities of MPO and H3 （P<0.01）， and up-regulated protein and mRNA levels of NE， H3， CD54， PI3K， and Akt （P<0.01）. Compared with the model group， the mTOR agonist group showed increased fibroblasts and cytoplasmic folds， absence of mitochondrial cristae， endoplasmic reticulum dilation， and evident collagen fiber hyperplasia. Pelvic adhesions were observed to cause aggravated damage to the uterine， fallopian tube， and ovarian tissues. The levels of IL-1β， IL-17， and TNF-α in the peritoneal lavage fluid elevated （P<0.01） and the average fluorescence intensities of MPO and H3 enhanced （P<0.01） in the mTOR agonist group. In contrast， the XYSKD group and the mTOR inhibitor group showcased decreased fibroblasts and collagen fibers， alleviated mitochondrial crista loss and endoplasmic reticulum dilation， improved morphology and appearance of the uterine， fallopian tube， and ovarian tissues， lowered levels of IL-1β， IL-17， and TNF-α in the peritoneal lavage fluid （P<0.01）， decreased average fluorescence intensities of MPO and H3 （P<0.01）， and down-regulated protein and mRNA levels of NE， H3， CD54， PI3K， and Akt （P<0.05）. Compared with the mTOR agonist group， the mTOR agonist + XYSKD group showed alleviated pathological changes in the pelvic tissue， declined levels of IL-1β， IL-17， and TNF-α （P<0.01）， decreased average fluorescence intensities of MPO and H3 （P<0.01）， and down-regulated protein levels of NE， H3， CD54， α-SMA， p-PI3K/PI3K， and p-Akt/Akt （P<0.01） and mRNA levels of NE， H3， CD54， α-SMA， PI3K， and Akt （P<0.01）. Compared with the mTOR inhibitor group， the mTOR inhibitor + XYSKD group demonstrated reduced pathological severity of the pelvic tissue， reduced levels of IL-1β， IL-17， and TNF-α （P<0.01）， decreased average fluorescence intensities of MPO and H3 （P<0.01）， and down-regulated protein and mRNA levels of NE and CD54 （P<0.05）. ConclusionXYSKD can inhibit the excessive formation and release of NETs via PI3K/Akt/mTOR to ameliorate the inflammatory environment and reduce fibrosis and adhesion of the pelvic tissue， thereby playing a role in the treatment of SPID. It may exert the effects by lowering the levels of IL-1β， IL-17， and TNF-α and down-regulating the expression of NE， H3， CD54， α-SMA， PI3K， and Akt in the pelvic adhesion tissue.

Objective In order to provide a scientific and reasonable method for the optimization of drug mix design,the application of PSO-BP neural network modeling after Logratio transformation and nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)optimization in the optimization of drug prescription ratio of multi-objective mix design was explored.Methods Based on the analysis of experimental data in literature,after Logratio transformation of experimental data of compound glycyrrhiza microemulsion,PSO-BP neural network model was constructed by taking particle size and skin retention of active components as evaluation indexes,and then NSGA-Ⅱ was adopted for multi-objective optimization of the network.Finally,the optimization scheme in this paper was compared with that in the original paper.Results The fitting effect of PSO-BP neural network using particle size and active component skin retention as output is R2=0.97298 and R2=0.96334,respectively,indicating that the fitting effect of PSO-BP is better,and the fitting effect is improved compared with the Scheffe polynomial model used in the original paper.In this paper,PSO-BP was used to construct the model,and NSGA-Ⅱ scheme 3、4、6、7、10、11 etc.were superior to the original scheme.Compared with the original scheme,the microemulsion particle size was reduced by 3.02nm and the skin retention of the active ingredient was increased by 18.31 μg.Conclusion In theory,it is feasible and reasonable to use Logratio transformation and PSO-BP neural network in the model construction of mixed data and NSGA-Ⅱalgorithm to obtain the optimal ratio of drug prescription.

Objective To investigate the value of cranial CT cisternal grading combined with D-dimer(D-D)and Glasgow Coma Scale(GCS)score in predicting the short-term postoperative prog-nosis of patients with severe traumatic brain injury.Methods A total of 165 patients with severe trau-matic brain injury who were treated in the hospital from January 2019 to May 2024 were selected as study subjects,all underwent craniotomy surgery.Postoperative follow-up was conducted for 3 months to analyze the differences in clinical data and preoperative indicators such as cranial CT cisternal grad-ing,D-D levels,and GCS scores between patients with poor and good prognosis.The value of cranial CT cisternal grading,D-D levels,and GCS scores in predicting short-term postoperative poor prognosis in patients with severe traumatic brain injury was also analyzed.Results Compared with patients with good prognosis,patients with poor prognosis had higher proportion of age,cranial CT cisternal grading of Ⅰ to Ⅱ,D-D levels,and GCS scores＜6(P＜0.05).There were no statistically significant differences in C-reactive protein,prothrombin time,activated partial thromboplastin time,international normalized ratio,total cholesterol,triglycerides,high-density lipoprotein cholesterol,and low-density lipoprotein cholesterol levels between patients with poor and good prognosis(P＞0.05).Cranial CT cisternal grading,D-D levels,and GCS scores were influencing factors for short-term postoperative poor prognosis in patients with severe traumatic brain injury(P＜0.05).The area under the curve for poor prognosis by three indicators in combination was 0.941(95％CI,0.906 to 0.975),which was higher than the area under the curve for the individual predictions of cranial CT cisternal grad-ing,D-D levels,and GCS scores(P＜0.05).Conclusion The influencing factors for short-term postoperative prognosis in patients with severe traumatic brain injury include cranial CT cisternal grading,D-D levels,and GCS scores.The model based on these three indicators has certain appli-cation value in predicting patient prognosis.

COMPERA 2.0 risk stratification has been demonstrated to be useful in patients with precapillary pulmonary hypertension (PH). However, its suitability for patients at risk for post-capillary PH or PH associated with left heart disease (PH-LHD) is unclear. To investigate the use of COMPERA 2.0 in patients with severe aortic stenosis (SAS) undergoing transcatheter aortic valve replacement (TAVR), who are at risk for post-capillary PH, a total of 327 eligible SAS patients undergoing TAVR at our institution between September 2015 and November 2020 were included in the study. Patients were classified into four strata before and after TAVR using the COMPERA 2.0 risk score. The primary endpoint was all-cause mortality. Survival analysis was performed using Kaplan-Meier curves, log-rank test, and Cox proportional hazards regression model. The study cohort had a median (interquartile range) age of 76 (70‒80) years and a pulmonary arterial systolic pressure of 33 (27‒43) mmHg (1 mmHg=0.133 kPa) before TAVR. The overall mortality was 11.9% during 26 (15‒47) months of follow-up. Before TAVR, cumulative mortality was higher with an increase in the risk stratum level (log-rank, both P<0.001); each increase in the risk stratum level resulted in an increased risk of death (hazard ratio (HR) 2.53, 95% confidential interval (CI) 1.54‒4.18, P<0.001), which was independent of age, sex, estimated glomerular filtration rate (eGFR), hemoglobin, albumin, and valve type (HR 1.76, 95% CI 1.01‒3.07, P=0.047). Similar results were observed at 30 d after TAVR. COMPERA 2.0 can serve as a useful tool for risk stratification in patients with SAS undergoing TAVR, indicating its potential application in the management of PH-LHD. Further validation is needed in patients with confirmed post-capillary PH by right heart catheterization.
Humans ; Aortic Valve Stenosis/complications* ; Aged ; Hypertension, Pulmonary/mortality* ; Male ; Female ; Transcatheter Aortic Valve Replacement ; Aged, 80 and over ; Risk Assessment/methods* ; Proportional Hazards Models ; Kaplan-Meier Estimate ; Retrospective Studies

Cancer stem cells (CSCs) are widely acknowledged as primary mediators to the initiation and progression of tumors. The association between microbial infection and cancer stemness has garnered considerable scholarly interest in recent years. Porphyromonas gingivalis (P. gingivalis) is increasingly considered to be closely related to the development of oral squamous cell carcinoma (OSCC). Nevertheless, the role of P. gingivalis in the stemness of OSCC cells remains uncertain. Herein, we showed that P. gingivalis was positively correlated with CSC markers expression in human OSCC specimens, promoted the stemness and tumorigenicity of OSCC cells, and enhanced tumor formation in nude mice. Mechanistically, P. gingivalis increased lipid synthesis in OSCC cells by upregulating the expression of stearoyl-CoA desaturase 1 (SCD1) expression, a key enzyme involved in lipid metabolism, which ultimately resulted in enhanced acquisition of stemness. Moreover, SCD1 suppression attenuated P. gingivalis-induced stemness of OSCC cells, including CSCs markers expression, sphere formation ability, chemoresistance, and tumor growth, in OSCC cells both in vitro and in vivo. Additionally, upregulation of SCD1 in P. gingivalis-infected OSCC cells was associated with the expression of KLF5, and that was modulated by P. gingivalis-activated NOD1 signaling. Taken together, these findings highlight the importance of SCD1-dependent lipid synthesis in P. gingivalis-induced stemness acquisition in OSCC cells, suggest that the NOD1/KLF5 axis may play a key role in regulating SCD1 expression and provide a molecular basis for targeting SCD1 as a new option for attenuating OSCC cells stemness.
Porphyromonas gingivalis/pathogenicity* ; Stearoyl-CoA Desaturase/metabolism* ; Humans ; Carcinoma, Squamous Cell/pathology* ; Mouth Neoplasms/metabolism* ; Animals ; Neoplastic Stem Cells/microbiology* ; Mice, Nude ; Mice ; Nod1 Signaling Adaptor Protein/metabolism* ; Kruppel-Like Transcription Factors/metabolism* ; Cell Line, Tumor

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.

Objective In order to provide a scientific and reasonable method for the optimization of drug mix design,the application of PSO-BP neural network modeling after Logratio transformation and nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)optimization in the optimization of drug prescription ratio of multi-objective mix design was explored.Methods Based on the analysis of experimental data in literature,after Logratio transformation of experimental data of compound glycyrrhiza microemulsion,PSO-BP neural network model was constructed by taking particle size and skin retention of active components as evaluation indexes,and then NSGA-Ⅱ was adopted for multi-objective optimization of the network.Finally,the optimization scheme in this paper was compared with that in the original paper.Results The fitting effect of PSO-BP neural network using particle size and active component skin retention as output is R2=0.97298 and R2=0.96334,respectively,indicating that the fitting effect of PSO-BP is better,and the fitting effect is improved compared with the Scheffe polynomial model used in the original paper.In this paper,PSO-BP was used to construct the model,and NSGA-Ⅱ scheme 3、4、6、7、10、11 etc.were superior to the original scheme.Compared with the original scheme,the microemulsion particle size was reduced by 3.02nm and the skin retention of the active ingredient was increased by 18.31 μg.Conclusion In theory,it is feasible and reasonable to use Logratio transformation and PSO-BP neural network in the model construction of mixed data and NSGA-Ⅱalgorithm to obtain the optimal ratio of drug prescription.

Objective To construct a swallowing dynamic model for simulating dysphagia caused by reduced tongue movement am-plitude.Methods A swallowing dynamic model was established based on medical imaging data from CT and videofluoroscopic swallowing study(VFSS).The finite element method was used to simulate soft tissues,while the smoothed parti-cle hydrodynamics method(SPH)was used to simulate bolus.The model's posture at each time point was com-pared with the imaging data of VFSS from twelve patients with dysphagia,and a normalization method was used for quantitative evaluation of the model's validity.By adjusting the tongue movement amplitude under different viscosity conditions,the role of tongue movement in the swallowing process was investigated,and the swallow-ing safety and efficiency were assessed.Results The tongue posture and bolus trajectory presented by the swallowing dynamic model were consistent with the VFSS imaging.The brightness in the epiglottis area in VFSS images correlated with the equivalent brightness of SPH particles in the simulation results(r=0.97).As the tongue movement amplitude reducing by 20%,the num-ber of aspirated particles,swallowing efficiency and the average velocity of bolus particles in the oropharyngeal cavity all performed well.Pudding-like fluids exhibited favorable swallowing characteristics even when tongue movement amplitude reducing significantly.Conclusion The swallowing dynamic model can simulate the human swallowing process,providing good support for re-habilitation training of patients with dysphagia and the development of specialized medical foods,demonstrating significant potential for clinical applications.

Objective:To investigate the value of coronary CTA (CCTA)-based traditional features and radiomics of plaque in the identification of culprit lesions that caused acute myocardial infarction (AMI).Methods:This was a retrospective multicenter study. From July 2016 to November 2023, a total of 344 patients from the Affiliated Hospital of Qingdao University (training cohort, n=184), Shandong Provincial Hospital Affiliated to Shandong First Medical University (validation cohort, n=88) and Qilu Hospital of Shandong University (test cohort, n=72) who received percutaneous coronary intervention (PCI) due to AMI and underwent CCTA within 48 hours of AMI were enrolled. The culprit plaques and non-culprit plaques were identified using a combination of electrocardiogram, CCTA, and angiographic findings. The vessel, plaque location, plaque type, Coronary Artery Disease-Reporting and Data System (CAD-RADS) score, high-risk plaque characteristics, plaque length, plaque volume, and burden were analyzed, and 1 904 radiomics features were extracted for each plaque. The traditional imaging model, the radiomics model, and the combined model were established by using multivariate Logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each model in identifying culprit lesions. The DeLong test was used for the comparison of AUC between every two models. The net reclassification index (NRI) was used to evaluate the incremental value of the combined model to the traditional imaging model and the radiomics model. The decision curve analysis (DCA) was used to assess the clinical net benefit of these models. A correlation heatmap was used to evaluate the correlation between the radiomics score and traditional CCTA factors. The interpretable analysis of the decision process of the combined model was performed by the Shapley Additive exPlanations (SHAP). Results:In the validation cohort and the test cohort, the AUC of the traditional imaging model developed by the vessel, plaque type, positive remodeling and CAD-RADS score was 0.898 (95% CI 0.869-0.922) and 0.881 (95% CI 0.848-0.910), respectively. The radiomics model developed by six radiomics features was 0.863 (95% CI 0.831-0.891) and 0.863 (95% CI 0.827-0.864), respectively. The AUC of the combined model was 0.930 (95% CI 0.905-0.950)and 0.919 (95% CI 0.889-0.942), respectively. In the validation cohort and the test cohort, the AUC of the combined model was higher than that of the traditional imaging model ( Z=4.013, 4.272, P<0.001) and that of the radiomics model ( Z=4.819, 3.784, P<0.001), respectively. In the validation cohort, the combined model yielded an NRI of 20.43% (95% CI 10.43%-30.44%, P<0.001) and 20.21% (95% CI 9.62%-30.80%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. In the test cohort, the combined model yielded an NRI of 28.05% (95% CI 16.72%-39.38%, P<0.001) and 23.57% (95% CI 13.58%-33.56%, P<0.001) for identifying culprit lesions compared with the traditional imaging model and the radiomics model, respectively. DCA showed the combined model had the highest clinical net benefit. The correlation heatmap showed the radiomics score was not correlated or only weakly correlated with traditional CCTA factors. SHAP indicated the radiomics and CAD-RADS score contributed significantly to the model. Conclusion:The CCTA-based traditional features and radiomics of plaque have favorable performance for the identification of culprit plaques in patients with AMI.