Objective:To analyze the risk factors of Mycoplasma pneumoniae（MP）lobar pneumonia with plastic bronchitis（PB）in pediatric patients，and to establish a risk nomogram prediction model.Methods:The medical informations were collected from pediatric patients diagnosed with MP lobar pneumonia who performed bronchoscopy during hospitalization in the Department of Pediatrics at the Second Affiliated Hospital of Air Force Military Medical University from April 2023 to December 2023.According to the bronchoscopic findings，the patients were divided into PB group and non-PB group.The clinical medical records and ancillary diagnostic findings were retrospectively analyzed.A multivariate Logistic regression model was used to analyze the independent risk factors for children with MP lobar pneumonia complicated with PB.A nomogram model was constructed to predict the risk of PB occurrence. Calibration curves and Hosmer-Lemeshow goodness-of-fit test were used to evaluate the predictive value of the nomogram model for MP lobar pneumonia with PB. The receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy.Results:A total of 357 pediatric patients diagnosed with MP lobar pneumonia were included，with 92 cases in PB group and 265 cases in non-PB group. No statistically significant differences in gender and age were observed between the two groups（ P>0.05）.The duration of fever and the hospitalization time in PB group were longer than those in non-PB group. The incidences of pleural effusion，consolidation area of a single lung lobe ≥2/3 and atelectasis on chest CT were higher in PB group compared to non-PB group. Additionally,the levels of neutrophil/lymphocyte ratio，C-reactive protein，procalcitonin，D-dimer（D-D），alanine aminotransferase(ALT)，aspartate aminotransferase，lactate dehydrogenase，α-hydroxybutyrate dehydrogenase，interferon-γ（IFN-γ），interleukin（IL）-6，IL-10 and IFN-γ/IL-4 ratio in PB group were higher than those in non-PB group（all P<0.05）.Logistic regression analysis showed elevated D-D, ALT and IFN-γ, pleural effusion and consolidation area of a single lung lobe ≥2/3 were independent risk factors for PB.The nomogram prediction model constructed by the model demonstrated good goodness-of-fit (χ 2=11.316， P=0.184) and provided significant clinical net benefits within a risk threshold range of 0.09–0.65. The area under the ROC curve for combined prediction was 0.771（95% CI 0.716-0.826），with a sensitivity of 0.707 and specificity of 0.706. Conclusion:In children with MP lobar pneumonia, elevated laboratory markers (D-D, ALT, IFN-γ) and imaging features (pleural effusion, consolidation area of a single lung lobe ≥2/3) are critical predictors for early diagnosis of PB.The nomogram prediction model can be used to predict MP lobar pneumonia with PB in early stage.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Aim To investigate the expression levels of cytoplasmic FMR1-interacting protein-1(CYFIP1)in colorectal cancer and assess the impact of CYFIP1 interaction on the proliferation and apoptosis of colorec-tal cancer cell HT29,along with its potential mecha-nisms.Methods Immunohistochemistry was em-ployed to assess CYFIP1 expression in 32 colorectal cancer tissues and adjacent tissues.Coexpressed genes were identified using the GEPIA2 website to predict potential correlations and binding sites.Following the construction of a siRNA-CYFIP1,alterations in cell proliferation,apoptosis,and levels of apoptosis-related proteins were evaluated through CCK-8 assay,Hoechst 33342/PI double staining assay,and Western blot a-nalysis,respectively.Results The immunohisto-chemical findings revealed a significantly elevated level of CYFIP1 expression in colorectal cancer tissues com-pared to paracancer tissues(P＜0.05).The expres-sion of CYFIP1 did not show any correlation with age and gender,but exhibited associations with TNM stage and lymph node metastasis(P＜0.05).A conserved TP53 binding site was predicted in the 3kbps DNA re-gion upstream of the CYFIP1 gene using GEPIA2,JASPAR databases,and rVista 2.0 promoter prediction software.Following transfection of HT29 cells with siRNA-CYFIP1,the clonogenesis and proliferation of cells significantly decreased(P＜0.05).Additional-ly,the levels of cleaved caspase-3 were elevated,while the expression levels of caspase-3 and Bcl-2 were reduced after transfection with siRNA-CYFIP1(P＜0.05),which might be related to the interaction be-tween CYFIP1 and TP53.Conclusions The upregu-lation of CYFIP1 in colorectal cancer is associated with TNM stage and lymph node metastasis.Upon silen-cing,CYFIP1 demonstrates the ability to suppress pro-liferation in HT29 cells and modulate the expression of apoptotic proteins.

Aim To investigate the effect of cordycepin(COR)on gentamicin(GEN)-induced nephrotoxicity and the molecular mechanism of inhibiting oxidative stress and ferroptosis induced by GEN.Methods The oral SD rats were divided into a control group,GEN group,and GEN+COR group.Following the success-ful setting up of the animal model,the serum creatinine(CR)and urea nitrogen(BUN)levels of rats were measured,and renal tissue injury was assessed using HE staining.In addition,the contents of malondialde-hyde and glutathione in kidney tissues of SD rats in each group were detected,and the expressions of fer-roptosis markers GPX4 and SLC7A11 were analyzed by Western blot.Results Compared with the control group,CR and BUN in GEN-stimulated group signifi-cantly increased(P＜0.01),and the level of CR and BUN was effectively reduced after 50 mg·kg-1 COR oral administration.HE results also showed that COR could alleviate the kidney tissue damage caused by GEN.COR could reverse the increase of malondialde-hyde level and the decrease of glutathione level caused by GEN in rat kidney tissue,and COR could restore the decrease of GPX4 and SLC7A11 protein levels induced by GEN.Conclusion COR can reduce GEN-induced kidney injury by inhibiting oxidative stress and ferrop-tosis.

Ivonescimab is a humanized bispecific antibody targeting human vascular endothelial growth factor-A(VEGF-A)and programmed death protein-1(PD-1).It was approved by National Medi-cal Products Administration on May 24th,2024,and can be used in combination with pemetrexed and carboplatin for locally advanced or positive EG-FR gene mutation after treatment with epidermal growth factor receptor(EGFR)tyrosine kinase inhib-itor.This paper mainly introduces the research progress of the world's first PD-1/VEGF bispecific antibody ivonescimab,and summarizes the mecha-nism of action,pharmacokinetics,phase Ⅰ-Ⅲ clinical trials and drug safety.

Objective:To assess the feasibility and efficacy of transperineal laser ablation(TPLA)with single laser fiber in treating benign prostatic obstruction(BPO).Methods:From April 2021 to March 2024,a total of 13 BPO patients were selected from Beijing Friendship Hospital.TPLA was performed using a single laser fiber,which was guided by transrectal biplane ultrasound.The single laser fiber was used to undergo TPLA under the guidance of trans-rectal dual-plane ultrasound.The intraoperative time,ablation time,energy consumption,indwelling time of catheter,and complications were observed.The postoperative 6 months was chosen as the cut-off point of follow-up,and the pre and postoperative changes of international prostate symptom score(IPSS),quality of life index(QoL),prostate volume(PV),residual urine volume(RUV)and the maximum urine flow rate(Qmax)were compared.Results:All 13 patients successfully underwent TPLA with single laser fiber.The average operation time was(55.1±18.3)min,and the average ablation time was(16.3±1.7)min,and average energy consumption was(3951.6±459.7)J,and the median value of indwelling time of catheter was 7(7,10)days.The number of postoperative complication was 2 cases,and both them belonged to Clavien-Dindo grade II complication.At the postoperative 6th month,the IPSS,QoL,PV,Qmax and RUV of all patients were improved,all of which were better than preoperative these indicators,and the differences were significant(t=12.102,-3.228,-3.181,-2.581,-2.936,P<0.05).Conclusion:The application of single laser fiber in conducting TPLA operation is feasibility at technical aspect,and it can achieve the therapeutic goals of improving patients'symptoms and enhancing their quality of life.Although its operational time is slightly longer than that of using multiple fibers simultaneously,it can effectively reduce the cost of expenditure for consumables.

AIM To study the effects of Jiaotai Pills and their single composition drugs on high-fat diet-induced hypothalamic inflammation in obese mice.METHODS C57BL/6J mice were randomly divided into the normal group(15 mice)and the high-fat group(75 mice).The mice given 12 weeks of high-fat diet feeding were further randomly divided into the model group,the Jiaotai Pills group,the Coptis chinensis group,the Cinnamomum cassia group and the positive metformin group,with 15 mice in each group.After 6 weeks of administration,the mice had their body weight and fasting blood glucose(FBG)levels detected;their hypothalamic expressions of IL-1β,IL-6,TNF-α and Socs3 mRNA detected by RT-qPCR;their hypothalamic expressions of TLR4,MyD88,IKKβ and activated NF-κB protein detected by Western blot;their hypothalamic expressions of Iba1 and GFAP detected by immunohistochemistry;and their ultrastructural changes of nerve tissues observed using transmission electron microscopy(TEM).RESULTS Compared with the model group,each drug group displayed decreased hypothalamic expressions of IL-1β,IL-6,TNF-α and Socs3 mRNA(P＜0.01),and improved number and morphology of nerve cells revealed by TEM.The groups intervened with Jiaotai Pills,or Coptis chinensis,or metformin shared decreased body weight and FBG levels(P＜0.05);decreased protein expressions of TLR4,MyD88,IKKβ and p-NF-κB(P＜0.05);and decreased number of hypothalamic astrocytes and microglia(P＜0.05).Additionally,decreased p-NF-κB protein expression was observed in the Cinnamomum cassia group(P＜0.05).CONCLUSION Jiaotai Pills and their single composition drugs can improve high-fat diet-induced hypothalamic inflammation in obese mice.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Ivonescimab is a humanized bispecific antibody targeting human vascular endothelial growth factor-A(VEGF-A)and programmed death protein-1(PD-1).It was approved by National Medi-cal Products Administration on May 24th,2024,and can be used in combination with pemetrexed and carboplatin for locally advanced or positive EG-FR gene mutation after treatment with epidermal growth factor receptor(EGFR)tyrosine kinase inhib-itor.This paper mainly introduces the research progress of the world's first PD-1/VEGF bispecific antibody ivonescimab,and summarizes the mecha-nism of action,pharmacokinetics,phase Ⅰ-Ⅲ clinical trials and drug safety.

Objective:To evaluate the role of NOD-like receptor protein 3 (NLRP3) inflammasome-mediated microglia activation in myocardial ischaemia-reperfusion-induced brain injury in mice.Methods:Fifty-two SPF healthy male wild-type C57BL/6 mice and 52 NLRP3 -/- mice, aged 8-10 weeks, were divided into 4 groups ( n=26 each) using a random number table method: wild type sham operation group (W-S group), wild type myocardial ischemia-reperfusion group (W-IR group), NLRP3 -/- sham operation group (NLRP3 -/--S group), and NLRP3 -/- myocardial ischemia-reperfusion group (NLRP3 -/--IR group). The myocardial ischemia-reperfusion-induced brain injury model was established by ligating the left anterior descending coronary artery for 45 min followed by 24 h of reperfusion in anesthetized mice. The cognitive function was evaluated using the modified Morris water maze test at 24 h of reperfusion. The mice were sacrificed after blood specimens were collected, and brain tissues were obtained for measurement of the blood-brain barrier permeability and water content, for microscopic examination of the pathological changes of brain tissues, and for determination of serum S-100β protein and neuron-specific enolase (NSE) concentrations, contents of interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNF-α) in hippocampal tissues (by enzyme-linked immunosorbent assay), expression of NLRP3, apoptosis-associated speck-like protein (ASC), cleaved cysteine aspartate protease 1 (cleaved-caspase-1), gasdermin D (GSDMD), ionized calcium-binding adapter molecule 1 (Iba-1), and occludin in hippocampal tissues (by immunofluorescence and/or Western blot). The apoptosis rate of neurons and density of dendritic spine were calculated. Results:Compared with sham operation group, the escape latency was significantly prolonged, the number of crossing the original platform was decreased, and the time spent in the target quadrant was shortened, the concentrations of serum S-100β protein and NSE were increased, the blood-brain barrier permeability and brain water content were increased, the dendritic spine density in the hippocampal CA1 area was decreased, the contents of IL-1β, IL-6 and TNF-α were increased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was up-regulated, and the expression of occludin was down-regulated ( P<0.05), and the pathological injury to brain tissues was found in ischemia-reperfusion group. Compared with W-IR group, the escape latency was significantly shortened, the number of crossing the original platform was increased, and the time spent in the target quadrant was prolonged, the concentrations of serum S-100β protein and NSE were decreased, the blood-brain barrier permeability and brain water content were decreased, the dendritic spine density in the hippocampal CA1 area was increased, the contents of IL-1β, IL-6 and TNF-α were decreased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was down-regulated, and the expression of occludin was up-regulated ( P<0.05), and the pathological injury to brain tissues was alleviated in NLRP3 -/--IR group. Conclusions:NLRP3 inflammasome-mediated microglia activation is involved in myocardial ischaemia-reperfusion-induced brain injury in mice.