Objective To explore the clinical efficacy of robot-assisted coronary artery bypass grafting through a small incision in the left intercostal space in the treatment of multivessel coronary disease. Methods A retrospective analysis was conducted on the clinical data of patients who underwent coronary artery bypass grafting through a small incision in the left intercostal space at Central China Fuwai Hospital of Zhengzhou University from January 1, 2023 to October 15, 2024. Patients were divided into a robotic group and a minimally invasive group based on whether the surgery was assisted by the Da Vinci robot. Results A total of 81 patients were included, with 57 in the minimally invasive group, including 41 males and 16 females, with a median age of 65.0 (57.5, 69.5) years; and 24 in the robotic group, including 17 males and 7 females, with a median age of 61.0 (56.0, 69.0) years. There was no statistically significant difference in baseline data between the two groups (P>0.05). The robotic group had less intraoperative bleeding [300 (200, 438) mL vs. 500 (375, 600) mL, P=0.006], shorter postoperative mechanical ventilation time [15.0 (13.3, 23.5) h vs. 22.0 (15.5, 39.5) h, P=0.037], and lower incidence of postoperative pain [8 (33.3%) vs. 33 (57.9%), P=0.043]. The hospitalization cost in the robotic group was higher than that in the minimally invasive group [130491 (123298, 135691) yuan vs. 123892 (115543, 133449) yuan, P=0.023]. There was no statistical difference in postoperative laboratory indicators between the two groups (P>0.05). There was also no statistical difference in the duration of surgery, postoperative 24 h drainage volume, ICU stay time, postoperative hospital stay or incidences of perioperative compications including pleural effusion, transfusion, new-onset atrial fibrillation, acute kidney injury, non-union of incision, major cardiovascular and cerebrovascular adverse events, and reoperation between the two groups (P>0.05). Conclusion Compared with the minimally invasive group, the robotic group shows satisfactory efficacy and can effectively reduce postoperative pain and intraoperative bleeding, and shorten postoperative mechanical ventilation time.

BackgroundIndividuals with schizophrenia are prone to higher rates of hospital readmission， presenting significant clinical challenges and imposing considerable social burdens within the mental health domain. In recent years， various risk prediction models have been developed to forecast readmission in patients with schizophrenia and support clinical decision-making， but their predictive performance and clinical applicability require comprehensive evaluation. ObjectiveTo systematically evaluate the risk prediction models for readmission in patients with schizophrenia， so as to provide insights for the development of high-performance and highly applicable readmission risk prediction models for patients with schizophrenia. MethodsOn July 5， 2025， a systematic literature search was conducted across multiple electronic databases， including PubMed， Embase， Cochrane Library， Web of Science， CINAHL， CNKI， China Biomedical Literature Database， Wanfang Database， and VIP Database， to identify risk prediction models for readmission in patients with schizophrenia. The search period was from the establishment of the databases to July 1， 2025. Two researchers independently performed literature screening， data extraction， risk of bias assessment， and applicability assessment. ResultsA total of 9 studies were included in this review， encompassing 18 risk prediction models for readmission in patients with schizophrenia. Among them， 4 models reported the area under the receiver operating characteristic （ROC） curve （AUC）， ranging from 0.734 to 0.820， 16 models provided AUC values of 0.642–0.879 for internal validation， and 1 model demonstrated an AUC of 0.841 for external validation. Key predictors included disease duration and the concomitant therapy of antipsychotic medications. The risk of bias was assessed as "high" in all included studies. ConclusionThe development of risk prediction models for readmission in patients with schizophrenia remains in an exploratory stage. Although the model exhibits favorable predictive performance， it is associated with a high risk of bias and insufficient performance evaluation.

Objectives:To assess the impact of intravascular ultrasound(IVUS)guidance for drug-eluting stent(DES)implantation on the long-term outcomes in coronary artery disease(CAD)patients with chronic kidney disease(CKD).Methods:A retrospective study was conducted on 1 800 CAD and CKD patients who underwent coronary DES implantation at Nanjing First Hospital from January 2018 to December 2022.Patients were divided into IVUS-guided group(IVUS group,n=333)and angiography-guided group(angiography group,n=1 467).Propensity score matching(PSM)was performed at a 1:2 ratio to adjust for differences in baseline clinical and angiographic characteristics between the two groups.Major adverse cardiovascular events(MACE),including cardiac death,target vessel myocardial infarction,and clinically driven target vessel revascularization,were evaluated over a 2-year follow-up.Cox proportional hazards regression was performed to identify independent predictors of MACE.Results:After propensity score matching,333 patients in the IVUS group were matched with 666 patients in the angiography group.Following matching,there were no significant differences in clinical characteristics and angiographic data between the two groups(all P>0.05).Regarding procedural data,IVUS group had a higher number of stents implanted,longer total stent length,larger average stent diameter,more frequent use of post-dilation balloons,larger post-dilation balloon diameter,and greater contrast agent usage(all P<0.05).MACE rate was significantly higher in the angiography group than that in the IVUS group(23.9%vs.18.0%,P=0.037).The difference was mainly attributed to a higher rate of cardiac death in the angiography group(16.1%vs.10.8%,P=0.013).Additionally,patients in angiography group had a significantly higher all-cause mortality rate compared to IVUS group(19.7%vs.13.8%,P=0.022).Multivariate cox regression analysis showed that IVUS guidance(HR=0.73,95%CI:0.55-0.99,P=0.042)was an independent protective factor,while hypertension(HR=1.60,95%CI:1.13-2.26,P=0.008),type 2 diabetes(HR=1.56,95%CI:1.19-2.05,P=0.001),acute coronary syndrome(HR=1.92,95%CI:1.12-3.30,P=0.018),and moderate to severe calcification(HR=1.55,95%CI:1.18-2.04,P=0.002)were independent risk factors for MACE at 2 years after DES implantation in CKD patients.Conclusions:Patients with CAD and CKD,IVUS-guided DES implantation is associated with a reduced risk of MACE and all-cause mortality at 2 years post-procedure compared to coronary angiography-guided implantation.

Objective To explore the risk prediction value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid enhanced magnetic resonance imaging(Gd-EOB-MRI)liver imaging reporting and data system version 2018(LI-RADS v2018)with contrast-enhanced ultrasound(CEUS)LI-RADS version 2017(LI-RADS v2017)in high-risk patients with hepatocellular carcinoma(HCC).Methods The clinical and imaging data of 122 high-risk patients for HCC(with a total of 134 liver lesions)who underwent both Gd-EOB-MRI and CEUS examination at the same time and obtained pathological results within one month were retrospectively col-lected.The nodules were classified according to the CT/MRI LI-RADS v2018 and CEUS LI-RADS v2017 criteria,and the LI-RADS classification results of the two imaging methods were subjected to Cohen's Kappa test.Using pathological results as the gold stand-ard,the diagnostic efficacy of LI-RADS v2018 and LI-RADS v2017 with LR-5 as the standard for HCC was calculated separately.Results The overall consistency between the Gd-EOB-MRI LI-RADS v2018 and CEUS LI-RADS v2017 classification standards was good(Kappa=0.691,P＜0.001).Using LR-5 as the standard for diagnosing HCC,the sensitivity of Gd-EOB-MRI and CEUS was 84.7％and 81.2％,the specificity was 79.6％and 73.5％,the positive predictive value was 87.8％and 84.1％,the negative predictive value was 75.0％and 69.2％,and the accuracy was 82.8％and 78.4％,respectively.There was no statistically significant difference in the diagnostic efficacy of the diagnosis of HCC by LR-5 between the two imaging methods(P＞0.05).Conclusion The Gd-EOB-MRI LI-RADS v2018 and CEUS LI-RADS v2017 classifica-tion standards show good overall agreement.The diagnostic efficacy of Gd-EOB-MRI for HCC using LR-5 classification is better than that of CEUS.

Objective:To explore the relationship between tumor necrosis factor-like weak inducer of apoptosis(TWEAK),kidney injury molecule 1(KIM-1),RBC distribution width/platelet ratio(RPR)and disease activity,renal injury in children with sys-temic lupus erythematosus(SLE).Methods:A total of 42 children with newly diagnosed SLE in Xiangyang Central Hospital were en-rolled as SLE group between February 2020 and February 2022,while another 40 healthy children undergoing physical examination during the same period were enrolled as control group.According to scores of systemic lupus erythematosus disease activity index(SLEDAI),SLE children were divided into low disease activity group(≤9 points)24 cases and high disease activity group(>9 points)18 cases.According to presence or absence of renal injury,SLE children were divided into LN group 31 cases and non-LN group 11 cases.The red blood cell distribution width(RDW)and platelet count(PLT)in all objects were detected.RPR was calculated.The levels of serum TWEAK and KIM-1 were detected by ELISA.Results:The levels of serum TWEAK,KIM-1 and RPR in SLE group were significantly higher than those in control group(P<0.05).The levels of serum TWEAK,KIM-1 and RPR in high disease activity group were significantly higher than those in low disease activity group(P<0.05),which were significantly higher in LN group than non-LN group(P<0.05).Correlation analysis showed that RPR was significantly positively correlated with SLEDAI score(P<0.05),TWEAK and KIM-1 were significantly positively correlated with 24 h urinary protein(P<0.05).AUC of RPR for assessing high disease activity of SLE was 0.744(95%CI:0.657～0.818),significantly greater than that of TWEAK and KIM-1[0.678(95%CI:0.588～0.760),0.598(95%CI:0.505～0.686),P<0.05].AUC values of TWEAK and KIM-1 for predicting LN 0.847(95%CI:0.773～0.905)and 0.773(95%CI:0.690～0.842),significantly greater than that of RPR AUC values[0.645(95%CI:0.555～0.727),P<0.05].Conclusion:The levels of TWEAK,KIM-1 and RPR are changed in children with SLE.RPR has higher diagnostic efficiency for SLE activity,while TWEAK and KIM-1 have higher predictive value for LN.

To construct a recombinant fowl adenovirus 4(FAdV-4)expressing the Cap protein of goose astrovirus genotype 2(GoAstV-2),the expression cassette of Cap gene was inserted into the natural 1 966 bp deletion region of the FAdV-4 genome in the infectious clone p15A-cm-FAdV4-HNJZ.The resulted recombinant plasmid p15A-cm-FAdV4-HNJZ-Cap/GoAstV-2 was linearized with restriction enzyme and transfected into chicken hepatoma cell line(LMH)to rescue the recombinant FAdV-4 expressing the Cap protein of GoAstV-2,rF Ad V4-Cap/GoAstV-2.After 15 passages in LMH cells,the recombinant rFAdV4-Cap/GoAstV-2 was identified by PCR using primers flanking the insertion site of the Cap gene expression cassette and using viral genome DNA extracted from rFAdV4-Cap/GoAstV-2 infected LMH cells as template.LMH cells were in-fected with 15th passage rFAdV4-Cap/GoAstV-2 and indirect immunofluorescence was performed with a polyclonal antibody against Cap protein as the primary antibody.Western blot was carried out with lysates of rFAdV4-Cap/GoAstV-2 infected LMH cells.The in vitro replication dynamic of the 15th passage of the rFAdV4-Cap/GoAstV-2 was also investigated in LMH cells.The results demonstrated that the Cap gene of GoAstV-2 was presented in the genome of the recombinant vi-rus rF AdV4-Cap/Go Ast V-2,and could be expressed stably.The prepared recombinant virus in this study will lay a foundation for developing inactivated bivalent vaccine candidate against co-in-fection of FAdV-4 and GoAstV-2 in goose.

There is significant inter-individual variation of pharmacokinetics and pharmacody-namics in patients receiving tacrolimus(TAC)for an-ti-rejection therapy,which cause the rejection or toxic action.Based on results of therapeutic drug monitoring and pathophysiological index of trans-plant patients,the individualized dosing regimen can be designed and adjusted by using model in-formed precision dosing(MIPD).The patients'clini-cal outcome can be improved.In the consensus,the different methods of MIPD used for patients re-ceived TAC for anti-rejection therapy were intro-duced,which can be used for the designing and ad-justing doing regimen,predicting adverse drug reac-tion,improving medication adherence and econom-ics during therapy.

AIM:To clarify the molecular mechanism by which astragaloside Ⅳ(AS-Ⅳ)suppresses oxida-tive stress and alleviates cerebral ischemia-reperfusion injury(CIRI)via the PTEN-induced kinase 1(PINK1)/parkin mi-tophagy-associated pathway.METHODS:A middle cerebral artery occlusion/reperfusion(MCAO/R)model was estab-lished in Sprague-Dawley rats.The animals were allocated to sham,MCAO/R,AS-Ⅳ,and mitochondrial division inhibi-tor-1(Mdivi-1)treatment groups.The rats in AS-Ⅳ and Mdivi-1 groups were intraperitoneally injected once daily with AS-Ⅳ(20 mg/kg)for 7 d,while those in Midivi-1 group also received intraperitoneal injection of Mdivi-1(1.2 mg·kg-1·d-1).The rats in sham and MCAO/R groups were given equivalent volume of distilled water.Neurological deficits were as-sessed using Zea Longa scoring,infarcted area volumes were measured using TTC staining,and brain tissue pathology was examined using hematoxylin and eosin staining.The levels of malondialdehyde(MDA)and superoxide dismutase(SOD)were assessed by ELISA,while those of reactive oxygen species(ROS)were measured using flow cytometry.The expres-sion levels of PINK1,parkin and microtubule-associated protein 1 light chain 3(LC3)were quantified using Western blot and RT-qPCR.RESULTS:AS-Ⅳ administration significantly alleviated neuronal and mitochondrial damage in MCAO/R model rat brains(P＜0.05),together with significant reductions in the cerebral infarct volume and neurological dysfunc-tion(P＜0.05).Significant increases in PINK1,parkin and LC3 protein and mRNA levels were observed in response to AS-Ⅳ(P＜0.05),SOD activity rose,and ROS and MDA levels declined significantly(P＜0.05).The co-administration of Mdivi-1 abrogated the protective benefits of AS-Ⅳ,inhibited activation of the PINK1/parkin pathway,down-regulated LC3 at the mRNA and protein levels,and significantly increased mitochondrial damage.Mdivi-1 also markedly reduced autophagosome formation and SOD activity level,but increased both ROS and MDA levels,cerebral infarct volume,and the severity of neurological deficits(P＜0.05).CONCLUSION:Astragaloside Ⅳ activates the PINK/parkin-mediated mitophagy pathway,inhibits oxidative stress and alleviates CIRI in rats.

Objective:To explore associated factors of post-discharge depressive symptom severity in patients with bipolar disorder.Methods:A longitudinal follow-up was conducted to investigate the demographic,behavioral,and clinical characteristics,and social function among discharged patients with bipolar disorder who met the DSM-5 diagnostic criteria.Clinical characteristics were assessed with the Hamilton Depression Scale(HAMD)and Brief Psychiatric Rating Scale(BPRS).Single factor and multivariate regression were carried out to explore the associat-ed factors of depressive symptom severity in patients with bipolar disorder.Results:A total of 298 discharged pa-tients with bipolar disorder were face-to-face interviewed to complete the follow-up survey.At follow-up time,psy-chotic symptoms(standardized(β)=0.18),housework((β)=0.23),social interaction((β)=0.17)and BPRS total score((β)=0.46)were positively associated with HAMD total score.Productive labor and work((β)=-0.27)and person-al life management((β)=-0.15)were negatively associated with HAMD total scores.Conclusion:Post-discharge depressive symptom severity in bipolar disorder patients is influenced by multiple factors.Effective management of psychotic symptoms,combined with enhanced community-based social rehabilitation and functional recovery,may help reduce the persistence or worsening of depressive symptoms and improve prognosis.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. 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. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.