Purpose To evaluate the predictive value of multifunctional parameters of single photon emission computed tomography gated myocardial perfusion imaging(SPECT G-MPI)for major adverse cardiovascular events(MACE)in chronic kidney disease(CKD)with abnormal stress myocardial perfusion.Materials and Methods A total of 99 patients diagnosed with CKD from June 2017 to March 2024 who underwent stress and rest G-MPI indicating abnormal myocardial perfusion in Beijing Anzhen Hospital,Capital Medical University.The American Heart Association 17-segment 5-point method and PHASE software were used to obtain the left ventricular myocardial perfusion,functional and synchronization parameters.According to the occurrence of MACE,the patients were divided into MACE group and non-MACE group.Cox regression was used to analyze the predictors related to MACE.The receiver operator characteristic curve was used to analyze the performance of predictors,the survival curves were obtained by the Kaplan-Meier method,Log-rank test was used to compare the differences in different groups.Results Finally,we enrolled 99 CKD patients with abnormal stress myocardial perfusion.35 patients(35.35%)developed MACE during the follow-up period.Cox regression analysis showed that stress phase bandwidth(SPBW)(HR=1.015,95%CI 1.002-1.028)and sum difference score(SDS)(HR=1.105,95%CI 1.008-1.211)were independent risk factors for predicting MACE(both P<0.05).The optimal cut-off value of SPBW and SDS for predicting MACE were 69° and 6 points,the area under the curve was 0.801 and 0.778,respectively.The incidence of MACE in the SPBW≥69° group and SDS≥6 points group was higher than that in SPBW<69° group and SDS<6 points group(66.6%vs.13.2%,53.3%vs.20.4%,both P<0.05).Conclusion SPECT G-MPI multifunctional parameters can be used to predict the prognosis of CKD patients with abnormal stress myocardial perfusion.SPBW and SDS are independent risk factors for MACE in these patients.

Purpose To evaluate the predictive value of multifunctional parameters of single photon emission computed tomography gated myocardial perfusion imaging(SPECT G-MPI)for major adverse cardiovascular events(MACE)in chronic kidney disease(CKD)with abnormal stress myocardial perfusion.Materials and Methods A total of 99 patients diagnosed with CKD from June 2017 to March 2024 who underwent stress and rest G-MPI indicating abnormal myocardial perfusion in Beijing Anzhen Hospital,Capital Medical University.The American Heart Association 17-segment 5-point method and PHASE software were used to obtain the left ventricular myocardial perfusion,functional and synchronization parameters.According to the occurrence of MACE,the patients were divided into MACE group and non-MACE group.Cox regression was used to analyze the predictors related to MACE.The receiver operator characteristic curve was used to analyze the performance of predictors,the survival curves were obtained by the Kaplan-Meier method,Log-rank test was used to compare the differences in different groups.Results Finally,we enrolled 99 CKD patients with abnormal stress myocardial perfusion.35 patients(35.35%)developed MACE during the follow-up period.Cox regression analysis showed that stress phase bandwidth(SPBW)(HR=1.015,95%CI 1.002-1.028)and sum difference score(SDS)(HR=1.105,95%CI 1.008-1.211)were independent risk factors for predicting MACE(both P<0.05).The optimal cut-off value of SPBW and SDS for predicting MACE were 69° and 6 points,the area under the curve was 0.801 and 0.778,respectively.The incidence of MACE in the SPBW≥69° group and SDS≥6 points group was higher than that in SPBW<69° group and SDS<6 points group(66.6%vs.13.2%,53.3%vs.20.4%,both P<0.05).Conclusion SPECT G-MPI multifunctional parameters can be used to predict the prognosis of CKD patients with abnormal stress myocardial perfusion.SPBW and SDS are independent risk factors for MACE in these patients.

Objective:To evaluate the clinical predictive value of SPECT myocardial perfusion imaging (MPI) in the occurrence of cardiac death in patients with chronic kidney disease (CKD).Methods:A retrospective follow-up was performed for 160 patients (109 males, 51 females; age: 68.5(61.0, 74.0) years) who underwent MPI in Beijing Anzhen Hospital, Capital Medical University between June 2017 and March 2024. The 17-segment 5-point method was used for image analysis to obtain the left ventricular myocardial perfusion and functional parameters. The patients were followed up for cardiac death, and divided into death group and survival group. Clinical data of those 2 groups were compare by χ2 test, the independent-sample t test or Mann-Whitney U test. Cox proportional hazards regression analysis was used to analyze the predictors related to cardiac death. The ROC curve was used to analyze the performance of predictors. Survival curves were obtained by the Kaplan-Meier method, and log-rank test was performed to compare the difference between 2 groups. Results:The follow-up time of 160 patients with CKD was 26.0(10.0, 46.5) months. Of 160 patients, 17 died and 143 survived. There were statistically significant differences in body mass index (BMI), previous myocardial infarction, previous revascularization, hypersensitive C-reactive protein (hs-CRP), positive MPI, left ventricular end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), summed stress score (SSS), summed rest score (SRS) and summed difference score (SDS) between the death group and the survival group ( χ2 values: 4.58-16.13, t values: -2.34, -3.97, Z values: from -2.81 to 5.02, all P<0.05). Multivariate Cox regression analysis showed that SSS (hazard ratio ( HR)=1.153, 95% CI: 1.062-1.252, P=0.001) and hs-CRP ( HR=1.031, 95% CI: 1.004-1.058, P=0.023) were independent risk factors for cardiac death in patients with CKD. The optimal cut-off value of SSS for predicting cardiac death in those patients was determined to be 8 with the AUC of 0.815, and the incidence of cardiac death in the SSS ≥8 group was significantly higher than that in the SSS<8 group (33.3%(12/36) vs 4.0%(5/124); χ2 = 25.44, P<0.001). Conclusion:MPI is an important imaging method for the evaluation of cardiac death in patients with CKD, SSS and hs-CRP are important risk factors in predicting cardiac death in those patients.

Radiotherapy remains one of the primary therapeutic modalities for various cancers. However, individual heterogeneity exists in treatment outcomes and adverse reactions. In recent years, the interaction between the gut microbiota and radiotherapy has garnered increasing attention. The gut microbiota plays a crucial role in modulating host immune responses, maintaining intestinal barrier integrity, and participating in metabolic processes, thereby influencing both the efficacy and tolerance of radiotherapy. Modulating gut microbiota composition through probiotics, antibiotics, or dietary interventions may reduce the toxic side effects induced by radiotherapy, thereby enhancing therapeutic outcomes. Despite numerous challenges in mechanistic studies and clinical application, current research has shed light on cancer therapies. This review emphasizes the significant role of gut microbiota in radiotherapy, impacting treatment outcomes and patients’ tolerance and quality of life. Future research should further explore the links between microbiota regulation and optimization of radiotherapy outcomes, with the prospect of translating these strategies into clinical practice to provide more personalized treatment options for patients.

Objective:To evaluate the clinical predictive value of SPECT myocardial perfusion imaging (MPI) in the occurrence of cardiac death in patients with chronic kidney disease (CKD).Methods:A retrospective follow-up was performed for 160 patients (109 males, 51 females; age: 68.5(61.0, 74.0) years) who underwent MPI in Beijing Anzhen Hospital, Capital Medical University between June 2017 and March 2024. The 17-segment 5-point method was used for image analysis to obtain the left ventricular myocardial perfusion and functional parameters. The patients were followed up for cardiac death, and divided into death group and survival group. Clinical data of those 2 groups were compare by χ2 test, the independent-sample t test or Mann-Whitney U test. Cox proportional hazards regression analysis was used to analyze the predictors related to cardiac death. The ROC curve was used to analyze the performance of predictors. Survival curves were obtained by the Kaplan-Meier method, and log-rank test was performed to compare the difference between 2 groups. Results:The follow-up time of 160 patients with CKD was 26.0(10.0, 46.5) months. Of 160 patients, 17 died and 143 survived. There were statistically significant differences in body mass index (BMI), previous myocardial infarction, previous revascularization, hypersensitive C-reactive protein (hs-CRP), positive MPI, left ventricular end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), summed stress score (SSS), summed rest score (SRS) and summed difference score (SDS) between the death group and the survival group ( χ2 values: 4.58-16.13, t values: -2.34, -3.97, Z values: from -2.81 to 5.02, all P<0.05). Multivariate Cox regression analysis showed that SSS (hazard ratio ( HR)=1.153, 95% CI: 1.062-1.252, P=0.001) and hs-CRP ( HR=1.031, 95% CI: 1.004-1.058, P=0.023) were independent risk factors for cardiac death in patients with CKD. The optimal cut-off value of SSS for predicting cardiac death in those patients was determined to be 8 with the AUC of 0.815, and the incidence of cardiac death in the SSS ≥8 group was significantly higher than that in the SSS<8 group (33.3%(12/36) vs 4.0%(5/124); χ2 = 25.44, P<0.001). Conclusion:MPI is an important imaging method for the evaluation of cardiac death in patients with CKD, SSS and hs-CRP are important risk factors in predicting cardiac death in those patients.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

Radiotherapy remains one of the primary therapeutic modalities for various cancers. However, individual heterogeneity exists in treatment outcomes and adverse reactions. In recent years, the interaction between the gut microbiota and radiotherapy has garnered increasing attention. The gut microbiota plays a crucial role in modulating host immune responses, maintaining intestinal barrier integrity, and participating in metabolic processes, thereby influencing both the efficacy and tolerance of radiotherapy. Modulating gut microbiota composition through probiotics, antibiotics, or dietary interventions may reduce the toxic side effects induced by radiotherapy, thereby enhancing therapeutic outcomes. Despite numerous challenges in mechanistic studies and clinical application, current research has shed light on cancer therapies. This review emphasizes the significant role of gut microbiota in radiotherapy, impacting treatment outcomes and patients’ tolerance and quality of life. Future research should further explore the links between microbiota regulation and optimization of radiotherapy outcomes, with the prospect of translating these strategies into clinical practice to provide more personalized treatment options for patients.

Avian orthoreovirus(ARV)is a globally significant immunosuppressive pathogen in poultry,capable of infecting a range of avian species and inducing conditions such as viral arthritis,tenosynovitis,and growth retardation,thereby presenting substantial challenges to the health and economic productivity of poultry.Under persistent immune selection pressure,ARV has evolved various immune evasion strategies,facilitating virus proliferation and the spread of progeny viru-ses.This article reviews the advancements in understanding the mechanisms by which ARV antag-onizes the host immune response during infection,aiming to inform research into ARV pathogene-sis and the identification of novel vaccine targets.

Avian orthoreovirus(ARV)is a globally significant immunosuppressive pathogen in poultry,capable of infecting a range of avian species and inducing conditions such as viral arthritis,tenosynovitis,and growth retardation,thereby presenting substantial challenges to the health and economic productivity of poultry.Under persistent immune selection pressure,ARV has evolved various immune evasion strategies,facilitating virus proliferation and the spread of progeny viru-ses.This article reviews the advancements in understanding the mechanisms by which ARV antag-onizes the host immune response during infection,aiming to inform research into ARV pathogene-sis and the identification of novel vaccine targets.

Objective The monosaccharide composition of Abelmoschus Manihot Radix polysaccharide was identified and then we evaluated the therapeutic effect on slow transit constipation.Finally,the pharmacodynamic substances and molecular mechanisms in the polysaccharide from Abelmoschus Manihot Radix to improve slow transit constipation were explored by network pharmacology and molecular docking techniques.Methods The polysaccharide from Abelmoschus Manihot Radix has been prepared by aqueous-alcoholic precipitation and has been determined by HPAEC method;The mice model of slow transit constipation was made by sc loperamide(10 mg?kg-1)and the therapeutic effect for the treatment of constipation was evaluated by two indicators:fecal water content and small intestinal propulsion rate;Pathological changes in the colon tissue of STC mice were observed by HE.Immunohistochemical method was used to detect zonula occludens-1(ZO-1)and claudin-1 expression in colon tissue of STC mice;qRT-PCR method was used to detect mRNA expressions of AQP3,AQP4,TNF-α,IL-1β and IL-6 in each group;Network pharmacology and molecular docking technology were used to explore the potential targets and pathways of the polysaccharide from Abelmoschus Manihot Radix in treating slow transit constipation.Results The Polysaccharide from Abelmoschus Manihot Radix significantly increased fecal water content and intestinal propulsion rate in mice caused by slow transit constipation,decreased the expressions of AQP3,AQP4,TNF-α,IL-1β and IL-6 mRNA(P<0.01,P<0.001),protected the integrity of the colonic barrier in STC mice,and increased the protein expressions of ZO-1 and claudin-1 in colon tissues of STC mice.By network pharmacology,it was found that monosaccharides such as rhamnose,fucose and glucuronic acid could mainly act on key targets such as STAT3,JUN,CASP3,HSP90AA1,VEGFA and IL-1β and regulate the Inflammatory mediator regulation of TRP channels,EGFR tyrosine kinase inhibitor resistance and AGE-RAGE signaling pathway in diabetic complications to improve the symptoms of constipation in mice.Western blot results showed that intervention with polysaccharides from the roots of Solanum palmatum significantly reduced the expression of CASP3,VEGFA and IL-1β protein in the colon tissue of constipated mice.Conclusion The polysaccharide from Abelmoschus Manihot Radix can treat slow transit constipation through multi-component,multi-target and multi-pathway therapy.It provides a scientific basis for the further clinical application of Abelmoschus Manihot and drug development(National patent ZL202310894613.3.has been authorized)and it is expected to promote the efficient utilisation of resources from the non-medicinal parts of Abelmoschus Manihot.