Objective To explore the clinical characteristics and short-term prognosis of postoperative acute myocardial infarction (PAMI) after cardiac surgery. Methods A retrospective study was conducted on 45 PAMI patients who underwent coronary angiography after cardiac surgery at Zhongshan Hospital, Fudan University, from February 2015 to February 2025. General information, type of surgery, onset time and causes of PAMI, and in-hospital recovery condition were collected. Univariate logistic regression was used to analyze factors related to in-hospital survival. Results The patients’ age was (62.40±11.72) years, 31 cases (68.89%) were male, 21 cases (46.67%) had hypertension. Twenty-six patients (57.78%) underwent cardiac valve surgery, and 16 patients (35.56%) received coronary artery bypass grafting. The onset time of PAMI was 24 (0, 48) hours, with 38 cases (84.44%) developing within 48 hours after surgery. Twenty-eight patients (62.22%) presented with new lesions in coronary arteries/bypass grafts, and 20 patients (44.44%) were considered to have thrombus-induced PAMI. Nine patients died in hospital, 13 patients failed to recover and were discharged automatically, and 23 patients were discharged after recovery. Univariate logistic regression analysis showed that new lesion in coronary arteries/bypass grafts was risk factor for in-hospital survival (OR=0.235, P=0.036). Conclusions PAMI frequently occurs within 48 hours post-cardiac surgery, and thrombosis is the most common cause of PAMI. New lesions in coronary arteries/bypass grafts are closely correlated with poor short-term prognosis.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Purpose To explore the predictive value of hepatosplenic hemodynamic indexes obtained by dual-energy CT combined with clinical features in non-invasive assessment of high-risk esophageal varices(EV)in cirrhosis.Materials and Methods We retrospectively collected 93 patients with cirrhosis from March 2022 to May 2023 in Zengcheng Branch of Nanfang Hospital.All patients underwent epigastric enhanced energy spectrum scan and gastroscopy.EV severity as assessed by gastroscopy(none,EV0;mild,EV1;medium,EV2;severe,EV3)were divided into low-risk EV group(EV0 and EV1)and high-risk EV group(EV2 and EV3).Age,gender,Child-Pugh grade,ascites and platelet of the two groups were collected,and dual-energy CT parameters including liver and spleen volume,mean iodine content in liver and spleen and liver and spleen iodine volume were measured.The difference of parameters between the low-risk EV and the high-risk EV group were analyzed,and the model was constructed by Logistic regression analysis.Receiver operating characteristic curve was used to evaluate the diagnostic performance of the model for high-risk EV.Results There were significant differences in age,Child-Pugh grade,ascites,platelet,liver and spleen volume,liver and spleen iodine volume between low-risk EV and high-risk EV groups(Z/χ2=-5.921-16.343,all P<0.05).Ascites,platelet,liver and spleen volume,liver and spleen iodine volume were included in multivariate regression analysis to construct regression models,and the results showed that spleen iodine volume(OR=1.002,P<0.001),ascites(OR=5.319,P=0.009),platelet(OR=0.99,P=0.062)were independent risk factors for predicting the high risk of EV.Hosmer-Lemeshow test showed that the regression model fit the observed values well(P=0.303),with accuracy of 83.9%,sensitivity of 78.6%,specificity of 88.2%,positive prediction rate of 84.6%and negative prediction rate of 83.3%.The area under the curve of this regression model was 0.894.Conclusion Spleen iodine content based on dual-energy CT,platelet and ascites can noninvasively predict high-risk EV.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To explore the potential risk factors for cancer in patients with adenomatous polyps based on tumor markers and polyp lesion characteristics.Methods:A retrospective analysis was conducted to collect clinical data of 115 patients with adenomatous intestinal polyps who visited Nanjing Jiangbei Hospital from November 2022 to November 2024. They were divided into a cancerous group (17 cases) and a non cancerous group (98 cases) based on whether they were cancerous or not. Clinical data such as tissue type and polyp site and tumor marker levels such as carcinoembryonic antigen (CEA) and cancer antigen 72-4 (CA72-4) were collected at the first visit of all patients. The potential risk factors of adenomatous intestinal polyp canceration were investigated by Logistic regression analysis.Results:Univariate analysis revealed that the proportion of villous tubular adenomas, central depression of polyps, and lobulated polyps in the cancerous group were higher than those in the non cancerous group. Serum levels of CEA and CA72-4 were also higher in the cancerous group than in the non cancerous group : 13/17 vs.47.96% (47/98), 7/17 vs. 15.31% (15/98), 6/17 vs. 8.16% (8/98), (6.41 ± 1.81) μg/L vs. (4.23 ± 1.48) μg/L, (6.98 ± 1.83) kU/L vs. (5.66 ± 1.78) kU/L, respectively. The difference was statistically significant ( P<0.05). The results of Logistic regression analysis showed that the histological subtype of villous tubular adenoma, central depression of polyps, lobulated polyps, and high levels of CEA and CA72-4 were independent risk factors for cancer in patients with adenomatous intestinal polyps ( P<0.05). A nomogram risk model was constructed based on the influencing factors of canceration in patients with adenomatous intestinal polyps. The calibration curve was drawn, and the calibration curve was similar to the Y-X straight line, suggesting that the evaluation results of the nomogram risk model were highly consistent with the actual observation results. The receiver operating characteristic (ROC) curve was drawn. The results showed that the area under the curve (AUC) of the nomogram risk model for evaluating the canceration of patients with adenomatous intestinal polyps was 0.956, and the evaluation value was high. The decision curve was drawn, with the threshold of high risk as the horizontal coordinate and the net rate of return as the vertical coordinate. The results showed that when the threshold was in the range of 0 - 0.85, 0.96 - 0.99, the net benefit rate of predicting the cancer risk of patients with adenomatous intestinal polyps was greater than 0 and the maximum net benefit rate was 0.148. Conclusions:The histological classification of villous tubular adenoma, central depression of polyps, lobulated polyps, and high levels of CEA and CA72-4 are independent risk factors for cancer in patients with adenomatous intestinal polyps; The evaluation efficiency of the column chart risk model constructed based on the above factors is good.

A 57-year-old female patient with small cell lung cancer was treated with serplulimab (100 mg by intravenous infusion on the first day, 21 days as a cycle). After the 10th cycle of treatment, the patient suddenly presented drowsiness, shortness of breath, dry mouth, nausea, vomiting, fatigue and other symptoms. Laboratory tests showed fasting blood glucose 38.9 mmol/L, glycosylated hemoglobin 7.9%, serum C peptide 0.2 μg/L, pH value 7.05, anion gap 31 mmol/L, anti-islet cell antibody 36 kU/L, anti-glutamic acid decarboxylase antibody 131 kU/L, urine ketone body (+++). The patient was diagnosed with fulminant type 1 diabetic ketoacidosis, which was considered to be related to serplulimab. The drug was stopped, and insulin, rehydration, correction of acid-base imbalance, and other treatments were given. After 3 days, the patient′s consciousness returned to normal. After 12 days, her breathing was stable, dry mouth, nausea, vomiting and other symptoms were relieved. Laboratory tests showed random blood glucose 13.6 mmol/L, pH value 7.44, anion gap 6 mmol/L, and urine ketone body negative. After 26 days, her random blood glucose was controlled at 10.0-12.0 mmol/L.

Diabetic retinopathy, a prevalent and vision-threatening microvascular complication of diabetes mellitus, is the leading cause of blindness among middle-aged and elderly individuals. Natural diterpenoids isolated from Siegesbeckia pubescens demonstrate potent anti-inflammatory properties. This study aimed to identify novel bioactive diterpenoids from S. pubescens and investigate their effects on oxidative stress and inflammatory responses in diabetic retinopathy, both in vitro and in vivo. Three new ent-pimarane-type diterpenoids (1-3) and six known compounds (4-9) were isolated from the aerial parts of S. pubescens. Their structures were elucidated through spectroscopic data interpretation, and absolute configurations were determined by comparing calculated and experimental electronic circular dichroism (ECD) spectra. Among these compounds, 14β,16-epoxy-ent-3β,15α,19-trihydroxypimar-7-ene (5) exhibited the most potent protective effect against high glucose and interleukin-1β (IL-1β)-stimulated human retinal endothelial cells. Mechanistically, compound 5 promoted endothelial cell survival while ameliorating oxidative stress and inflammatory response in diabetic retinopathy, both in vivo and in vitro. These findings not only suggest that diterpenoids such as compound 5 are important anti-inflammatory constituents in S. pubescens, but also indicate that compound 5 may serve as a lead compound for preventing or treating vascular complications associated with diabetic retinopathy.
Diabetic Retinopathy/metabolism* ; Humans ; Oxidative Stress/drug effects* ; Animals ; Diterpenes, Kaurane/administration & dosage* ; Asteraceae/chemistry* ; Male ; Endothelial Cells/drug effects* ; Abietanes/administration & dosage* ; Molecular Structure ; Mice ; Anti-Inflammatory Agents/chemistry* ; Plant Extracts/chemistry* ; Mice, Inbred C57BL