The accurate and timely detection of biochemical coagulation indicators is pivotal in pulmonary and critical care medicine. Despite their reliability, traditional laboratories often lag in terms of rapid diagnosis. Point-of-care testing (POCT) has emerged as a promising alternative, which is awaiting rigorous validation. We assessed 226 samples from patients at the First Affiliated Hospital of Guangzhou Medical University using a Beckman Coulter AU5821 and a PUSHKANG POCT Biochemistry Analyzer MS100. Furthermore, 350 samples were evaluated with a Stago coagulation analyzer STAR MAX and a PUSHKANG POCT Coagulation Analyzer MC100. Metrics included thirteen biochemical indexes, such as albumin, and five coagulation indices, such as prothrombin time. Comparisons were drawn against the PUSHKANG POCT analyzer. Bland-Altman plots (MS100: 0.8206‒0.9995; MC100: 0.8318‒0.9911) evinced significant consistency between methodologies. Spearman correlation pinpointed a potent linear association between conventional devices and the PUSHKANG POCT analyzer, further underscored by a robust correlation coefficient (MS100: 0.713‒0.949; MC100: 0.593‒0.950). The PUSHKANG POCT was validated as a dependable tool for serum and whole blood biochemical and coagulation diagnostics. This emphasizes its prospective clinical efficacy, offering clinicians a swift diagnostic tool and heralding a new era of enhanced patient care outcomes.
Humans ; Point-of-Care Testing ; Critical Care ; Blood Coagulation Tests/methods* ; Male ; Blood Coagulation ; Female ; Middle Aged ; Reproducibility of Results ; Prothrombin Time ; Aged ; Adult ; Point-of-Care Systems

Objective:To compare the diagnostic efficacy of the added 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT/CT imaging (tomography imaging) after early or delayed planar imaging in different types of hyperparathyroidism, and to seek for the optimal timing of preoperative imaging. Methods:A total of 339 patients (148 males, 191 females, age (52±13) years) with primary or secondary hyperparathyroidism and pathological results from January 2016 to June 2024 in the Second Affiliated Hospital of Soochow University were retrospectively analyzed. The patients were divided into primary early tomography imaging group ( n=63) and delayed tomography imaging group ( n=47), secondary early tomography imaging group ( n=89) and delayed tomography imaging group ( n=140). χ2 test was used to compare the diagnostic efficacies of early and delayed tomography imaging. Results:The difference of accuracy between primary delayed (98.40%(185/188)) and early tomography imaging (94.84%(239/252)) was statistically significant ( χ2=3.90, P=0.048). There were significant differences in sensitivity (77.29%(405/524) and 85.40%(275/322)), accuracy (75.89%(425/560) and 83.99%(299/356)) and negative predictive value (14.39%(20/139) and 33.80%(24/71)) between secondary delayed and early tomography imaging ( χ2 values: 8.33, 8.61, 10.70, all P<0.01). Conclusion:The optimal timing of preoperative 99Tc m-MIBI SPECT/CT imaging for primary and secondary hyperparathyroidism is after delayed planar imaging and after early planar imaging respectively.

Objective:To compare the diagnostic efficacy of the added 99Tc m-methoxyisobutylisonitrile (MIBI) SPECT/CT imaging (tomography imaging) after early or delayed planar imaging in different types of hyperparathyroidism, and to seek for the optimal timing of preoperative imaging. Methods:A total of 339 patients (148 males, 191 females, age (52±13) years) with primary or secondary hyperparathyroidism and pathological results from January 2016 to June 2024 in the Second Affiliated Hospital of Soochow University were retrospectively analyzed. The patients were divided into primary early tomography imaging group ( n=63) and delayed tomography imaging group ( n=47), secondary early tomography imaging group ( n=89) and delayed tomography imaging group ( n=140). χ2 test was used to compare the diagnostic efficacies of early and delayed tomography imaging. Results:The difference of accuracy between primary delayed (98.40%(185/188)) and early tomography imaging (94.84%(239/252)) was statistically significant ( χ2=3.90, P=0.048). There were significant differences in sensitivity (77.29%(405/524) and 85.40%(275/322)), accuracy (75.89%(425/560) and 83.99%(299/356)) and negative predictive value (14.39%(20/139) and 33.80%(24/71)) between secondary delayed and early tomography imaging ( χ2 values: 8.33, 8.61, 10.70, all P<0.01). Conclusion:The optimal timing of preoperative 99Tc m-MIBI SPECT/CT imaging for primary and secondary hyperparathyroidism is after delayed planar imaging and after early planar imaging respectively.

Objective To compare the effect of palliative mitral valve surgeries and medication therapies for secondary non-ischemic mitral regurgitation. Methods The clinical data of patients with non-ischemic functional mitral regurgitation treated in our hospital between 2009 and 2019 were retrospectively analyzed. Patients with a left ventricular ejection fraction (LVEF)＜40% underwent a dobutamine stress test, and a positive result was determined when the LVEF improved by more than 15% compared to the baseline value. Positive patients were divided into a surgery group and a medication group. The surgery group underwent surgical mitral valve repair or replacement, while the medication group received simple medication treatment. Follow-up on survival and cardiac function status through outpatient or telephone visits every six months after surgery, and patients underwent cardiac ultrasound examination one year after surgery. The main research endpoint was a composite endpoint of all-cause death, heart failure readmission, and heart transplantation, and the differences in cardiac function and cardiac ultrasound parameters between the two groups were compared. Results Ultimately 41 patients were collected, including 28 males and 13 females with an average age of 55.5±11.1 years. Twenty-five patients were in the surgery group and sixteen patients in the medication group. The median follow-up time was 16 months, ranging 1-96 months. The occurrence of all-cause death in the surgery group was lower than that in the medication group (HR=0.124, 95%CI 0.024-0.641, P=0.034). The difference between the two groups was not statistically significant in the composite endpoint (HR=0.499, 95%CI 0.523-1.631, P=0.229). The New York Heart Association (NYHA) grade of the surgery group was better (NYHA Ⅰ-Ⅱ accounted for 68.0% in the surgury group and 18.8% in the medication group, P<0.01) as well as the grade of mitral valve regurgitation (87.5% of the patients in the medication group had moderate or above regurgitation at follow-up, while all the patients in the surgery group had moderate below regurgitation, P<0.01). There was no statistical difference in preoperative and follow-up changes in echocardiograph parameters between the two groups (P＞0.05). Conclusion For non-ischemic functional mitral regurgitation, if the cardiac systolic function is well reserved, mitral valve surgery can improve survival and quality of life compare to simple medication therapy.

With persistent breakthrough and maturity of surgical procedures and postoperative immunosuppressive therapy, the survival rate of liver transplant recipients and grafts has been significantly increased. The shortage of donor liver has become the main obstacle for clinical development of liver transplantation. How to expand the source of donor liver has become an urgent issue. Groundbreaking progresses have been made in the use of common marginal donor livers in clinical liver transplantation, such as elderly donor liver, steatosis donor liver, viral hepatitis donor liver and liver from donation after cardiac death. Nevertheless, multiple restrictions still exist regarding the use of marginal donor liver. Consequently, the definition of marginal donor liver and research progress in the application of common marginal donor livers were reviewed, and the opportunities and challenges of mariginal donoor liver were illustrated, aiming to provide reference for expanding the donor pool for clinical liver transplantation and bringing benefits to more patients with end-stage liver disease.

Objective To investigate the effect of silencing alpha tubulin acetyltransferase 1(α-TAT1)on migration behavior of endothelial cells induced by hepatopulmonary syndrome(HPS).Methods Online database Tabula Muris was used to analyze the expression of α-TAT1 in various cell subsets in the lungs.Twenty-four male SD rats were randomly divided into control group(Sham group,n=6)and common bile duct ligation group(HPS group,n=18).The rats in HPS group were euthanasized at 2 and 4 weeks after modelling,and then the expression of α-TAT1 in pulmonary vascular endothelial cells was detected by immunofluorescence colocalization.The sera from the Sham and HPS rats were used to stimulate human umbilical vein endothelial cells(HUVECs)for 12 and 24 h,respectively.Then the obtained HUVECs were divided into 4 groups:Sham serum+siRNA NC group,Sham serum+siRNA α-TAT1 group,HPS serum+siRNA NC group,HPS serum+siRNA α-TAT1 group.The expression levels of α-TAT1 and Ace-α-tubulin in HUVECs were detected by Western blotting.Immunofluorescence assay was applied to observe the levels of polymerized microtubules of α-Tubulin in HUVECs after nocodazole(10 μmol/L)pretreatment to evaluate the stability of microtubule structure.Cell scratch assay combined with cell immunofluorescence assay was employed to observe the nuclear localization of Golgi apparatus and cell migration ability of HUVECs.The angiogenesis ability of HUVECs was tested by in vitro angiogenesis test.Results In vivo and in vitro experiments showed that the expression of α-TAT1 in endothelial cells was significantly increased after HPS inducement.The expression levels of α-TAT1 and Ace-α-tubulin were significantly down-regulated,and the stability of microtubules was weakened in the siRNA α-TAT1 interference group(P＜0.01).In addition,the distribution of GM 130 labeled Golgi apparatus in the protrusion of HUVECs was down-regulated in the siRNAα-TAT1 interference group,as well as the migration ability(P＜0.01).And the length of angiogenesis and network level were also significantly declined(P＜0.01).Conclusion Silencing α-TAT1 reduces the migrαtion and angiogenesis of endothelial cells in HPS,which was associated with weakened stabilization of microtubule.

Objective:To design and synthesize 89Zr-deferoxamine(DFO)-G4C2, a novel molecular probe targeting programmed cell death receptor 1(PD-1), and evaluate its in vivo biodistribution and microPET/CT imaging characteristics in tumor-bearing mice. Methods:DFO-G4C2 was prepared by coupling DFO with G4C2, a monoclonal antibody targeting PD-1. The affinity and binding specificity of this amalgamation were subsequently assessed through the implementation of flow cytometry and surface plasmon resonance techniques. The molecular probe 89Zr-DFO-G4C2 was achieved by labeling DFO-G4C2 with the radioisotope 89Zr, and the labeling efficiency and in vitro stability of 89Zr-DFO-G4C2 were determined. Mouse models laden with CT26 colorectal cancer cells expressing PD-1 were established, followed by in vivo biodistribution and microPET/CT imaging studies, to explore the potential clinical value of 89Zr-DFO-G4C2. Additionally, the validity of this molecular probe was verified in 4T1 breast cancer models, affirming its efficacy as an imaging tool across different tumor models. Independent-sample t test was used to analyze the data. Results:DFO-G4C2 exhibited an affinity constant KD of (0.55±0.02) μmol/L, indicating a strong binding affinity. The binding rate to mouse PD-1 protein was determined to be (61.82±8.49)%. The labeling rate of 89Zr-DFO-G4C2 reached a high level of (98.76±0.51)%. Furthermore, the labeling rates in lysate and human serum after 144 h were measured to be (93.07±2.16)% and (83.42±3.21)%, respectively. MicroPET/CT imaging of CT26 tumor-bearing mice injected with 89Zr-DFO-G4C2 showcased pronounced radioactivity uptake in the tumor tissue. At 72 h post-injection, the tumor uptake value reached (10.47±0.34) percentage activity of injection dose per gram of tissue (%ID/g). The tumor uptake observed in the blocked experimental group, wherein an excess of unlabeled antibody was administered, was significantly lower at (6.26±1.03) %ID/g in comparison to the non-blocked group ( t=6.67, P=0.003). The in vivo biodistribution results were consistent with the observed microPET/CT imaging outcomes. MicroPET/CT imaging observations in the 4T1 breast cancer bearing mouse model were analogous to those obtained from the CT26 model. Conclusion:ImmunoPET based on the 89Zr-DFO-G4C2 molecular probe can non-invasively and visually assess the PD-1 expression level of tumors in vivo, and it is expected to be a new molecular imaging technique for immunotherapy monitoring of PD-1 inhibitors.

Objective:To develop deep learning models for ulcerative colitis (UC) classification based on Mayo endoscopic score.Methods:A total of 2400 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital of Soochow University and the HyperKvasir database were extracted for training classification models, and 200 endoscopic images from Affiliated Jintan Hospital of Jiangsu University were extracted for evaluating the models, both scored by endoscopists according to Mayo endoscopic score (score 0-3). Four deep convolutional neural networks (MobileNetV2, ResNetV2, Xception, EfficientNetV2S), which were pre-trained in the ImageNet database, were used to develop the UC classification models by transfer learning. Models were evaluated in the test set based on the confusion matrix using accuracy, Matthews correlation coefficient (MCC) and Cohen′s kappa, and compared with the performance of senior and junior physicians. Meanwhile, the model was visualized by gradient-weighted class activation mapping.Results:Four deep learning Mayo score models based on UC endoscopic image classification models were successfully developed. In the test set, the accuracy of MobileNetV2, ResNetV2, Xception and EfficientNetV2S was 0.785, 0.800, 0.815, 0.830, respectively (average accuracy 0.808). Amoug them, EfficientNetV2S model was the best, higher than junior physician′s accuracy (accuracy 0.785), and slightly lower than senior physician′s (accuracy 0.870) .Conclusions:The UC endoscopic severity classification models developed by deep learning show good performance, which can be further improved by larger sample size and optimizing the framework.

Objective:To develop deep learning models for ulcerative colitis (UC) classification based on Mayo endoscopic score.Methods:A total of 2400 endoscopic images from the Gastrointestinal Endoscopy Centre of the First Affiliated Hospital of Soochow University and the HyperKvasir database were extracted for training classification models, and 200 endoscopic images from Affiliated Jintan Hospital of Jiangsu University were extracted for evaluating the models, both scored by endoscopists according to Mayo endoscopic score (score 0-3). Four deep convolutional neural networks (MobileNetV2, ResNetV2, Xception, EfficientNetV2S), which were pre-trained in the ImageNet database, were used to develop the UC classification models by transfer learning. Models were evaluated in the test set based on the confusion matrix using accuracy, Matthews correlation coefficient (MCC) and Cohen′s kappa, and compared with the performance of senior and junior physicians. Meanwhile, the model was visualized by gradient-weighted class activation mapping.Results:Four deep learning Mayo score models based on UC endoscopic image classification models were successfully developed. In the test set, the accuracy of MobileNetV2, ResNetV2, Xception and EfficientNetV2S was 0.785, 0.800, 0.815, 0.830, respectively (average accuracy 0.808). Amoug them, EfficientNetV2S model was the best, higher than junior physician′s accuracy (accuracy 0.785), and slightly lower than senior physician′s (accuracy 0.870) .Conclusions:The UC endoscopic severity classification models developed by deep learning show good performance, which can be further improved by larger sample size and optimizing the framework.

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms