Objective:To compare the clinical effects of restrictive transfusion strategy and liberal transfusion strategy for cardiac valve surgery.Methods:This study employed a prospective, randomized controlled superiority design, enrolling 439 patients undergoing non-emergency cardiac valve surgery with cardiopulmonary bypass at Department of Cardiovascular Surgery, Guangdong Provincial People′s Hospital, Southern Medical University from June 2023 to October 2024 who met the inclusion and exclusion criteria. While all the patients appeared hematocrit (Hct)≤24% or hemoglobin (Hb)≤80 g/L during the cardiopulmonary bypass. A simple random design was adopted to generate a random sequence and participants were randomized into a restrictive transfusion group (restrictive criteria: Hct≤18% or Hb≤60 g/L during cardiopulmonary bypass, and Hct≤21% or Hb≤70 g/L postoperatively) or a liberal transfusion group (liberal criteria: Hct≤24% or Hb≤80 g/L during cardiopulmonary bypass and Hct≤30% or Hb≤100 g/L postoperatively). If Hb or Hct fell below the respective thresholds, 2 units of red blood cells were transfused, followed by re-evaluation. If levels remained below the threshold, an additional 2 units were transfused until the criteria were met. The primary outcome was a composite of postoperative 3-month mortality, infection, ischemic events, and new-onset renal failure requiring dialysis. Secondary outcomes included blood product utilization, length of stay in the ICU and so on. Intergroup comparisons were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher′s exact test, and analyses were conducted using a binary multivariable Logistic regression model. Results:A total of 439 patients were included in this study. The restrictive roup included 221 patients, including 75 males and 146 females, aged ( M(IQR)) 57.0 (14.0) years (range: 21 to 76 years). The liberal group included 218 patients, including 67 males and 151 females, aged 56.0 (20.0) years (range: 19 to 74 years). No statistically significant difference was observed in the incidence of primary outcome (restrictive group: 10.9%(24/221) vs. liberal group: 9.6%(21/218), χ2=0.180, P>0.05), 2 patints in the restrictive group died and 3 patints in liberal group died ( P=0.684). The transfusion rate was significantly lower in the restrictive group(19.0%(42/221) vs. 100%(218/218), P<0.01), with no significant differences in other secondary outcomes (all P>0.05). Subgroup analysis revealed an interaction between sex and transfusion strategy ( P=0.023), suggesting that using liberal transfusion strategy in male patients might increase the risk of the primary outcome. Conclusion:The mid-term results do not show that the restrictive transfusion strategy is superior to the liberal transfusion strategy in reducing the incidence of postoperative outcome events in patients undergoing cardiac valve surgery.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

Objective:To construct fused cancer cell/neutrophil membrane-coated polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNP@FMs) and explore the potential for targeted pancreatic cancer fluorescence imaging and MRI.Methods:Cancer cell membranes fused with neutrophil membranes were encapsulated on the surface of polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNPs) to prepare PMNP@FMs. The morphology, structure, and MRI performance of the product were characterized. The cytotoxicity of PMNP@FMs towards human pancreatic cancer cells (PANC-1) and normal human pancreatic ductal epithelial cells (hTERT-HPNE) was evaluated using cell counting kit (CCK)-8, and in vivo toxicity was assessed in healthy mice. PANC-1 pancreatic cancer xenograft nude mouse models were established for in vivo fluorescence imaging and MRI. Data were analyzed using the independent-sample t test, repeated measures analysis of variance and the least significance difference method. Results:PMNP@FMs exhibited a core-shell structure with a diameter of (112.81±8.64) nm, negative surface charge, and good dispersibility. The T 1 relaxivity of PMNPs was 18.81±0.22, which was 4.1 times higher than that of gadopentetate dimeglumine (Gd-DTPA) (4.55±0.24; t=75.54, P<0.001). Co-culture of PMNPs and PMNP@FMs with hTERT-HPNE and PANC-1 cells for 24 h resulted in cell viability above 90% within the concentration range of 0-500 μg/ml. PMNP@FMs did not affect mouse survival and showed no apparent organ damage. In vivo fluorescence imaging and MRI revealed that PMNP@FMs accumulated highly in tumors and reached the peak 24 h post intravenous administration (relative MR signal: 1.35±0.01, fluorescence intensity: (1.20±0.25)×10 10), surpassing the peak observed in the control group (1.22±0.01, (3.87±0.50)×10 9;F values: 11.03-188.01, t values: 18.20, 5.64, all P<0.05), with hepatic metabolism being the primary route of clearance. Conclusion:PMNP@FMs demonstrate a potential for targeted pancreatic cancer fluorescence imaging and MRI, offering promising prospect for precise diagnosis of early-stage pancreatic cancer.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective:To construct fused cancer cell/neutrophil membrane-coated polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNP@FMs) and explore the potential for targeted pancreatic cancer fluorescence imaging and MRI.Methods:Cancer cell membranes fused with neutrophil membranes were encapsulated on the surface of polydopamine nanoparticles chelated with manganese ions (Ⅱ) (PMNPs) to prepare PMNP@FMs. The morphology, structure, and MRI performance of the product were characterized. The cytotoxicity of PMNP@FMs towards human pancreatic cancer cells (PANC-1) and normal human pancreatic ductal epithelial cells (hTERT-HPNE) was evaluated using cell counting kit (CCK)-8, and in vivo toxicity was assessed in healthy mice. PANC-1 pancreatic cancer xenograft nude mouse models were established for in vivo fluorescence imaging and MRI. Data were analyzed using the independent-sample t test, repeated measures analysis of variance and the least significance difference method. Results:PMNP@FMs exhibited a core-shell structure with a diameter of (112.81±8.64) nm, negative surface charge, and good dispersibility. The T 1 relaxivity of PMNPs was 18.81±0.22, which was 4.1 times higher than that of gadopentetate dimeglumine (Gd-DTPA) (4.55±0.24; t=75.54, P<0.001). Co-culture of PMNPs and PMNP@FMs with hTERT-HPNE and PANC-1 cells for 24 h resulted in cell viability above 90% within the concentration range of 0-500 μg/ml. PMNP@FMs did not affect mouse survival and showed no apparent organ damage. In vivo fluorescence imaging and MRI revealed that PMNP@FMs accumulated highly in tumors and reached the peak 24 h post intravenous administration (relative MR signal: 1.35±0.01, fluorescence intensity: (1.20±0.25)×10 10), surpassing the peak observed in the control group (1.22±0.01, (3.87±0.50)×10 9;F values: 11.03-188.01, t values: 18.20, 5.64, all P<0.05), with hepatic metabolism being the primary route of clearance. Conclusion:PMNP@FMs demonstrate a potential for targeted pancreatic cancer fluorescence imaging and MRI, offering promising prospect for precise diagnosis of early-stage pancreatic cancer.

Objective:To compare the clinical effects of restrictive transfusion strategy and liberal transfusion strategy for cardiac valve surgery.Methods:This study employed a prospective, randomized controlled superiority design, enrolling 439 patients undergoing non-emergency cardiac valve surgery with cardiopulmonary bypass at Department of Cardiovascular Surgery, Guangdong Provincial People′s Hospital, Southern Medical University from June 2023 to October 2024 who met the inclusion and exclusion criteria. While all the patients appeared hematocrit (Hct)≤24% or hemoglobin (Hb)≤80 g/L during the cardiopulmonary bypass. A simple random design was adopted to generate a random sequence and participants were randomized into a restrictive transfusion group (restrictive criteria: Hct≤18% or Hb≤60 g/L during cardiopulmonary bypass, and Hct≤21% or Hb≤70 g/L postoperatively) or a liberal transfusion group (liberal criteria: Hct≤24% or Hb≤80 g/L during cardiopulmonary bypass and Hct≤30% or Hb≤100 g/L postoperatively). If Hb or Hct fell below the respective thresholds, 2 units of red blood cells were transfused, followed by re-evaluation. If levels remained below the threshold, an additional 2 units were transfused until the criteria were met. The primary outcome was a composite of postoperative 3-month mortality, infection, ischemic events, and new-onset renal failure requiring dialysis. Secondary outcomes included blood product utilization, length of stay in the ICU and so on. Intergroup comparisons were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher′s exact test, and analyses were conducted using a binary multivariable Logistic regression model. Results:A total of 439 patients were included in this study. The restrictive roup included 221 patients, including 75 males and 146 females, aged ( M(IQR)) 57.0 (14.0) years (range: 21 to 76 years). The liberal group included 218 patients, including 67 males and 151 females, aged 56.0 (20.0) years (range: 19 to 74 years). No statistically significant difference was observed in the incidence of primary outcome (restrictive group: 10.9%(24/221) vs. liberal group: 9.6%(21/218), χ2=0.180, P>0.05), 2 patints in the restrictive group died and 3 patints in liberal group died ( P=0.684). The transfusion rate was significantly lower in the restrictive group(19.0%(42/221) vs. 100%(218/218), P<0.01), with no significant differences in other secondary outcomes (all P>0.05). Subgroup analysis revealed an interaction between sex and transfusion strategy ( P=0.023), suggesting that using liberal transfusion strategy in male patients might increase the risk of the primary outcome. Conclusion:The mid-term results do not show that the restrictive transfusion strategy is superior to the liberal transfusion strategy in reducing the incidence of postoperative outcome events in patients undergoing cardiac valve surgery.

Objective:To identify the types of fear of progression in patients after percutaneous coronary intervention (PCI) based on latent profile analysis, and to explore the influencing factors of different types.Methods:Cross-sectional survey method was used to select the patients with coronary heart disease and underwent PCI in Anhui Public Health Clinical Center from April to December 2023 as the research object. The general information questionnaire, Fear of Progression Questionnaire-Short Form, Ruminative Response Scale and Brief Illness Perception Questionnaire were used to investigate them. Mplus8.3 software was used to construct the latent profile model.Results:A total of 240 patients with complete data were enrolled, including 176 males and 64 females, aged 28-84 (62.94 ± 11.20) years. The results of latent profile analysis showed that the fear of progression of patients after PCI could be divided into three latent categories: There were 59 cases (24.6%) in the low fear group, 111 cases (46.3%) in the medium fear group, and 70 cases (29.1%) in the high fear-worried family group. The results of multiple logistic regression analysis showed that compared with the low fear group, the probability of having primary school education or below was higher in the medium fear group ( OR=4.054, 95% CI 1.370-11.996) and the high fear-worry family group ( OR=5.996, 95% CI 1.562-23.014), secondary school was more likely in the moderate fear group ( OR=3.096, 95% CI 1.104-8.682, all P<0.05);Living in rural areas were more likely to be in the moderate fear group ( OR=2.587, 95% CI 1.187-5.637) and the high few-worry family group ( OR=6.958, 95% CI 2.567-18.856, all P<0.05); The probability of the first interventional therapy was higher in the moderate fear group ( OR=2.496, 95% CI 1.107-5.630) and the high fear-worry family group ( OR=4.924, 95% CI=1.809-13.402, all P<0.05). In addition, compared with the low fear group, patients with higher rumination were more likely to belong to the high few-worry family working group ( OR=1.130, 95% CI 1.055-1.210, P<0.05);Moderate fear group ( OR=1.181, 95% CI 1.046-1.334) and high fear family working group ( OR=1.349, 95% CI 1.164-1.562, all P<0.05) had a higher level of illness perception. Conclusions:There is significant heterogeneity in the fear of progression among patients after PCI. Medical staff can implement precise intervention according to the potential category characteristics of patients′ fear of progression, so as to reduce the level of fear of disease progression.

Objective To analyze the influencing factors of oral frailty in elderly patients with type 2 diabetes and construct a nomogram prediction model. Methods A total of 370 elderly patients with type 2 diabetes were selected as the research subjects, including 284 patients in the modeling group and 86 patients in the validation group. The Oral Frailty Index-8 (OF-8) Scale was used for oral frailty screening, and a score of ≥4 was considered positive for oral frailty. General information of the two groups was collected through a self-made questionnaire. Multivariate Logistic regression analysis was used to analyze the influencing factors of oral frailty in patients with type 2 diabetes, and nomogram model was constructed. The goodness-of-fit and predictive performance of the model were verified using the Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic curve (ROC). Results The incidence of oral frailty among elderly patients with type 2 diabetes was 45.4% (129/284). Age, body mass index (BMI), appendicular skeletal muscle mass index (ASMI), smoking, monthly income and subjective chewing difficulty were identified as influencing factors for oral frailty in elderly diabetic patients (P < 0.05). The areas under the ROC for internal and external validation were 0.887 (95%CI, 0.847 to 0.925) and 0.839 (95%CI, 0.755 to 0.923), respectively. The Hosmer-Lemeshow test showed that χ²=4.852, P=0.773, indicating good goodness-of-fit for the nomogram model. Conclusion Age, ASMI, BMI, smoking, monthly income and subjective chewing difficulty are influencing factors for oral frailty in elderly diabetic patients. The nomogram prediction model constructed based on these influencing factors demonstrates good performance.