Objective To understand the sleep status in patients with Parkinson's disease (PD), and to explore its relationship with dyskinesia and negative emotions. Methods A total of 308 patients with PD who met the inclusion and exclusion criteria in the hospital from September 2022 to May 2024 were selected as the research subjects. The scores of sleep status [Pittsburgh Sleep Quality Index (PSQI)], dyskinesia [Simplified Fugl-Meyer Motor Assessment (FMA)] and negative emotions [Beck Anxiety Inventory (BAI), Beck Depression Inventory-II (BDI-II)] were analyzed, and the PSQI score was compared among patients with different demographic characteristics. Pearson correlation analysis was performed to analyze the correlation of sleep with dyskinesia and negative emotions in patients with PD. Results The total score of PSQI scale was (6.16±0.97) points in 308 PD patients, of which 208 cases (67.53%) were complicated with sleep disorders. The proportions of female, 61-75 years old, technical secondary school or below, disease course of 4 years and above, Hoehn-Yahr stage IV and unmarried status in the sleep disorder group were higher than those in the non-sleep disorder group (P<0.05). Compared with the non-sleep disorder group, the FMA score in the sleep disorder group was lower (P<0.05) while the BAI score and BDI-II score were higher (P<0.05). Pearson correlation analysis revealed that PSQI was negatively correlated with FMA (r=-0.489, P<0.05), and was positively correlated with BAI and BDI-II (r=0.476, 0.502, P<0.05). Conclusion The incidence rate of sleep disorders in PD patients is high. PSQI is negatively correlated with FMA, and is positively correlated with BAI and BDI-II.

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 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

Human NAD(P)H: quinone oxidoreductase 1 (NQO1) is a flavoenzyme expressed at high levels in multiple solid tumors, making it an attractive target for anticancer drugs. Bioactivatable drugs targeting NQO1, such as β-lapachone (β-lap), are currently in clinical trials for the treatment of cancer. β-Lap selectively kills NQO1-positive (NQO1⁺) cancer cells by inducing reactive oxygen species (ROS) via catalytic activation of NQO1. In this study, we demonstrated that cryptotanshinone (CTS), a naturally occurring compound, induces NQO1-dependent necrosis without affecting NQO1 activity. CTS selectively kills NQO1⁺ cancer cells by inducing NQO1-dependent necrosis. Interestingly, CTS directly binds to NQO1 but does not activate its catalytic activity. In addition, CTS enables activation of JNK1/2 and PARP, accumulation of iron and Ca²⁺, and depletion of ATP and NAD⁺. Furthermore, CTS selectively suppressed tumor growth in the NQO1⁺ xenograft models, which was reversed by NQO1 inhibitor and NQO1 shRNA. In conclusion, CTS induces NQO1-dependent necrosis via the JNK1/2/iron/PARP/NAD⁺/Ca²⁺ signaling pathway. This study demonstrates the non-enzymatic function of NQO1 in inducing cell death and provides new avenues for the design and development of NQO1-targeted anticancer drugs.

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 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.

BACKGROUND:At present,osteoarthritis has become a major disease affecting the quality of life of the elderly,and the therapeutic effect is poor,often focusing on preventing the disease process,and the pathogenesis of osteoarthritis is still not fully understood.Bioinformatics analysis was carried out to explore the main pathogenesis of osteoarthritis and related mechanisms of gene coding regulation. OBJECTIVE:To screen core differential genes with a major role in osteoarthritis by gene expression profiling. METHODS:Datasets were downloaded from the Gene Expression Omnibus(GEO):GSE114007,GSE117999,and GSE129147.Differential genes in the GSE114007 and GSE117999 data collections were screened using R software,performing differential genes to weighted gene co-expression network analysis.The module genes most relevant to osteoarthritis were selected to perform protein interaction analysis.Candidate core genes were selected using the cytocape software.The candidate core genes were subsequently subjected to least absolute shrinkage and selection operator regression and COX analysis to identify the core genes with a key role in osteoarthritis.The accuracy of the core genes was validated using an external dataset,GSE129147. RESULTS AND CONCLUSION:(1)A total of 477 differential genes were identified,265 differential genes associated with osteoarthritis were obtained by weighted gene co-expression network analysis,and 8 candidate core genes were identified.The least absolute shrinkage and selection operator regression analysis finally yielded a differential gene ASPM with core value that was externally validated.(2)It is concluded that abnormal gene ASPM expression screened by bioinformatics plays a key central role in osteoarthritis.

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.

Icaritin(ICT)is an 8-isopentenylflavonoid,which is the main effective component of the tra-ditional Chinese medicine Epimedium.Previously,we found that Icaritin inhibits the growth of glioblasto-ma(GBM)cells.Herein we aim to study the in vivo anti-GBM effectiveness of Icaritin and explore its mechanism.The results of MTT assay,flow cytometry,comet assay and cellular immunofluorescence as-say in vitro showed that ICT inhibited the proliferation of four kinds of GBM cells,U87,U251,U118 and A172,induced early apoptosis(P＜0.001)and late apoptosis(P＜0.05)in U87 cells,induced DNA damage in U87 cells,and blocked the growth of U87 cells at the G0/G1 phase(P＜0.0001)in a concen-tration-time-dependent manner.In vivo subcutaneous tumor transplantation tumor experiments showed that feeding 200 mg/kg(P＜0.01)and 400 mg/kg(P＜0.001)ICT had a significant inhibitory effect on the growth of GBM subcutaneous tumors,and had no significant toxic effects on heart,liver,spleen,lung and kidney tissues.The results of network pharmacological analysis,molecular docking and cellular thermodynamic experiments showed that there were 26 possible target proteins between ICT and GBM,a-mong which the expression of p53 in GBM tissues was significantly(P＜0.001)higher than in normal tis-sues,and the binding energy of ICT and p53 was lower;cellular thermodynamic experiments verified that ICT significantly enriched the level of p53 in the living cells of GBM,which indicated that ICT could tar-get p53.The expression of key proteins in the DNA damage repair and apoptosis-associated FOXO signa-ling pathway was detected by ICT.The results showed that the expression of ATR(P＜0.01),P53(P＜0.001),P21(P＜0.05)and γ-H2AX(P＜0.05)was up-regulated,whereas the expression of Cyc-lin E1(P＜0.01),E2F1(P＜0.05),CDK2(P＜0.01),Rb(P＜0.001),p-Rb(P＜0.0001)and WRN(P＜0.0001)expression were down-regulated.There was no significant change in the expres-sion of FOXO 1 in the FOXO pathway or a significant down-regulation of its phosphorylation level.This study demonstrated that ICT could effectively inhibit the growth of GBM cells in vivo.It targets p53 to regulate the DNA damage repair pathway and FOXO signaling pathway to induce GBM cell cycle arrest and apoptosis.

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.