Objective This research focused on the synthesis and characterization of a dual-modal nanoprobe (Gal-MnO₂/CDDP@PDA-Cy5.5) for fluorescence and magnetic resonance imaging of hepatocellular carcinoma. The study further explored the probe's specific targeting efficacy against ASGPR-expressing Huh-7 cells and evaluated its performance in magnetic resonance imaging through in vitro experiments. Methods Potassium permanganate solution was added to silica and etched with anhydrous sodium carbonate. The resulting solution was conjugated with polydopamine (DA), galactosamine (Gal), and fluorescent Cy5.5 to obtain Gal-MnO₂@PDA-Cy5.5. Cisplatin (CDDP) was introduced into the solution, followed by overnight incubation at room temperature and subsequent centrifugation to remove unreacted CDDP. Cytotoxicity and cellular uptake were assessed using the CCK-8 assay and flow cytometry, respectively. The relaxation rate was measured by Niumag small-scale NMR spectroscopy, and the enhancement degree was evaluated using magnetic resonance imaging. Results The prepared nanoprobes, as observed by transmission electron microscopy, exhibited uniform size and granular morphology with a particle size of 185.0±6.3 nm. The hydrodynamic diameter was measured to be 185.1±16.4 nm, with a zeta potential of 22.5±0.3 mV and a relaxivity of 14.589 (mmol/L)^-1s^-1. The magnetic resonance imaging signal intensity progressively enhanced with increasing nanoprobe concentration. Cytotoxicity assays demonstrated minimal toxicity of the nanoprobes. Conclusion This study successfully synthesized a dual-modal nanoprobe targeting ASGPR, designated as Gal-MnO₂/CDDP@PDA-Cy5.5, which demonstrates specific binding to target cells in vitro. Validation results confirmed that the synthesized nanoprobe exhibits excellent stability and biosafety, along with specific targeting capability toward Huh-7 cells. Furthermore, it possesses imaging functionality that enhances T1 contrast in magnetic resonance imaging.

Objective Based on head and neck CT angiography imaging data, this study aimed to apply computational fluid dynamics technology to analyze the influence of atherosclerosis (AS) on the hemodynamics of intracranial aneurysms and explore its guiding value for clinical evaluation. Methods A retrospective analysis was conducted on the clinical data of 178 patients with intracranial aneurysms admitted to The Second Affiliated Hospital of Bengbu Medical University from June 2023 to January 2025. Patients were divided into four groups based on the presence of AS and the occurrence of subarachnoid hemorrhage (SAH): intracranial aneurysm (IA) group, AS+IA group, SAH group, and AS+SAH group. All patients underwent head and neck CT angiography examinations. Three-dimensional vascular models were reconstructed, and computational fluid dynamics simulations were performed to calculate hemodynamic parameters including wall shear stress (WSS), time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time, and blood flow velocity. Hemodynamic differences between AS-complicated aneurysms and simple aneurysms were compared, and their correlation with SAH was analyzed. Results No significant difference in aneurysm size was observed between groups (P>0.05). The aspect ratio (AR) and size ratio (SR) were significantly higher in the AS group than in the non-AS group (P<0.05), and SAH patients also showed significantly higher AR and SR values than non-SAH patients (P<0.05). Hemodynamic analysis revealed significantly increased blood flow velocity and OSI values, along with decreased TAWSS and normalized WSS in the AS group (P<0.05). Similar hemodynamic alterations were observed in SAH patients. Conclusion Through numerical simulation and imaging analysis, AS significantly altered the hemodynamic characteristics of intracranial aneurysms, manifested as increased blood flow velocity, elevated OSI, and reduced WSS. These changes were closely associated with an increased risk of aneurysm rupture. Computational fluid dynamics technology can provide important reference for clinical evaluation of aneurysm stability and SAH risk prediction.

Objective To develop a preoperative model combining MRI features and clinical variables for predicting high Ki-67 expression in hepatocellular carcinoma (HCC) and to assess its prognostic value. Methods A total of 344 patients with solitary HCC who underwent hepatectomy at Zhongshan Hospital, Fudan University from January to December 2020 were retrospectively enrolled. Among them, 191 patients showed high Ki-67 expression (>25%) and 153 patients had low Ki-67 expression (≤25%) based on postoperative pathological findings. Preoperative MRI features, clinical variables, and pathological data were collected, and each HCC lesion was assigned a LI-RADS. The relationship between MRI, clinical, and pathological features and high Ki-67 expression was compared. The model's performance was evaluated using ROC curves, and the recurrence-free survival (RFS) was compared using the Kaplan-Meier method. Results Multivariable logistic regression identified age and lower alpha-fetoprotein (AFP) as protective factors, whereas high Edmondson-Steiner grade, corona enhancement and LI-RADS were independent risk factors for high Ki-67 (P<0.05). A composite score incorporating these five variables yielded an AUC of 0.747 (sensitivity 68.1%, specificity 71.9%), outperforming any single predictor (P<0.05). Overall RFS did not differ between high and low Ki-67 groups (P>0.05). Among patients with high Ki-67, those with microvascular invasion had significantly shorter RFS than those without microvascular invasion (P<0.05); no such difference was observed in the low Ki-67 subgroup (P>0.05). Conclusion Preoperative MRI features (corona enhancement, LI-RADS) combined with clinical variables (age, AFP, Edmondson-Steiner grade) reliably predict high Ki-67 expression in HCC and provide imaging evidence for prognostic stratification.

Objective To develop an efficient and robust machine learning model for predicting neonatal acute bilirubin encephalopathy based on T1WI radiomics features using six algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest, K-Nearest Neighbors, Naive Bayes, and Multilayer Perceptron. Methods A retrospective analysis was conducted involving 54 neonates clinically diagnosed with ABE admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2019 to August 2023, with a mean gestational age of 37⁺² to 40⁺¹(38.03±2.57) weeks. Additionally, 47 healthy neonates were selected as controls, with a mean gestational age of 37⁺⁴ to 40⁺⁵ (38.05±2.61) weeks. High-throughput radiomics features were extracted from T1WI images using Python and Pyradiomics software. Feature selection was performed using Pearson correlation coefficients and least absolute shrinkage and selection operator (LASSO) regression. Subsequently, machine learning models were constructed based on the selected radiomics features, and the classification performance of each algorithm was compared. Results After feature extraction and selection, eight representative radiomics features were identified to construct the ABE radiomics prediction model. Among the algorithms tested, SVM achieved the highest accuracy of 0.739, surpassing the performance of the other five methods. Conclusion Machine learning models based on MRI radiomics show significant clinical potential for diagnosing neonatal ABE. Particularly, the SVM algorithm demonstrates superior classification performance and model stability, offering a novel approach to early ABE diagnosis with promising clinical application prospect.

Objective To evaluate left ventricular function in obese patients using left ventricular pressure-strain loops (LVPSL), measure their epicardial adipose tissue (EAT) thickness, and explore the value of EAT thickness in predicting LV dysfunction in obese patients. Methods A total of 116 obese patients who visited Shanxi Bethune Hospital from December 2021 to August 2023 were selected for echocardiography. According to the median thickness of EAT, obese patients were divided into EAT <4.8 mm group and EAT≥4.8 mm group. The parameters of conventional echocardiography, global longitudinal strain (GLS), and myocardial work (MW) parameters obtained through LVPSL analysis were compared between the two groups, and the correlation between left ventricular MW parameters and EAT was analyzed. Results Compared with the EAT <4.8 mm group, the EAT≥4.8 mm group had a decrease in left ventricular GLS, GCW, GWI, GWE, and an increase in GWW, with statistically significant differences (P<0.05). Pearson correlation analysis showed that GLS, GCW, and GWI were negatively correlated with EAT in obese patients (r=-0.286, -0.313, -0.367, P<0.05), while GWW and GWE were not significantly correlated with EAT (P>0.05). Conclusion Non-invasive LVPSL can early and accurately assess the left ventricular myocardial function of obese patients, which has important clinical value. And the thicker the eat thickness in obese patients, the more serious the left ventricular myocardial function damage. EAT can be regarded as a useful indicator for early identification of impaired left ventricular myocardial function in obese patients.

Objective To evaluate the feasibility, efficacy, and clinical value of the prolonged intermittent Pringle maneuver (LIPM) in technically challenging laparoscopic hepatectomy. Methods A retrospective analysis was conducted on 128 patients who underwent laparoscopic hepatectomy in the Department of Hepatobiliary Surgery from August 1st, 2018 to December 31st, 2022. According to the intermittent Pringle maneuver (IPM) strategy used, patients were divided into two groups: the conventional IPM group (n=65) and the prolonged IPM (LIPM) group (n=63). The following perioperative and short-term outcome parameters were compared: operative time, number and total duration of Pringle maneuvers, intraoperative blood loss, frequency of instrument switching, hospitalization costs, perioperative transfusion rate, postoperative complications, liver function recovery, and the 2-year recurrence rate. Results Compared with the conventional IPM procedure, the LIPM technique demonstrated significant advantages in multiple perioperative metrics. These included shorter liver transection time, faster transection speed, reduced reliance on the Pringle maneuver, decreased intraoperative blood loss, fewer instrument changes, lower total hospitalization costs, and a reduced perioperative transfusion rate (P<0.05). ostoperative recovery was comparable between the two groups, with no significant differences observed in the length of hospital stay, incidence of complications, or the 2-year recurrence rate. Peak values for liver function markers (ALT, AST, TBIL, DBIL) and trough values for hematological parameters (WBC, HGB, PLT) also did not differ significantly (P>0.05). Conclusion In difficult laparoscopic hepatectomy, the prolonged intermittent Pringle maneuver demonstrates safety and feasibility. This technique effectively improves key surgical efficiency metrics-including the number of Pringle maneuvers, transection time, blood loss, and instrument switching-while simultaneously reducing perioperative transfusion rates and total hospitalization costs, with no increase in postoperative complications.

Objective To systematically analyze the research dynamics and core progress of electroencephalography (EEG) applied to acupuncture in the past 20 years by using bibliometric methods, to reveal the development trend of the discipline, the international cooperation mode, and the potential of clinical translation. Methods Based on the Web of Science core ensemble database, 181 documents were included and visualized using CiteSpace software, including annual publication volume, national/regional collaborative networks, journal co-citation, keyword co-occurrence and clustering analyses, and combined with emergent words to detect the stage-by-stage evolution of research hotspots. Results The number of articles published each year has shown a steady upward trend, with China (excluding Taiwan Province of China) publishing the most articles (n=97), and the highest collaborative centrality in the UK (0.59). The high-frequency cited journals were concentrated in the fields of neuroimaging and alternative medicine. Keyword analysis showed that research hotspots gradually shifted from pain and electroacupuncture to brain network regulation, randomized controlled trials and multimodal imaging techniques, and clustering themes focused on acupuncture neuromodulation mechanisms and autonomic nervous system regulation. The emergent words indicate that brain modeling and functional connectivity have become emerging directions, and EEG technology confirms that acupuncture enhances alpha rhythm synchronization and improves brain network topology, providing electrophysiological evidence for clinical efficacy such as stroke rehabilitation. Conclusion The cross-study of EEG and acupuncture has shifted from single-mechanism exploration to complex system analysis, and in the future, it is necessary to deeply integrate the multimodal technologies such as EEG-fMRI, artificial intelligence algorithms, and clinical data, to construct a dynamic assessment system of acupuncture efficacy, and to promote the optimization of personalized treatment plans and the translation of precision medicine.

Objective To explore the risk factors influencing the prognosis of breast cancer and establish a predictive model by analyzing the ultrasound, contrast-enhanced ultrasound, clinical and pathological data of breast cancer patients. Methods A cohort of 365 breast cancer patients diagnosed by needle biopsy and postoperative pathological examination at the First Affiliated Hospital of Xinjiang Medical University from January 2014 to January 2022 was stratified and analyzed according to prognostic outcomes. The cohort comprised 302 patients without distant metastasis who survived, and 63 patients who either developed distant metastasis or succumbed to the disease.The follow-up duration ranged from a minimum of 36 months to a maximum of 96 months, with an average follow-up time of 48.2 months. Univariate and multivariate Cox regression analyses were conducted to evaluate the impact of ultrasound findings, contrast-enhanced ultrasound results, and clinical risk factors on the prognosis of breast cancer patients. According to the ratio of 7:3, 365 breast cancer cases were randomly divided into a training set (n=256) and a validation set (n=109). The model was visualized and plotted using a line graph and survival curve with the R software; the ROC curve was drawn and the area under the curve (AUC) was calculated; the accuracy of the prediction was evaluated using the calibration curve; and the clinical benefit of the model was quantified using the decision curve analysis. Results The patient had a family history (P<0.001), the maximum diameter of the lesion was≥3 cm (P<0.001), axillary lymph node metastasis (P<0.001), contrast-enhanced ultrasound had peripheral convergence phenomenon (P<0.001), and the enhancement amplitude (P=0.036) , the area under the time-intensity curve after enhancement (P=0.005), chemotherapy status (P<0.001), and triple-negative molecular subtype (P<0.001) between the two groups of patients were statistically significant. Chemotherapy status (P=0.047), the maximum diameter of the lesion≥3 cm (P=0.002), contrast-enhanced ultrasound with peripheral convergence phenomenon (P=0.002), and the tumor molecular subtype being triple-negative (P=0.009) were independent risk factors affecting distant metastasis or death events in breast cancer patients. Based on the screened independent risk factors, a Nomogram was established. The predicted AUCs of distant metastasis-free survival rates in the model training set at 3 and 5 years were 0.80 and 0.84, respectively, and those in the validation set at 3- and 5- years were 0.77 and 0.80, respectively. Conclusion A prognostic model for predicting 3-year and 5-year survival rate of breast cancer patients without distant metastasis was established based on ultrasound, contrast-enhanced ultrasound, clinical and pathological data. The model shows good results in predicting the prognosis of breast cancer patients, and it could have a positive impact on the decision making of clinicians.

Objective To explore the application value of multi-slice spiral CT (MSCT) in the diagnosis and risk classification of gastrointestinal stromal tumor (GIST), and analyze image features of GISTs at different risk levels. Methods From January 2019 to December 2024, 104 patients with GIST admitted to China Rongtong Medical and Health Group Co., Ltd. Anqing 116 Hospital were selected. All of the patients underwent MSCT plain scan and multi-phase enhanced scan. With surgical and pathological results as the gold standard, cases at extremely low risk and low risk were included in the low-risk group, while those at intermediate risk and high risk were included in the high-risk group. The differences in MSCT features between the two groups, and the correlation between MSCT features and risk level were analyzed. The ROC curves were used to evaluate the predictive efficacy of MSCT features for high-risk GIST, and the predictive model was clinically validated. Results Multivariate logistic regression analysis identified tumor diameter, lesion morphology, growth pattern, and ulceration as independent influencing factors for risk stratification (P<0.05).A logistic regression model was constructed based on MSCT features, and ROC curve analysis results showed that the area under the curve of the model for evaluating the risk level of GIST was 0.951 (P<0.001). The sensitivity and specificity were 94.74% and 84.85%, indicating higher diagnostic efficacy. TheHosmer Lemeshow goodness of fit test was used to evaluate the calibration of the risk grading diagnostic prediction model for GIST patients, and the results showed that the fitting level of the GIST patient risk grading diagnostic prediction model was good (P=0.675, adjusted R²=0.754). Clinical verification shows that the sensitivity of this prediction model was 100%, the specificity was 88.89%, and the accuracy was 92.31%. The consistency between the prediction model and the actual clinical situation was relatively high (Kappa=0.831). Conclusion MSCT can effectively help evaluate the risk level of GIST based on features such as tumor diameter, shape, growth pattern, and ulcer. Among them, tumor diameter >5 cm, irregular shape, transmural growth, and ulcer are independent imaging indicators for predicting high-risk GIST, which provides an important basis for preoperative risk assessment in clinical practice.