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 establish an ion chromatography-integrated amperometric detection method for iodide in water.Methods:After the water sample was filtered through a filter membrane, the AS 11-HC anion chromatography column of ion chromatography method was used to separate iodide ions under the conditions of 70 mmol/L sodium hydroxide solution as the eluent, injection volume of 100 μl, column temperature of 30 ℃, and flow rate of 1.0 ml/min. The results were determined by silver working electrode integral amperometric detection method. Under the optimized experimental conditions, methodological evaluations such as method calibration curves, detection limits, quantification limits, precision, and accuracy were conducted.Results:Iodide followed a square correction curve within the concentration range of 0 - 100 μg/L, with a correlation coefficient ( r) > 0.999 9. The detection limit of the method was 0.30 μg/L, and the quantification limit was 1.00 μg/L. The determination results of the national standard substances GBW09113f and GBW09114f for iodine composition analysis in water were within the reference range [(8.4 ± 1.2), (55 ± 6) μg/L]. The recovery rates of low, medium, and high concentration spiked samples with low background values ranged from 91.7% to 97.2%, and the relative standard deviation ranged from 0.40% to 1.60%. Conclusion:This method has the characteristics of simple water sample pretreatment, high sensitivity, and good accuracy, which can meet the determination of trace iodides in bulk water samples for iodine deficiency disorders monitoring.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective To investigate the potential value of a deep learning(DL)model based on non-contrast CT images in predicting contrast medium extravasation in contrast-enhanced CT scans of tumor patients.Methods A total of 298 tumor patients were retrospectively selected,including 90 patients with extravasation and 208 without extravasation,and divided into training set(207 patients),validation set(46 patients),and external test set(45 patients)in a ratio of 7︰1.5︰1.5.U-Net was employed to segment the right common carotid artery/internal jugular vein and right subclavian artery/vein in non-contrast CT images,and ResNet50 was utilized to extract imaging features to construct the DL model,which was subsequently integrated with independent clinical predictors to establish the combined model.The segmentation performance of the DL model was evaluated using Dice similarity coefficient(DSC)and Intersection over Union(IoU),while the area under the curve(AUC),accuracy,sensitivity,and specificity of the model were calculated.Results The DL model demonstrated superior vascular segmentation(DSC 0.81-0.95,IoU 0.79-0.90).The combined model achieved optimal predictive performance,with AUC of 0.961[95%confidence interval(CI)0.924-0.983],0.949(95%CI 0.840-0.992),and 0.891(95%CI 0.762-0.964)in the training,validation,and external test sets,respectively.Its accuracy,sensitivity,and specificity were consistently higher than those of the standalone clinical model.Conclusion The DL model based on non-contrast CT images shows significant potential value in predicting contrast medium extravasation risk in tumor patients,providing an objective and intelligent tool for clinical risk assessment.

Objective:To establish a method for determination of fluoride in water using a fully automated biochemical analyzer (abbreviated as this method).Methods:Based on the parameters of the fully automatic biochemical analyzer, appropriate reagent volumes and reaction time were optimized to determine fluoride ions in water within the standard curve range (0.0 - 2.0 mg/L). The method was validated through evaluation of linear range, detection limit, precision, accuracy, and comparative analysis with the manual measurement results of fluoride reagent spectrophotometric method specified in the "Standard Examination Methods for Drinking Water - Part 5: Inorganic Nonmetallic indices" (GB/T 5750.5-2023).Results:Within the range of 0.0 - 2.0 mg/L fluoride mass concentration, the absolute values of the correlation coefficients were > 0.999 0 and the detection limit of this method was 0.082 mg/L. The relative standard deviation for the determination of fluoride level in water samples with low, medium and high fluoride mass concentrations was 4.03%, 2.16% and 1.68%, respectively. The spiked recovery rates were 98.1%, 99.5%, and 100.2%, respectively. There was no statistically significant difference between the results obtained by this method and manual measurement ( t = 1.07, P = 0.295). Conclusion:This method exhibits high sensitivity, accuracy, and efficiency in detecting fluoride, enabling rapid sample detection through instrumental automation instead of manual operation.

Objective:To establish a method for determination of fluoride in water using a fully automated biochemical analyzer (abbreviated as this method).Methods:Based on the parameters of the fully automatic biochemical analyzer, appropriate reagent volumes and reaction time were optimized to determine fluoride ions in water within the standard curve range (0.0 - 2.0 mg/L). The method was validated through evaluation of linear range, detection limit, precision, accuracy, and comparative analysis with the manual measurement results of fluoride reagent spectrophotometric method specified in the "Standard Examination Methods for Drinking Water - Part 5: Inorganic Nonmetallic indices" (GB/T 5750.5-2023).Results:Within the range of 0.0 - 2.0 mg/L fluoride mass concentration, the absolute values of the correlation coefficients were > 0.999 0 and the detection limit of this method was 0.082 mg/L. The relative standard deviation for the determination of fluoride level in water samples with low, medium and high fluoride mass concentrations was 4.03%, 2.16% and 1.68%, respectively. The spiked recovery rates were 98.1%, 99.5%, and 100.2%, respectively. There was no statistically significant difference between the results obtained by this method and manual measurement ( t = 1.07, P = 0.295). Conclusion:This method exhibits high sensitivity, accuracy, and efficiency in detecting fluoride, enabling rapid sample detection through instrumental automation instead of manual operation.