Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.

Objective:To investigate the application value of dual-energy computer tomo-graphy (CT) multi-parameter imaging in predicting the pathological grade of pancreatic ductal adeno-carcinoma (PDAC).Methods:The retrospective cohort study was conducted. The clinicopatholo-gical data of 147 patients with PDAC who were admitted to The Fifth Affiliated Hospital of Wenzhou Medical University from January 2017 to August 2023 were collected. There were 102 males and 45 females, aged (59±10)years. All patients underwent preoperative dual-energy CT examination and postoperative histopathological examination. The 147 patients were divided into a training set of 103 cases and a test set of 44 cases by stratified random sampling at a ratio of 7∶3. The training set was used to construct the prediction model, and the test set was used to verify the effectiveness of prediction model. Observation indicators: (1) analysis of factors affecting the pathological grade of PDAC patients in the training set; (2) construction and evaluation of the fusion prediction model for pathological grade of PDAC. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. The performance of the model was evaluated by receiver operating characteristic (ROC) curve, and the area under the curve (AUC), accuracy, sensitivity and specificity were calculated. The Delong test was used to analyze the effec-tiveness of model. The calibration curve and decision curve of Hosmer-Lemeshow test were used to evaluate the consistency and clinical application value of the nomogram, respectively. Results:(1) Analysis of factors affecting the pathological grade of PDAC patients in the training set. Results of multivariate analysis showed that tumor cystic necrosis, vascular invasion, standardized iodine concentration (NIC) in venous phase, effective atomic number (Zeff) in venous phase, and energy spectrum curve slope (λ HU) in venous phase were all independent factors affecting the pathological grade of PDAC patients in the training set ( odds ratio=4.326, 3.887, 4.155, 5.389, 3.164, 95% confidence interval as 1.167-16.033, 1.111-13.592, 1.707-10.113, 1.284-22.613, 1.247-8.028, P<0.05). (2) Construction and evaluation of the fusion prediction model for pathological grade of PDAC. Accor-ding to the results of multivariate analysis, tumor cystic necrosis, vascular invasion, NIC in venous phase, Zeff in venous phase and λ HU in venous phase were all included to construct the clinical-imaging fusion prediction nomogram model. The AUC, accuracy, sensitivity and specificity of the fusion prediction model in the training set were 0.938 (95% confidence interval as 0.896-0.981), 87.38%, 89.74% and 85.94%, respectively. The above indicators of the fusion prediction model in the test set were 0.893 (95% confidence interval as 0.802-0.985), 84.09%, 82.35% and 85.19%, respectively. Results of Delong test showed that there was no significant difference in AUC between the training set and the test set ( Z=0.343, P>0.05). Results of Hosmer-Lemeshow test showed that the fusion prediction model had a good fit in the training set and the test set ( χ2=3.042, 7.545, P>0.05). Results of calibration curve showed that the predictive ability of the fusion prediction model was good. Conclusions:Multiple parameters in venous phase of the dual-energy CT can be used as imaging markers for preoperative evaluation of the pathological grade of patients with PDAC. Establishing a clinical-imaging fusion prediction model can effectively predict the pathological grade of PDAC.

Objective:To calculate the relative biological effectiveness (RBE) value of the released low-energy electrons in gadolinium neutron capture therapy ( 157GdNCT) based on microdosimetry. Methods:The Monte Carlo (MC) code Geant4-DNA package was used to simulate the energy deposition distribution and microdosimetry parameters of low-energy electrons released during gadolinium neutron capture treatment in different sensitive target volumes and physical models on track structures. On this basis, RBE value was obtained based on the microdosimetry kinetic model (MKM).Results:The low-energy electron RBE value was highly variable in different sensitive target volumes and decreases with increasing sensitive target volumes. With 6-nm-diameter sensitive target as reference, RBE value was 1.77 for 6-nm diameter, 1.53 for 10 nm diameter with percentage difference 13%, and 1.40 for 15-nm diameter with percentage difference of 21%, respectively. The effect of different Geant4-DNA physical models on the RBE of low-energy electrons was small. Using the RBE value of 1.53 for physical model option2 as reference, the RBE values of option6 and option7 were 1.49 and 1.52, respectively, with the percentage differences of 2.6% and 0.6%, respectively.Conclusions:The RBE values of low energy electrons released by 157GdNCT in different sensitive target volumes and physical models were calculated by MKM to be 1.40-1.77.

Brain metastasis is a common and serious complication of breast cancer, which is commonly associated with poor survival and prognosis. In particular, the treatment of brain metastasis from triple-negative breast cancer (BM-TNBC) has to face the distinct therapeutic challenges from tumor heterogeneity, circulating tumor cells (CTCs), blood-brain barrier (BBB) and blood-tumor barrier (BTB), which is in unmet clinical needs. Herein, combining with the advantages of synthetic and natural targeting moieties, we develop a "Y-shaped" peptide pVAP-decorated platelet-hybrid liposome drug delivery system to address the all-stage targeted drug delivery for the whole progression of BM-TNBC. Inherited from the activated platelet, the hybrid liposomes still retain the native affinity toward CTCs. Further, the peptide-mediated targeting to breast cancer cells and transport across BBB/BTB are demonstrated in vitro and in vivo. The resultant delivery platform significantly improves the drug accumulation both in orthotopic breast tumors and brain metastatic lesions, and eventually exhibits an outperformance in the inhibition of BM-TNBC compared with the free drug. Overall, this work provides a promising prospect for the comprehensive treatment of BM-TNBC, which could be generalized to other cell types or used in imaging platforms in the future.

AL3810, a molecular dual inhibitor of the vascular endothelial growth factor receptor (VEGFR) and fibroblast growth factor receptor (FGFR), has earned the permission of phase II clinical trial for tumor treatment by China FDA. As a reversible ATP-competitive inhibitor, AL3810 targets ATP-binding site on intracellular region of VEGFR and FGFR, whereas, AL3810 lacking interplay with extracellular region of receptors rendered deficient blood-brain tumor barrier (BBTB) recognition, poor brain penetration and unsatisfactory anti-glioma efficacy. Integrin

Malignant glioma is usually accompanied by vigorous angiogenesis to provide essential nutrients. An effective glioma targeting moiety should include excellent tumor-cell homing ability as well as good neovasculature-targeting efficiency, and should be highly resistant to enzyme degradation in the bloodstream. The phage display-selected heptapeptide, the glioma-initiating cell peptide (GICP), was previously reported as a ligand for the VAV3 protein (a Rho-GTPase guanine nucleotide exchange factor), which is mainly expressed on glioma cells; the stabilized heptapeptide A7R has been shown to be the ligand of both vascular endothelial growth factor receptor 2 (VEGFR2) and neuropilin-1 (NRP-1), and has demonstrated good neovasculature-targeting ability. By linking A7R and GICP, a multi-receptor targeting molecule was obtained. The stability of these three peptides was evaluated and their targeting efficiency on tumor-related cells and models was compared. The ability of these peptides to cross the blood--tumor barrier (BTB) was also determined. The results indicate that the coupled Y-shaped peptide A7R-GICP exhibited improved tumor and neovasculature targeting ability and had higher efficiency in crossing the BTB than either individual peptide.

The dense extracellular matrix and high interstitial fluid pressure of tumor tissues prevent the ability of anti-tumor agents to penetrate deep into the tumor parenchyma for treatment effects. C-end rule (CendR) peptides can enhance the permeability of tumor blood vessels and tumor tissues binding to neuropilin-1 (NRP-1), thus aiding in drug delivery. In this study, we selected one of the CendR peptides (sequence RGERPPR) as the parent l-peptide and substituted d-amino acids for the l-amino acids to synthesize its inverso peptide (RGERPPR). We investigated the NRP-1 binding activity and tumor-penetrating ability of (RGERPPR). We found that the binding affinity of (RGERPPR) with NRP-1 and the cellular uptake was significantly higher than that of RGERPPR. Evans Blue tests revealed that (RGERPPR) exhibited improved tumor-penetrating ability in C6, U87 and A549 tumor-bearing nude mice. Using nude mice bearing A549 xenograft tumors as a model, we found that the rate of tumor growth in the group co-administered with (RGERPPR) and gemcitabine (Gem) was significantly lower than the gemcitabine-treated group with a tumor suppression rate (TSR%) of 55.4%. Together, our results demonstrate that (RGERPPR) is a potential tumor-penetrating peptide.

The blood-brain barrier (BBB) and the blood-brain tumor barrier (BBTB) prevent drug and nano-drug delivery systems from entering the brain. However, ligand-mediated nano-drug delivery systems have significantly enhanced the therapeutic treatment of glioma. In this study we investigated the mechanism especially the integrity of liposomes and lipid disks while traversing the BBB and BBTB both and . Fluorophores (DiO, DiI and DiD) were loaded into liposomes and lipid disks to form Förster resonance energy transfer (FRET) nano-drug delivery systems. Using brain capillary endothelial cells as a BBB model, we show that liposomes and disks are present in the cytoplasm as their intact forms and traverse the BBB with a ratio of 0.68‰ and 1.67‰, respectively. Using human umbilical vein endothelial cells as BBTB model, liposomes and disks remained intact and traversed the BBTB with a ratio of 2.31‰ and 8.32‰ at 3 h. imaging and immunohistochemical results revealed that liposomes and disks could traverse the BBB and BBTB as intact forms. In conclusion, these observations explain in part the mechanism by which nano-drug delivery systems increase the therapeutic treatment of glioma.

Objective To investigate the protective effect of Shen Mai injection in the treatment of lung injury induced by radiotherapy in patients with advanced breast cancer. Methods 80 cases with lung injury induced by radiotherapy in patients with advanced breast cancer from our hospital from 2015.1 to 2016.5 were selected and randomly divided into 2 groups, 40 cases in control group were given routine treatment, 40 cases in the experimental group were treated based on the control group treated with Shenmai injection treatment, and 2 groups were treated for 4 weeks. The pulmonary function, T lymphocyte subsets, clinical symptoms, quality of life scores and clinical efficacy were observed before and after treatment in two groups. The results were analyzed by statistical software SPSS 19.0. Results Compared with before treatment, levels of FVC, FEV1 and TLC in 2 groups after treatment elevated, scores of respiratory symptoms, activities, and daily life effect decreased, levels of serum CD3+, CD4+, CD4+/CD8+elevated, levels of CD8+ decreased, and compared with the control group, levels of FVC, FEV1, TLC, CD3+, CD4+, CD4+/CD8+ in the experimental group were higher, scores of respiratory symptoms, activities, and daily life effect were lower, clinical effect rate was higher, the differences were statistically significant (P<0.05). Conclusion Shen Mai injection can effectively improve lung function and immunity, improve respiratory symptoms and improve the quality of life in patients with advanced breast cancer radiotherapy for lung injury, and has better clinical efficacy.

Lack of mucoadhesive properties is the major drawback to poloxamer 407 (F127)-basedhydrogels for mucosal administration. The objective of the present study was to construct a novel mucoadhesive and thermosensitivehydrogel drug delivery system based on an amino-functionalized poloxamer for vaginal administration. First, amino-functionalized poloxamer 407 (F127-NH) was synthesized and characterized with respect to its micellization behavior and interaction with mucin. Then using acetate gossypol (AG) as model drug, AG-loaded F127-NH-basedhydrogels (NFGs) were evaluated with respect to rheology, drug release,vaginal mucosal adhesion,intravaginal retention and local irritation after vaginal administration to healthy female mice. The results show that F127-NHis capable of forming a thermosensitivehydrogel with sustained drug release properties. An interaction between positively charged F127-NHand negatively charged mucin was revealed by changes in the particle size and zeta potential of mucin particles as well as an increase in the complex modulus of NFG caused by mucin.andfluorescence imaging and quantitative analysis of the amount of AG remaining in mouse vaginal lavage all demonstrated greater intravaginal retention of NFG than that of an unmodified F127-basedhydrogel. In conclusion, amino group functionalization confers valuable mucoadhesive properties on poloxamer 407.