Structural characteristics of a low molecular weight velvet antler protein and the anti-tumor activity on S180 tumor-bearing mice.

Velvet antler is a traditional Chinese medicine with various pharmacological values, which is an important raw material for traditional Chinese medicinal wine. Nevertheless, the chemical compositions and bioactivities of velvet antler residue used for making medicinal wine are rarely reported, leading to a waste of resources. In this study, a velvet antler protein (VA-pro) was extracted from velvet antler residue by simulating the gastrointestinal digestion, and its composition, structural characteristics and in vivo anti-tumor activities were determined and investigated. VA-pro possessed high purity with a relatively low molecular weight as 22.589 kDa under HPLC, one- and two-dimensional electrophoresis, and it contained high contents of Pro, Gly, Glu and Ala. Besides, the secondary structure of VA-pro was dominated by ß-turn and ß-sheet, and VA-pro possessed similar protein sequence, isoelectric point and amino acid compositions to hypothetical protein G4228_020061. The in vivo results substantiated that VA-pro could improve the body weights and immune organ indices, increase the expressions of sera cytokines and regulate the distributions of T and B lymphocytes subsets in peripheral blood of S180 tumor-bearing mice. Furthermore, VA-pro could effectively inhibit solid S180 tumors growth by inducing S phase cell cycle arrest mediated through mitochondria. To summarize, our study provided theoretical support that VA-pro had the potential to be used as an immunopotentiator in immunocompromised or cancer-bearing hosts.

A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model.

OBJECTIVE: To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). MATERIALS AND METHODS: This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. RESULTS: The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842-0.993) and 0.894 (95% CI: 0.766-0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p > 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. CONCLUSION: Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.

Extracellular matrix modulating enzyme functionalized biomimetic Au nanoplatform-mediated enhanced tumor penetration and synergistic antitumor therapy for pancreatic cancer.

BACKGROUND: Excessive extracellular matrix (ECM) deposition in pancreatic ductal adenocarcinoma (PDAC) severely limits therapeutic drug penetration into tumors and is associated with poor prognosis. Collagen is the most abundant matrix protein in the tumor ECM, which is the main obstacle that severely hinders the diffusion of chemotherapeutic drugs or nanomedicines. METHODS: We designed a collagenase-functionalized biomimetic drug-loaded Au nanoplatform that combined ECM degradation, active targeting, immune evasion, near-infrared (NIR) light-triggered drug release, and synergistic antitumor therapy and diagnosis into one nanoplatform. PDAC tumor cell membranes were extracted and coated onto doxorubicin (Dox)-loaded Au nanocages, and then collagenase was added to functionalize the cell membrane through lipid insertion. We evaluated the physicochemical properties, in vitro and in vivo targeting, penetration and therapeutic efficacy of the nanoplatform. RESULTS: Upon intravenous injection, this nanoplatform efficiently targeted the tumor through the homologous targeting properties of the coated cell membrane. During penetration into the tumor tissue, the dense ECM in the PDAC tissues was gradually degraded by collagenase, leading to a looser ECM structure and deep penetration within the tumor parenchyma. Under NIR irradiation, both photothermal and photodynamic effects were produced and the encapsulated chemotherapeutic drugs were released effectively, exerting a strong synergistic antitumor effect. Moreover, this nanoplatform has X-ray attenuation properties that could serve to guide and monitor treatment by CT imaging. CONCLUSION: This work presented a unique and facile yet effective strategy to modulate ECM components in PDAC, enhance tumor penetration and tumor-killing effects and provide therapeutic guidance and monitoring.

Ultra-thin layered double hydroxide-mediated photothermal therapy combine with asynchronous blockade of PD-L1 and NR2F6 inhibit hepatocellular carcinoma.

BACKGROUND: The efficacy of immune checkpoint blockade (ICB), in the treatment of hepatocellular carcinoma (HCC), is limited due to low levels of tumor-infiltrating T lymphocytes and deficient checkpoint blockade in this immunologically "cool" tumor. Thus, combination approaches are needed to increase the response rates of ICB and induce synergistic antitumor immunity. METHODS: Herein, we designed a pH-sensitive multifunctional nanoplatform based on layered double hydroxides (LDHs) loaded with siRNA to block the intracellular immune checkpoint NR2F6, together with the asynchronous blockade surface receptor PD-L1 to induce strong synergistic antitumor immunity. Moreover, photothermal therapy (PTT) generated by LDHs after laser irradiation modified an immunologically "cold" microenvironment to potentiate Nr2f6-siRNA and anti-PD-L1 immunotherapy. Flow cytometry was performed to assess the immune responses initiated by the multifunctional nanoplatform. RESULTS: Under the slightly acidic tumor extracellular environment, PEG detached and the re-exposed positively charged LDHs enhanced tumor accumulation and cell uptake. The accumulated siRNA suppressed the signal of dual protumor activity in both immune and H22 tumor cells by silencing the NR2F6 gene, which further reduced the tumor burden and enhanced systemic antitumor immunity. The responses include enhanced tumor infiltration by CD4+ helper T cells, CD8+ cytotoxic T cells, and mature dendritic cells; the significantly decreased level of immune suppressed regulator T cells. The therapeutic responses were also attributed to the production of IL-2, IFN-γ, and TNF-α. The prepared nanoparticles also exhibited potential magnetic resonance imaging (MRI) ability, which could serve to guide synergistic immunotherapy treatment. CONCLUSIONS: In summary, the three combinations of PTT, NR2F6 gene ablation and anti-PD-L1 can promote a synergistic immune response to inhibit the progression of primary HCC tumors and prevent metastasis. This study can be considered a proof-of-concept for the targeting of surface and intracellular immune checkpoints to supplement the existing HCC immunotherapy treatments.

Self-amplification of oxidative stress with tumour microenvironment-activatable iron-doped nanoplatform for targeting hepatocellular carcinoma synergistic cascade therapy and diagnosis.

BACKGROUND: Hepatocellular carcinoma is insensitive to many chemotherapeutic agents. Ferroptosis is a form of programmed cell death with a Fenton reaction mechanism. It converts endogenous hydrogen peroxide into highly toxic hydroxyl radicals, which inhibit hepatocellular carcinoma progression. METHODS: The morphology, elemental composition, and tumour microenvironment responses of various organic/inorganic nanoplatforms were characterised by different analytical methods. Their in vivo and in vitro tumour-targeting efficacy and imaging capability were analysed by magnetic resonance imaging. Confocal microscopy, flow cytometry, and western blotting were used to investigate the therapeutic efficacy and mechanisms of complementary ferroptosis/apoptosis mediated by the nanoplatforms. RESULTS: The nanoplatform consisted of a silica shell doped with iron and disulphide bonds and an etched core loaded with doxorubicin that generates hydrogen peroxide in situ and enhances ferroptosis. It relied upon transferrin for targeted drug delivery and could be activated by the tumour microenvironment. Glutathione-responsive biodegradability could operate synergistically with the therapeutic interaction between doxorubicin and iron and induce tumour cell death through complementary ferroptosis and apoptosis. The nanoplatform also has a superparamagnetic framework that could serve to guide and monitor treatment under T2-weighted magnetic resonance imaging. CONCLUSION: This rationally designed nanoplatform is expected to integrate cancer diagnosis, treatment, and monitoring and provide a novel clinical antitumour therapeutic strategy.

Clinical and CT Quantitative Features for Predicting Liver Metastases in Patients with Pancreatic Neuroendocrine Tumors: A Study with Prospective/External Validation.

RATIONALE AND OBJECTIVES: We aimed to evaluate clinical characteristics and quantitative CT imaging features for the prediction of liver metastases (LMs) in patients with pancreatic neuroendocrine tumors (PNETs). METHODS: Patients diagnosed with pathologically confirmed PNETs were included, 133 patients were in the training group, 22 patients in the prospective internal validation group, and 28 patients in the external validation group. Clinical information and quantitative features were collected. The independent variables for predicting LMs were confirmed through the implementation of univariate and multivariate logistic analyses. The diagnostic performance was evaluated by conducting receiver operating characteristic curves for predicting LMs in the training and validation groups. RESULTS: PNETs with LMs demonstrated significantly larger diameter and lower arterial/portal tumor-parenchymal enhancement ratio, arterial/portal absolute enhancement value (AAE/PAE value) (p < 0.05). After multivariate analyses, A high level of tumor marker (odds ratio (OR): 5.32; 95% CI, 1.54-18.35), maximum diameter larger than 24.6 mm (OR: 7.46; 95% CI, 1.70-32.72), and AAE value ≤ 51 HU (OR: 4.99; 95% CI, 0.93-26.95) were independent positive predictors of LMs in patients with PNETs, with area under curve (AUC) of 0.852 (95%CI, 0.781-0.907). The AUCs for prospective internal and external validation groups were 0.883 (95% CI, 0.686-0.977) and 0.789 (95% CI, 0.602-0.916), respectively. CONCLUSION: Tumor marker, maximum diameter and absolute enhancement value in arterial phase were independent predictors with good predictive performance for the prediction of LMs in patients with PNETs. Combining clinical and quantitative features may facilitate the attainment of good predictive precision in predicting LMs.

Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis.

Background: Xanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making. Methods: We investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model. Results: Based on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC. Conclusions: The diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.

A radiologic diagnostic scoring model based on CT features for differentiating gastric schwannoma from gastric gastrointestinal stromal tumors.

We aimed to further explore the CT features of gastric schwannoma (GS), propose and validate a convenient diagnostic scoring system to distinguish GS from gastric gastrointestinal stromal tumors (GISTs) preoperatively. 170 patients with submucosal tumors pathologically confirmed (GS n=35; gastric GISTs n=135) from Hospital 1 were analyzed retrospectively as the training cohort, and 72 patients (GS=11; gastric GISTs=61) from Hospital 2 were enrolled as the validation cohort. We searched for significant CT imaging characteristics and constructed the scoring system via binary logistic regression and converted regression coefficients to weighted scores. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. For convenient assessment, the system was further divided into four score ranges and their diagnostic probability of GS was calculated respectively. Four CT imaging characteristics were ultimately enrolled in this scoring system, including transverse position (2 points), location (5 points), perilesional lymph nodes (6 points) and pattern of enhancement (2 points). The AUC of the scoring model in the training cohort were 0.873 (95% CI, 0.816-0.929) and the cutoff point was 6 points. In the validation cohort, the AUC was 0.898 (95% CI, 0.804-0.957) and the cutoff value was 5 points. Four score ranges were as follows: 0-3 points for very low probability of GS, 4-7 points for low probability; 8-9 points for middle probability; 10-15 points for very high probability. A convenient scoring model to preoperatively discriminate GS from gastric GISTs was finally proposed.

Radiographic Features and Clinical Factor for Preoperative Prediction in the Bulging Duodenal Papilla With Malignancy.

BACKGROUND: To investigate characteristic clinical and imaging features and establish a scoring system for preoperative prediction of malignancy in the bulging duodenal papilla. METHODS: A total of 147 patients with bulging duodenal papilla (Benign enlargement n = 67; malignant enlargement n = 80) from our hospital between 2010 and 2020 were retrospectively analyzed. We investigated meaningful clinical and CT imaging features and established the score model through logistic regression and weighted. The calibration test, the ROC, AUC, and cut-off points were performed in score model. The model was also divided into three score ranges for convenient clinical evaluation. RESULTS: Three clinical and CT imaging features were finally included in the score model including direct bilirubin (DBil) increase >7 umol/L (3 points), pancreatic duct (PD) dilation >5 mm (2 points), and irregular shape (2 points). The AUCs of the primary predictive model and score model were 0.896 (95% CI, 0.835-0.940) and 0.896 (95% CI, 0.835-0.940), respectively. This scoring system presented with a sensitivity of 78.8% and a specificity of 88.1% when using 2.5 points as cutoff value. Three score ranges were also proposed for convenient clinical use as follows: 0-2 points; 3-4 points; 5-7 points. The number of patients with malignant duodenal papillary enlargement increased with the increasing scores. CONCLUSIONS: We proposed a convenient scoring system to preoperative predict malignancy in the bulging duodenal papilla.

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features.

BACKGROUND: Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging. METHODS: A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated. RESULTS: Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956-0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954-1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854-0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2-4 points; >4 to ≤11 points) significantly increased with increasing scores. CONCLUSION: This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

Guanidinothiosialoside-Human Serum Albumin Conjugate Mimics mucin Barrier to Restrict Influenza Infection.

A guanidinothiosialoside-human serum albumin conjugate as mucin mimic was prepared via a copper-free click reaction. Matrix-Assisted Laser Desorption/Ionization-Time of Flight-Mass Spectrometry (MALDI-TOF-MS) indicated that three sialoside groups were grafted onto the protein backbone. The synthetic glycoconjugate exhibited strong influenza virion capture and trapping capability. Further mechanistic studies showed that this neomucin bound tightly to neuraminidase on the surface of influenza virus with a dissociation constant (KD) in the nanomolar range and had potent antiviral activity against a broad spectrum of virus strains. Most notably, the glycoconjugate acted as a biobarrier was able to protect Madin-Darby canine kidney (MDCK) cells from influenza viral infection with 50% effective concentrations (EC50) in the nanomolar range and showed no cytotoxicity towards Human Umbilical Vein Endothelial Cells (HUVEC) at high concentrations. This research establishes an attractive strategy for the development of new multivalent antiviral agents based on mucin structure. Moreover, the method for the functionalization of the natural biological macromolecular scaffold with bioactive small molecules also lays the experimental foundation for potential biomedical and biomaterial applications.

Thiosialoside-decorated polymers use a two-step mechanism to inhibit both early and late stages of influenza virus infection.

We describe the preparation of thiosialoside-modified poly (methyl vinyl ether-alt-maleic anhydride) as second-generation polymeric conjugates for the inhibition of influenza virus infection. These synthetic glycopolymers show significantly enhanced neuraminidase inhibitory and antiviral activity in enzyme and cellular levels, respectively. The polyvalent thiosialosides also exhibit comparable inhibitory activity to the first-line anti-influenza drugs Zanamivir® and Oseltamivir® against the PR8 influenza virus strain in virus growth inhibition assays, which may be attributed to multivalent binding to neuraminidase on the virion particles, leading to the virion aggregation and further inhibiting the attaching/fusion and releasing steps in the influenza virus life-cycle. These findings suggest that attaching monomeric sialoside with neuraminidase inhibitory activity to a polymeric scaffold will synergistically disturb both the early and late stages of influenza virus infection, and provides a basis for the development of efficacious anti-viral agents against both wild-type and drug-resistant mutant strains.