Efficient healing of diabetic wounds by MSC-EV-7A composite hydrogel via suppression of inflammation and enhancement of angiogenesis.

Diabetes mellitus (DM) is characterized by prolonged hyperglycemia, impaired vascularization, and serious complications, such as blindness and chronic diabetic wounds. About 25% of patients with DM are estimated to encounter impaired healing of diabetic wounds, often leading to lower limb amputation. Multiple factors are attributed to the non-healing of diabetic wounds, including hyperglycaemia, chronic inflammation, and impaired angiogenesis. It is imperative to develop more efficient treatment strategies to tackle healing difficulties in diabetic wounds. Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising for diabetic wound healing considering their anti-inflammatory, pro-angiogenic and pro-proliferative activities. A histone deacetylase 7 (HDAC7)-derived 7-amino-acid peptide (7A) was shown to be highly effective for angiogenesis. However, it has never been investigated whether MSC-EVs are synergistic with 7A for the healing of diabetic wounds. Herein, we propose that MSC-EVs can be combined with 7A to greatly promote diabetic wound healing. The combination of EVs and 7A significantly improved the migration and proliferation of skin fibroblasts. Moreover, EVs alone significantly suppressed LPS-induced inflammation in macrophages, and notably, the combination treatment showed an even better suppression effect. Importantly, the in vivo study revealed that the combination therapy consisting of EVs and 7A in an alginate hydrogel was more efficient for the healing of diabetic wounds in rats than monotherapy using either EV or 7A hydrogels. The underlying mechanisms include suppression of inflammation, improvement of skin cell proliferation and migration, and enhanced collagen fiber disposition and angiogenesis in wounds. In summary, the MSC-EV-7A hydrogel potentially constitutes a novel therapy for efficient healing of chronic diabetic wounds.

Enhanced cartilage regeneration by icariin and mesenchymal stem cell-derived extracellular vesicles combined in alginate-hyaluronic acid hydrogel.

OBJECTIVE: Osteoarthritis (OA) is characterized by progressive cartilage degeneration and absence of curative therapies. Therefore, more efficient therapies are compellingly needed. Both mesenchymal stem cells (MSCs)-derived extracellular vesicles (EVs) and Icariin (ICA) are promising for repair of cartilage defect. This study proposes that ICA may be combined to potentiate the cartilage repair capacity of MSC-EVs. MATERIALS AND METHODS: MSC-EVs were isolated from sodium alginate (SA) and hyaluronic acid (HA) composite hydrogel (SA-HA) cell spheroid culture. EVs and ICA were combined in SA-HA hydrogel to test therapeutic efficacy on cartilage defect in vivo. RESULTS: EVs and ICA were synergistic for promoting both proliferation and migration of MSCs and inflammatory chondrocytes. The combination therapy led to strikingly enhanced repair on cartilage defect in rats, with mechanisms involved in the concomitant modulation of both cartilage degradation and synthesis makers. CONCLUSION: The MSC-EVs-ICA/SA-HA hydrogel potentially constitutes a novel therapy for cartilage defect in OA.

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

RATIONALE AND OBJECTIVES: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I-II and stage III-IV, and also compared these models with radiologists. MATERIALS AND METHODS: Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96). In the training set, the radiomics features with inter- and intraclass correlation coefficients (ICCs)> 0.75 were screened by Spearman correlation analysis and recursive feature elimination (RFE); then support vector machine (SVM) classifier was applied to develop the radiomics model. The DL model was built using ResNet 18 by the cropped 2D regions of interest (ROIs) in the maximum tumor ROI slices and the last fully connected layer of this network served as the DL feature extractor. Finally, a combined model was developed by pooling the radiomics features and extracted DL features to predict the staging. RESULTS: The area under the curves (AUCs) for radiomics model, DL model, and combined model in the test set were 0.704 (95% confidence interval [CI]: 0.588-0.820), 0.724 (95% CI: 0.613-0.835), and 0.849 (95% CI: 0.755-0.943), respectively. The combined model outperformed the radiomics model and the DL model in discriminating stage I-II from stage III-IV (p = 0.031 and p = 0.020, respectively). Only the combined model performed significantly better than radiologists (p < 0.050 for both). CONCLUSION: The combined model can help tailor the therapeutic strategy for laryngeal carcinoma patients by enabling more accurate preoperative staging.

IKBIP is a Predictive Biomarker Related to Immunosuppressive Microenvironment in Digestive System Malignancies.

OBJECTIVE: IKBKB-interacting protein (IKBIP) has rarely been reported in tumor research. This study aimed to evaluate IKBIP role in tumor progression. mRNA (messenger ribonucleic acid) expression, clinical characteristics and predictive values of IKBIP were assessed. METHODS: R package "clusterProfiler" was used to examine the potential mechanisms in which IKBIP may involve. Immune cell infiltration and its correlation with IKBIP was also analyzed. We further evaluated IKBIP influence on drug resistance. RESULTS: It was found that IKBIP was overexpressed and related to poorer survival in most types of tumors. IKBIP expression was strongly related to immunosuppressive cells in the TCGA (The Cancer Genome Atlas) pan-cancer samples. These immunosuppressive cells included tumor-related macrophages, tumor-related fibroblasts, and regulatory T cells. Moreover, immunosuppressive genes and immune checkpoints were positively related to IKBIP expression in several tumor types. Furthermore, patients with IKBIP overexpressed did not respond to most anti-cancer medications. It was also found that compared to control group, the number of invasive cells is four times that of IKBIP overexpression group, and the number of clone forming cells is six times that of IKBIP overexpression group. IKBIP overexpression promoted colon cancer cells invasiveness and clonogenesis by Transwell assay and colon formation assay. CONCLUSIONS: According to current findings, IKBIP is a probable oncogene and predictive marker for most of tumor types. High IKBIP expression is associated with tumor immunosuppression.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.

OBJECTIVES: To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant parotid tumors. METHODS: Data from 573 patients with histopathologically confirmed parotid tumors from center 1 (training set: n = 269; internal-testing set: n = 116) and center 2 (external-testing set: n = 188) were retrospectively collected. Six deep learning models (MobileNet V3, ShuffleNet V2, Inception V3, DenseNet 121, ResNet 50, and VGG 19) based on arterial-phase CT images, and a baseline support vector machine (SVM) model integrating clinical-radiological features with handcrafted radiomics signatures were constructed. The performance of senior and junior radiologists with and without optimal model assistance was compared. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the clinical benefit of using the optimal model. RESULTS: MobileNet V3 had the best predictive performance, with sensitivity increases of 0.111 and 0.207 (p < 0.05) in the internal- and external-testing sets, respectively, relative to the SVM model. Clinical benefit and overall efficiency of junior radiologist were significantly improved with model assistance; for the internal- and external-testing sets, respectively, the AUCs improved by 0.128 and 0.102 (p < 0.05), the sensitivity improved by 0.194 and 0.120 (p < 0.05), the NRIs were 0.257 and 0.205 (p < 0.001), and the IDIs were 0.316 and 0.252 (p < 0.001). CONCLUSIONS: The developed deep learning models can assist radiologists in achieving higher diagnostic performance and hopefully provide more valuable information for clinical decision-making in patients with parotid tumors. KEY POINTS: • The developed deep learning models outperformed the traditional SVM model in predicting benign and malignant parotid tumors. • Junior radiologist can obtain greater clinical benefits with assistance from the optimal deep learning model. • The clinical decision-making process can be accelerated in patients with parotid tumors using the established deep learning model.

Selective CDK9 knockdown sensitizes TRAIL response by suppression of antiapoptotic factors and NF-kappaB pathway.

The aberrantly up-regulated CDK9 can be targeted for cancer therapy. The CDK inhibitor dinaciclib (Dina) has been found to drastically sensitizes cancer response to TRAIL-expressing extracellular vesicle (EV-T). However, the low selectivity of Dina has limited its application for cancer. We propose that CDK9-targeted siRNA (siCDK9) may be a good alternative to Dina. The siCDK9 molecules were encapsulated into EV-Ts to prepare a complexed nanodrug (siEV-T). It was shown to efficiently suppress CDK9 expression and overcome TRAIL resistance to induce strikingly augmented apoptosis in lung cancer both in vitro and in vivo, with a mechanism related to suppression of both anti-apoptotic factors and nuclear factor-kappa B pathway. Therefore, siEV-T potentially constitutes a novel, highly effective and safe therapy for cancers.

The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I-II and III-IVa Nasopharyngeal Carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I-II) and advanced-stage (III-IVa) NPC based on MR images. METHODS: 329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment. RESULTS: Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC. CONCLUSION: Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.

MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.

Objective: To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic-radiological features can produce a superior diagnostic performance. Methods: The database of 247 patients with SNIP (n=106) or MST (n=141) were analyzed. Dataset from scanner A were randomly divided into training set (n=135) and test set 1 (n=58) in a ratio of 7:3, and dataset from scanner B and C were used as an additional independent test set 2 (n=54). Fourteen clinic-radiological features were analyzed by using univariate analysis, and those with significant differences were applied to construct clinical model. Based on the radiomics features extracted from single sequence (T2WI or CE-T1WI) and combined sequence, four commonly used classifiers (logistic regression (LR), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN)) were employed to constitute twelve different machine learning models, and the best-performing one was confirmed as the optimal radiomics model. Furthermore, a combined model incorporated best radiomics feature subsets and clinic-radiological features was developed. The diagnostic performances of these models were assessed by the area under the receiver operating characteristic (ROC) curve (AUC) and the calibration curves. Results: Five clinic-radiological features (age, convoluted cerebriform pattern sign, heterogeneity, adjacent bone involvement and infiltration of surrounding tissue) were considered to be significantly different between the tumor groups (P < 0.05). Among the twelve machine learning models, the T2WI-SVM model exhibited optimal predictive efficacy for classification tasks on the two test sets, with the AUC of 0.878 and 0.914, respectively. For three types of diagnostic models, the combined model achieved highest AUC of 0.912 (95%CI: 0.807-0.970) and 0.927 (95%CI: 0.823-0.980) for differentiation of SNIP and MST in test 1 and test 2 sets, which performed prominently better than clinical model (P=0.011, 0.005), but not significantly different from the optimal radiomics model (P=0.100, 0.452). Conclusion: The machine learning model based on T2WI sequence and SVM classifier achieved best performance in differentiation of SNIP and MST, and the combination of radiomics features and clinic-radiological features significantly improved the diagnostic capability of the model.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.

Objective: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves. Results: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888). Conclusion: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

Differentiation of Intraspinal Tuberculosis and Metastatic Cancer Using Magnetic Resonance Imaging.

PURPOSE: This study aimed to explore the differences in the magnetic resonance imaging (MRI) findings between intraspinal tuberculosis and metastatic cancer, which may aid in making the correct diagnosis. PATIENTS AND METHODS: The clinical features and MRI findings of 15 patients with intraspinal tuberculosis and 11 patients with intraspinal metastatic cancers were retrospectively analyzed. RESULTS: The mean ages of the patients with intraspinal tuberculosis and metastatic cancer were 26.3 (15-42) and 52.1 (38-67) years, respectively. All intraspinal tuberculosis cases were secondary to primary extraspinal tuberculosis, including tuberculous meningitis (11/15), as well as pulmonary (9/15), vertebral (5/15), urinary tract (1/15), abdominal (1/15), cervical lymph node (1/15), and multisystem tuberculosis (9/15). The intraspinal metastases originated from the breast (5/11), lung (3/11), kidney (1/11), ovarian (1/11), and nasopharyngeal cancers (1/11). Both intraspinal tuberculosis and metastatic cancers presented with multiple intra- and extramedullary lesions throughout all regional segments of the spinal canal, accompanied by irregularly thickened meninges. Intraspinal tuberculous lesions had indistinct edges that integrated with each other, most of them exhibiting obvious enhancement on MRI. Conversely, intraspinal metastatic lesions were distinctly separated with clear edges and exhibited lesser enhanced MRI than intraspinal tuberculosis. CONCLUSION: A combined analysis of clinical features and MRI findings may be helpful in differentiating intraspinal tuberculosis from metastatic cancer.

Hedgehog Signaling Non-Canonical Activated by Pro-Inflammatory Cytokines in Pancreatic Ductal Adenocarcinoma.

Hedgehog(HH) pathway is found to be activated through a manner of canonical, or the non-canonical HH pathways. Distinct hyperplasia stroma around tumor cells is supposed to express pro-inflammatory cytokines abundantly, such as tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß), etc. in pancreatic ductal adenocarcinoma (PDAC) tissues. In this study we observed the effects of TNF-α and IL-1ß on HH pathway activation in PDAC cells, and explored their activation manners. Our results showed that pro-inflammatory cytokines, TNF-α and IL-1ß, could up-regulate the expression of GLI1 gene, increase its nuclear protein expression and promote malignant cell behaviors including migration, invasion, epithelial-mesenchymal transition (EMT) and drug resistance as well. Moreover, GLI1 promoter-reporter assay in combination with blocking either NF-κB or Smoothened (SMO) suggested that TNF-α and IL-1ß could transcriptionally up-regulate expression of GLI1 completely via NF-κB, whereas ablation of SMO could not completely attenuate the regulation effects of TNF-α and IL-1ß on GLI1 expression. Collectively, our results indicated that TNF-α and IL-1ß in hyperplasia stroma can promote the PDAC cell development by activating HH pathway, through both the canonical and non-canonical HH activation ways.

Detection of gastric carcinoma-associated antigen MG7-Ag in human sera using surface plasmon resonance sensor.

BACKGROUND: MG7-Ag is a kind of gastric cancer-specific tumor-associated antigen and has been investigated to serve as a marker of gastric cancer for early diagnosis. METHODS: Surface plasmon resonance (SPR) sensor was used for the detection of MG7-Ag in the sera of gastric cancer patients to develop an innovative, simple and rapid assay method for early diagnosis. The specific monoclonal MG7 antibodies were used as capture and detection receptors which were immobilized on the surface of SPR sensor chips for MG7-Ag identification in the human sera. The measurements include 9 cases of gastric cancer patients and 2 cases of healthy blood donors and a MKN45 cancer cell lysate solution sample for positive control. RESULTS: The binding of MG7-Ag onto the sensor surface was observed from SPR spectra. The sera of most gastric cancer patients revealed much higher expression level of MG7-Ag than healthy human sera did in SPR measurement. CONCLUSION: The initial results demonstrate that the SPR biosensor has the potential for its application in the early diagnosis of gastric cancer. However, more tests need to be done to confirm the detection limitation and the criterion for cancer risk evaluation in early diagnosis.

MiR-218 inhibits invasion and metastasis of gastric cancer by targeting the Robo1 receptor.

MicroRNAs play key roles in tumor metastasis. Here, we describe the regulation and function of miR-218 in gastric cancer (GC) metastasis. miR-218 expression is decreased along with the expression of one of its host genes, Slit3 in metastatic GC. However, Robo1, one of several Slit receptors, is negatively regulated by miR-218, thus establishing a negative feedback loop. Decreased miR-218 levels eliminate Robo1 repression, which activates the Slit-Robo1 pathway through the interaction between Robo1 and Slit2, thus triggering tumor metastasis. The restoration of miR-218 suppresses Robo1 expression and inhibits tumor cell invasion and metastasis in vitro and in vivo. Taken together, our results describe a Slit-miR-218-Robo1 regulatory circuit whose disruption may contribute to GC metastasis. Targeting miR-218 may provide a strategy for blocking tumor metastasis.

Monoclonal antibody MG7 as a screening tool for gastric cancer.

MG7 is a gastric cancer-specific MAb with high specificity and sensitivity. By using MG7 MAb, we found that MG7Ag was increasingly detected in superficial gastritis, atrophic gastritis, intestinal metaplasia, atypical hyperplasia, and gastric cancer, indicating that MG7Ag could be considered an important early warning molecule of gastric cancer and MG7 MAb could be used as a tool for screening gastric cancer. We have developed a new and sensitive system, immuno-realtime PCR, for detection of MG7Ag in the serum. The use of qIPCR assays enabled the detection of MG7Ag in complex biological samples that were poorly accessible by conventional immunoassays.