Coaxial 3D Bioprinting Process Research and Performance Tests on Vascular Scaffolds.

Three-dimensionally printed vascularized tissue, which is suitable for treating human cardiovascular diseases, should possess excellent biocompatibility, mechanical performance, and the structure of complex vascular networks. In this paper, we propose a method for fabricating vascularized tissue based on coaxial 3D bioprinting technology combined with the mold method. Sodium alginate (SA) solution was chosen as the bioink material, while the cross-linking agent was a calcium chloride (CaCl2) solution. To obtain the optimal parameters for the fabrication of vascular scaffolds, we first formulated theoretical models of a coaxial jet and a vascular network. Subsequently, we conducted a simulation analysis to obtain preliminary process parameters. Based on the aforementioned research, experiments of vascular scaffold fabrication based on the coaxial jet model and experiments of vascular network fabrication were carried out. Finally, we optimized various parameters, such as the flow rate of internal and external solutions, bioink concentration, and cross-linking agent concentration. The performance tests showed that the fabricated vascular scaffolds had levels of satisfactory degradability, water absorption, and mechanical properties that meet the requirements for practical applications. Cellular experiments with stained samples demonstrated satisfactory proliferation of human umbilical vein endothelial cells (HUVECs) within the vascular scaffold over a seven-day period, observed under a fluorescent inverted microscope. The cells showed good biocompatibility with the vascular scaffold. The above results indicate that the fabricated vascular structure initially meet the requirements of vascular scaffolds.

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning.

Introduction: This study aimed to develop and assess a deep-learning model based on CT images for distinguishing infectivity in patients with pulmonary tuberculosis (PTB). Methods: We labeled all 925 patients from four centers with weak and strong infectivity based on multiple sputum smears within a month for our deep-learning model named TBINet's training. We compared TBINet's performance in identifying infectious patients to that of the conventional 3D ResNet model. For model explainability, we used gradient-weighted class activation mapping (Grad-CAM) technology to identify the site of lesion activation in the CT images. Results: The TBINet model demonstrated superior performance with an area under the curve (AUC) of 0.819 and 0.753 on the validation and external test sets, respectively, compared to existing deep learning methods. Furthermore, using Grad-CAM, we observed that CT images with higher levels of consolidation, voids, upper lobe involvement, and enlarged lymph nodes were more likely to come from patients with highly infectious forms of PTB. Conclusion: Our study proves the feasibility of using CT images to identify the infectivity of PTB patients based on the deep learning method.

Noninvasive evaluation of neutrophil extracellular traps signature predicts clinical outcomes and immunotherapy response in hepatocellular carcinoma.

Background: Neutrophil extracellular traps (NETs) have been shown to play a pivotal role in promoting metastasis and immune escape in hepatocellular carcinoma (HCC). Therefore, noninvasive tests to detect the formation of NETs in tumors can have significant implications for the treatment and prognoses of patients. Here, we sought to develop and validate a computed tomography (CT)-based radiomics model to predict the gene expression profiles that regulate the formation of NETs in HCC. Methods: This study included 1133 HCC patients from five retrospective cohorts. Based on the mRNA expression levels of 69 biomarkers correlated with NET formation, a 6-gene score (NETs score, NETS) was constructed in cohort 1 from TCIA database (n=52) and validated in cohort 2 (n=232) from ICGC database and cohort 3 (n=365) from TCGA database. And then based on the radiomics features of CT images, a radiomics signature (RNETS) was developed in cohort 1 to predict NETS status (high- or low-NETS). We further employed two cohorts from Nanfang Hospital (Guangzhou, China) to evaluate the predictive power of RNETS in predicting prognosis in cohort 4 (n=347) and the responses to PD-1 inhibitor of HCC patients in cohort 5 (n=137). Results: For NETS, in cohort 1, the area under the curve (AUC) values predicting 1, 2, and 3-year overall survival (OS) were 0.836, 0.879, and 0.902, respectively. The low-NETS was associated with better survival and higher levels of immune cell infiltration. The RNETS yielded an AUC value of 0.853 in distinguishing between high-NETS or low-NETS and patients with low-RNETS were associated with significantly longer survival time in cohort 1 (P<0.001). Notably, the RNETS was competent in predicting disease-free survival (DFS) and OS in cohort 4 (P<0.001). In cohort 5, the RNETS was found to be an independent risk factor for progression-free survival (PFS) (P<0.001). In addition, the objective response rate of HCC patients treated with PD-1 inhibitor was significantly higher in the low-RNETS group (27.8%) than in the high-RNETS group (10.8%). Conclusions: This study revealed that RNETS as a radiomics biomarker could effectively predict prognosis and immunotherapy response in HCC patients.

Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study.

OBJECTIVES: In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND METHODS: A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model. RESULTS: The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts. CONCLUSIONS: This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.

A Novel Post-Operative ALRI Model Accurately Predicts Clinical Outcomes of Resected Hepatocellular Carcinoma Patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading malignant tumors worldwide. Prognosis and long-term survival of HCC remain unsatisfactory, even after radical resection, and many non-invasive predictors have been explored for post-operative patients. Most prognostic prediction models were based on preoperative clinical characteristics and pathological findings. This study aimed to investigate the prognostic value of a newly constructed nomogram, which incorporated post-operative aspartate aminotransferase to lymphocyte ratio index (ALRI). METHODS: A total of 771 HCC patients underwent radical resection from three medical centers were enrolled and grouped into the training cohort (n = 416) and validation cohort (n = 355). Prognostic prediction potential of ALRI was assessed by receiver operating curve (ROC) analysis. The Cox regression model was used to identify independent prognostic factors. Nomograms for overall survival (OS) and disease-free survival (DFS) were constructed and further validated externally. RESULTS: The ROC analysis ranked ALRI as the most effective prediction marker for resected HCC patients, with the cut-off value determined at 22.6. Higher ALRI level positively correlated with larger tumor size, higher tumor node metastasis (TNM) stage, and inversely with lower albumin level and shorter OS and DFS. Nomograms for OS and DFS were capable of discriminating HCC patients into different risk-groups. CONCLUSIONS: Post-operative ALRI was of prediction value for HCC prognosis. This novel nomogram may categorize HCC patients into different risk groups, and offer individualized surveillance reference for post-operative patients.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy.

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

Inhibitory effect of estrogen receptor beta on P2X3 receptors during inflammation in rats.

Estrogen receptor beta (ERß) has been shown to play a therapeutic role in inflammatory bowel disease (IBD). However, the mechanism underlying how ERß exerts therapeutic effects and its relationship with P2X3 receptors (P2X3R) in rats with inflammation is not known. In our study, animal behavior tests, visceromotor reflex recording, and Western blotting were used to determine whether the therapeutic effect of ERß in rats with inflammation was related with P2X3R. In complete Freund adjuvant (CFA)-induced chronic inflammation in rats, paw withdrawal threshold was significantly decreased which were then reversed by systemic injection of ERß agonists, DPN or ERB-041. In 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis in rats, weight loss, higher DAI scores, increased visceromotor responses, and inflammatory responses were reversed by application of DPN or ERB-041. The higher expressions of P2X3R in dorsal root ganglia (DRG) of CFA-treated rats and those in rectocolon and DRG of TNBS-treated rats were all decreased by injection of DPN or ERB-041. DPN application also inhibited P2X3R-evoked inward currents in DRG neurons from TNBS rats. Mechanical hyperalgesia and increased P2X3 expression in ovariectomized (OVX) CFA-treated rats were reversed by estrogen replacements. Furthermore, the expressions of extracellular signal-regulated kinase (ERK) in DRG and spinal cord dorsal horn (SCDH) and c-fos in SCDH were significantly decreased after estrogen replacement compared with those of OVX rats. The ERK antagonist U0126 significantly reversed mechanical hyperalgesia in the OVX rats. These results suggest that estrogen may play an important therapeutic role in inflammation through down-regulation of P2X3R in peripheral tissues and the nervous system, probably via ERß, suggesting a novel therapeutic strategy for clinical treatment of inflammation.