MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS: The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION: We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Biodegradation of highly crystallized poly(ethylene terephthalate) through cell surface codisplay of bacterial PETase and hydrophobin.

The process of recycling poly(ethylene terephthalate) (PET) remains a major challenge due to the enzymatic degradation of high-crystallinity PET (hcPET). Recently, a bacterial PET-degrading enzyme, PETase, was found to have the ability to degrade the hcPET, but with low enzymatic activity. Here we present an engineered whole-cell biocatalyst to simulate both the adsorption and degradation steps in the enzymatic degradation process of PETase to achieve the efficient degradation of hcPET. Our data shows that the adhesive unit hydrophobin and degradation unit PETase are functionally displayed on the surface of yeast cells. The turnover rate of the whole-cell biocatalyst toward hcPET (crystallinity of 45%) dramatically increases approximately 328.8-fold compared with that of purified PETase at 30 °C. In addition, molecular dynamics simulations explain how the enhanced adhesion can promote the enzymatic degradation of PET. This study demonstrates engineering the whole-cell catalyst is an efficient strategy for biodegradation of PET.

Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning.

Background: Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery. Methods: A total of 353 patients with acute type A aortic dissection transferred to ICU after surgery from September 2016 to August 2019 were included. The patients were randomly divided into the training dataset (70%) and the validation dataset (30%). Eighty-four preoperative and intraoperative factors were collected for each patient. ICU-LOS was divided into four intervals (<4, 4-7, 7-10, and >10 days) according to interquartile range. Kendall correlation coefficient was used to identify factors associated with ICU-LOS. Five classic classifiers, Naive Bayes, Linear Regression, Decision Tree, Random Forest, and Gradient Boosting Decision Tree, were developed to predict ICU-LOS. Area under the curve (AUC) was used to evaluate the models' performance. Results: The mean age of patients was 51.0 ± 10.9 years and 307 (87.0%) were males. Twelve predictors were identified for ICU-LOS, namely, D-dimer, serum creatinine, lactate dehydrogenase, cardiopulmonary bypass time, fasting blood glucose, white blood cell count, surgical time, aortic cross-clamping time, with Marfan's syndrome, without Marfan's syndrome, without aortic aneurysm, and platelet count. Random Forest yielded the highest performance, with an AUC of 0.991 (95% confidence interval [CI]: 0.978-1.000) and 0.837 (95% CI: 0.766-0.908) in the training and validation datasets, respectively. Conclusions: Machine learning has the potential to predict ICU-LOS for acute type A aortic dissection. This tool could improve the management of ICU resources and patient-throughput planning, and allow better communication with patients and their families.

Three-Dimensional CT for Quantification of Longitudinal Lung and Pneumonia Variations in COVID-19 Patients.

Objectives: Visual chest CT is subjective with interobserver variability. We aimed to quantify the dynamic changes of lung and pneumonia on three-dimensional CT (3D-CT) images in coronavirus disease 2019 (COVID-19) patients during hospitalization. Methods: A total of 110 laboratory-confirmed COVID-19 patients who underwent chest CT from January 3 to February 29, 2020 were retrospectively reviewed. Pneumonia lesions were classified as four stages: early, progressive, peak, and absorption stages on chest CT. A computer-aided diagnostic (CAD) system calculated the total lung volume (TLV), the percentage of low attenuation areas (LAA%), the volume of pneumonia, the volume of ground-glass opacities (GGO), the volume of consolidation plus the GGO/consolidation ratio. The CT score was visually assessed by radiologists. Comparisons of lung and pneumonia parameters among the four stages were performed by one-way ANOVA with post-hoc tests. The relationship between the CT score and the volume of pneumonia, and between LAA% and the volume of pneumonia in four stages was assessed by Spearman's rank correlation analysis. Results: A total of 534 chest CT scans were performed with a median interval of 4 days. TLV, LAA%, and the GGO/consolidation ratio were significantly decreased, while the volume of pneumonia, GGO, and consolidation were significantly increased in the progressive and peak stages (for all, P < 0.05). The CT score was significantly correlated with the pneumonia volume in the four stages (r = 0.731, 0.761, 0.715, and 0.669, respectively, P < 0.001). Conclusion: 3D-CT could be used as a useful quantification method in monitoring the dynamic changes of COVID-19 pneumonia.

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve. RESULTS: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%. CONCLUSIONS: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation. ADVANCES IN KNOWLEDGE: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.

Concurrent chemoradiotherapy with additional chemotherapy for nasopharyngeal carcinoma: A pooled analysis of propensity score-matching studies.

OBJECTIVE: To determine the benefits of adding induction chemotherapy (IC) and adjuvant chemotherapy (AC) to concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC) based on propensity score-matching (PSM) studies. METHODS: Eligible PSM studies were searched in the PubMed, Web of Science, and Embase databases from inception to September 1, 2020. The primary endpoints included overall survival (OS), distant metastasis-free survival (DMFS), and locoregional recurrence-free survival (LRFS). RESULTS: A total of 14 trials consisting of 4086 participants were included. Significant benefits were observed between IC + CCRT and CCRT for OS (hazard ratio [HR], 0.76; 95% confidence interval [CI]: 0.64-0.91) and DMFS (HR, 0.77; 95% CI: 0.64-0.94) with the exception of LRFS (HR, 1.14; 95% CI: 0.90-1.43). However, CCRT + AC did not achieve significant improvements. CONCLUSIONS: IC with CCRT yields significant survival benefits in terms of OS and DMFS, whereas CCRT with AC fails to achieve any additional benefit in all endpoints.

Predicting hyperprogressive disease in patients with advanced hepatocellular carcinoma treated with anti-programmed cell death 1 therapy.

BACKGROUND: Hyperprogressive disease (HPD) is a new progressive pattern in patients with advanced hepatocellular carcinoma (HCC) treated with programmed cell death 1 (PD-1) inhibitors. We aimed to investigate risk factors associated with HPD in advanced HCC patients undergoing anti-PD-1 therapy. METHODS: A total of 69 patients treated with anti-PD-1 therapy between March 2017 and January 2020 were included. HPD was determined according to the time to treatment failure, tumour growth rate, and tumour growth rate ratio. Univariate and multivariate analyses were performed to identify clinical variables significantly associated with HPD. A risk model was constructed based on clinical variables with prognostic significance for HPD. FINDINGS: Overall, 10 (14·49%) had HPD. Haemoglobin level, portal vein tumour thrombus, and Child-Pugh score were significantly associated with HPD. The risk model had an area under the curve of 0·931 (95% confidence interval, 0·844-1·000). Patients with HPD had a significantly shorter overall survival (OS) than that of the patients with non-HPD (p < 0·001). However, there was no significant difference in OS between PD (progressive disease) patients with and without HPD (p = 0·05). INTERPRETATION: We identified three clinical variables as risk factors for HPD, providing an opportunity to aid the pre-treatment evaluation of the risk of HPD in patients treated with immunotherapy. FUNDING: This study was funded by the National Natural Science Foundation of China (81571664, 81871323, and 81801665); National Natural Science Foundation of Guangdong Province (2018B030311024); Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010,328); and China Postdoctoral Science Foundation (2016M600145).

Yeast cell surface display of bacterial PET hydrolase as a sustainable biocatalyst for the degradation of polyethylene terephthalate.

Enzymatic hydrolysis of polyethylene terephthalate (PET) is considered to be an environmentally friendly method for the recycling of plastic waste. Recently, a bacterial enzyme named IsPETase was found in Ideonella sakaiensis with the ability to degrade amorphous PET at ambient temperature suggesting its possible use in recycling of PET. However, applying the purified IsPETase in large-scale PET recycling has limitations, i.e., a complicated production process, high cost of single-use, and instability of the enzyme. Yeast cell surface display has proven to be an effectual alternative for improving enzyme degradation efficiency and realizing industrial applications. This chapter deals with the construction and application of a whole-cell biocatalyst by displaying IsPETase on the surface of yeast (Pichia pastoris) cells.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters.

PURPOSE: To quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters. METHODS: Patients with real-time fluorescence polymerase chain reaction (RT-PCR)-confirmed COVID-19 between January 01 and February 18, 2020, were included in this study. Laboratory parameters were retrospectively collected from medical records. Severity of lung changes on chest CT of early, progressive, peak, and absorption stages was scored according to the percentage of lung involvement (5 lobes, scores 1-5 for each lobe, range 0-20). Relationship between CT scores and laboratory parameters was evaluated by the Spearman rank correlation. The Bonferroni correction adjusted significance level was at 0.05/4 = 0.0125. RESULTS: A total of 84 patients (mean age, 47.8 ± 12.0 years [standard deviation]; age range, 24-80 years) were evaluated. The patients underwent a total of 339 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). Median chest CT scores peaked at 4 days after the beginning of treatment and then declined. CT score of the early stage was correlated with neutrophil count (r = 0.531, P = 0.011). CT score of the progressive stage was correlated with neutrophil count (r = 0.502, P < 0.001), white blood cell count (r = 0.414, P = 0.001), C-reactive protein (r = 0.511, P < 0.001), procalcitonin (r = 0.423, P = 0.004), and lactose dehydrogenase (r = 0.369, P = 0.010). However, CT scores of the peak and absorption stages were not correlated with any parameter (P > 0.0125). No sex difference occurred regarding CT score (P > 0.05). CONCLUSION: Severity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

Soluble hydrophobin mutants produced in Escherichia coli can self-assemble at various interfaces.

Hydrophobins are small, secreted amphiphilic proteins produced by filamentous fungi. Due to their charming ability to self-assemble at different interfaces, several efforts have been made in recent years to produce hydrophobins at a large scale for industrial applications. However, producing soluble and functional hydrophobins in bacterial expression systems is challenging because all hydrophobins contain eight conserved cysteine residues, resulting in the formation of inclusion bodies. Here, two cysteine mutants for both class I and class II hydrophobins were successfully produced in Escherichia coli in soluble form. Subsequent experiments systematically demonstrated that those two mutants preserved the ability to self-assemble at water-water, air-water and oil-water interfaces similarly to native hydrophobins. We also found that disulfide bridges differently influenced the self-assembly of hydrophobins. They were not involved in the self-assembly of the class I hydrophobin HGFI, but directly affected the self-assembly of the class II hydrophobin HFBI. Our study demonstrated that the bacterial expression system was suitable for producing soluble and functional hydrophobin mutants, which have the potential to replace native hydrophobins produced in other complicated production systems.

Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer.

Pre-treatment survival prediction plays a key role in many diseases. We aimed to determine the prognostic value of pre-treatment Magnetic Resonance Imaging (MRI) based radiomic score for disease-free survival (DFS) in patients with early-stage (IB-IIA) cervical cancer. Methods: A total of 248 patients with early-stage cervical cancer underwent radical hysterectomy were included from two institutions between January 1, 2011 and December 31, 2017, whose MR imaging data, clinicopathological data and DFS data were collected. Patients data were randomly divided into the training cohort (n = 166) and the validation cohort (n=82). Radiomic features were extracted from the pre-treatment T2-weighted (T2w) and contrast-enhanced T1-weighted (CET1w) MR imagings for each patient. Least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazard model were applied to construct radiomic score (Rad-score). According to the cutoff of Rad-score, patients were divided into low- and high- risk groups. Pearson's correlation and Kaplan-Meier analysis were used to evaluate the association of Rad-score with DFS. A combined model incorporating Rad-score, lymph node metastasis (LNM) and lymphovascular space invasion (LVI) by multivariate Cox proportional hazard model was constructed to estimate DFS individually. Results: Higher Rad-scores were significantly associated with worse DFS in the training and validation cohorts (P<0.001 and P=0.011, respectively). The Rad-score demonstrated better prognostic performance in estimating DFS (C-index, 0.753; 95% CI: 0.696-0.805) than the clinicopathological features (C-index, 0.632; 95% CI: 0.567-0.700). However, the combined model showed no significant improvement (C-index, 0.714; 95%CI: 0.642-0.784). Conclusion: The results demonstrated that MRI-derived Rad-score can be used as a prognostic biomarker for patients with early-stage (IB-IIA) cervical cancer, which can facilitate clinical decision-making.

Immune-checkpoint inhibitor plus chemotherapy versus conventional chemotherapy for treatment of recurrent or metastatic head and neck squamous cell carcinoma: a systematic review and network meta-analysis.

BACKGROUND: Multiple therapies including immune-checkpoint inhibitors are emerging as effective treatment for patients with recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSSC). However, the optimal first-line and second-line treatments remains controversial. METHODS: We systematically searched databases and conducted a systematic review of phase II/III randomized controlled trials (RCTs) that compared two or more treatments for R/M HNSSC. Progression-free survival (PFS), overall survival (OS) and adverse events (AEs) ⩾3 with hazard ratios (HRs) were extracted and synthesized based on a frequentist network meta-analysis. RESULTS: Twenty-six trials involving 8908 patients were included. Of first-line treatments, pembrolizumab plus cisplatin plus 5-fluorouracil is associated with significantly improved OS (P-score = 0.91) and TPEx ranked first for prolonging PFS (0.91). EXTREME plus docetaxel (0.18) ranked lowest for AEs ⩾3. Of second-line treatments, nivolumab was the highest-ranked treatment for prolonging OS (0.95), while buparlisib plus paclitaxel was the highest-ranked treatment for PFS (0.94). Subgroup analyses suggested that nivolumab was significantly associated with improvement of OS in patients with high PD-L1 expression (HR 0.55, 0.43-0.70), whereas its OS benefit is similar with conventional chemotherapy for those with low PD-L1 expression. Buparlisib plus paclitaxel showed the best OS benefit in subgroups of patients with HPV-negative status, and with oral cavity or larynx as primary tumor sites. CONCLUSIONS: Pembrolizumab plus cisplatin plus 5-fluorouracil is likely to be the best first-line treatment when OS is a priority. Otherwise, TPEx should be the optimal first-line option due to its superior PFS prolongation efficacy, best safety profile, and similar OS benefit with pembrolizumab plus cisplatin plus 5-fluorouracil. Nivolumab appears to be the best second-line option with best OS prolongation efficacy and outstanding safety profile in the overall population. Future RCTs with meticulous grouping of patients and detailed reporting are urgently needed for individualized treatment.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19. Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness. Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram. Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Efficient biodegradation of highly crystallized polyethylene terephthalate through cell surface display of bacterial PETase.

Polyethylene terephthalate (PET) is one of the most widely used plastics in the world. Accumulation of the discarded PET in the environment is creating a global environmental problem. Recently, a bacterial enzyme named PETase was found to have the novel ability to degrade the highly crystallized PET. However, the enzymatic activity of native PETase is still low limiting its possible use in recycling of PET. In this study, we developed a whole-cell biocatalyst by displaying PETase on the surface of yeast (Pichia pastoris) cell to improve its degradation efficiency. Our data shows that PETase could be functionally displayed on the yeast cell with enhanced pH and thermal stability. The turnover rate of the PETase-displaying yeast whole-cell biocatalyst towards highly crystallized PET dramatically increased about 36-fold compared with that of purified PETase. Furthermore, the whole-cell biocatalyst showed stable turnover rate after seven repeated use and under some chemical/solvent conditions, and its ability to degrade different commercial highly crystallized PET bottles. Our results reveal that PETase-displaying whole-cell biocatalyst affords a promising route for efficient biological recycling of PET.

Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma.

Surgical decision-making on advanced laryngeal carcinoma is heavily depended on the identification of preoperative T category (T3 vs. T4), which is challenging for surgeons. A T category prediction radiomics (TCPR) model would be helpful for subsequent surgery. A total of 211 patients with locally advanced laryngeal cancer who had undergone total laryngectomy were randomly classified into the training cohort (n = 150) and the validation cohort (n = 61). We extracted 1,390 radiomic features from the contrast-enhanced computed tomography images. Interclass correlation coefficient and the least absolute shrinkage and selection operator (LASSO) analyses were performed to select features associated with pathology-confirmed T category. Eight radiomic features were found associated with preoperative T category. The radiomic signature was constructed by Support Vector Machine algorithm with the radiomic features. We developed a nomogram incorporating radiomic signature and T category reported by experienced radiologists. The performance of the model was evaluated by the area under the curve (AUC). The T category reported by radiologists achieved an AUC of 0.775 (95% CI: 0.667-0.883); while the radiomic signature yielded a significantly higher AUC of 0.862 (95% CI: 0.772-0.952). The predictive performance of the nomogram incorporating radiomic signature and T category reported by radiologists further improved, with an AUC of 0.892 (95% CI: 0.811-0.974). Consequently, for locally advanced laryngeal cancer, the TCPR model incorporating radiomic signature and T category reported by experienced radiologists have great potential to be applied for individual accurate preoperative T category. The TCPR model may benefit decision-making regarding total laryngectomy or larynx-preserving treatment.

Potential Role of circPVT1 as a proliferative factor and treatment target in esophageal carcinoma.

BACKGROUND: Many circRNAs have been reported to play important roles in cancer development and have the potential to serve as a novel class of biomarkers for clinical diagnosis. However, the role of circRNAs in esophageal carcinoma (EC) remains unclear. In the current study, we investigated the potential role of circPVT1 in esophageal carcinoma. METHODS: Quantitative real-time PCR was performed to detect circPVT1 levels. CircPVT1-specific siRNA or plasmids were used to knock down or overexpression the target RNA. Hoechst Staining was implemented to evaluate the ratio of cell apoptosis. Transwell migration assays were carried out to study the effects of circPVT1 on esophageal squamous cell carcinoma cell invasion. RegRNA 2.0 was used for bioinformatics analysis. The expression levels of Pax-4, Pax-6, PPARα and PPAR-γ were assessed using Western blot. RESULTS: In the present study, we demonstrated a significant up-regulation of circPVT1 levels in EC tissues and cancer cell lines. The levels of circPVT1 decreased significantly when the cells were maintained to over-confluence. These results suggested a potential role for circPVT1 in cell proliferation. In addition, overexpressing circPVT1 in TE-10 cell promoted invasive ability of cancer cell. In contrast, siRNA knockdown of circPVT1 inhibited this phenomenon, leading to increased apoptosis levels of TE-10 cell. What's more, miR-4663 had the effect of inhibiting tumor growth by downregulated Paxs and upregulated PPARs. Whereas, after the addition of circPVT1, this effect no longer worked, suggesting that circPVT1 may affect the malignancy of the tumor by affecting miRNA and regulating the levels of Paxs and PPARs. CONCLUSIONS: Collectively, our study reveals a critical role for circPVT1 in esophageal carcinoma, which may provide new insights of this circRNA as a biomarker for the diagnosis and treatment target of EC.

An integrated targeting drug delivery system based on the hybridization of graphdiyne and MOFs for visualized cancer therapy.

Multimodal therapies have been regarded as promising strategies for cancer treatment as compared to conventional drug delivery systems that have various drawbacks in either low loading content, uncontrolled release, non-targeting or biotoxicity. We have developed a multifunctional three-dimensional tumor-targeting drug delivery system, Fe3O4@UIO-66-NH2/graphdiyne (FUGY), based on the hybridization of a novel two-dimensional material, graphdiyne (GDY), with a metal organic framework (MOFs) structure, Fe3O4@UIO-66-NH2 (FU). The FU MOF structure has superior ability for magnetic targeting, and was constructed by an in situ growth method in which it was surface-installed with GDY via amide bonds, as a carrier of anticancer drugs. The anticancer drug doxorubicin (DOX) was loaded onto FUGY and served as both an anticancer drug to treat the tumor and a fluorescence probe to ascertain the location of FUGY. The results show that FUGY exhibits a high drug loading content of 43.8% and an effective drug release around the tumor cells at pH 5.0. In particular, fluorescence imaging demonstrates that FUGY can deliver more anticancer drugs to tumor tissue than conventional drug delivery systems. Furthermore, FUGY exhibits superior therapeutic efficiencies with negligible side effects as compared to the direct administration of free DOX, both in vitro and in vivo. The obtained FUGY drug delivery system possesses ideal biocompatibility, sustained drug release, effective chemotherapeutic efficacy, and specific targeting abilities. Such a multimodal therapeutic system can facilitate new possibilities for multifunctional drug delivery systems.

The self-assembly of monosubstituted BODIPY and HFBI-RGD.

A novel fluorescent probe was constructed by the self-assembly of monosubstituted BODIPY and a novel targeted hydrophobin named hereafter as HFBI-RGD. Optical measurements and theoretical calculations confirmed that the spectral properties of the probe were greatly influenced by the BODIPY structure, the appropriate volume of BODIPY and the cavity of HFBI-RGD. The experiments in vivo and ex vivo demonstrated that the probe had excellent ability for tumor labelling.