Fabrication of Self-Wrinkling Polymer Films with Tunable Patterns through an Interfacial-Fuming-Induced Surface Instability Process.

Inspired by the superglue fuming method for fingerprint collection, this study developed a novel interfacial-fuming-induced surface instability process to generate wrinkled patterns on polymeric substrates. High-electronegativity groups are introduced on the substrate surface to initiate the polymerization of monomer vapors, such as ethyl cyanoacrylate, which results in the formation of a stiff poly(ethyl cyanoacrylate) capping layer. Moreover, interfacial polymerization resulted in the covalent bonding of the substrate, which led to the volumetric shrinkage of the composite and the accumulation of compressive strain. This process ultimately resulted in the development and stabilization of wrinkled surface morphologies. The authors systematically examined parameters such as the modulus of the epoxy substrate, prestrain, the flow rate of fuming, and operating temperature. The aforementioned technique can be easily applied to architectures with complex outer morphologies and inner surfaces, thereby enabling the construction of surface patterns under ambient conditions without vacuum limitations or precise process control. This study is the first to combine fuming-induced interfacial polymerization with surface instability to create robust wrinkles. The proposed method enables the fabrication of intricate microwrinkled patterns and has considerable potential for use in various practical applications, including microfluidics, optical components, bioinspired adhesive devices, and interfacial engineering.

Liproxstatin-1 alleviates cartilage degradation by inhibiting chondrocyte ferroptosis in the temporomandibular joint.

BGROUND INFORMATION: Ferroptosis contributes to temporomandibular joint osteoarthritis (TMJOA) lesion development and is still poorly understood. RESULTS: In this study, we used different TMJOA animal models to examine whether ferroptosis was related to disease onset in TMJOA induced by monosodium iodoacetate (MIA), IL-1ß, occlusion disorder (OD), and unilateral anterior crossbite (UAC). Immunohistochemical staining and Western blot analysis were used to detect ferroptosis- and cartilage degradation-related protein expression. Our results revealed reduced levels of the ferroptosis-related protein GPX4 in the cartilage layer, but the levels of ACSL4 and P53 were increased in the condyle. Injection of the ferroptosis inhibitor liproxstatin-1 (Lip-1) effectively decreased ACSL4, P53 and TRF expression. In vitro, IL-1ß reduced cartilage extracellular matrix expression in mandibular condylar chondrocytes (MCCs). Lip-1 maintained the morphology and function of mitochondria and ameliorated the exacerbation of lipid peroxidation and reactive oxygen species (ROS) production induced by IL-1ß. CONCLUSION: These results suggest that chondrocyte ferroptosis plays an important role in the development and progression of TMJOA. SIGNIFICANCE: Inhibiting condylar chondrocyte ferroptosis could be a promising therapeutic strategy for TMJOA.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

OBJECTIVES: To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS: A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS: The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS: The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT: An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS: • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.

Background: The current study aimed to construct a computed tomography (CT)-based decision tree algorithm (DTA) model to predict the epidermal growth factor receptor (EGFR) mutation status in synchronous multiple primary lung cancers (SMPLCs). Methods: The demographic and CT findings of 85 patients with molecular profiling for surgically resected SMPLCs were reviewed retrospectively. Least absolute shrinkage and selection operator (LASSO) regression was used to select the potential predictors of EGFR mutation, and a CT-DTA model was developed. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the performance of this CT-DTA model. Results: The CT-DTA model was applied to predict the EGFR mutant that had ten binary split, of which eight parameters to accurately categorize the lesions as follows: the presence of bubble-like vacuole sign (19.4% importance in the development of the model), presence of air bronchogram sign (17.4% importance), smoking status (15.7% importance), types of the lesions (14.8% importance), histology (12.6% importance), presence of pleural indentation sign (7.6% importance), gender (6.9% importance), and presence of lobulation sign (5.6% importance). The ROC analysis achieved an area under the curve (AUC) of 0.854. Multivariate logistic regression analysis demonstrated that this CT-DTA model was an independent predictor of EGFR mutation (P<0.001). Conclusions: CT-DTA model is a simple tool to predict the status of EGFR mutation in SMPLC patients and could be considered for treatment decision-making.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

BACKGROUND: There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS: This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS: A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS: The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Application of synthetic magnetic resonance imaging and DWI for evaluation of prognostic factors in cervical carcinoma: a prospective preliminary study.

OBJECTIVES: To determine the values of quantitative metrics derived from synthetic MRI (SyMRI) and apparent diffusion coefficient (ADC) in evaluating the prognostic factors of cervical carcinoma (CC). METHODS: In this prospective study, 74 patients with pathologically confirmed CC were enrolled. Pretreatment quantitative metrics including T1, T2 and ADC values were obtained from SyMRI and diffusion-weighted imaging (DWI) sequences. The values of all metrics were compared for different prognostic features using Student's t-test or Mann-Whitney U-test. The receiver operating characteristic (ROC) curve and multivariate logistic regression analysis were utilized to evaluate the diagnostic performance of quantitative variables. RESULTS: T1 and T2 values of parametrial involvement (PMI)-negative were significantly higher than those of PMI-positive (p = 0.002 and < 0.001), while ADC values did not show a significant difference. The area under curve (AUC) of T1 and T2 values for identifying PMI were 0.743 and 0.831. Only the T2 values showed a significant difference between the lymphovascular space involvement (LVSI)-negative and LVSI-positive (p < 0.001), and the AUC of T2 values for discriminating LVSI was 0.814. The differences of T1, T2, and ADC values between the well/moderately and the poorly differentiated CC were significant (all p < 0.001). The AUCs of T1, T2 and ADC values for predicting differentiation grades were 0.762, 0.830, and 0.808. The combined model of all metrics proved to achieve good diagnostic performance with the AUC of 0.866. CONCLUSION: SyMRI may be a potential noninvasive tool for assessing the prognostic factors such as PMI, LVSI, and differentiation grades in CC. Moreover, the overall diagnostic performances of synthetic quantitative metrics were superior to the ADC values, especially in identifying PMI and LVSI. ADVANCES IN KNOWLEDGE: This is the first study to assess the utility of SyMRI-derived parameters and ADC value in evaluating the prognostic factors in CC.

False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study.

BACKGROUND: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. METHODS: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1,066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. RESULTS: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees (P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. CONCLUSIONS: Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists.

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases.

Backgrounds: A significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM. Methods: The receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM. Results: Discordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set. Conclusions: Receptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.

Nomograms for Predicting Hepatocellular Carcinoma Recurrence and Overall Postoperative Patient Survival.

Background: Few studies have focused on the prognosis of patients with hepatocellular carcinoma (HCC) of Barcelona Clinic Liver Cancer (BCLC) stage 0‒C in terms of early recurrence and 5-years overall survival (OS). We sought to develop nomograms for predicting 5-year OS and early recurrence after curative resection of HCC, based on a clinicopathological‒radiological model. We also investigated whether different treatment methods influenced the OS of patients with early recurrence. Methods: Retrospective data, including clinical pathology, radiology, and follow-up data, were collected for 494 patients with HCC who underwent hepatectomy. Nomograms estimating OS and early recurrence were constructed using multivariate Cox regression analysis, based on the random survival forest (RSF) model. We evaluated the discrimination and calibration abilities of the nomograms using concordance indices (C-index), calibration curves, and Kaplan‒Meier curves. OS curves of different treatments for patients who had recurrence within 2 years after curative surgery were depicted and compared using the Kaplan-Meier method and the log-rank test. Results: Multivariate Cox regression revealed that BCLC stage, non-smooth margin, maximum tumor diameter, age, aspartate aminotransferase levels, microvascular invasion, and differentiation were prognostic factors for OS and were incorporated into the nomogram with good predictive performance in the training (C-index: 0.787) and testing cohorts (C-index: 0.711). A nomogram for recurrence-free survival was also developed based on four prognostic factors (BCLC stage, non-smooth margin, maximum tumor diameter, and microvascular invasion) with good predictive performance in the training (C-index: 0.717) and testing cohorts (C-index: 0.701). In comparison to the BCLC staging system, the C-index (training cohort: 0.787 vs. 0.678, 0.717 vs. 0.675; external cohort 2: 0.748 vs. 0.624, 0.729 vs. 0.587 respectively, for OS and RFS; external cohort1:0.716 vs. 0.627 for RFS, all p value<0.05), and model calibration curves all showed improved performance. Patients who underwent surgery after tumor recurrence had a higher reOS than those who underwent comprehensive treatments and supportive care. Conclusions: The nomogram, based on clinical, pathological, and radiological factors, demonstrated good accuracy in estimating OS and recurrence, which can guide follow-up and treatment of individual patients. Reoperation may be the best option for patients with recurrence in good condition.

Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

BACKGROUND: Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. METHODS: Five hundred seventy-three consecutive patients (10 448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. RESULTS: The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6-94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7-71.3%] to 80.4% [95% CI, 78.0-82.8%], all P < .001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. CONCLUSIONS: BMD enables accurate BM detection. Reading with BMD improves radiologists' detection sensitivity and reduces their reading times.

Managing Emergent Surgery for Ruptured Abdominal Aortic Aneurysm during the COVID-19 Pandemic.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a global pandemic which may compromise the management of vascular emergencies. An uncompromised treatment for ruptured abdominal aortic aneurysm (rAAA) during such a health crisis represents a challenge. This study aimed to demonstrate the treatment outcomes of rAAA and the perioperative prevention of cross-infection under the COVID-19 pandemic. METHODS: In cases of rAAA during the pandemic, a perioperative workflow was applied to expedite coronavirus testing and avoid pre-operative delay, combined with a strategy for preventing cross-infection. Data of rAAA treated in 11 vascular centers between January-March 2020 collected retrospectively were compared to the corresponding period in 2018 and 2019. RESULTS: Eight, 12, and 14 rAAA patients were treated in 11 centers in January-March 2018, 2019, and 2020, respectively. An increased portion were treated at local hospitals with a comparable outcome compared with large centers in Guangzhou. With EVAR-first strategy, 85.7% patients with rAAA in 2020 underwent endovascular repair, similar to that in 2018 and 2019. The surgical outcomes during the pandemic were not inferior to that in 2018 and 2019. The average length of ICU stay was 1.8 ± 3.4 days in 2020, tending to be shorter than that in 2018 and 2019, whereas the length of hospital stay was similar among 3 years. The in-hospital mortality of 2018, 2019, and 2020 was 37.5%, 25.0%, and 14.3%, respectively. Three patients undergoing emergent surgeries were suspected of COVID-19, though turned out to be negative after surgery. CONCLUSIONS: Our experience for emergency management of rAAA and infection prevention for healthcare providers is effective in optimizing emergent surgical outcomes during the COVID-19 pandemic.

PRMT1 promotes extracellular matrix degradation and apoptosis of chondrocytes in temporomandibular joint osteoarthritis via the AKT/FOXO1 signaling pathway.

Temporomandibular joint osteoarthritis (TMJOA) is a chronic degenerative joint disease characterized by extracellular matrix (ECM) degradation and chondrocyte apoptosis. The aim of this study was to investigate the role of PRMT1 in TMJOA pathogenesis and its underlying molecular mechanism. Compared to the control group, PRMT1 was highly expressed in IL-1ß-treated chondrocytes and articular cartilage following MIA injection into rat TMJs. Furthermore, knocking down PRMT1 considerably inhibited ECM degradation and apoptosis induced by IL-1ß. Mechanistic analyses further revealed that PRMT1 knockdown activated the PI3K/AKT signaling pathway and prevented FOXO1 from translocating to the nucleus. Moreover, an inhibitor of AKT (LY294002) rescued the effect of PRMT1 knockdown on IL-1ß-induced ECM degradation and apoptosis, and AMI-1, a selective inhibitor of PRMT1, inhibited PRMT1 expression and reversed the pathological progress of TMJOA. Thus, our findings suggest that PRMT1 plays an essential role in ECM degradation and chondrocyte apoptosis in TMJOA via the AKT/FOXO1 signaling pathway.

Activated E2F activity induces cell death in papillary thyroid carcinoma K1 cells with enhanced Wnt signaling.

Disruption of Wnt signaling often happens in tumorigenesis, but whether Wnt signaling affects the early stages of thyroid tumor, such as papillary thyroid carcinoma, is still a question, especially in the papillary thyroid carcinoma without genomic RET/PTC mutation. In this study, we demonstrated the important function of Wnt signaling in papillary thyroid carcinoma K1 cells, which have no RET/PTC mutation. We found that K1 cells have enhanced Wnt signaling in comparison to normal thyroid cells. We further demonstrated that K1 cells require the enhanced Wnt signaling for growth and survival. Interestingly, we identified that enhancing E2F activity by either knockdown of Rb or overexpression of Cyclin D1 induces cell death in K1 cells. And we further revealed that the cell death is caused by enhanced oxidative stress. Our studies present a novel cell model to support the key roles of Wnt signaling in early stage of thyroid tumor, and also provide an alternative way to limit thyroid cancer.

A comparison of surgical outcomes and complications between hemostatic devices for thyroid surgery: a network meta-analysis.

This meta-analysis compared harmonic scalpel and LigaSure® systems with the conventional clamp-and-tie technique in thyroidectomy. Medline, Cochrane, EMBASE, and Google Scholar databases were searched until December 30, 2015. Randomized controlled studies (RCTs) or two-arm prospective studies were included. The primary outcome was operation time. The data were evaluated both by pair-wise meta-analyses and network meta-analysis within a Bayesian framework using Markov chain Monte Carlo methods. Compared with the conventional hemostasis, there was a significant reduction in operation time with harmonic scalpel (HS) and LigaSure (LS) (difference in means = -24.27 min, 95 % CI -28.11 to -20.44 min, P < 0.001; and difference in means = -13.08 min, 95 % CI -16.88 to -9.27 min, P < 0.001, respectively). For total thyroidectomy and hemi-thyroidectomy, subgroup pair-wise meta-analyses found a reduction of 26.31 and 21.90 min in operation time for harmonic scalpel, and a reduction of 12.77 and 17.48 min for LigaSure, respectively. Among studies with mixed total and hemi-thyroidectomy, no significant difference in operation time was seen between harmonic scalpel and the conventional hemostasis (P = 0.313). Network meta-analysis also found harmonic scalpel and LigaSure to have less operation time than the conventional hemostasis, and that harmonic scalpel was associated with a significant 9.78 min reduction in operation time than LigaSure which was not seen in pair-wise comparison. Harmonic scalpel had significantly less risk of definitive recurrent laryngeal nerve palsy, intra-operation blood loss, and post-operation bleeding than the conventional hemostasis. LigaSure was associated with significantly less intra-operative blood loss than the conventional hemostasis (P = 0.023). There was no significant difference among three different procedures in rates of transient recurrent laryngeal nerve palsy. This study found that harmonic scalpel and LigaSure decreased operation time compared with the conventional hemostasis and that harmonic scalpels was associated with the lowest operation time.

microRNA-137 is downregulated in thyroid cancer and inhibits proliferation and invasion by targeting EGFR.

microRNA-137 expression is downregulated in several tumors. To date, its expression and function in human thyroid cancer remain unexplored. The aim of this study is to identify its expression, function, and molecular mechanism in thyroid cancer. microRNA-137 (miR-137) downregulation was observed in thyroid cancer tissues compared with normal thyroid tissues. miR-137 mimics downregulated B-CPAP cell proliferation, colony formation ability, and invasion, with suppressed expression of cyclin E, MMP2, p-ERK, and p-AKT. miR-137 inhibitor transfection in TPC-1 cell line showed the opposite effects. With prediction software and luciferase reporter assay, we found that epidermal growth factor receptor (EGFR) was a target of miR-137. Transfection of miR-137 mimic suppressed EGFR protein and messenger RNA (mRNA) expression. EGFR small interfering RNA (siRNA) abrogated the role of miR-137 inhibitor on cyclin E, MMP2, p-ERK, and p-AKT. In addition, we found a negative correlation of EGFR and miR-137 in thyroid cancer tissues. In conclusion, the present study showed that miR-137 downregulation is associated with malignant progression of thyroid cancer. miR-137 inhibits growth and invasion by targeting EGFR in thyroid cancer cells.

Recombinant ING4 suppresses the migration of SW579 thyroid cancer cells via epithelial to mesenchymal transition.

Thyroid cancer is a common endocrine malignancy that has rapidly increased in global incidence. Inhibitor of growth 4 (ING4) has been identified in various types of carcinoma; however, to the best of our knowledge, no previous studies have investigated the effects of ING4 on thyroid cancer. In the present study, SW579 thyroid cancer cells were treated with recombinant ING4 protein, and the results confirmed that recombinant ING4 protein was able to reduce the rate of proliferation, increase the rate of apoptosis and inhibit the mobility of SW579 cells. These results were obtained using a colony formation, fluoroscein isothiocyanate/propidium iodide double staining and Transwell assays, respectively. Furthermore, in the western blot analysis assays, ING4 was demonstrated to inhibit the Wnt/ß catenin signaling pathway and epithelial to mesenchymal transition (EMT). Therefore, the present study demonstrated the antitumor activities of recombinant ING4 and identified ING4 could inhibit EMT in thyroid cancer cell. However, additional studies are required to confirm these results in other cell types.

RhoBTB2 (DBC2) functions as a multifunctional tumor suppressor in thyroid cancer cells via mitochondrial apoptotic pathway.

Thyroid cancer is the most common endocrine malignancy worldwide. Tumor suppressor gene RhoBTB2 (also known as Deleted in Breast Cancer 2, DBC2) was observed in various carcinomas, however, no reports showed the effects of RhoBTB2 on thyroid cancer. In our study, we found that RhoBTB2 decreases proliferation, increases apoptosis, inhibits mobility, and induces mitochondria damage in SW579 cells through increased Bax and decreased Bcl-2 and Bcl-xL protein expression. The effects of RhoBTB2 on SW579 cells were inversed by using butin (an inhibitor of the mitochondrial apoptosis pathway). Our results suggest that RhoBTB2 suppresses the growth of SW579 cells through a mitochondrial apoptosis pathway.