EIF4A3-mediated biogenesis of circSTX6 promotes bladder cancer metastasis and cisplatin resistance.

BACKGROUND: Cisplatin (CDDP)-based chemotherapy is a standard first-line treatment for metastatic bladder cancer (BCa) patients, and chemoresistance remains a major challenge in clinical practice. Circular RNAs (circRNAs) have emerged as essential regulators in carcinogenesis and cancer progression. However, the role of circRNAs in mediating CDDP chemosensitivity has yet to be well elucidated in BCa. METHODS: CircSTX6 (hsa_circ_0007905) was identified by mining the public circRNA datasets and verified by Sanger sequencing, agarose gel electrophoresis, RNase R treatment and qRT-PCR assays. Then, function experiments were performed to evaluate the effects of circSTX6 on BCa metastasis. Luciferase reporter assay, RNA pull-down, RNA immunoprecipitation (RIP), RNA stability assay, Fluorescence in situ hybridization (FISH) and Immunofluorescence (IF) were conducted to evaluate the interaction among circSTX6, miR-515-3p, PABPC1 and SUZ12. Animal experiments were performed to explore the function of circSTX6 in tumor metastasis and CDDP sensitivity. RESULTS: We identified that circSTX6 was significantly upregulated in clinical samples and cells of BCa. Functionally, circSTX6 promoted cell migration and invasion both in vitro and in vivo. Mechanistically, circSTX6 could act as a miR-515-3p sponge and abolish its effect on SUZ12. Moreover, circSTX6 was confirmed to increase the stability of SUZ12 mRNA by interacting with a mRNA stabilizer PABPC1 and subsequently promote the expression of SUZ12. Importantly, silencing of circSTX6 improved the chemosensitivity of CDDP-resistant bladder cancer cells to CDDP. Furthermore, in vivo analysis supported that knockdown of circSTX6 attenuated CDDP resistance in BCa tumors. CONCLUSION: These studies demonstrate that circSTX6 plays a pivotal role in BCa metastasis and chemoresistance, and has potential to serve as a therapeutic target for treatment of BCa.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing.

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

Qualitative and Quantitative Determination of Decoquinate in Chicken Tissues by Gas Chromatography Tandem Mass Spectrometry.

A novel precolumn derivatization-GC-MS/MS method was developed for the determination of decoquinate residues in chicken tissues (muscle, liver, and kidney). The samples were extracted and purified by liquid-liquid extraction combined with solid-phase extraction and derivatized with acetic anhydride and pyridine. The recovery rates for decoquinate were 77.38~89.65%, and the intra-day and inter-day RSDs were 1.63~5.74% and 2.27~8.06%, respectively. The technique parameters meet the necessities for veterinary drug residue detection in China, the US, and the EU. Finally, the method was applied to analyze tissues of 60 chickens bought from a neighborhood supermarket, and solely one sample of chicken muscle contained 15.6 µg/kg decoquinate residue.

Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

Remdesivir Alleviates Acute Kidney Injury by Inhibiting the Activation of NLRP3 Inflammasome.

Acute kidney injury (AKI) is a frequent clinical complication in critically ill patients, and it rapidly develops into renal failure with high morbidity and mortality. However, other than dialysis, no effective therapeutic interventions can offer reliable treatment to limit renal injury and improve survival. Here, we firstly reported that remdesivir (RDV, GS-5734), a broad-spectrum antiviral nucleotide prodrug, alleviated AKI by specifically inhibiting NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome activation in macrophages. Mechanically, RDV effectively suppressed the activities of nuclear transcription factor (NF)-κB, mitogen-activated protein kinase (MAPK), which further led to the reduction of the inflammasome genes of NLRP3 transcription, limiting the activation of NLRP3 inflammasome in vivo and in vitro. RDV also inhibited other pro-inflammatory genes including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), IL-12, IL-1ß, and interferon-ß (IFN-ß), leading to the reduction of inflammatory factors release. Thus, RDV can ameliorate AKI via modulating macrophage inflammasome activation and inflammatory immune responses and may have a therapeutic potential for patients with AKI in clinical application.

The Hemodynamics of Patent Ductus Arteriosus in Patients after Central Shunt Operation.

A central shunt (CS) was an important surgery of systemic-to-pulmonary shunt (SPS) for the treatment of complex congenital heart diseases with decreased pulmonary blood flow (CCHDs-DPBF). There was no clear conclusion on how to deal with unclosed patent ductus arteriosus (PDA) during CS surgery. This study expanded the knowledge base on PDA by exploring the influence of the closing process of the PDA on the hemodynamic parameters for the CS model. The initial three-dimensional (3D) geometry was reconstructed based on the patient's computed tomography (CT) data. Then, a CS configuration with three typical pulmonary artery (PA) dysplasia structures and different sizes of PDA was established. The three-element windkessel (3WK) multiscale coupling model was used to define boundary conditions for transient simulation through computational fluid dynamics (CFD). The results showed that the larger size of PDA led to a greater systemic-to-pulmonary shunt ratio (Q S/A), and the flow ratio of the left pulmonary artery (LPA) to right pulmonary artery (RPA) (Q L/R) was more close to 1, while both the proportion of high wall shear stress (WSS) areas and power loss decreased. The case of PDA nonclosure demonstrates that the aortic oxygen saturation (Sao2) increased, while the systemic oxygen delivery (Do2) decreased. In general, for the CS model with three typical PA dysplasia, the changing trends of hemodynamic parameters during the spontaneous closing process of PDA were roughly identical, and nonclosure of PDA had a series of hemodynamic advantages, but a larger PDA may cause excessive PA perfusion and was not conducive to reducing cyanosis symptoms.

A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image.

PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT images jointly. METHOD: Patients diagnosed with supracardiac TPAVC from January 1, 2009, to December 31, 2018, in Guangdong Province People's Hospital were enrolled. Logistic regression were applied for clinical data features selection, while a convolutional neural network was used to extract CT images features. The prediction model was established by integrating the above two kinds of features for PVO prediction. And the proposed methods were evaluated using fourfold cross-validation. RESULT: Finally, 131 patients were enrolled in our study. Results show that compared with traditional approaches, the machine learning-based joint method using clinical data and CT image achieved the highest average AUC score of 0.943. In addition, the joint method also achieved a higher sensitivity of 0.828 and a higher positive prediction value of 0.864. CONCLUSION: Using clinical data and CT images jointly can improve the performance significantly compared with other methods that using only clinical data or CT images. The proposed machine learning-based joint method demonstrates the practicability of fully using multi-modality clinical data.

Cellular microRNAs influence replication of H3N2 canine influenza virus in infected cells.

MicroRNAs (miRNAs) are known to play important regulatory roles in host-virus interactions. Avian-origin H3N2 canine influenza virus (CIV) has emerged as the most prevalent subtype among dogs in Asia since 2007. To evaluate the roles of host miRNAs in H3N2 CIV infection, here, miRNA profiles obtained from primary canine bronchiolar epithelial cells (CBECs) and canine alveolar macrophages (CAMCs) were compared between infected and mock-infected cells with the H3N2 CIV JS/10. It was found that the expressions of cfa-miR-125b and cfa-miR-151, which have been reported to be associated with innate immunity and inflammatory response, were significantly decreased in CIV-infected canine primary cells. Bioinformatics prediction indicated that 5' seed regions of the two miRNAs are partially complementary to the mRNAs of nucleoprotein (NP) and non-structural protein 1 (NS1) of JS/10. As determined by virus titration, quantitative real-time PCR (qRT-PCR) and western blotting, overexpression of the two miRNAs inhibited CIV replication in cell culture, while their inhibition facilitated this replication, suggesting that the two miRNAs could act as negative regulators of CIV replication. Our findings support the notion that some cellular miRNAs can influence the outcome of virus infection, which helps to elucidate the resistance of host cells to viral infection and to clarify the pathogenesis of H3N2 CIV.

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery.

Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected. The multi-causes discovering with structure learning (McDSL) algorithm, least absolute shrinkage and selection operator regression (LASSO), and Cox proportional hazards regression model were employed to discover the predictors for 3-year PFS and OS, respectively. Eight machine learning classifiers with 5-fold cross-validation were developed to predict 3-year PFS and OS. The area under the curve (AUC) was used to evaluate the prognostic performance of classifiers. Results: McDSL identified four causal factors (tumor location, WHO grade, histologic type, and molecular genetic group) for 3-year PFS and OS, whereas LASSO and Cox identified wide-range number of factors associated with 3-year PFS and OS. The performance of each machine learning classifier based on McDSL, LASSO, and Cox was not significantly different. Logistic regression yielded the optimal performance in predicting 3-year PFS based on the McDSL (AUC, 0.872, 95% confidence interval [CI]: 0.828-0.916) and 3-year OS based on the LASSO (AUC, 0.901, 95% CI: 0.861-0.940). Conclusions: McDSL is more reproducible than LASSO and Cox model in the feature selection process. Logistic regression model may have the highest performance in predicting 3-year PFS and OS of gliomas.

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI. METHODS: We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging. For each MRI sequence, four non-texture and 10,320 texture features were extracted from medial temporal lobe, gray matter, and white matter, respectively. The relief and 0.632 + bootstrap algorithms were applied for initial and subsequent feature selection, respectively. Random forest method was used to construct the prediction model. Three models, 1, 2 and 3, were developed for predicting the results of the last three follow-up MRI scans at different times before RTLI onset, respectively. The area under the curve (AUC) was used to evaluate the performance of models. RESULTS: Of the 242 patients, 171 (70.7%) were men, and the mean age of all the patients was 48.5 ± 10.4 years. The median follow-up and latency from radiotherapy until RTLI were 46 and 41 months, respectively. In the testing cohort, models 1, 2, and 3, with 20 texture features derived from the medial temporal lobe, yielded mean AUCs of 0.830 (95% CI: 0.823-0.837), 0.773 (95% CI: 0.763-0.782), and 0.716 (95% CI: 0.699-0.733), respectively. CONCLUSION: The three developed radiomic models can dynamically predict RTLI in advance, enabling early detection and allowing clinicians to take preventive measures to stop or slow down the deterioration of RTLI.

Ultrasound Real-Time Tissue Elastography Improves the Diagnostic Performance of the ACR Thyroid Imaging Reporting and Data System in Differentiating Malignant from Benign Thyroid Nodules: A Summary of 1525 Thyroid Nodules.

BACKGROUND: To explore the correlation between the ultrasound elasticity score (ES) of real-time tissue elastography (RTE) and the malignant risk stratification of the Thyroid Imaging Reporting and Data System (TI-RADS) and to evaluate the added value of RTE to TI-RADS in differentiating malignant nodules from benign ones. METHODS: A total of 1,498 patients (885 women and 613 men; mean age of 43.5 ± 12.4 years) with 1,525 confirmed thyroid nodules (D = maximum diameter, D ≤ 2.5 cm) confirmed by fine-needle aspiration (FNA) and/or surgery were included. The nodules were divided into four groups based on their sizes (D ≤ 0.5 cm, 0.5 < D ≤ 1.0 cm, 1.0 < D ≤ 2.0 cm, and 2.0 < D ≤ 2.5 cm). We assigned an ES of RTE and malignant risk stratification of the TI-RADS category to each nodule. The correlation between the ES of RTE and the malignant risk stratification of TI-RADS category was analyzed by the Spearman's rank correlation. The diagnostic performances of RTE, TI-RADS, and their combination were compared by the receiver operator characteristic (ROC) analysis. RESULTS: The ES of RTE and the malignant risk stratification of TI-RADS showed a strong correlation in the size intervals of 0.5 < D ≤ 1.0 cm, 1.0 < D ≤ 2.0 cm, and 2.0 < D ≤ 2.5 cm (r = 0.768, 0.711, and 0.743, respectively). The diagnostic performance of their combination for each size interval was always better than RTE or TI-RADS alone (for all, P < 0.001). CONCLUSIONS: Overall, The ES of RTE was strongly correlated with the malignant risk stratification of TI-RADS. The diagnostic performance of the combination of RTE and TI-RADS outperformed RTE or TI-RADS alone. Therefore, RTE may be an adjunctive tool to the current TI-RADS system for differentiating malignant from benign thyroid nodules.

Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.

OBJECTIVES: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography. DESIGN: Retrospective cohort study. PATIENTS: A total of 2818 patients (1890 female, mean age, 45.5 ± 13.2 years) with 2850 thyroid nodules were retrospectively evaluated between April 2011 and October 2016. 26.8% nodules were malignant. MEASUREMENTS: We used a randomly divided sample of 80% of the nodules to perform a multivariate logistic regression analysis. Cut-points were determined to create a risk-stratification scoring system. Patients were classified as having low, moderate and high probability of malignancy according to their scores. We validated the models to the remaining 20% of the nodules. The area under the curve (AUC) was used to evaluate the discrimination ability of the systems. RESULTS: Ten variables were selected as predictors of malignancy. The point-based scoring systems with and without elasticity score achieved similar AUCs of 0.916 (95% confidence interval [CI]: 0.885-0.948) and 0.906 (95% CI: 0.872-0.941) when validated. Malignancy risk was segmented from 0% to 100.0% and was positively associated with an increase in risk scores. We then developed a Web-based risk-stratification system of thyroid nodules (http: thynodscore.com). CONCLUSION: A simple and reliable Web-based risk-stratification system could be practically used in stratifying the risk of malignancy in thyroid nodules.

Evaluating primary intra-arterial chemotherapy versus intravenous plus intra-arterial chemotherapy for advanced intraocular retinoblastoma.

PURPOSE: Although intra-arterial chemotherapy (IAC) is commonly used for treating intraocular retinoblastoma, it is not a systemic therapy. We aimed to investigate whether the addition of intravenous chemotherapy (IVC) before IAC administration had any effects (whether beneficial or adverse) on patient outcomes. METHODS: This multicenter retrospective cohort study included 213 patients with advanced intraocular retinoblastoma who received IVC plus IAC (n = 103) or IAC alone (n = 110) between April 2009 and January 2017. Eyes were grouped according to the International Intraocular Retinoblastoma Classification. Kaplan-Meier and Cox regression analyses were performed to compare survival outcomes between the two groups. Moreover, details regarding enucleation were recorded. RESULTS: The 3-year ocular survival rates were 62% in the IVC plus IAC group and 68% in the IAC group (hazard ratio (HR) 0.88, 95% confidence interval (CI) 0.55-1.43, P = 0.61). Moreover, the corresponding 3-year overall survival rates were 97% and 93%, respectively (HR 1.56, 95% CI 0.41-5.90, P = 0.51), while the 3-year event-free survival rates were 76% and 72%, respectively (HR 0.96, 95% CI 0.56-1.65, P = 0.89). CONCLUSIONS: Within a 3-year follow-up period, IVC plus IAC produced no additional benefit over primary IAC for treating advanced intraocular retinoblastoma in terms of local tumor control and extending survival. Longer follow-up periods are required to assess long-term efficacy.

Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation.

PURPOSE: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans. MATERIALS AND METHODS: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model. The concordance index (C-index) was applied to evaluate the model's prognostic performance. A Kaplan-Meier analysis and the log-rank test were used to assess risk stratification ability of models in predicting progression. A nomogram was plotted to predict individual risk of progression. RESULTS: Composed of four significant features, the radiomic model showed good performance in stratifying patients into high- and low-risk groups of progression in both the training and validation cohorts (log-rank test, p = 0.00016, p = 0.0063, respectively). Peripheral invasion and metastasis were selected as significant clinical variables. The combined radiomic-clinical model showed good discriminative performance, with C-indices 0.804 (95% confidence interval (CI), 0.688-0.920) and 0.756 (95% CI, 0.605-0.907) in the training and validation cohorts, respectively. The median progression-free survival (PFS) in the high-risk group was significantly shorter than that in the low-risk group in the training (median PFS, 9.5 m and 19.0 m, respectively; p [log-rank] < 0.0001) and validation (median PFS, 11.3 m and 22.5 m, respectively; p [log-rank] = 0.0063) cohorts. CONCLUSIONS: A radiomics-based model was established to predict the risk of progression in hypopharyngeal cancer with chemoradiotherapy. KEY POINTS: • Clinical information showed limited performance in stratifying the risk of progression among patients with hypopharyngeal cancer. • Imaging features extracted from CECT and NCCT images were independent predictors of PFS. • We combined significant features and valuable clinical variables to establish a nomogram to predict individual risk of progression.

Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging.

Background: To develop and validate a radiomic nomogram incorporating radiomic features with clinical variables for individual local recurrence risk assessment in nasopharyngeal carcinoma (NPC) patients before initial treatment. Methods: One hundred and forty patients were randomly divided into a training cohort (n = 80) and a validation cohort (n = 60). A total of 970 radiomic features were extracted from pretreatment magnetic resonance (MR) images of NPC patients from May 2007 to December 2013. Univariate and multivariate analyses were used for selecting radiomic features associated with local recurrence, and multivariate analyses was used for building radiomic nomogram. Results: Eight contrast-enhanced T1-weighted (CET1-w) image features and seven T2-weighted (T2-w) image features were selected to build a Cox proportional hazard model in the training cohort, respectively. The radiomic nomogram, which combined radiomic features and multiple clinical variables, had a good evaluation ability (C-index: 0.74 [95% CI: 0.58, 0.85]) in the validation cohort. The radiomic nomogram successfully categorized those patients into low- and high-risk groups with significant differences in the rate of local recurrence-free survival (P <0.05). Conclusions: This study demonstrates that MR imaging-based radiomics can be used as an aid tool for the evaluation of local recurrence, in order to develop tailored treatment targeting specific characteristics of individual patients.

Intra-arterial chemotherapy as primary or secondary treatment for infants diagnosed with advanced retinoblastoma before 3 months of age.

BACKGROUND: To evaluate the safety and efficacy of intra-arterial chemotherapy (IAC) for the primary or secondary treatment of infants diagnosed with advanced retinoblastoma before 3 months of age. METHODS: This single-center retrospective study included 39 infants (42 eyes) aged ≤3 months who were diagnosed with unilateral or bilateral advanced intraocular retinoblastoma (group D and E eyes) and received IAC as primary or secondary treatment between June 2012 and February 2017. Based on each patient's therapeutic history and response to chemotherapeutic drugs, melphalan, topotecan, and/or carboplatin were used for IAC. The main outcomes included the technical success rate for IAC, survival rates, and adverse events. RESULTS: In total, 29 and 13 eyes received IAC as primary and secondary treatments, respectively. Catheterization was successful in 136 of 137 procedures. All eyes in the secondary IAC group had previously received intravenous chemotherapy. The mean number of IAC sessions for each eye was 3 (range, 2-6). The 2-year ocular survival rates were 80.7% (95% confidence interval [CI], 58.9-91.7) in the primary IAC group and 91.7% (95% CI, 53.9-98.8) in the secondary IAC group. During the follow-up period, 1 patient with unilateral disease (group E) developed extraocular disease and died. The 2-year recurrence-free survival rates in the primary and secondary IAC groups were 71.9% (95% CI, 49.4-85.7) and 75.0% (95% CI, 40.8-91.2), respectively. During each catheterization procedure, the main complications included eyelid erythema (2.4%), fundus hemorrhage (11.9%), myelosuppression (7.7%), transient vomiting and hair loss (2.6%), and transient pancytopenia (2.6%). Prolonged complications included phthisis bulbi (19.0%), vision loss (19.0%), poor vision (9.5%), and cataract (2.4%). There was no case of stroke, neurological impairment, secondary malignant tumor, or metastasis. CONCLUSIONS: Our findings suggest that IAC, whether primary or secondary, is effective and fairly safe for the management of advanced retinoblastoma in infants aged < 3 months. However, adverse events related to intra-arterial injection and the visual outcomes cannot be neglected and require further investigation.

Roles of three TonB systems in the iron utilization and virulence of the Aeromonas hydrophila Chinese epidemic strain NJ-35.

The TonB system functions in iron transport and has been identified in certain Gram-negative bacteria. Recently, we reported three TonB systems in the Aeromonas hydrophila Chinese epidemic strain NJ-35, but the functions of these systems have not been thoroughly elucidated to date. In this study, we investigated the role of these TonB systems in A. hydrophila iron utilization and virulence. We found that tonB1 and tonB2 were preferentially transcribed in iron-chelated conditions, where gene expression levels were approximately 8- and 68-fold higher compared with iron-rich conditions, respectively; tonB3 was consistently transcribed at a low level under iron-repleted and iron-depleted conditions. Only the TonB2 system was required to utilize iron-binding proteins. The tonB123 mutant showed increased susceptibility to erythromycin and roxithromycin. In addition, all three tonB genes were involved in A. hydrophila virulence in zebrafish, and various phenotypes associated with environmental survival were changed with varying degrees in each tonB mutant. TonB2 plays a relatively major role in adhesion, motility, and biofilm formation, while TonB3 is more involved in the anti-phagocytosis of A. hydrophila. In each observed phenotype, no significant difference was found between the single- and double-deletion mutants, whereas the triple-deletion mutant exhibited the most serious defects, indicating that all three TonB systems of A. hydrophila coordinately complement one another. In conclusion, this study elucidates the importance of TonB in iron acquisition and virulence of A. hydrophila, which lays the foundation for future studies regarding the survival mechanisms of this bacterium in iron-restricted environments.

Machine Learning-Assisted System for Thyroid Nodule Diagnosis.

Background: Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning. Methods: A total of 2064 thyroid nodules (2032 patients, 695 male; Mage = 45.25 ± 13.49 years) met all of the following inclusion criteria: (i) hemi- or total thyroidectomy, (ii) maximum nodule diameter 2.5 cm, (iii) examination by conventional US and real-time elastography within one month before surgery, and (iv) no previous thyroid surgery or percutaneous thermotherapy. Models were developed using 60% of randomly selected samples based on nine commonly used algorithms, and validated using the remaining 40% of cases. All models function with a validation data set that has a pretest probability of malignancy of 10%. The models were refined with machine learning that consisted of 1000 repetitions of derivatization and validation, and compared to diagnosis by an experienced radiologist. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results: A random forest algorithm led to the best diagnostic model, which performed better than radiologist diagnosis based on conventional US only (AUC = 0.924 [confidence interval (CI) 0.895-0.953] vs. 0.834 [CI 0.815-0.853]) and based on both conventional US and real-time elastography (AUC = 0.938 [CI 0.914-0.961] vs. 0.843 [CI 0.829-0.857]). Conclusions: Machine-learning algorithms based on US examinations, particularly the random forest classifier, may diagnose malignant thyroid nodules better than radiologists.

Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.

BACKGROUND: We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC). METHODS: This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis. FINDINGS: In total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high- and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P < 0·001). A radiomics nomogram based on radiomic features and clinical variables was developed for DM risk assessment in each patient, and it showed a significant predictive ability in the training [area under the curve (AUC), 0·827; 95% confidence interval (CI), 0.754-0.900] and validation (AUC, 0.792; 95% CI, 0.633-0.952) cohorts. INTERPRETATION: DMMM can serve as a visual prognostic tool for DM prediction in NPC, and it can improve treatment decisions by aiding in the differentiation of patients with high and low risks of DM. FUND: This research received financial support from the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81771924, 81501616, 81671851, and 81527805); the National Natural Science Foundation of Guangdong Province (2018B030311024); the Science and Technology Planning Project of Guangdong Province (2016A020216020); the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328); the China Postdoctoral Science Foundation (2016M600145); and the National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, and 2017YFC1309100).