Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer

BACKGROUND

Vesical Imaging-Reporting and Data System (VI-RADS) is a pathway for the standardized imaging and reporting of bladder cancer staging using multiparametric (mp) MRI.

PURPOSE

To investigate additional role of morphological (MOR) measurements to VI-RADS for the detection of muscle-invasive bladder cancer (MIBC) with mpMRI.

STUDY TYPE

Retrospective.

POPULATION

A total of 198 patients (72 MIBC and 126 NMIBC) underwent bladder mpMRI was included.

FIELD STRENGTH/SEQUENCE

3.0 T/T2-weighted imaging with fast-spin-echo sequence, spin-echo-planar diffusion-weighted imaging and dynamic contrast-enhanced imaging with fast 3D gradient-echo sequence.

ASSESSMENT

VI-RADS score and MOR measurement including tumor location, number, stalk, cauliflower-like surface, type of tumor growth, tumor-muscle contact margin (TCM), tumor-longitudinal length (TLL), and tumor cellularity index (TCI) were analyzed by three uroradiologists (3-year, 8-year, and 15-year experience of bladder MRI, respectively) who were blinded to histopathology.

STATISTICAL TESTS

Significant MOR measurements associated with MIBC were tested by univariable and multivariable logistic regression (LR) analysis with odds ratio (OR). Area under receiver operating characteristic curve (AUC) with DeLong's test and decision curve analysis (DCA) were used to compared the performance of unadjusted vs. adjusted VI-RADS. A P-value <0.05 was considered statistically significant.

RESULTS

TCM (OR 9.98; 95% confidence interval [CI] 4.77-20.8), TCI (OR 5.72; 95% CI 2.37-13.8), and TLL (OR 3.35; 95% CI 1.40-8.03) were independently associated with MIBC at multivariable LR analysis. VI-RADS adjusted by three MORs achieved significantly higher AUC (reader 1 0.908 vs. 0.798; reader 2 0.906 vs. 0.855; reader 3 0.907 vs. 0.831) and better clinical benefits than unadjusted VI-RADS at DCA. Specially in VI-RADS-defined equivocal lesions, MOR-based adjustment resulted in 55.5% (25/45), 70.4% (38/54), and 46.4% (26/56) improvement in accuracy for discriminating MIBC in three readers, respectively.

DATA CONCLUSION

MOR measurements improved the performance of VI-RADS in detecting MIBC with mpMRI, especially for equivocal lesions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

[Ewing's sarcoma of central nervous system: a clinicopathological analysis of six cases]

Objective: To investigate the clinicopathological characteristics, pathological diagnosis of Ewing's sarcoma of the central nervous system. Methods: Six cases of Ewing's sarcoma of the central nervous system diagnosed at the First Affiliated Hospital of Nanjing Medical University, Nanjing, China from 2015 to 2022 were collected. The clinical manifestations, histological morphology, immunophenotype and molecular genetics of these cases were analyzed. The related literature was reviewed. Results: There were four males and two females, with a male to female ratio of 2∶1. The onset age was 17-40 years, with a median age of 23 years. All 6 tumors were located in the spinal cord (2 cases of cervical vertebra, 1 case of thoracic vertebra, 2 cases of lumbar vertebra, and 1 case of sacral vertebra). The patients' clinical manifestations were mostly lumbago, weakness and numbness of lower limbs/limbs. In 1 case, the tumor recurred and metastasized to the suprasellar region and the third ventricle. Microscopically, the tumor showed diffuse infiltrative growth. In some cases, the tumor was closely related to the spinal meninges. The tumor cells were arranged in sheet, lobular, thin-rope, and nest-like patterns. Homer-Wright rosette was visible. The tumor cells were small to medium in size, and most of them had scant cytoplasm. A few cells had clear cytoplasm. Some areas were rhabdoid. The tumor cell nuclei showed focal mild pleomorphism. The chromatin was uniform and delicate while the nucleoli were not obvious. Mitosis was commonly seen. The tumor was separated by fibrous connective tissue and may be accompanied by mucinous degeneration. Immunohistochemistry showed that all tumors were positive for CD99, NKX2.2, Fli1, ERG. ATRX, H3K27me3, INI1 and BRG1 were all retained. Immunohistochemical stains for EMA, GFAP and Olig2 were negative. The Ki-67 proliferation index was 30%-70%. EWSR1 break-apart FISH test was positive. Conclusions: Ewing's sarcoma is rare in the central nervous system and needs to be distinguished from a variety of neoplasms with primitive undifferentiated small cell morphology. Immunohistochemistry and molecular genetics may be required for a proper diagnosis.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence

BACKGROUND

To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.

METHODS

Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.

RESULTS

RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score.

CONCLUSIONS

AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Enzalutamide and olaparib synergistically suppress castration-resistant prostate cancer progression by promoting apoptosis through inhibiting nonhomologous end joining pathway

Recent studies revealed the relationship among homologous recombination repair (HRR), androgen receptor (AR), and poly(adenosine diphosphate-ribose) polymerase (PARP); however, the synergy between anti-androgen enzalutamide (ENZ) and PARP inhibitor olaparib (OLA) remains unclear. Here, we showed that the synergistic effect of ENZ and OLA significantly reduced proliferation and induced apoptosis in AR-positive prostate cancer cell lines. Next-generation sequencing followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed the significant effects of ENZ plus OLA on nonhomologous end joining (NHEJ) and apoptosis pathways. ENZ combined with OLA synergistically inhibited the NHEJ pathway by repressing DNA-dependent protein kinase catalytic subunit (DNA-PKcs) and X-ray repair cross complementing 4 (XRCC4). Moreover, our data showed that ENZ could enhance the response of prostate cancer cells to the combination therapy by reversing the anti-apoptotic effect of OLA through the downregulation of anti-apoptotic gene insulin-like growth factor 1 receptor ( IGF1R ) and the upregulation of pro-apoptotic gene death-associated protein kinase 1 ( DAPK1 ). Collectively, our results suggested that ENZ combined with OLA can promote prostate cancer cell apoptosis by multiple pathways other than inducing HRR defects, providing evidence for the combined use of ENZ and OLA in prostate cancer regardless of HRR gene mutation status.

[Gonadoblastoma: Clinicopathological study and literature review of 3 cases]

OBJECTIVE

To investigate the clinical feature, pathological morphology, special histopathological subtype and immunohistochemical characteristic of gonadoblastoma.

METHODS

Three patients of gonadoblastoma treated from 2014 to 2020 were enrolled, and the clinical characteristics, histological morphology and immunophenotype were analyzed, and the literatures were also reviewed.

RESULT

Three phenotypical females were 14,17 and 27 years old. Case 1 was 46,XX with normal gonadal development. Case 2 was 46,XY and case 3 was chromosomal chimeric type (46, XY 90%/45,X 10%), both with dysgenetic gonads. Microscopically, the morphology of classic type was observed in all cases more or less, manifesting small nests of primitive germ cells and surrounding clustered sex cord-like cells, usually with Call-Exner like bodies and calcification. In additon, the morphology of special subtype can be seen in case 1,exhibiting cord-like tumor cells, which was segmentated by cellular fibrous stroma. Cases 2 and 3 were accompanied by dysgerminoma components. Immunohistochemically,all the primal germ cells were positive for OCT3/4, PLAP and CDll7 , and sexcord-like cells were positive for inhibin, SF-1, SOX9 and FOXL2 . Patients were followed up for 10 years, 6 years and 4 years respectively without recurrence.

CONCLUSION

Gonadoblastoma is a rare germ cell-sex cord stromal tumor, which is usually accompanied by gonadal hypoplasia. As a special subtype, dissecting gonadoblastoma will be easily confused with dysgerminoma/seminoma, but the prognosis is better. So we should improve the understanding of this subtype and avoid overdiagnosis.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study

BACKGROUND

The high level of expertise required for accurate interpretation of prostate MRI.

PURPOSE

To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI.

STUDY TYPE

Retrospective.

SUBJECTS

One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test).

FIELD STRENGTH/SEQUENCE

3.0T/scanners, T₂ -weighted imaging (T₂ WI), diffusion-weighted imaging, and apparent diffusion coefficient map.

ASSESSMENT

Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology.

STATISTICAL TESTS

Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I² analysis.

RESULTS

In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I²  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05).

DATA CONCLUSION

Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Longitudinal Multiparametric MRI Assessment of Irradiated Salivary Gland in a Rat Model: Correlated With Histological Findings

BACKGROUND

Several magnetic resonance imaging (MRI) sequences have been applied to assess injured glands but without histological validation.

PURPOSE

To evaluate longitudinal changes in multiparametric MRI (mp-MRI) of irradiated salivary glands in a rat model and investigate correlations between mp-MRI and histological findings.

STUDY TYPE

Prospective.

ANIMAL MODEL

Submandibular glands of 36 rats were radiated using a single dose of 15 Gy X-ray (irradiation [IR] group), and 6 other rats were enrolled into sham-IR group. mp-MRI were scanned 1 day after sham-IR (n = 6), or 1, 2, 4, 8, 12, 24 weeks after IR (n = 36, 6 per subgroup).

FIELD STRENGTH/SEQUENCE

A 3.0-T/Diffusion-weighted imaging (DWI), readout-segmented echo-planar imaging (EPI) sequence; intravoxel incoherent motion DWI, single-shot EPI sequence; T1 mapping, dual-flip-angle gradient-echo sequence with volumetric interpolated breath-hold examination; T2 mapping, turbo spin-echo sequence.

ASSESSMENT

Parameters including apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D^* ), perfusion fraction (f), T1 and T2 value were obtained. Histological examinations, including hematoxylin and eosin staining (for acinar cell fraction [AC%] detection), Masson's trichrome staining (for degree of fibrosis [F%] determination) and CD34-immunohistochemical staining (for microvessel density [MVD] calculation), were performed at corresponding time points.

STATISTICAL TESTS

One-way analysis of variance was used to compare the mp-MRI and histological parameters among different groups. Spearman correlation analysis was applied to determine the correlation between mp-MRI and histological parameters. Two-sided P ≤ 0.05 was considered statistically significant.

RESULTS

Changes of mp-MRI parameters (ADC, D, D^* , f, T1, T2) and histological results (AC%, F%, MVD) among the seven groups were all significant. ADC, D, and T2 values negatively correlated with AC% (ADC, r = -0.728; D, r = -0.773; T2, r = -0.600), f positively correlated with MVD (r = 0.496), and T1 values positively correlated with F% (r = 0.714). DATA CONCLUSION: mp-MRI might be able to noninvasively and quantitatively evaluate the dynamic pathological changes within the irradiated salivary glands.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

Background

Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods

The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

Findings

The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.

Interpretation

The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).

Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

PURPOSE

A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)-based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI).

METHODS

Eight hundred and forty nine consecutive PCa patients who underwent mpMRI and prostatectomy without previous radio- or hormonal therapy from two medical centers were retrospectively included. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts' prior knowledge (PAGNet) from 596 training patients. Model validation was performed in 150 internal and 103 external patients. Performance comparison was made between AI, two experts using a criteria-based ECE grading system, and expert-AI interaction.

RESULTS

An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867), and 0.728 (95% CI, 0.631-0.811) in training, internal, and external validation data, respectively. The performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When experts' interpretations were adjusted by AI assessments, the performance of two experts was improved.

CONCLUSION

Our AI tool, showing improved accuracy, offers a promising alternative to human experts for ECE staging using mpMRI.

[Clinical characteristics and treatment strategies of prostatic mucinous adenocarcinoma: A report of 10 cases and literature review]

OBJECTIVE

To investigate the clinical characteristics and treatment strategies of prostatic mucinous adenocarcinoma (PMAC).

METHODS

We retrospectively analyzed the clinical data on 10 cases of PMAC treated in the First Affiliated Hospital of Nanjing Medical University from January 2014 to June 2018. The patients were aged 51－79 (65 ± 14) years, with a medium PSA level of 89 (14.63－128.05) μg/L and Gleason scores of 3 + 3 in 1 case, 3 + 4 in 2, 4 + 3 in 1 and 8 in 6 cases preoperatively, 1 treated by robot-assisted radical prostatectomy and the other 9 by laparoscopic radical prostatectomy. We conducted pelvic cavity lymph node dissection for all the patients and analyzed their prognosis and survival.

RESULTS

Operations were successfully completed in all the cases. Pathological examination revealed 2 cases of mucinous adenocarcinoma with signet ring cell carcinoma in the 10 PMAC patients, 2 at stage ≤T2b, 5 at stage ≥T2c, 3 positive at pelvic lymph node dissection and 5 positive at the incision margin. The patients were followed up for 6－48 (median 26) months. Four of the patients were found with biochemical recurrence within 2 years after operation and treated by androgen-deprivation therapy, radiotherapy and chemotherapy, which reduced the PSA level to <1.0 μg/ml in all the 4 cases.

CONCLUSIONS

PMAC has a good prognosis. Radical surgery is recommended for moderate and low-risk PMAC and the patients with postoperative biochemical recurrence can benefit from comprehensive treatment of total androgen blockade.

A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection

OBJECTIVES

To develop a machine learning (ML)-assisted model to identify candidates for extended pelvic lymph node dissection (ePLND) in prostate cancer by integrating clinical, biopsy, and precisely defined magnetic resonance imaging (MRI) findings.

PATIENTS AND METHODS

In all, 248 patients treated with radical prostatectomy and ePLND or PLND were included. ML-assisted models were developed from 18 integrated features using logistic regression (LR), support vector machine (SVM), and random forests (RFs). The models were compared to the Memorial SloanKettering Cancer Center (MSKCC) nomogram using receiver operating characteristic-derived area under the curve (AUC) calibration plots and decision curve analysis (DCA).

RESULTS

A total of 59/248 (23.8%) lymph node invasions (LNIs) were identified at surgery. The predictive accuracy of the ML-based models, with (+) or without (-) MRI-reported LNI, yielded similar AUCs (RFs⁺ /RFs^- : 0.906/0.885; SVM⁺ /SVM^- : 0.891/0.868; LR⁺ /LR^- : 0.886/0.882) and were higher than the MSKCC nomogram (0.816; P < 0.001). The calibration of the MSKCC nomogram tended to underestimate LNI risk across the entire range of predicted probabilities compared to the ML-assisted models. The DCA showed that the ML-assisted models significantly improved risk prediction at a risk threshold of ≤80% compared to the MSKCC nomogram. If ePLNDs missed was controlled at <3%, both RFs⁺ and RFs^- resulted in a higher positive predictive value (51.4%/49.6% vs 40.3%), similar negative predictive value (97.2%/97.8% vs 97.2%), and higher number of ePLNDs spared (56.9%/54.4% vs 43.9%) compared to the MSKCC nomogram.

CONCLUSIONS

Our ML-based model, with a 5-15% cutoff, is superior to the MSKCC nomogram, sparing ≥50% of ePLNDs with a risk of missing <3% of LNIs.

An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification

Preoperatively predict the probability of Prostate cancer (PCa) biochemical recurrence (BCR) is of definite clinical relevance. The purpose of this study was to develop an imaging-based approach in the prediction of 3-years BCR through a novel support vector machine (SVM) classification. We collected clinicopathologic and MR imaging datasets in 205 patients pathologically confirmed PCa after radical prostatectomy. Univariable and multivariable analyses were used to assess the association between MR findings and 3-years BCR, and modeled the imaging variables and follow-up data to predict 3-year PCa BCR using SVM analysis. The performance of SVM was compared with conventional Logistic regression (LR) and D'Amico risk stratification scheme by area under the receiver operating characteristic curve (Az) analysis. We found that SVM had significantly higher Az (0.959 vs. 0.886; p = 0.007), sensitivity (93.3% vs. 83.3%; p = 0.025), specificity (91.7% vs. 77.2%; p = 0.009) and accuracy (92.2% vs. 79.0%; p = 0.006) than LR analysis. Performance of popularized D'Amico scheme was effectively improved by adding MRI-derived variables (Az: 0.970 vs. 0.859, p < 0.001; sensitivity: 91.7% vs. 86.7%, p = 0.031; specificity: 94.5% vs. 78.6%, p = 0.001; and accuracy: 93.7% vs. 81.0%, p = 0.007). Additionally, beside pathological Gleason score (hazard ratio [HR] = 1.560, p = 0.008), surgical-T3b (HR = 4.525, p < 0.001) and positive surgical margin (HR = 1.314, p = 0.007), apparent diffusion coefficient (HR = 0.149, p = 0.035) was the only independent imaging predictor of time to PSA failure. Therefore, We concluded that imaging-based approach using SVM was superior to LR analysis in predicting PCa outcome. Adding MR variables improved the performance of D'Amico scheme.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer

OBJECTIVE

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa).

METHODS

This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis.

RESULTS

For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]).

CONCLUSION

Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa.

KEY POINTS

• Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Genome-wide identification and analysis of the SGR gene family in Cucumis melo L

Chlorophyll (CHL) is present in many plant organs, and its metabolism is strongly regulated throughout plant development. Understanding the fate of CHL in senescent leaves or during fruit ripening is a complex process. The stay-green (SGR) protein has been shown to affect CHL degradation. In this study, we used the conserved sequences of STAY-GREEN domain protein (NP_567673) in Arabidopsis thaliana as a probe to search SGR family genes in the genome-wide melon protein database. Four candidate SGR family genes were identified in melon (Cucumis melo L. Hetao). The phylogenetic evolution, gene structure, and conserved motifs were subsequently analyzed. In order to verify the function of CmSGR genes in CHL degradation, CmSGR1 and CmSGR2 were transiently overexpressed and silenced using different plasmids in melon. Overexpression of CmSGR1 or CmSGR2 induced leaf yellowing or fruit ripening, while silencing of CmSGR1 or CmSGR2 via RNA interference delayed CHL breakdown during fruit ripening or leaf senescence compared with the wild type. Next, the expression profile was analyzed, and we found that CmSGR genes were expressed ubiquitously. Moreover, CmSGR1 and CmSGR2 were upregulated, and promoted fruit ripening. CmSGR3 and CmSGR4 were more highly expressed in leaves, cotyledon, and stem compared with CmSGR1 or CmSGR2. Thus, we conclude that CmSGR genes are crucial for fruit ripening and leaf senescence. CmSGR protein structure and function were further clarified to provide a theoretical foundation and valuable information for improved performance of melon.

Acute kidney damage induced by low- and iso-osmolar contrast media in rats: Comparison study with physiologic MRI and histologic-gene examination

PURPOSE

To investigate the physiopathological effects of low- and iso-osmolar contrast media (CM) on renal function with physiologic MRI and histologic-gene examination.

MATERIALS AND METHODS

Forty-eight rats underwent time-course DWI and DCE-MRI at 3.0 Tesla (T) before and 5-15 min after exposure of CM or saline (Iop.370: 370 mgI/mL iopromide; Iod.320: 320 mgI/mL iodixanol; Iod.270: 270 mgI/mL iodixanol; 4 gI/kg body weight). Intrarenal viscosity was reflected by apparent diffusion coefficient (ADC). Renal physiologies were evaluated by DCE-derived glomerular filtration rate (GFR), renal blood flow (RBF), and renal blood volume (RBV). Potential acute kidney injury (AKI) was determined by histology and the expression of kidney injury molecule 1 (Kim-1).

RESULTS

Iop.370 mainly increased ADC in inner-medulla (△ADC_IM : 12.3 ± 11.1%; P < 0.001). Iod.320 and Iod.270 mainly decreased ADC in outer-medulla (△ADC_IM ; Iod.320: 16.8 ± 7.5%; Iod.270: 18.1 ± 9.5%; P < 0.001) and inner-medulla (△ADC_IM ; Iod.320: 28.4 ± 9.3%; Iod.270: 30.3 ± 6.3%; P < 0.001). GFR, RBF and RBV were significantly decreased by Iod.320 (△GFR: 45.5 ± 24.1%; △RBF: 44.6 ± 19.0%; △RBV: 35.2 ± 10.1%; P < 0.001) and Iod.270 (33.2 ± 19.0%; 38.1 ± 15.6%; 30.1 ± 10.1%; P < 0.001), while rarely changed by Iop.370 and saline. Formation of vacuoles and increase in Kim-1 expression was prominently detected in group of Iod.320, while rarely in Iod.270 and Iop.370.

CONCLUSION

Iso-osmolar iodixanol, given at high-dose, produced prominent AKI in nonhydrated rats. This renal dysfunction could be assessed noninvasively by physiologic MRI.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:291-302.

Determination of carbonyl compounds in water by derivatization-solid-phase microextraction and gas chromatographic analysis

The solid-phase microextraction (SPME) technique was evaluated for the determination of 23 carbonyl compounds in water. The carbonyl compounds in water were derivatized with omicron-(2,3,4,5,6-pentafluorobenzyl)-hydroxylamine hydrochloride (PFBHA), extracted with SPME from liquid or headspace and analyzed by GC with electron capture detection (GC-ECD). The effects of agitation techniques and the addition of salt (NaCl) on extraction, the absorption-time and absorption-concentration profiles were examined. The precision of the SPME technique for the determination of carbonyl compounds was evaluated with spiked bidistilled water, ozonated drinking water, and rain water. The relative standard deviations obtained from different spiked water matrix were similar, and in the range of 5.7-21.1%. The precision can be further improved by using an internal standard. With 4 ml of water sample, the limits of detection for most of the tested carbonyl compounds using liquid or headspace SPME-GC-ECD were similar and in the range of 0.006-0.2 micrograms/l, except for glyoxal and methylglyoxal, which showed low sensitivity when using headspace SPME. In the analysis of an ozonated drinking water sample, the SPME techniques gave comparable results to those of the conventional liquid-liquid extraction method.