Differentiating mixed epithelial and stromal tumor family from predominantly cystic renal cell carcinoma using magnetic resonance imaging-based Bosniak classification system version 2019.

PURPOSE: To differentiate mixed epithelial and stromal tumor family (MESTF) of the kidney from predominantly cystic renal cell carcinoma (RCC) using the magnetic resonance imaging (MRI)-based Bosniak classification system version 2019 (v2019). MATERIALS AND METHODS: The study included 36 consecutive patients with MESTF and 77 with predominantly cystic RCC who underwent preoperative renal MRI. One radiologist evaluated and documented the clinical and MRI characteristics (age, sex, laterality, R.E.N.A.L. Nephrometry Score [RNS], surgical approach, the signal intensity on T2-weighted imaging, restricted diffusion and enhancement features in corticomedullary phase). Blinded to clinical and pathological information, another two radiologists independently evaluated Bosniak category of all masses. Interobserver agreement based on Bosniak classification system v2019 was measured by the weighted Cohen/Conger's Kappa coefficient. Furthermore, predominantly cystic RCCs and MESTFs were divided into low (categories I, II, and IIF) and high-class (categories III, and IV) tumors. The independent sample t test (Mann-Whitney U test) or Pearson Chi-square test (Fisher's exact probability test) was utilized to compare clinical and imaging characteristics between MESTFs and predominantly cystic RCCs. The performance of the Bosniak classification system v2019 in distinguishing MESTF from predominantly cystic RCC was investigated via receiver operating characteristic curve analysis. RESULTS: MESTF and predominantly cystic RCC groups significantly differed in terms of age, lesion size, RNS, restricted diffusion, and obvious enhancement in corticomedullary phase, but not sex, laterality, surgical approach, and the signal intensity on T2WI. Interobserver agreement was substantially based on the Bosniak classification system v2019. There were 24 low-class tumors and 12 high-class tumors in the MESTF group. Meanwhile, 13 low-class tumors and 64 high-class tumors were observed in the predominantly cystic RCC group. The distribution of low- or high-class tumors significantly differed between the MESTF and predominantly cystic RCC groups. Bosniak classification system v2019 had excellent discrimination (cutoff value = category III), and an area under curve value was 0.81; accuracy, 80.5%; sensitivity, 87.0%; and specificity, 66.7%. CONCLUSION: The MRI-based Bosniak classification system v2019 can effectively distinguish MESTF from predominantly cystic RCC if category III was used as a cutoff reference.

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study.

BACKGROUND: Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension. PURPOSE: To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs. STUDY TYPE: Retrospective. POPULATION: 797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses. FIELD STRENGTH/SEQUENCES: 3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging. ASSESSMENT: Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions. STATISTICAL TESTS: Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05. RESULTS: The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993). CONCLUSION: The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 2.

Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score.

PURPOSE: Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC. METHODS: We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)]. RESULTS: In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780). CONCLUSION: The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.

A multiomic study uncovers a bZIP23-PER1A-mediated detoxification pathway to enhance seed vigor in rice.

Seed vigor in crops is important in terms of improving grain quality and germplasm conservation; however, little is known about its regulatory mechanisms through the encoded proteome and gene network. Comparative analyses of transcriptome (RNA sequencing [RNA-seq]) and broadly targeted metabolic profiling of two subspecific rice cultivars with distinct seed vigor during accelerated aging revealed various biological pathways and metabolic processes as key influences explaining trait differences. RNA-seq coexpression regulatory network analyses identified several transcription factors, including bZIP23 and bZIP42, that act as nodes in the gene network. Importantly, transgenic seeds of overexpression of bZIP23 enhanced seed vigor, whereas its gene knockout reduced seed vigor, suggesting that the protein it encodes functions as a positive regulator. Similarly, overexpression and knockout of PER1A that encodes a key player in the detoxification pathway enhanced and decreased seed vigor, respectively. We further demonstrated a direct interaction of the PER1A promoter with bZIP23 in seeds, which activates the expression of PER1A, and the genetic evidence suggested that bZIP23 most likely functions in a common pathway with and acts upstream of PER1A to modulate seed vigor. In addition, the control of seed vigor by the bZIP23-PER1A module was connected with that of the abscisic acid signaling pathway. Collectively, we revealed the genetic architecture of variation in seed vigor and uncovered the bZIP23-PER1A-mediated detoxification pathway that enhances the trait in rice.

Identification of key genes and pathways associated with resting mast cells in meningioma.

BACKGROUND: To identify candidate key genes and pathways related to resting mast cells in meningioma and the underlying molecular mechanisms of meningioma. METHODS: Gene expression profiles of the used microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichments of DEGs were analyzed using the ClusterProfiler package in R. The protein-protein interaction network (PPI), and TF-miRNA- mRNA co-expression networks were constructed. Further, the difference in immune infiltration was investigated using the CIBERSORT algorithm. RESULTS: A total of 1499 DEGs were identified between tumor and normal controls. The analysis of the immune cell infiltration landscape showed that the probability of distribution of memory B cells, regulatory T cells (Tregs), and resting mast cells in tumor samples were significantly higher than those in the controls. Moreover, through WGCNA analysis, the module related to resting mast cells contained 158 DEGs, and KEGG pathway analysis revealed that the DEGs were dominant in the TNF signaling pathway, cytokine-cytokine receptor interaction, and IL-17 signaling pathway. Survival analysis of hub genes related to resting mast cells showed that the risk model was constructed based on 9 key genes. The TF-miRNA- mRNA co-regulation network, including MYC-miR-145-5p, TNFAIP3-miR-29c-3p, and TNFAIP3-hsa-miR-335-3p, were obtained. Further, 36 nodes and 197 interactions in the PPI network were identified. CONCLUSION: The results of this study revealed candidate key genes, miRNAs, and pathways related to resting mast cells involved in meningioma development, providing potential therapeutic targets for meningioma treatment.

[Modeling and Predicting of MODIS Leaf Area Index Time Series Based on a Hybrid SARIMA and BP Neural Network Method].

The modeling and predicting of vegetation Leaf area index (LAI) is an important component of land surface model and assimilation of remote sensing data. The MODIS LAI product (i.e. MOD15A2) is one of the most widely used LAI data sources. However, the time series of MODIS LAI contains some data of low quality. For example, because of the influence of the cloud, aerosol, etc., the MODIS LAI presents the characteristics of the discontinuous in time and space. In fact, the time series of MODIS LAI include both linear and nonlinear components, which cannot be accurately modeled and predicted by either linear method or nonlinear method alone. In this paper, the original LAI time series data were first smoothed with Savitzky-Golay (SG) filtration and linear interpolation; SARIMA, BP neural network and a hybrid method of SARIMA-BP neural network were then used for modeling and predicting MODIS LAI time series. The SARIMA-BP neural network combined both SARIMA and BP neural network, which could model the linear and the nonlinear component of MODIS LAI time series respectively. That is, the final result of SARIMA-BP neural network was the sum of results of the two methods. Experiments showed that the time series of MODIS LAI that were smoothed with the SG filtration and linear interpolation were more smooth than original time series, with a determination coefficient up to 0.981, closer to 1 than that of SARIMA (0.941) and BP neural network (0.884); the correlation coefficient between SARIMA-BP neural network and the observation is 0.991, higher than that of between SARIMA (0.971) or BP neural network (0.942) SARIMA and the observation. Thus, it can be concluded that, the proposed SARIMA-BP neural network method can better adapt to the LAI time series, and it outperforms the SARIMA and BP neural network methods.

Rapid construction of multiple sgRNA vectors and knockout of the Arabidopsis IAA2 gene using the CRISPR/Cas9 genomic editing technology.

IAA2 is a member of the Aux/IAA auxin responsive gene family in Arabidopsis thaliana. No iaa2 mutant has been reported until now, thus hindering its further mechanistic investigations. The normal genomic editing technology of CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) uses only a single guide RNA (sgRNA) to target one site in a specific gene, and the gene knockout efficiency is not high. Instead, multiple sgRNAs can target multiple sites; therefore, the efficiency may be improved. In the present investigation, we used the golden-gate cloning strategy and two rounds of PCR reactions to combine three sgRNAs in the same entry vector. The final expression vector was obtained by LR reactions with the destination vector containing the Cas9 expression cassette. Four out of the six sgRNAs were effective, and we also obtained a lot of insertion and deletion mutations. Compared with one sgRNA approach, multiple sgRNAs displayed higher gene-knockout efficiency and produced more germ-line mutants. Thus, we established a more rapid and efficient method and generated five mutants for further studies of IAA2 functions.

[Vulvar intraepithelial neoplasia: a clinicopathologic study of twenty cases].

OBJECTIVE: To investigate the clinical, pathological and immunohistochemical features of vulvar intraepithelial neoplasia (VIN). METHODS: According to the 2004 modified terminology of International Society for the Study of Vulvovaginal Diseases (ISSVD), the cases were diagnosed as VIN from patients who had performed vulvar biopsy in Beijing Wuzhou Women's Hospital from February 2009 to December 2011, which were reclassified as usual VIN and differentiated VIN. The clinical and pathological studies were conducted respectively. MaxVision immunohistochemical staining was used to detect the expression of Ki-67, p16 and p53 proteins. RESULTS: There were 20 cases of VIN in 237 patients, and the incidence of VIN was 8.4% in all of contemporary vulvar biopsy. In 17 cases of usual VIN, mean age was 29.6 years, the lesion typically presented with atypical cells involving almost all layers of the epithelium, which was equivalent to the high-grade squamous intraepithelial neoplasia of cervix. Immunohistochemistry for Ki-67 and p16 was strongly positive in usual VIN. High risk human papillomavirus (HPV) detection was also positive. The incidence of differentiated VIN was less than usual VIN, and there were only 3 cases in this study. In differentiated VIN, patients aged over 50 years, with mean of 53.7 years, and the lesion most commonly presented with lichen sclerosis background. There were epithelial thickening and extending, and parakeratosis, and atypia was strictly confined to the basal and parabasal layers of the epithelium where the cells enlarged with abundant eosinophilic cytoplasm, presented with prominent nucleoli, increased cellularity and abnormal keratinization. In differentiated VIN, p53 was strongly positive, Ki-67 and p16 immunohistochemical expression was confined to the basal layer only. CONCLUSIONS: VIN is a precursor of invasive squamous cell carcinoma of the vulva. The modified terminology of ISSVD classifies VIN as high-grade lesions. Definitive pathological diagnosis of VIN plays an important role in its timely treatment and the prevention of vulvar carcinoma.