World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading in fine‐needle aspiration biopsies of renal masses

Background Utilization of fine‐needle aspiration (FNA) biopsy for the evaluation of renal masses has been increasing at our institution. At times diagnostic material on direct smears is superior to that in the cell block/core biopsy, therefore assigning an accurate nuclear grade in the cytopathology report would provide useful prognostic information. Methods Search of the pathology database identified renal FNAs performed during an 11‐year period (2006–2017). Corresponding core biopsies and resections were identified. Cases with a diagnosis of primary renal neoplasia on FNA, core biopsy, and/or resection were included. Two pathologists reviewed all cases and assigned a World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grade to each FNA, core biopsy, and resection case. Results A total of 162 kidney FNAs were identified. Primary renal neoplasia was diagnosed in 137 cases on core biopsy/resection. Among diagnostic FNAs of clear cell RCC and papillary RCC with core biopsy/resection specimens for re‐review (n = 52), reviewers assigned a concordant WHO/ISUP grade to 83% (43/52) of cases. Among 9 cases with discrepant scores, all had a discrepancy of 1 grade and were undergraded on FNA. Using a two tier grading system (low vs. high grade), reviewers assigned a concordant grade to 88% (46/52) of cases. Among 6 cases with discrepant scores, all were classified as low grade (WHO/ISUP grade 2) on FNA versus high grade (WHO/ISUP grade 3) on resection. Conclusion The WHO/ISUP grade assigned on FNA shows good concordance with subsequent resection/core specimens (83%), with all discrepant cases being undergraded by one grade

Android application collusion demystified

Application collusion is an emerging threat to Android based devices. In app collusion, two or more apps collude in some manner to perform a malicious action that they are unable to do independently. Detection of colluding apps is a challenging task. Existing commercial malware detection systems analyse each app separately, hence fail to detect any joint malicious action performed by multiple apps through collusion. In this paper, we discuss the current state of research on app collusion and open challenges to the detection of colluding apps. We compare existing approaches and present an integrated approach to effectively detect app collusion

Predicting Gonadal Germ Cell Cancer in People with Disorders of Sex Development; Insights from Developmental Biology

The risk of gonadal germ cell cancer (GGCC) is increased in selective subgroups, amongst others, defined patients with disorders of sex development (DSD). The increased risk is due to the presence of part of the Y chromosome, i.e., GonadoBlastoma on Y chromosome GBY region, as well as anatomical localization and degree of testicularization and maturation of the gonad. The latter specifically relates to the germ cells present being at risk when blocked in an embryonic stage of development. GGCC originates from either germ cell neoplasia in situ (testicular environment) or gonadoblastoma (ovarian-like environment). These precursors are characterized by presence of the markers OCT3/4 (POU5F1), SOX17, NANOG, as well as TSPY, and cKIT and its ligand KITLG. One of the aims is to stratify individuals with an increased risk based on other parameters than histological investigation of a gonadal biopsy. These might include evaluation of defined susceptibility alleles, as identified by Genome Wide Association Studies, and detailed evaluation of the molecular mechanism underlying the DSD in the individual patient, combined with DNA, mRNA, and microRNA profiling of liquid biopsies. This review will discuss the current opportunities as well as limitations of available knowledge in the context of predicting the risk of GGCC in individual patients

Percutaneous Biopsy of the Renal Mass: Fine Needle Aspiration or Core Biopsy?

BACKGROUND In recent years, there have been increasing indications for percutaneous renal biopsy. Fine-needle aspiration (FNA), with or without core needle biopsy (CB), has been used increasingly in the management of renal tumors at the study institution. METHODS A computerized search of laboratory records was conducted to retrieve FNA cases of renal masses as well as the correlating CB and/or nephrectomy specimens. The cases spanned a period of 10 years (2006-2015). The diagnoses were classified into 5 categories: malignant, suspicious for malignancy, neoplastic, atypical, and negative/nondiagnostic. Based on the results of the nephrectomy specimens, the diagnostic rate, sensitivity, and diagnostic accuracy were calculated among 3 groups of specimens: FNA only, CB only, and combined FNA and CB. RESULTS A total of 247 cases of FNA with 123 correlating CB and 101 follow-up nephrectomy specimens were identified. The diagnostic rate, sensitivity, and diagnostic accuracy were 72%, 78%, and 96%, respectively, for FNA; 87%, 92%, and 94%, respectively, for CB; and 92%, 92%, and 94%, respectively, for the combined FNA and CB group. Renal cell carcinoma and its variants were the most common histologic diagnoses (112 of 174 cases; 64%). Significant diagnostic discrepancy was noted in one case: a malignant melanoma that was misdiagnosed as renal cell carcinoma in both the preoperative FNA specimen and in the CB specimen. CONCLUSIONS In the current study, both FNA and CB demonstrated excellent diagnostic accuracy (96% and 94%, respectively). The combination of FNA and CB was found to significantly improve the diagnostic rate when compared with either FNA alone (92% vs 72%; P<.05) or CB alone (92% vs 87%)

Class-Agnostic Counting

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201

TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.Comment: 15 page

Managing the Quality of Chromium Sulphate during the Recycling From Tanning Waste Water

Quality management is a big issue during recovery and recycling process because if desired quality is not received during chromium recovery or recycling process, we may be faced another problem of recycled materials. This also seen that most important that the production processes is useless without taking specific required quality of chromium., in real way about 60%-70% of chromium salt is used as chemical interaction with the hides but 30%-40% of chemical chromium salt is wasted as the solid and liquid form. Therefore, the quality during the recovery process of the chromium sulphate from chromium wastewater that is most important step for controlling environmental pollution with some economical benefits. Recycling of chromium sulphate is possible by using chemical precipitation method for water treatment, two precipitating agents' magnesium oxide and calcium hydroxide plus alum are used for this purpose. Final findings showed that the optimum pH for efficient recovery with required quality was 8 and the Recycling of chromium sulphate was about 99(%) at pH 8 with good sludge with high settling rate. on the Base of these findings an economical production plant can be designed which are useful for quality improvement

Nucleotide identity and variability among different Pakistani hepatitis C virus isolates

<p>Abstract</p> <p>Background</p> <p>The variability within the hepatitis C virus (HCV) genome has formed the basis for several genotyping methods and used widely for HCV genotyping worldwide.</p> <p>Aim</p> <p>The aim of the present study was to determine percent nucleotide identity and variability in HCV isolates prevalent in different geographical regions of Pakistan.</p> <p>Methods</p> <p>Sequencing analysis of the 5'noncoding region (5'-NCR) of 100 HCV RNA-positive patients representing all the four provinces of Pakistan were carried out using ABI PRISM 3100 Genetic Analyzer.</p> <p>Results</p> <p>The results showed that type 3 is the predominant genotypes circulating in Pakistan, with an overall prevalence of 50%. Types 1 and 4 viruses were 9% and 6% respectively. The overall nucleotide similarity among different Pakistani isolates was 92.50% ± 0.50%. Pakistani isolates from different areas showed 7.5% ± 0.50% nucleotide variability in 5'NCR region. The percent nucleotide identity (PNI) was 98.11% ± 0.50% within Pakistani type 1 sequences, 98.10% ± 0.60% for type 3 sequences, and 99.80% ± 0.20% for type 4 sequences. The PNI between different genotypes was 93.90% ± 0.20% for type 1 and type 3, 94.80% ± 0.12% for type 1 and type 4, and 94.40% ± 0.22% for type 3 and type 4.</p> <p>Conclusion</p> <p>Genotype 3 is the most prevalent HCV genotype in Pakistan. Minimum and maximum percent nucleotide divergences were noted between genotype 1 and 4 and 1 and 3 respectively.</p