Primary peritoneal clear cell carcinoma - A diagnostic dilemma

Peritoneum is a site for both primary and secondary tumors. Clear cell carcinoma of the peritoneum is an extremely rare primarytumor. The diagnosis of primary peritoneal tumors is based on the gynecology oncology group criteria, originally described forprimary adenocarcinoma of the peritoneum. A 55-year-old female presented with non-specific pain in the lower abdomen of 1-monthduration. Contrast-enhanced computed tomography findings of the abdomen were suggestive of suspected neoplastic mass mostlikely arising from the left ovary. Pre-operative serum cancer antigen-125 levels were 252 U/ml. Upon exploratory laparotomy, ahard nodular mass appearing to arise from anterior abdominal wall (15 × 12 cm) was found in the left iliac fossa. Histopathologicaldiagnosis of primary peritoneal clear cell carcinoma was made and immunohistochemistry was performed for confirmation

STUDY ON FRICTION STIR WELDING OF ALUMINIUM PLATES USING AN ARTIFICIAL NEURAL NETWORK

For attaching solid materials, friction stir welding (FSW) is a relatively novelmethod recently developed. Compared to fusion welding processes, it has many benefits,such as reduced distortion, porosity, shrinkage, and cracking. FSW was first used to linkaluminum alloys with limited weldability, but it has since been used to join other metallicalloys and other dissimilar alloys. It is possible to fuse two plates using FSW by inserting anon-consumable rotating tool with a specifically designed pin between them and moving italong the welding line. Multiple applications in the aerospace and shipbuilding industries andthe automobile sector have seen success with this approach owing to its many benefits.Computer-aided artificial neural network (ANN) modelling may be used in material scienceand engineering to improve the FSW process. In the same manner, as the brain processesinformation, ANN is a computer processing paradigm inspired by the brain's workings. Thereare many nerve cells in the system. ANNs, like humans, are taught by examples and maybeboth a teaching and a forecasting tool. Well-trained neural networks are excellent predictiontools and can predict results for inputs it has never seen. It may therefore be considered as anapproach to automating FSW.A wide range of variables influences the FSW process. To better understand the relationshipbetween welded material's mechanical characteristics, such as ultimate tensile strength (UTS)and hardness, this study considers three parameters: tool rotation speed, welding speed, andaxial force. An artificial neural network (ANN) is developed and then evaluated to determinethe mechanical characteristics of welded materials

PREDICTION OF WELD SHAPE FACTOR IN FLUX BONDED GAS TUNGSTEN ARC WELDING FOR AISI 1020 STEEL

GTAW welding has a number of drawbacks, the most significant of which is the limitedthickness of material that can be welded in a single pass, resulting in a decreased productionrate. Thus, a new welding technique Flux Bonded Gas Tungsten Arc Welding used whichlimits the drawbacks of the GTAW process. In the present work, GTAW process is carriedout on AISI 1020 carbon steel plates of 10mm thickness. The specimens were welded as beadon plate. Shape factor is calculated for FB-GTAW and compared with GTAW process. Themicrostructures and micro hardness are compared with flux and without flux at different heatinputs. The simulation of FB-GTAW process was done by NASTRAN® software. The shapefactor predicted by simulation and compared with experimental shape factor at different heatinputs. Time temperature data was measured by NASTRAN® software and compared withexperimental time temperature data. The shape factor decreases by FB-GTAW as comparedwith GTAW process. The shape factor by FB-GTAW decrease by 29.22%, 19.59% and31.13% at 120, 140 and 160 A respectively. The Micro-hardness of more than 300HV wasmeasured in FB-GTAW process. The Micro-hardness measured was about 200HV. TheMicro-hardness measured with FB-GTAW is more than normal GTAW process. This isbecause due to high cooling rate the martensite formation takes place in the weld pool by FBGTAWprocess. While with normal GTAW process acicular ferrite and pearlite are observedin the weld metal. The FB-GTAW process shows that there was maximum error of 0.395%calculated after comparing simulation and experimental shape factor. The simulation peaktemperature was 1387°C, 1300°C and 1250°C at 160 A, 140 A and 120 A respectively.Experimental results were 1350°C, 1280°C and 1235°C at above respective currents

Role of intrauterine insemination in infertile couple seeking care at Acharya Vinobha Bhave rural tertiary care hospital

Background: Fibroids Infertility affects between 60 million to 68 million people worldwide; generally one in ten couples experience primary or secondary infertility. The majority of those who suffer, live in the developing world. Universally, the prevalence rises significantly with a woman’s age. The WHO estimates the overall prevalence of infertility in India to be between 3.9 and 16.8 per cent. Objective: To assess the outcome of intrauterine insemination (IUI) amongst infertile couples seeking care at AVBRH. And to correlate the outcome of IUI with various parameters associated with infertility.Methods: 50 patients undergoing 72 stimulated IUI cycles between September 2012 and august 2014. It was a prospective interventional study. Interventions: Ovarian stimulation with clomifene citrate & gonadotrophins was initiated and a single IUI was performed 36 h after triggering ovulation. Main outcome: Pregnancy rates by urine pregnancy test per couple. Pregnancy rates by urine pregnancy test per cycle. Secondary outcome: livebirth, on-going PR, abortion rate.Results: The pregnancy rate per couple and per cycle were 22% and 15.27% respectively. Live birth were 8%, on-going pregnancy was 4%, abortions were 10%.  Conclusions: On the basis of analysis of successful outcome of IUI it can be said that the patients with best prognosis are the one with age <30 years, lesser duration of infertility, good motile spermatozoa, preovulatory follicle, good endometrial thickness, & semen preparation are certainly the cornerstones for successful intrauterine insemination

A multicentre phase III study comparing efficacy and safety of novel extended-release versus conventional formulation of dydrogesterone in Indian patients with endometriosis

Background: The aim of the study was to compare the efficacy and safety of novel once-daily extended-release (ER) dydrogesterone 20 mg versus conventional twice-daily dydrogesterone 10 mg in Indian patients with endometriosis. Methods: A phase III prospective, randomized, double-blind, single-dummy, two-arm, active-controlled, parallel, multicenter study was performed in six gynecology centers across India. The patients of 18 to 45 years of age with a confirmed diagnosis of endometriosis on ultrasonography (USG) and having endometriosis-associated pelvic pain score (EAPP) of at least 30 mm on a 100 mm visual analog scale (VAS) were randomly assigned to a 1:1 ratio to either once-daily dydrogesterone ER 20 mg or twice-daily dydrogesterone 10 mg arms for a treatment period of 90 days. The primary outcome was a change from baseline in EAPP score at the end of the treatment. Results: A total of 228 patients with a mean age of 31.8±6.9 years were enrolled in the study. At day 90, both the treatment arms showed a significant reduction (p<0.05) in EAPP score from baseline (i.e. -34.2±15.3 mm and -33.1±14.8 mm in once daily dydrogesterone ER and twice daily dydrogesterone 10 mg, respectively), with no significant difference between the two arms (p=0.53). With both formulations, patients experienced a significant reduction in the size of endometrioma, serum vascular endothelial growth factors (VEGF) levels, use of rescue analgesics, and significant improvement in the health-related quality-of-life parameters. A favorable safety profile of dydrogesterone was confirmed, and no significant safety concerns were reported during the study. Conclusions: Once daily dydrogesterone ER 20 mg and twice daily dydrogesterone 10 mg demonstrated a significant and similar reduction in EAPP and all other secondary parameters along with marked improvements in parameters related to quality of life

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination

A proteomics sample metadata representation for multiomics integration and big data analysis

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.publishedVersio

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease