Rapid Spectrophotometric Determination of Phenoxazine

A rapid high sensitive and inexpensive economic method has been developed for the Determination of phenoxazine by using molecular spectrophotometry. The method is based on the oxidation of phenoxazine by potassium (meta)periodate in acidic medium. The oxidation conditions were selected to enhance the sensitivity and the stability of the pink colored species which shows an absorption maximum at 530 nm. The Beer’s law was obeyed for phenoxazine concentration range from 1 to 6 µg mL-1 with 0.003 µg mL-1 detection limit and provided variation coefficients between 0.4 to 1.7 %. This method was successfully applied for the determination of phenoxazine in aqueous sample

Spectrofluorimetric method for the determination of glibenclamide in pharmaceutical formulations

A sensitive spectrofluorimetric method for the determination of glibenclamide in its tablet formulations has been proposed. The method is based on the dissolving of glibenclamide in absolute ethanol and measuring the native fluorescence at 354 nm after excitation at 302 nm. Beers law is obeyed in the concentration of 1.4 to 10 µg.ml-1 of glibenclamide with a limit of detection (LD) of 0.067 µg.ml-1 and a standard deviation of 0.614. The range percent recoveries (N=3) is 94 - 103

Detection of Toxoplasmosis in Rat (Rattus rattus) in Baghdad governorate/Iraq

The Toxoplasma gondii infects human beings and wild rats (Rattus rattus) worldwide. Wild rats are infected with T. gondii due to ingestion of food or water contaminated with oocysts and may play a significant role in the transmission of T. gondii infection to the humans. The aim of the present study was to determine the seroprevalence of T. gondii among wild rats. Acute and chronic cases of toxoplasmosis in rats caught from old buildings and garbage in Baghdad city/Iraq were determined serologically. The percentage of positive rats for anti-T. gondii antibodies was 45%. Moreover, the higher infection rate observed in male rats. The percentages of acute and chronic infected rats were 10% and 35% respectively. The association between the presence of infection with the rat sex and age and their collection sites was insignificant (p>0.05). In conclusion, this study approved the presence of acute and chronic toxoplasmosis in wild rats in Baghdad city. However, the insignificant correlation between rat’s sex and age and its collection sites also observed. The authors recommend another future study including large numbers of rats in extended geographical area in Baghdad governorate to determine the incidence of Toxoplasma gondii in wild rats that might have an impact on the public health

Evaluation ofthe Middle East and North Africa Land Data Assimilation System

The Middle East and North Africa (MENA) region is dominated by dry, warm deserts, areas of dense population, and inefficient use of fresh water resources. Due to the scarcity, high intensity, and short duration of rainfall in the MENA, the region is prone to hydro climatic extremes that are realized by devastating floods and times of drought. However, given its widespread water stress and the considerable demand for water, the MENA remains relatively poorly monitored. This is due in part to the shortage of meteorological observations and the lack of data sharing between nations. As a result, the accurate monitoring of the dynamics of the water cycle in the MENA is difficult. The Land Data Assimilation System for the MENA region (MENA LDAS) has been developed to provide regional, gridded fields of hydrological states and fluxes relevant for water resources assessments. As an extension of the Global Land Data Assimilation System (GLDAS), the MENA LDAS was designed to aid in the identification and evaluation of regional hydrological anomalies by synergistically combining the physically-based Catchment Land Surface Model (CLSM) with observations from several independent data products including soil-water storage variations from the Gravity Recovery and Climate Experiment (GRACE) and irrigation intensity derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). In this fashion, we estimate the mean and seasonal cycle of the water budget components across the MENA

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Overexpression of EpCAM in uterine serous papillary carcinoma : implications for EpCAM-specific immunotherapy with human monoclonal antibody adecatumumab (MT201)

We evaluated the expression of epithelial cell adhesion molecule (EpCAM) and the potential of MT201 (adecatumumab), a human monoclonal antibody against EpCAM, in uterine serous papillary carcinoma (USPC). EpCAM expression was evaluated by real-time PCR and immunohistochemistry in a total of 56 USPC fresh-frozen biopsies and paraffin-embedded tissues. EpCAM surface expression was also evaluated by flow cytometry and immunohistochemistry in six USPC cell lines. Sensitivity to MT201 antibodydependent cellular cytotoxicity and complement-dependent cytotoxicity was tested against a panel of primary USPC cell lines expressing different levels of EpCAM in standard 5-h 51Cr release assays. EpCAM transcript was significantly overexpressed in fresh-frozen USPC when compared with normal endometrial cells (NEC). Median (minimum\u2013maximum) copy number was 943.8 (31.5\u20131568.3) in tumor samples versus 12.9 (1.0\u201337.0) in NEC (P < 0.001). By immunohistochemistry, EpCAM expression was found in 96% (26 out of 27) of USPC samples with significantly higher expression compared with NECs (P < 0.001). High surface expression of EpCAM was found in 83% (five out of six) of the USPC cell lines tested by flow cytometry. EpCAM-positive cell lines were found highly sensitive to MT201-mediated antibody-dependent cellular cytotoxicity in vitro, whereas primary USPC cell lines were resistant to natural killer cell\u2013dependent cytotoxicity. Human plasma IgG did not significantly inhibit MT201-mediated cytotoxicity against USPC. EpCAM is highly expressed in uterine serous carcinoma at mRNA and protein levels, and primary USPC are highly sensitivity to MT201-mediated cytotoxicity. MT201 might represent a novel therapeutic strategy in patients harboring advanced/recurrent or metastatic USPC refractory to standard treatment modalities

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press