IntroductionLittle is known about the pattern and outcome of Acute Kidney injury (AKI) in Sudan. This study aimed to determine the etiology and outcome of AKI among Sudanese adults.MethodsA retrospective cohort study was conducted in a tertiary level hospital, Soba University Hospital, Sudan. The medical records of all adults admitted to hospital from the 1st of January to 31st of December 2014 were reviewed. The diagnosis and severity of AKI was defined as per the Kidney Disease Improving Global Outcomes (KDIGO) recommendations.ResultsThe medical records of 6769 patients were reviewed. AKI was diagnosed in 384 patients (5.7%); being community acquired in 82.6% of cases. Sepsis, volume depletion, obstructive uropathy, heart failure, acute glomerulonephritis and severe malaria were the commonest causes of AKI diagnosed in 44%, 38.5%, 8.9%, 5.7%, 4.7% and 3.1% of patients, respectively. Following treatment complete renal recovery was seen in 35.7% of patients; whereas 31.2% of patients died. Predictors of increased risk of death were old age [OR 1.03, 95% CI (1.01-1.057); P=0.003], presence of chronic liver disease [OR 2.877, 95% CI (1.5-5.5); P=0.001], sepsis [OR 2.51, 95% CI (1.912-4.493);P=0.002] and the severity of AKI [OR 3.873, 95% CI(1.498-10.013);P=0.005].ConclusionAKI was diagnosed in 5.7% of adults admitted to hospital. Most patients were having community acquired AKI. Old age, the presence of chronic liver disease, sepsis, and the severity of AKI as per KDIQO staging were significant predictors of mortality.

The present study was conducted to find out the variation in the chemical

Model deficiency that results from incomplete training data is a form of structural blindness that leads to costly errors, oftentimes with high confidence. During the training of classification tasks, underrepresented class-conditional distributions that a given hypothesis space can recognize results in a mismatch between the model and the target space. To mitigate the consequences of this discrepancy, we propose Random Test Sampling and Cross-Validation (RTSCV) as a general algorithmic framework that aims to perform a post-training model rectification at deployment time in a supervised way. RTSCV extracts unknown unknowns (u.u.s), i.e., examples from the class-conditional distributions that a classifier is oblivious to, and works in combination with a diverse family of modern prediction models. RTSCV augments the training set with a sample of the test set (or deployment data) and uses this redefined class layout to discover u.u.s via cross-validation, without relying on active learning or budgeted queries to an oracle. We contribute a theoretical analysis that establishes performance guarantees based on the design bases of modern classifiers. Our experimental evaluation demonstrates RTSCV's effectiveness, using 7 benchmark tabular and computer vision datasets, by reducing a performance gap as large as 41% from the respective pre-rectification models. Last we show that RTSCV consistently outperforms state-of-the-art approaches.

Field experiments were carried out for 2017 agricultural season in Babylon / Musaib - Albojasem region 35 km west  north of the governorate to evaluate the performance of seven genotypes of rice (Oryza sativa L.) Genetic, environmental and phenotypic variances, heritability percent in the broad sense, genetic and phenotypic Different Coefficients, effect of irrigation methods (flooding and intermittent irrigation) of the genotypes (Amber33, Dijla, Mashkhab 2, Forat, Pernameg4, Yasmin and Ghadir)  The research center of the rice in Al-Mashkhab using the experiment of split plots in randomized complete Block design (RCBD) with three replicates. The results can be summarized as follows: 1.The genotypes showed significant differences in the 5% probability level for all studied traits. The genotype Amber 33 superior to all other traits except for the number of effective branches for genotype Forat. The method of irrigation by flooding showed significant superiority of all studied traits. The genetic variance values were higher than the environmental variability values of all traits except for the number of branches. 5.The estimates of heritability values in the broad sense indicated that they were high for all traits. 6.The values of the phenotypic and genetic differences were different between low values of the number of days from planting to 50% flowering and medium for the other traits except for the area of the leaf it was high for the irrigation methods and the low of the number of branches. panicle.