Abernathyarcher4083

A majority of multiple sclerosis patients experience visual impairment, often as the initial presenting symptom of the disease. While structural changes in the retinal nerve fiber layer and optic nerve have demonstrated correlations with brain atrophy in multiple sclerosis using magnetic resonance imaging, a non-invasive, cost-effective, and clinically efficacious modality to identify early damage and facilitate prompt therapeutic intervention to slow the progression of multiple sclerosis and its ocular manifestations, is still urgently needed. In this study, we sought to determine the role of macular sensitivity measured by microperimetry in the detection of subclinical multiple sclerosis-related retinal damage and visual dysfunction.

This cross-sectional observational case-control study involved population-based samples of multiple sclerosis patients and age-, race-, and gender-matched healthy control subjects. Among the key criteria for the multiple sclerosis patients were diagnosis by the McDonald criistory of optic neuritis. Furthermore, macular sensitivity demonstrated a positive correlation with macular thickness as measured by optical coherence tomography. As such, microperimetry may represent a non-invasive and efficient method to identify signs of subclinical visual dysfunction that correspond with early macular architectural changes characteristic of multiple sclerosis.

Macular sensitivity as measured by microperimetry was decreased in multiple sclerosis patients with normal visual acuity and no history of optic neuritis. Furthermore, macular sensitivity demonstrated a positive correlation with macular thickness as measured by optical coherence tomography. As such, microperimetry may represent a non-invasive and efficient method to identify signs of subclinical visual dysfunction that correspond with early macular architectural changes characteristic of multiple sclerosis.

DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. check details This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem.

The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene.

The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

The use of obstetric early-warning-systems (EWS) has been recommended to improve timely recognition, management and early referral of women who have or are developing a critical illness. Development of such prediction models should involve a statistical combination of predictor clinical observations into a multivariable model which should be validated. No obstetric EWS has been developed and validated for low resource settings. We report on the development and validation of a simple prediction model for obstetric morbidity and mortality in resource-limited settings.

We performed a multivariate logistic regression analysis using a retrospective case-control analysis of secondary data with clinical indices predictive of severe maternal outcome (SMO). Cases for design and validation were randomly selected (n = 500) from 4360 women diagnosed with SMO in 42 Nigerian tertiary-hospitals between June 2012 and mid-August 2013. Controls were 1000 obstetric admissions without SMO diagnosis. We used clinical observatbased obstetric EWS algorithm with RR, temperature, systolic BP, pulse rate, consciousness level, urinary output and mode of birth that has a potential for clinical use in low-resource settings..

Amebiasis is a rare condition in developed countries but epidemiologically growing. Clinical manifestation may range from asymptomatic to invasive disease, amoebic liver abscess being the most common manifestation. We report a peculiar case of left hepatic amoebic liver abscess in a patient without a well-known source of infection and presenting with left portal vein thrombosis.

Patient, working as longshoreman, presented with complaints of remittent-intermittent fever lasting from 2 weeks. Physical examination was normal. Blood tests showed mild anemia, neutrophilic leukocytosis and elevated inflammation markers. Chest x-rays was normal. Abdominal ultrasound showed multiple hypoechoic liver masses. CT-scan of abdomen showed enlarged left liver lobe due to the presence of large abscess cavity along with thrombosis of left portal vein. The indirect hemagglutination test for the detection of antibodies to Entamoeba histolytica (Eh) was positive. Ultrasound-guided percutaneous drainage revealed "anchovy sauce" pus.