Correlation of Fischer’s Ratio with Liver Fibrosis in Naive Chronic Hepatitis B Patients without Comorbidities

Introduction: Hepatitis B virus infection is one of the most common health problems in the world. 20% Chronic hepatitis B can can change into liver fibrosis. The liver is the center of the body's Amino Acids metabolism. Amino acid changes can occur due to impaired liver function. Fisher's ratio (BCAA/AAA) has become one of the sign of liver fibrosis. This study wanted to find a relationship between Fischer Ratio values and liver fibrosis in naive chronic hepatitis B patients without comorbidities. Method: The research was conducted from October 2020-May 2021 at DR. Moewardi Hospital, Surakarta, Indonesia. Subjects were naive chronic hepatitis B patients without comorbidities with a minimum age of 30 years. The study used a cross-sectional method. Fischer Ratio values were assessed by spectrophotometry and liver fibrosis was assessed by transient elastography (fibroscan). A correlation test was conducted to determine the relationship between variables. Result: 20 patients were included in the study. The average age of the research subjects was 47 ± 10 years. The average Fisher's ratio value was 2.83 ± 1.16 and the average fibroscan value was 17.31 ± 18.50 kPa. Ratio Fischer had a correlation with liver fibrosis with r= - 0.571 (p=0.004). Conclusions: Ratio Fischer has a negative correlation with liver fibrosis in naive chronic hepatitis B patients without comorbidities.

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.

Leveraging GWAS Data Derived From a Large Cooperative Group Trial to Identify a SNP Cluster Associated With the Risk of Taxane-induced Peripheral Neuropathy

BackgroundChemotherapy-induced peripheral neuropathy (CIPN) is a common toxicity of taxanes for which there is no effective intervention. Genomic CIPN risk determination has yielded promising, but inconsistent results. The present study assessed the utility of a collective SNP cluster identified using novel analytic to describe taxane-associated CIPN risk.MethodsWe analyzed GWAS data derived from ECOG-5103, first identifying SNPs that were most strongly associated with CIPN using Fisher’s ratio. We then ranked ordered those SNPs which discriminated CIPN-positive from CIPN-negative phenotypes based on their discriminatory power and developed the cluster of SNPs which provided the highest predictive accuracy using leave-one-out cross validation (LOOCV).ResultsUsing GWAS aggregate data, we identified a 267 SNP cluster which was associated with a CIPN+ phenotype with an accuracy of 96.1%. ConclusionsIdentification of a 267 SNP cluster could accurately predict CIPN risk. Validation using an independent patient cohort should be performed.

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher’s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

Methods of Forecasting the Development of the Nation's Economy Based on the Forecast of Automotive Products Export

Various forecasting methods and models have been analyzed, the possibility of their use in forecasting the automotive products export has been substantiated in the study. A mechanism for predicting the automotive products export based on regression analysis, Fisher's ratio, Pearson correlation coefficient has been proposed. The analysis of the automotive products market has been conducted, a mechanism has been designed to substantiate the connection between the export of automotive products and the development of the nation's economy. A methodology has been developed to forecast the development of the nation's economy based on the forecast for automotive products export. The reliability of the statement that the sale of cars characterizes the development of the economy has been proved. The proposed methodology is recommended to be used for forecasting GDP, development of the nation's economy, and at the strategic level of planning the development of the automotive industry.

Sampling Defective Pathways in Parkinson Disease

Discrimination of case-control status based on gene expression differences has potential to identify novel pathways relevant to neurodegenerative diseases including Parkinson’s disease (PD). In this paper we applied two different novel algorithms to predict dysregulated pathways of gene expression across several different regions of the brain in PD and controls. The Fisher’s ratio sampler uses the Fisher’s ratio of the most discriminatory genes as prior probability distribution to sample the genetic networks and their likelihood (accuracy) was established via Leave-One-Out-Cross Validation (LOOCV). The holdout sampler finds the minimum-scale signatures corresponding to different random holdouts, establishing their likelihood using the validation dataset in each holdout. Phenotype prediction problems have by genesis a very high underdetermined character. We used both approaches to sample different lists of genes that optimally discriminate PD from controls and subsequently used gene ontology to identify pathways affected by disease. Both algorithms identified common pathways of Insulin signaling, FOXA1 Transcription Factor Network, HIF-1 Signaling, p53 Signaling and Chromatin Regulation/Acetylation. This analysis provides new therapeutic targets to treat PD.