Genetic Bases of VitaminB12 Deficiency: Impact of MTHFR, TCN-II and GIF Polymorphisms on Vitamin B12 Level

Vitamin B12 deficiency is associated with serious health problems such as neurological disorders. In Jordan, few studies have evaluated the level of vitamin B21 in the Jordanian population with different prevalence. Genetic predisposition, lifestyle, environment, socioeconomic status, and geographic have been linked to vitamin B12 deficiency. Polymorphisms in the GIF, MTHFR, and Transcobalamins, have been proposed to be associated with the level of vitamin B12. The aim of the current study was to evaluate the impact of certain polymorphisms in MTHFR, TCN-II and GIF genes on the level of vitamin B12 in the Jordanian population. Polymorphic sites of the MTHFR (c.677 C>T, rs1801133 and c.1286A>C, rs1801131), TCN2-776C>G (Arg259Pro) (rs1801198) and GIF-68 A>G (Q5R) genes were analyzed by RFLP and DNA sequencing in a group of vitamin B12 deficient individuals (n = 100). The control group included 100 matching individuals with a normal level of vitamin B12 (>200 ng/mL). Our results showed a significant association between the homologous variant of the TCN2 gene (G776G) and MTHFR c.677C>T genes and vitamin B12 deficiency. On the other hand, The MTHFR c.1286A>C variant and GIF variants did not show significant association with vitamin B12 deficiency. This study expounds the association of TCN2 and MTHFR polymorphisms with cobalamin levels in a Jordanian population and highlights the necessity of further studies to elucidate the molecular basis and impact of TCN2, GIF, and MTHFR gene polymorphisms on vitamin B12 deficiency and associated disorders.

A Modified XG Boost Classifier Model for Detection of Seizures and Non-Seizures

Diagnosis of Epilepsy is immensely important but challenging process, especially while using traditional manual seizure detection methods with the help of neurologists or brain experts’ guidance which are time consuming. Thus, an automated classification method is require to quickly detect seizures and non-seizures. Therefore, a machine learning algorithm based on a modified XGboost classifier model is employed to detect seizures quickly and improve classification accuracy. A focal loss function is employed with traditional XGboost classifier model to minimize mismatch of training and testing samples and enhance efficiency of the classification model. Here, CHB-MIT SCALP Electroencephalography (EEG) dataset is utilized to test the proposed classification model. Here, data gathered for all 24 patients from CHB-MIT Database is used to analyze the performance of proposed classification model. Here, 2-class-seizure experimental results of proposed classification model are compared against several state-of-art-seizure classification models. Here, cross validation experiments determine nature of 2-class-seizure as the prediction is seizure or non-seizure. The metrics results for average sensitivity and average specificity are nearly 100%. The proposed model achieves improvement in terms of average sensitivity against the best traditional method as 0.05% and for average specificity as 1%. The proposed modified XGBoost classifier model outperforms all the state-of-art-seizure detection techniques in terms of average sensitivity, average specificity.

Using Machine Learning to Predict Heart Disease

Heart Disease has become one of the most leading cause of the death on the planet and it has become most life-threatening disease. The early prediction of the heart disease will help in reducing death rate. Predicting Heart Disease has become one of the most difficult challenges in the medical sector in recent years. As per recent statistics, about one person dies from heart disease every minute. In the realm of healthcare, a massive amount of data was discovered for which the data-science is critical for analyzing this massive amount of data. This paper proposes heart disease prediction using different machine-learning algorithms like logistic regression, naïve bayes, support vector machine, k nearest neighbor (KNN), random forest, extreme gradient boost, etc. These machine learning algorithm techniques we used to predict likelihood of person getting heart disease on the basis of features (such as cholesterol, blood pressure, age, sex, etc. which were extracted from the datasets. In our research we used two separate datasets. The first heart disease dataset we used was collected from very famous UCI machine learning repository which has 303 record instances with 14 different attributes (13 features and one target) and the second dataset that we used was collected from Kaggle website which contained 1190 patient’s record instances with 11 features and one target. This dataset is a combination of 5 popular datasets for heart disease. This study compares the accuracy of various machine learning techniques. In our research, for the first dataset we got the highest accuracy of 92% by Support Vector Machine (SVM). And for the second dataset, Random Forest gave us the highest accuracy of 94.12%. Then, we combined both the datasets which we used in our research for which we got the highest accuracy of 93.31% using Random Forest.

Estimation of the Ambit of Breast Cancer with a Modified Resnet Analysis Using Machine Learning Approach

Breast Cancer has been one of the most common reasons for mortality and morbidity among the females around the world especially in developing countries. In this regard, Mammography is a popular screening technique for breast cancer diagnosis so as to label the existence of cancerous cells. The present work encompasses the design and development of a M-ResNet (Modified ResNet) approach so as to classify the breast cancer into benign and malignant conditions with the inclusions for supervised classification models with the training of both upper as well as the lower layers of the designed networks. The efficacy of the developed approach was evaluated using various performance evaluators such as those of sensitivity, specificity, accuracy and F1-Score. Bi-Rads score was used as a basis for the classification process wherein a score of 0-3 correlated to benign and it is non-cancerous nature of tissues whereas malignancy was denoted by a score of 4 and above. InBreast dataset, a publicly available online dataset with 112 breast images were used for the evaluation of the developed paradigm. The present paradigm portrayed an accuracy of 96.43% with Area Under the Curve (AUC) of 95.63%.

Particition of Sodium-potassium Adenosine Triphosphatases in Homeostasis Regulation

A literature review presented an analysis of data regarding the mechanisms of the Na pump in nephron and hormonal regulators of enzyme activity, including enzymatic catalysts. Investigating the regulatory mechanisms of metabolic processes can facilitate the development of new strategies to repair various pathological conditions. Among these functional proteins, Na+/K+ATPase is responsible for the regulation of hydroionic homeostasis and signaling. Ion transport in different parts of the nephron is mediated via sodium transporters, which are characterized by a clear topographical expression. In the oligomeric Na+/K+ATPase molecule, the α-subunit comprises 10 transmembrane domains and performs a catalytic function. The signal function of Na+/K+ATPase and its interaction with the molecular environment in lipid microdomains involve rafts and caveolae. Analysis of the literature data demonstrated an important function of Na+/K+ATPase, along with its interaction with caveolin-1, in the regulation of intracellular cholesterol traffic. Moreover, reciprocal interactions of enzymes and cholesterol have been indicated. The status of Na+/K+ATPase activity is affected by hypoxia, reactive oxygen species, lipid peroxidation (LPO), increased cholesterol concentrations, and the viscosity of the cytoplasmic membrane. Ecological pollutants, including heavy metals, have significant effects on enzyme activity in nephron, hepatocytes and cardiomyocytes. Thus, available literature data indicate an important role of Na+/K+ATPase in the regulation of metabolic processes.

Cellular Automaton for Kidney Branching Morphogenesis

Epithelium is a complex component in the mammalian kidney that has a highly branched duct system. Branching morphogenesis has a hierarchy structure in the ureteric bud and produces the collecting duct tree through repetitive processes. Epithelial and mesenchymal cells surround the tips of growing branches, and their cellular reactions adjust the ureteric bud branching. Mesenchymal cells produce a small protein called glial cellline derived neurotrophic factor (GDNF) that connects to te Rearranged in Transfection (RET) receptors on the surface of epithelial cells. The identified reactions are a necessity for the normal branching growth and their roles exist for using biological features in the proposed model. This paper presents an agent-based model based on cellular automaton for kidney branching in ex-vivo using the features that are expressed as artificial patterns in algorithms. This model extending the groundbreaking approach of Lambert et al. is flexible in features and high compatibility with experimental data. Mesenchymal cells and RET receptors are also expressed as mathematical patterns in the algorithms. The growth mechanism is determined by the growth factor, which indicates the epithelial cell branch when its cell division depends on the local concentration growth factor. Cell division occurs when the level of stimulus growth factor exceeds the threshold. Comparison shows that the model mimics experimental data with high consistency and reveals the dependence between growth factor parameters and features. Results indicate the superiority of compatibility with nature when compared with the model mentioned above.

The New Model of Carcinogenic Activity of Polycyclic Hydrocarbons

A model is proposed that allows one to interpret the carcinogenic properties of polycyclic chemical compounds. Electronic, informational and structural molecular factors that characterize the molecule as a whole are proposed as explanatory variables. The factors limiting the carcinogenic activity of polycyclic compounds are analyzed. The model fully interprets all observable data that were used to support previous early models

Synthesis, Structure, and Radioprotective Activity of the Palladium (II) Complex With Mexidol

New complex compounds of palladium (II) with biologically active ligand 2 - ethyl - 6 methyl - 3 - hydroxy-pyridine - mexidol in acidic medium (pH = 5,3) of the following composition have been synthesized – (C8H12O⊕N)2 [PdCl4]. In this case, the ligand is protonated and as a single-charged cation occupies an external coordination sphere. The structure of the complex is proved by X-ray structure analysis. It is shown that the structure is constructed of an isolated complex anion [PdCl4]2- and cation C8H12O⊕N. The square planar coordination of the palladium atom is formed from three chlorine atoms and the formed tetraacidoanion ligand forms a hydrogen bond. The average length of Pd-Cl bond is 2, 3030 Α°, there are no deviations from 900 valence angles of Cl-Pd-Cl. The palladium atom is not shifted from the plane coordination polyhedron (square) and therefore trance angles of Cl-Pd-Cl are 1800. Two different lengths -2,289 Α° and 2,713 Α° of hydrogen bonds are related to the geometric location of the ligand functional group. The obtained 2 – ethyl – 6 – methyl – 3 -hydroxypyridinetrachloro - palladium - mexidazole was tested for radioprotective properties. Toxicity of the preparation is LD50 - 240 mg/kg of animal weight. Toxicological studies of mexidazole in mice, rats and dogs did not reveal cardiotoxic, immunotoxic, embryonic, nephrotoxic, hematoxic and other types of side effects. Mexidazole is removed from the body with urine 5-8 hours after intravenous injection. The carried out biological test showed that the compound, along with radioprotective properties, has some antitumor activity.

Discrete ARMA Model Applied for Tumor Growth Inhibition Modeling and LQR-based Chemotherapy Optimization

Mathematical models for tumor growth inhibition (TGI) are an important tool in the battle against cancer allowing preclinical evaluation of potential anti-cancer drugs and treatment schedules. In this article, an autoregressive moving average (ARMA) model for cancer tumor growth is estimated based on laboratory data of TGI in mice and presented. The model was proven capable of describing with accuracy the tumor growth under single-agent chemotherapy. At the same time, an optimal control problem was formulated to identify optimal drug dosages. The linear quadratic regulator (LQR) controller was used with success in optimizing both periodic and intermittent chemotherapy treatment schedules reducing the tumor mass while keeping dosages under acceptable toxicity

Post-Hoc Analysis of Data Received with Nuclear Medical Diagnostic in Bulgarien Patients

Fever of unknown origin (FUO) is a clinical condition extremely difficult to diagnose. For this purpose a number of methods are used – clinical and paraclinical, as well as medical imaging, of which only nuclear-medical diagnostic methods allow performance of full-body scanning, which avoids skipping areas, loss of time and resources. In this article we are looking for the interrelationship between two groups of factors: group 1 (body temperature; leukocytes and erythrocyte sedimentation rate (ESR) and group 2 (presence and severity of an inflammatory process). Analysis of variance (ANOVA) is used for analyzing observations of the index of accumulation that depends on the effects of the factors as patient’s temperature, leukocytes and ESR. The comparison of mean values of the statistically significant factors for the different categories of the index of accumulation and respectively activity of the inflammatory process is made. The study included 74 patients: 30 (40.54%) men aged 15 to 71 years. and 44 (59.46%) women aged 7 to 74 diagnosed in a hospital in Sofia, Bulgaria. However, ANOVA test results don’t map out which groups are different from other groups. To determine whether the mean differences between the factors are statistically significant post hoc test is used.