Objective : Multiple sclerosis (MS) is a common neurological disease deeply linked with the immune-inflammatory disorders whereas the term (multiple) mostly refers to the multi-focal zones of Inflammation caused by lymphocytes and macrophages infiltration besides oligodendrocytes death. Accordingly , the dysfunctional immune system able to damage myelin ( a pivotal component of the central nervous system ) which responsible for communication among neurons. The aim of the present study is to innovate a biochemical relationship between MS and thyroid hormones (THs) by highlighting immunological responses and also to examine the action of Interferon beta (IFNβ) drug on thyroid hormone (THs) and thyroid stimulation hormone (TSH). Materials and

The pre - equilibrium and equilibrium double differential cross
sections are calculated at different energies using Kalbach Systematic
approach in terms of Exciton model with Feshbach, Kerman and
Koonin (FKK) statistical theory. The angular distribution of nucleons
and light nuclei on 27Al target nuclei, at emission energy in the center
of mass system, are considered, using the Multistep Compound
(MSC) and Multistep Direct (MSD) reactions. The two-component
exciton model with different corrections have been implemented in
calculating the particle-hole state density towards calculating the
transition rates of the possible reactions and follow up the calculation
the differential cross-sections, that include MS

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

Objective: To establish growth curve for a sample of infertile women and to assess Body Mass Index.
Methodology: Non-probability (purposive sample) of (100) infertile women, who visit Kamal Al-Samaraee
Hospital/ fertility and IVF center . The data are collected through the use of constructed questionnaire, which
consists of two parts. Part 1: consists of (5) items about demographic characteristics, part 2: consists of (4) items
about reproductive status, descriptive statistical analysis procedures (frequency, percentage, Contingency
coefficients, polynomial cube order).
Results: Revealed that the infertile women in the study group had decrease in their Body Mass Index with aging
(with increase of infertility duration