Alzheimer's disease is a Declining neurological illness that mainly affects memory, thinking, and behaviour, leading to Significant deterioration in intellectual functioning of independence in daily routine. It impacts about 55 million people around the globe, with the number expected to rise significantly as the global population ages, making it one of the biggest public health challenges in current time. This study used a multi-case design to develop and test a deep learning model for classifying Alzheimer's disease stages from MRI scans. Two different datasets were used, the first one was used for the training and the other one was used for the validation task. Both of them used about 15000 images for the training and testing. A combination of VGG16 and LSTM neural networks was implemented using Keras and TensorFlow, with the VGG16 extracting visual features from MRI sequences which were then fed to an LSTM layer to classify sequences based on both visual and temporal dependencies. The model achieved 98.88% accuracy on the training set and 98.03% on the held-out validation set of 8,000 MRI images for classifying Alzheimer's disease stages. The combined VGG16-LSTM deep learning architecture demonstrated high performance in classifying Alzheimer's disease stages from MRI data, achieving over 98% accuracy. This research presents a promising automatic approach for diagnosis of Alzheimer's that has potential for clinical relevance
There has been significant interest in the field of diabetes complications, particularly diabetic nephropathy. This includes the use of biochemical factors as early diagnostic markers to prevent the deterioration of patients’ health. Accordingly, the current study aimed to identify the levels of angiopoietin-like protein-3 (ANGPTL3) and fibroblast growth factor-23 (FGF23) in Iraqi patients with diabetic nephropathy, which may serve as early indicators for the diagnosis of the disease. The study included 143 individuals aged 35-65 years who were visiting the Medical City of Baghdad Teaching Hospital from August to December 2024. The patients were divided into three groups based on the ACR criteria. The first group was the normoalbuminuria group (ACR<30 mg/g, 35 patients), and the second was the microalbuminuria group (ACR =30-300 mg/g, 33 patients). The third group was the macroalbuminuria group (ACR > 300 mg/g, 35 patients). The fourth group, which included 40 healthy individuals, represented the control group. The lipid profile, urea, and creatinine levels were measured using spectrophotometric methods. ELISA was used to measure ANGPL3 and FGF23 levels. The results showed a significant increase in both ANGPTL3 and FGF23 levels between the patient and healthy groups. The results of the lipid profile, urea, HbA1C, and creatinine were consistent with the results of ANGPTL3 and FGF23, which were found to be significantly increased in the patient groups compared with the control group. Also, the ROC results confirmed that these factors are perfect or excellent for this case. In conclusion, depending on the current outcomes, with ROC results, both ANGPTL3 and FGF23 appear to possess high detection sensitivity toward the targeted case. It may be used as an excellent factor for the early prediction of diabetic nephropathy development.
Translating Arabic dialects into modern standard Arabic is a difficult task due to the significant differences in grammar and vocabulary between them. Using neural machine translation approaches to translate Arabic dialects faces a significant challenge, which is the lack of parallel data. Unavailability of parallel data is a common problem in low-resource languages such as the Libyan dialect. To solve this problem, this paper presents a novel method to address this problem. This method uses both word replacement and fine-tuning techniques to build Libyan dialect-modern standard Arabic parallel data for neural machine translation. To build parallel data from monolingual modern standard Arabic sentences, it uses a word replacement to exchange modern standard Arabic words in the modern standard Arabic text with Libyan dialect equivalents. It also uses fine-tuning techniques to enhance the overall translation of the neural machine translation model, which is already pretrained on parallel data that is generated by word replacement. The method is evaluated using a test dataset of 1,000 parallel sentences. The results show that the method proposed in this paper achieves promising results compared to the baseline technique. It obtained a score of 1.37 for BLEU, a score of 12.87 for chrF, and 87.34 for TER. In contrast, the baseline model achieved a BLEU score of 0.12, a chrF score of 6.89, and a higher TER of 100.02.
Plant extracts are a rich source of active chemicals; they have lately been employed in environmentally friendly nanoparticle synthesis. In one step of environmentally friendly synthesis, it is crucial that the biomolecules present in plant extracts can transform metal ions into small particles. The study aimed to investigated the influence of silver nanoparticles (Ag NPs) synthesized from an aqueous extract of Cordia myxa and Ceratophyllum demersum on the osmotic fragility of human red blood cells (RBCs). Blood samples were treated with extracts of these two plants (0.125, 0.25 mg/ml, 0.357, and 0.75 mg/ml), and an osmotic fragility assay was done. The results showed a significant (p < 0.05) increase in RBC hemolysis in the presence of the extracts. Additionally, the results of the osmotic fragility assay revealed that the extracts of C. myxa and C. demersum have a hemolytic effect; these two plant extracts have similar effects on the fragility test of RBCs. In addition, the hemolytic impact of silver nanoparticles synthesized from C. myxa and C. demersum extracts is lower than that of the original extracts. These effects of the extracts and Ag NPs from these two plant extracts are linked to several pharmacological characteristics of these extracts and the chemical constituents of the nanoparticles.
The rapid growth of renewable energy has become a cornerstone of the contemporary global energy landscape. Solar energy, with its vast potential, holds promise for meeting the escalating energy demands and expanding its prominence within the energy sector. This research aims to assess feasible locations for the establishment of utility-scale solar photovoltaic (PV) plants in Karbala Governorate, situated in central Iraq. In this research, Geographic Information Systems (GIS) tools and techniques were used within the framework of a Multi-Criteria Decision-Making (MCDM) methodology. Data were collected from several reliable sources, including the Iraqi Ministry of Agriculture, the Iraqi Meteorological Organization, the Ministry of Planning, and the United States Geological Survey (USGS). These sources helped to create a realistic picture of the environmental conditions and infrastructure in the study area. The research first identifies and assigns weights to various factors (such as slope, solar radiation, proximity to power grid, distance from urban areas, proximity to road networks, distance from water bodies, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), precipitation, proximity to fault lines, and annual dust accumulation) that influence the suitability of potential solar PV plant sites. This is achieved through the Fuzzy Analytical Hierarchy Process (FAHP), which discerns distinct categories of suitability. Subsequently, all layers are transformed into fuzzy membership layers to delineate constraints across the study area, thereby accentuating unsuitable sites. The culmination of this effort involves multiplying the fuzzy-weighted set of relevant factors by the fuzzy-logic layer to produce the final suitability map. Upon scrutinizing the results, it becomes apparent that constraints envelop 83% of the study area, while a discernible 17% meets the requisite criteria, rendering them suitable for the establishment of utility-scale solar installations. Notably, a significant proportion of these potential areas is located near power transmission infrastructure and urban centers, highlighting their economic viability and potential impact.
