Deep Learning-Based Prediction of Alzheimer’s Disease Using Microarray Gene Expression Data

Alzheimer’s disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer’s disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer’s disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA–CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD–CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures

Using Voice Technologies to Support Disabled People

In recent years, significant strides have been made in speech and speaker recognition systems, owing to the rapid evolution of data processing capabilities. Utilizing a speech recognition system facilitates straightforward and efficient interaction, especially for individuals with disabilities. This article introduces an automatic speech recognition (ASR) system designed for seamless adaptation across diverse platforms. The model is meticulously described, emphasizing clarity and detail to ensure reproducibility for researchers advancing in this field. The model’s architecture encompasses four stages: data acquisition, preprocessing, feature extraction, and pattern recognition. Comprehensive insights into the system’s functionality are provided in the Experiments and Results section. In this study, an ASR system is introduced as a valuable addition to the advancement of educational platforms, enhancing accessibility for individuals with visual disabilities. While the achieved recognition accuracy levels are promising, they may not match those of certain commercial systems. Nevertheless, the proposed model offers a cost-effective solution with low computational requirements. It seamlessly integrates with various platforms, facilitates straightforward modifications for developers, and can be tailored to the specific needs of individual users. Additionally, the system allows for the effortless inclusion of new words in its database through a single recording process

Progress on lead-free metal halide perovskites for photovoltaic applications: a review

ABSTRACT: Metal halide perovskites have revolutionized the field of solution-processable photovoltaics. Within just a few years, the power conversion efficiencies of perovskite-based solar cells have been improved significantly to over 20%, which makes them now already comparably efficient to silicon-based photovoltaics. This breakthrough in solution-based photovoltaics, however, has the drawback that these high efficiencies can only be obtained with lead-based perovskites and this will arguably be a substantial hurdle for various applications of perovskite-based photovoltaics and their acceptance in society, even though the amounts of lead in the solar cells are low. This fact opened up a new research field on lead-free metal halide perovskites, which is currently remarkably vivid. We took this as incentive to review this emerging research field and discuss possible alternative elements to replace lead in metal halide perovskites and the properties of the corresponding perovskite materials based on recent theoretical and experimental studies. Up to now, tin-based perovskites turned out to be most promising in terms of power conversion efficiency; however, also the toxicity of these tin-based perovskites is argued. In the focus of the research community are other elements as well including germanium, copper, antimony, or bismuth, and the corresponding perovskite compounds are already showing promising properties. GRAPHICAL ABSTRACT: [Image: see text

Multidrug-Resistant Bacterial Pathogens and Public Health: The Antimicrobial Effect of Cyanobacterial-Biosynthesized Silver Nanoparticles

Background: Cyanobacteria are considered as green nano-factories. Manipulation of the size of biogenic silver nanoparticles is needed to produce particles that suit the different applications such as the use as antibacterial agents. The present study attempts to manipulate the size of biosynthesized silver nanoparticles produced by cyanobacteria and to test the different-sized nanoparticles against pathogenic clinical bacteria. Methods: Cyanothece-like. coccoid unicellular cyanobacterium was tested for its ability to biosynthesize nanosilver particles of different sizes. A stock solution of silver nitrate was prepared from which three different concentrations were added to cyanobacterial culture. UV-visible spectroscopy and FTIR were conducted to characterize the silver nanoparticles produced in the cell free filtrate. Dynamic Light Scattering (DLS) was performed to determine the size of the nanoparticles produced at each concentration. The antimicrobial bioassays were conducted on broad host methicillin-resistant Staphylococcus aureus (MRSA), and Streptococcus sp., was conducted to detect the nanoparticle size that was most efficient as an antimicrobial agent. Results. The UV-Visible spectra showed excellent congruence of the plasmon peak characteristic of nanosilver at 450 nm for all three different concentrations, varying peak heights were recorded according to the concentration used. The FTIR of the three solutions revealed the absence of characteristic functional groups in the solution. All three concentrations showed spectra at 1636 and 2050–2290 nm indicating uniformity of composition. Moreover, DLS analysis revealed that the silver nanoparticles produced with lowest concentration of precursor AgNO3 had smallest size followed by those resulting from the higher precursor concentration. The nanoparticles resulting from highest concentration of precursor AgNO3 were the biggest in size and tending to agglomerate when their size was above 100 nm. The three types of differently-sized silver nanoparticles were used against two bacterial pathogenic strains with broad host range; MRSA-(Methicillin-resistant Staphylococcus aureus) and Streptococcus sp. The three types of nanoparticles showed antimicrobial effects with the smallest nanoparticles being the most efficient in inhibiting bacterial growth. Discussion: Nanosilver particles biosynthesized by Cyanothece-like cyanobacterium can serve as antibacterial agent against pathogens including multi-drug resistant strains. The most appropriate nanoparticle size for efficient antimicrobial activity had to be identified. Hence, size-manipulation experiment was conducted to find the most effective size of nanosilver particles. This size manipulation was achieved by controlling the amount of starting precursor. Excessive precursor material resulted in the agglomeration of the silver nanoparticles to a size greater than 100 nm. Thereby decreasing their ability to penetrate into the inner vicinity of microbial cells and consequently decreasing their antibacterial potency. Conclusion: Antibacterial nanosilver particles can be biosynthesized and their size manipulated by green synthesis. The use of biogenic nanosilver particles as small as possible is recommended to obtain effective antibacterial agents

Integrating gene selection and deep learning for enhanced Autisms' disease prediction: a comparative study using microarray data

<abstract> <p>In this article, Autism Spectrum Disorder (ASD) is discussed, with an emphasis placed on the multidimensional nature of the disorder, which is anchored in genetic and neurological components. Identifying genes related to ASD is essential to comprehend the mechanisms that underlie the illness, yet the condition's complexity has impeded precise information in this field. In ASD research, the analysis of gene expression data helps choose and categorize significant genes. The study used microarray data to provide a novel approach that integrated gene selection techniques with deep learning models to improve the accuracy of ASD prediction. It offered a detailed comparative examination of gene selection approaches and deep learning architectures, including singular value decompositions (SVD), principal component analyses (PCA), and convolutional neural networks (CNNs). This paper combines gene selection methods (PCA and SVD) with deep learning models (CNN) to improve ASD prediction. Compared to more traditional approaches, the study revealed that its integrated methodology was more effective in improving the accuracy of ASD prediction results through experimentation. There was a difference in the accuracy between the PCA-CNN model, which achieved 94.33% with a loss of 0.4312, and the SVD-CNN model, which achieved 92.21% with a loss less than or equal to 0.3354. These discoveries help in the development of more accurate diagnostic and prognostic tools for ASD, which is a complicated neurodevelopmental disorder. Additionally, they provide insights into the molecular pathways that underlie ASD.</p> </abstract&gt

Cyanobacteria as Nanogold Factories: Chemical and Anti-Myocardial Infarction Properties of Gold Nanoparticles Synthesized by Lyngbya majuscula

To the best of our knowledge, cyanobacterial strains from the Arabian Gulf have never been investigated with respect to their potential for nanoparticle production. Lyngbya majuscula was isolated from the AlOqair area, Al-Ahsa Government, Eastern Province, Kingdom of Saudi Arabia. The cyanobacterium was initially incubated with 1500 mg/mL of HAuCl4 for two days. The blue-green strain turned purple, which indicated the intracellular formation of gold nanoparticles. Prolonged incubation for over two months triggered the extracellular production of nanogold particles. UV-visible spectroscopy measurements indicated the presence of a resonance plasmon band at ~535 nm, whereas electron microscopy scanning indicated the presence of gold nanoparticles with an average diameter of 41.7 ± 0.2 nm. The antioxidant and anti-myocardial infarction activities of the cyanobacterial extract, the gold nanoparticle solution, and a combination of both were investigated in animal models. Isoproterenol (100 mg/kg, SC (sub cutaneous)) was injected into experimental rats for three days to induce a state of myocardial infarction; then the animals were given cyanobacterial extract (200 mg/kg/day, IP (intra peritoneal)), gold nanoparticles (200 mg/kg/day, IP), ora mixture of both for 14 days. Cardiac biomarkers, electrocardiogram (ECG), blood pressure, and antioxidant enzymes were determined as indicators of myocardial infarction. The results showed that isoproterenol elevates ST and QT segments and increases heart rate and serum activities of creatine phosphokinase (CPK), creatine kinase-myocardial bound (CP-MB), and cardiac troponin T (cTnT). It also reduces heart tissue content of glutathione peroxidase (GRx) and superoxide dismutase (SOD), and the arterial pressure indices of systolic arterial pressure (SAP), diastolic arterial pressure (DAP), and mean arterial pressure (MAP). Gold nanoparticles alone or in combination with cyanobacterial extract produced an inhibitory effect on isoproterenol-induced changes in serum cardiac injury markers, ECG, arterial pressure indices, and antioxidant capabilities of the heart

The use of ractopamine as a feed additive: A review

Ractopamine hydrochloride is a β-adrenergic agonist that increases growth, feed efficiency, and fat deposition. Because of its ability to increase muscling, average daily gain, efficiency, and carcass weight, ractopamine hydrochloride has been used as a feed additive growth enhancer. Ractopamine is also a member of the phenylethanolamine class of chemicals, which is used as a feed supplement in meat-producing animals. This review threw the light on the use of ractopamine to improve weight gain and as a feed addition. Furthermore, the potential negative health effects of ractopamine were explored

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%