Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

Reconsidering red blood cells as the diagnostic potential for neurodegenerative disorders

BACKGROUND

Red blood cells (RBCs) are usually considered simple cells and transporters of gases to tissues.

HYPOTHESIS

However, recent research has suggested that RBCs may have diagnostic potential in major neurodegenerative disorders (NDDs).

RESULTS

This review summarizes the current knowledge on changes in RBC in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and other NDDs. It discusses the deposition of neuronal proteins like amyloid-β, tau, and α-synuclein, polyamines, changes in the proteins of RBCs like band-3, membrane transporter proteins, heat shock proteins, oxidative stress biomarkers, and altered metabolic pathways in RBCs during neurodegeneration. It also highlights the comparison of RBC diagnostic markers to other in-market diagnoses and discusses the challenges in utilizing RBCs as diagnostic tools, such as the need for standardized protocols and further validation studies.

SIGNIFICANCE STATEMENT

The evidence suggests that RBCs have diagnostic potential in neurodegenerative disorders, and this study can pave the foundation for further research which may lead to the development of novel diagnostic approaches and treatments.

Advances in Alzheimer's disease: A multifaceted review of potential therapies and diagnostic techniques for early detection

Alzheimer's disease (AD) remains one of the most formidable neurological disorders, affecting millions globally. This review provides a holistic overview of the therapeutic strategies, both conventional and novel, aimed at mitigating the impact of AD. Initially, we delve into the conventional approach, emphasizing the role of Acetylcholinesterase (AChE) inhibition, which has been a cornerstone in AD management. As our understanding of AD evolves, several novel potential approaches emerge. We discuss the promising roles of Butyrylcholinesterase (BChE) inhibition, Tau Protein inhibitors, COX-2 inhibition, PPAR-γ agonism, and FAHH inhibition, among others. The potential of the endocannabinoids (eCB) system, cholesterol-lowering drugs, metal chelators, and MMPs inhibitors are also explored, culminating in the exploration of the pivotal role of microRNA in AD progression. Parallel to these therapeutic insights, we shed light on the novel tools and methodologies revolutionizing AD research. From the quantitative analysis of gene expression by qRTPCR to the evaluation of mitochondrial function using induced pluripotent stem cells (iPSCs), the advances in diagnostic and research tools offer renewed hope. Moreover, we explore the current landscape of clinical trials, highlighting the leading drug interventions and their respective stages of development. This comprehensive review concludes with a look into the future perspectives, capturing the potential breakthroughs and innovations on the horizon. Through a synthesis of current knowledge and emerging research, this article aims to provide a consolidated resource for clinicians, researchers, and academicians in the realm of Alzheimer's disease.

Synthesis and evaluation of curcumin-based near-infrared fluorescent probes for detection of amyloid β peptide in Alzheimer mouse models

The abnormal accumulation of amyloid β protein (Aβ) is one of the most important causes of Alzheimer's disease (AD) and is usually a detecting biomarker. Curcumin and its derivatives have potential Aβ aggregate targeting ability; we synthesized a series of curcumin-based near-infrared fluorescence probes in this study. By characterizing the excitation wavelength and emission wavelength, the imaging characteristics of the investigation in the near-infrared light region were determined; with an increase in the concentration of the probe compounds, the fluorescence intensity showed an upward trend, demonstrating ideal optical characteristics. In vivo, imaging results showed that the synthesized probe compounds could penetrate the blood-brain barrier (BBB) and specifically bind to Aβ in the brain of APP/PS1 mice. Especially for compound 3b, the maximum emission wavelength was around 667 nm, and the fluorescence signal intensity in the brain of the APP/PS1 mice model was more than twice that of the wild control group at 120 min after administration, which could display Aβ pathological changes. The fluorescent probes designed in this study can become an effective tool for early AD diagnosis and visual detection.

Electrochemical Immunosensors Developed for Amyloid-Beta and Tau Proteins, Leading Biomarkers of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurological disease and a serious cause of dementia, which constitutes a threat to human health. The clinical evidence has found that extracellular amyloid-beta peptides (Aβ), phosphorylated tau (p-tau), and intracellular tau proteins, which are derived from the amyloid precursor protein (APP), are the leading biomarkers for accurate and early diagnosis of AD due to their central role in disease pathology, their correlation with disease progression, their diagnostic value, and their implications for therapeutic interventions. Their detection and monitoring contribute significantly to understanding AD and advancing clinical care. Available diagnostic techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are mainly used to validate AD diagnosis. However, these methods are expensive, yield results that are difficult to interpret, and have common side effects such as headaches, nausea, and vomiting. Therefore, researchers have focused on developing cost-effective, portable, and point-of-care alternative diagnostic devices to detect specific biomarkers in cerebrospinal fluid (CSF) and other biofluids. In this review, we summarized the recent progress in developing electrochemical immunosensors for detecting AD biomarkers (Aβ and p-tau protein) and their subtypes (AβO, Aβ_(1-40), Aβ_(1-42), t-tau, cleaved-tau (c-tau), p-tau₁₈₁, p-tau₂₃₁, p-tau₃₈₁, and p-tau₄₄₁). We also evaluated the key characteristics and electrochemical performance of developed immunosensing platforms, including signal interfaces, nanomaterials or other signal amplifiers, biofunctionalization methods, and even primary electrochemical sensing performances (i.e., sensitivity, linear detection range, the limit of detection (LOD), and clinical application).

Distribution of Copper, Iron, and Zinc in the Retina, Hippocampus, and Cortex of the Transgenic APP/PS1 Mouse Model of Alzheimer's Disease

A mis-metabolism of transition metals (i.e., copper, iron, and zinc) in the brain has been recognised as a precursor event for aggregation of Amyloid-β plaques, a pathological hallmark of Alzheimer's disease (AD). However, imaging cerebral transition metals in vivo can be extremely challenging. As the retina is a known accessible extension of the central nervous system, we examined whether changes in the hippocampus and cortex metal load are also mirrored in the retina. Laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS) was used to visualise and quantify the anatomical distribution and load of Cu, Fe, and Zn in the hippocampus, cortex, and retina of 9-month-old Amyloid Precursor Protein/Presenilin 1 (APP/PS1, n = 10) and Wild Type (WT, n = 10) mice. Our results show a similar metal load trend between the retina and the brain, with the WT mice displaying significantly higher concentrations of Cu, Fe, and Zn in the hippocampus (p < 0.05, p < 0.0001, p < 0.01), cortex (p < 0.05, p = 0.18, p < 0.0001) and the retina (p < 0.001, p = 0.01, p < 0.01) compared with the APP/PS1 mice. Our findings demonstrate that dysfunction of the cerebral transition metals in AD is also extended to the retina. This could lay the groundwork for future studies on the assessment of transition metal load in the retina in the context of early AD.

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.

Progress of advanced nanomaterials in diagnosis of neurodegenerative diseases

Neurodegenerative diseases (NDDs) encompass a wide range of clinically and pathologically diverse diseases characterized by progressive long-term cognitive decline, memory and function loss in daily life. Due to the lack of effective drugs and therapeutic strategies for preventing or delaying neurodegenerative progression, it is urgent to diagnose NDDs as early and accurately as possible. Nanomaterials, emerged as one of the most promising materials in the 21st century, have been widely applied and play a significant role in diagnosis and treatment of NDDs because of their remarkable properties including stability, prominent biocompatibility, unique structure, novel physical and chemical characteristics. In this review, we outlined general strategies for the application of different types of advanced materials in early and staged diagnosis of NDDs in vivo and in vitro. According to applied technology, in vivo research mainly involves magnetic resonance, fluorescence, and surface enhanced Raman imaging on structures of brain tissues, cerebral vessels and related distributions of biomarkers. In vitro research is focused on the detection of fluid biomarkers in cerebrospinal fluid and peripheral blood based on fluorescence, electrochemical, Raman and surface plasmon resonance techniques. Finally, we discussed the current challenges and future perspectives of biomarker-based NDDs diagnosis as well as potential applications regarding advanced nanomaterials.

A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.

Multimodal Medical Image Fusion of Positron Emission Tomography and Magnetic Resonance Imaging Using Generative Adversarial Networks

Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.