Electromagnetic Field Stimulation Therapy for Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative dementia worldwide. AD is a multifactorial disease that causes a progressive decline in memory and function precipitated by toxic beta-amyloid (Aβ) proteins, a key player in AD pathology. In 2022, 6.

Numerical Modeling and Computer Simulation of a Meander Line Antenna for Alzheimer's Disease Treatment, a Feasibility Study.

Alzheimer's disease (AD) is a brain disorder that eventually causes memory loss and the ability to perform simple cognitive functions; research efforts within pharmaceuticals and other medical treatments have minimal impact on the disease. Our preliminary biological studies showed that Repeated Electromagnetic Field Stimulation (REFMS) applying an EM frequency of 64 MHz and a specific absorption rate (SAR) of 0.4 - 0.

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images.

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described.

Numerical Analysis and Design of an EMF Birdcage Wearable Device for the Treatment of Alzheimer's Disease: A Feasibility Study.

In this study, we performed a numerical analysis of a novel EMF Birdcage wearable device for the treatment of Alzheimer's disease (AD). We designed the new device to generate and radiate a frequency of 64 MHz and a specific absorption rate (SAR) of 0.6 W/kg to a simulated human brain tissue.

Multidimensional insights into the repeated electromagnetic field stimulation and biosystems interaction in aging and age-related diseases.

We provide a multidimensional sequence of events that describe the electromagnetic field (EMF) stimulation and biological system interaction. We describe this process from the quantum to the molecular, cellular, and organismal levels. We hypothesized that the sequence of events of these interactions starts with the oscillatory effect of the repeated electromagnetic stimulation (REMFS).

EMF Antenna Exposure on a Multilayer Human Head Simulation for Alzheimer Disease Treatments.

In this paper, we follow up with our preliminary biological studies that showed that Repeated electromagnetic field stimulation (REMFS) decreased the toxic amyloid-beta (A) levels, which is considered to be the cause of Alzheimer's disease (AD). The REMFS parameters of these exposures were a frequency of 64 MHz and a Specific absorption rate (SAR) of 0.4 to 0.

Eletromagnetic Detection of Mild Brain Injury: A Novel Imaging Approach to Post Concussive Syndrome.

Introduction: Mild traumatic brain injury (mTBI) is a common injury, with nearly 3 - 4 million cases annually in the United States alone. Neuroimaging in patients with mTBI provides little benefit, and is usually not indicated as the diagnosis is primarily clinical. It is theorized that microvascular trauma to the brain may be present in mTBI, that may not be captured by routine MRI and CT scans.

TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining.

Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

Background: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer.

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis.

Electromagnetic simulation for diagnosing damage to femoral neck vasculature: A feasibility study.

Background: Femoral neck fractures are common injuries managed by orthopedic surgeons across the world. From pediatrics to geriatrics, disruption of the blood supply to the femoral neck is a well-recognized source of morbidity and mortality, oftentimes resulting in avascular necrosis of the femoral head. This devastating complication occurs in 10-45% of femoral neck fractures.

Electromagnetic simulation of non-invasive approach for the diagnosis of diabetic foot ulcers.

Diabetic foot ulcers are systemic diseases that affect all blood vessels within the human body. From major blood vessels to microvasculature, hardening, thickening, and narrowing of blood vessels ultimately results to diminished blood flow to end organs. The detrimental effects of peripheral vascular disease are well recognized across medicine, particularly with regards to diabetic foot ulcers.

Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans.

The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.

Antenna Design and SAR Analysis on Human Head Phantom Simulation for Future Clinical Applications.

Background: The rapid development of a variety of devices that emit Radiofrequency Electromagnetic fields (RF-EMF) has sparked growing interest in their interaction with biological systems and the beneficial effects on human health. As a result, investigations have been driven by the potential for therapeutic applications, as well as concern for any possible negative health implications of these EM energies [1-4]. Recent results have indicated specific tuning of experimental and clinical RF exposure may lead to their clinical application toward beneficial health outcomes [5].

Dynamic thermal/acoustic response for human bone materials at different energy levels: A diagnosis approach.

Background: The non-invasive diagnostic approaches have gained high attention in recent years, utilizing high technology sensor systems, including infrared, microwave devices, acoustic transducers, etc. The patient safety, high resolution images, and reliability are among the driving forces toward high technology approaches. The thermal and acoustic responses of the materials may reflect the important research parameters such as penetration depth, power consumption, and temperature change used for the practical models of the system.

Building a Surface Atlas of Hippocampal Subfields From High Resolution T2-weighted MRI Scans Using Landmark-free Surface Registration.

The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools.

Electromagnetic and Thermal Simulations of Human Neurons for SAR Applications.

The impact of the electromagnetic waves (EM) on human neurons (HN) has been under investigation for decades, in efforts to understand the impact of cell phones (radiation) on human health, or radiation absorption by HN for medical diagnosis and treatment. Research issues including the wave frequency, power intensity, reflections and scattering, and penetration depths are of important considerations to be incorporated into the research study. In this study, computer simulation for the EM exposure to HN was studied for the purpose of determining the upper limits of the electric and magnetic field intensities, power consumption, reflections and transmissions, and the change in temperature resulting from the power absorption by human neurons.

Non-invasive photo acoustic approach for human bone diagnosis.

The existing modalities of bone diagnosis including X-ray and ultrasound may cite drawback in some cases related to health issues and penetration depth, while the ultrasound modality may lack image quality. Photo acoustic approach however, provides light energy to the acoustic wave, enabling it to activate and respond according to the propagating media (which is type of bones in this case). At the same time, a differential temperature change may result in the bio heat response, resulting from the heat absorbed across the multiple materials under study.

Surface-Based Morphometric Analysis of Hippocampal Subfields in Mild Cognitive Impairment and Alzheimer's Disease.

The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for Alzheimer's disease (AD). Its complex folding anatomy often presents analytical challenges. In particular, the critical subfield information is typically not addressed by the existing hippocampal shape studies.

A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry.

In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer's disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, , to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data.

Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM.

The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies.

Application of thermography for non-invasive diagnosis of thyroid gland disease.

In this paper, a computer-based prototype device was designed based on an economical noninvasive system that could detect and display the relative skin temperature variations present in human patients suffering from thyroid disorders. Such a system could be used to augment the normal procedures followed by the physician in diagnosing the thyroid to detect areas of hyperactivity within the gland. Because a hyperactive nodule is a center of increased blood flow and chemical activity, it might be also a center of heat production that is detectable by thermal sensing.