An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals.

BACKGROUND: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer's disease (AD). The extraction of appropriate biomarkers to assess a subject's cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. METHODS: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study's feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. RESULTS: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. CONCLUSION: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.

Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020.

Background: COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown. Methods: Sample data was employed from the COVID-19 Impact Survey on April 20-26, 2020, May 4-10, 2020, and May 30-June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants' mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression. Results: The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle. Conclusion: This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions.

What is the fertility-enhancing effect of tubal flushing? A hypothesis article.

Hysterosalpingographies (HSGs) have formed an essential part of the fertility workup for more than a century. More recently, tubal flushing, especially with oil-based contrast, has been shown to significantly improve the natural conception rates. Critically, the mechanism of this fertility-enhancing effect during tubal flushing is still unclear. This article postulates hypotheses, based on published and own research, on the potential mechanisms and root cause of tubal flushing fertility enhancement. Possible explanations for the increased fertility rates, especially with oil-based contrast, are divided into the biochemical and interfacial effects derived from the contrast properties. The biochemical effects may include the immunological response of the endometrium or peritoneum, the impact on the endometrial opioid receptors or the iodine content. The interfacial effects may include improvement of interfacial factors due to the lubricant effect or dislodgement of mucus debris within the Fallopian tubes. Impact StatementWhat is already known on this subject? Tubal flushing during hysterosalpingographies (HSGs) increases natural conception rates, and using oil-based over water-based contrast increases that effect even further. However, the underlying mechanism of the observed fertility-enhancing effect is still poorly understood.What do the results of this study add? This article postulates different hypotheses on the potential mechanisms and root cause of the fertility enhancement from tubal flushing.What are the implications of these findings for clinical practice and/or further research? We suggest additional research on the different hypotheses, intending to determine which subfertile women will benefit most from tubal flushing using oil-based contrast and at which stage of their subfertility. Furthermore, we suggest research on administering tubal flushing with oil-based contrast, besides in HSG.

Diameter Estimation of Fallopian Tubes Using Visual Sensing.

Calculating an accurate diameter of arbitrary vessel-like shapes from 2D images is of great use in various applications within medical and biomedical fields. Understanding the changes in morphological dimensioning of the biological vessels provides a better understanding of their properties and functionality. Estimating the diameter of the tubes is very challenging as the dimensions change continuously along its length. This paper describes a novel algorithm that estimates the diameter of biological tubes with a continuously changing cross-section. The algorithm, evaluated using various controlled images, provides an automated diameter estimation with higher and better accuracy than manual measurements and provides precise information about the diametrical changes along the tube. It is demonstrated that the automated algorithm provides more accurate results in a much shorter time. This methodology has the potential to speed up diagnostic procedures in a wide range of medical fields.

Real-time deep learning-based image recognition for applications in automated positioning and injection of biological cells.

Biological cell injection is an effective method in which a foreign material is directly introduced into a biological cell. Since human involvement reduces the success rate of the biological microinjection procedure, an extensive research effort has been made towards its automation. The accurate positioning of a randomly placed biological cell in the microscope's field of view is a prerequisite for any automated injection procedure. Vision is the primary source for visual servoing in microinjection applications. For this reason, a visual sensing system is required to recognise, calculate, and manipulate the cell to the desired position. In this study, eight different pretrained neural networks were analysed and used as a backbone for the YOLOv2 object detection method, and the optimal network was evaluated based on mean Intersection over Union (IoU) accuracy, average precision (AP) at different thresholds, and frame rate (fps) in our dataset. YOLOv2 with Resnet-50 model demonstrated superior performance with 89% mean IoU accuracy and 100% detection accuracy at an average of 33 fps. Ten different sets of experiments were conducted to examine the algorithm by verifying the zebrafish embryo gradual presence within the field of view to bring the zebrafish embryo to the predefined position. Experimental results demonstrated that the developed solution performed real-time with high accuracy and illustrates auto-positioning with a 100% success rate regardless of the initial position of the biological cell within the Petri dish. Later, the generalization of the proposed solution was verified in a different dataset from the real microinjection setup.

Vision-Based Sensor for Three-Dimensional Vibrational Motion Detection in Biological Cell Injection.

Intracytoplasmic sperm injection (ICSI) is an infertility treatment where a single sperm is immobilised and injected into the egg using a glass injection pipette. Minimising vibration in three orthogonal axes is essential to have precise injector motion and full control during the egg injection procedure. Vibration displacement sensing using physical sensors in ICSI operation is challenging since the sensor interfacing is not practically feasible. This study proposes a non-invasive technique to measure the three-dimensional vibrational motion of the injection pipette by a single microscope camera during egg injection. The contrast-limited adaptive histogram equalization (CHALE) method and blob analyses technique were employed to measure the vibration displacement in axial and lateral axes, while the actual dimension of the focal axis was directly measured using the Brenner gradient algorithm as a focus measurement algorithm. The proposed algorithm operates between the magnifications range of 4× to 40× with a resolution of half a pixel. Experiments using the proposed vision-based algorithm were conducted to measure and verify the vibration displacement in axial and lateral axes at various magnifications. The results were compared against manual procedures and the differences in measurements were up to 2% among all magnifications. Additionally, the effect of injection speed on lateral vibration displacement was measured experimentally and was used to determine the values for egg deformation, force fluctuation, and penetration force. It was shown that increases in injection speed significantly increases the lateral vibration displacement of the injection pipette by as much as 54%. It has been demonstrated successfully that visual sensing has played a key role in identifying the limitation of the egg injection speed created by lateral vibration displacement of the injection pipette tip.

Effect of Injection Speed on Oocyte Deformation in ICSI.

Oocyte deformation during injection is a major cause of potential cell damage which can lead to failure in the Intracytoplasmic Sperm Injection (ICSI) operation used as an infertility treatment. Injection speed plays an important role in the deformation creation. In this paper the effect of different speeds on deformation of zebrafish embryos is studied using a specially designed experimental set-up. An analytical model is developed in order to link injection force, deformation, and injection speed. A finite element (FE) model is also developed to analyse the effect of injection speed, allowing the production of additional information that is difficult to obtain experimentally, e.g., deformation and stress fields on the oocyte. The numerical model is validated against experimental results. Experimental results indicate that by increasing the injection speed, the deformation decreases. However, higher speeds cause higher levels of injection force and force fluctuation, leading to a higher vibration during injection. For this reason, an optimum injection speed range is determined. Finally, the FE model was validated against experimental results. The FE model is able to predict the force-deformation variation during injection for different speeds. This proves to be useful for future studies investigating different injection conditions.

Oocyte Positional Recognition for Automatic Manipulation in ICSI.

Polar body position detection is a necessary process in the automation of micromanipulation systems specifically used in intracytoplasmic sperm injection (ICSI) applications. The polar body is an intracellular structure, which accommodates the chromosomes, and the injection must not only avoid this structure but be at the furthest point away from it. This paper aims to develop a vision recognition system for the recognition of the oocyte and its polar body in order to be used to inform the automated injection mechanism to avoid the polar body. The novelty of the paper is its capability to determine the position and orientation of the oocyte and its polar body. The gradient-weighted Hough transform method was employed for the detection of the location of the oocyte and its polar body. Moreover, a new elliptical fitting method was employed for size measurement of the polar bodies and oocytes for the allowance of morphological variance of the oocytes and their polar bodies. The proposed algorithm has been designed to be adaptable with typical commercial inverted microscopes with different criteria. The successful experimental results for this algorithm produce maximum errors of 5% for detection and 10% for reporting respectively.

Differential influence of ampullary and isthmic derived epithelial cells on zona pellucida hardening and in vitro fertilization in ovine.

The central role of the oviduct, as the site of zona pellucida (ZP) maturation, fertilization and early embryogenesis, has been recognized. The objective of this study was to investigate whether ampullary and isthmic derived epithelial cells have different effects on in vitro ZP hardening, in vitro fertilization (IVF) and in vitro culture (IVC) of the resulting embryos. Cumulus oocyte complexes (COCs) were matured in a coculture system with ampullary/isthmic epithelial cells, TCM199 supplemented with insulin-like growth factor I (IGF-I) and epithelial derived growth factor (EGF) (GF treated group), conditioned media produced using ampullary (ACM), isthmic (ICM), COCs+ampullary, and COCs+isthmic epithelial cells, contactless culture system, oviductal fluid, GF+ACM/ICM, and drops of TCM199 (control), for 24h. The matured oocytes were randomly divided into two groups: Group I was subjected to ZP digestion; Group II underwent IVF. The duration of the ZP digestion, in a coculture system with ampullary epithelial cells (AE) was significantly increased (p<0.05), compared with other groups. Penetrated oocytes and monospermic fertilization were significantly increased (p<0.05) in the AE group. The mean number of spermatozoa per penetrated oocyte was reduced dramatically for the AE group (p<0.05). A significant increase (p<0.05) in the embryo development was observed in all treated groups, compared to the control. Results revealed that epithelial cells harvested from the ampullary segment of the oviduct had in vitro specialized role in ZP hardening and have subsequent IVF and IVC outcomes.