Background: Type 2 Diabetes Mellitus (T2DM) and Alzheimer’s Disease (AD) are increasingly linked through shared pathophysiological mechanisms, giving rise to the concept of Type 3 Diabetes Mellitus (T3DM). Brain insulin resistance, oxidative stress, and neuroinflammation are central to both conditions, contributing to cognitive decline and AD progression. Aim: This review aims to explore this emerging relationship and its implications for prevention and management. Methods: Using an integrative review, 21 studies were systematically analyzed. The review focused on identifying demographic, genetic, and lifestyle factors contributing to T2DM and AD and examined shared molecular pathways such as insulin dysregulation and amyloid-beta accumulation. Results: The findings reveal that T3DM shares key features with T2DM and AD, including insulin resistance and chronic inflammation. Lifestyle interventions, such as diet and exercise, alongside routine cognitive and metabolic screenings, are critical in mitigating progression. Conclusions: Further research into diagnostic biomarkers and targeted therapies is essential to manage T3DM and its impact on AD. The role of nursing professionals in early detection, education, and holistic management is emphasized as vital in addressing this dual disease burden. This review offers actionable insights into integrated strategies for addressing these interconnected conditions.

A regular menstrual cycle is important to maintain a woman’s fertility. This cycle has been linked to optimal function of the thyroid gland in the production of its hormones. Disturbance of thyroid gland functions could result to female infertility due to changes in menstrual patterns. Aim of this research was to determine the correlation between thyroid gland functions and menstrual patterns amongst infertile and fertile women attending a tertiary care hospital in North-Central Nigeria. This comparative, cross-sectional study recruited 106 women who visited the hospital's Gynecology Clinic and Family Planning Clinic. 53 of the 106 patients were women with a history suggestive of either primary or secondary infertility and the remaining 53 women with no history of infertility served as the control. A well-structured questionnaire was used to obtain data on the patients’ menstrual patterns. Anthropometric data were measured and obtained. Collected blood samples were analyzed using Enzyme-Linked Immunosorbent Assay (ELISA) technique to determine the serum levels of thyroid hormones. All obtained data was analyzed, and the level of significance was set at p<0.05, at a 95% confidence interval. 33 patients had menstrual anomalies (78.8% infertile women; 21.2% fertile women who served as control, p=0.012). The incidence of menstrual anomalies in the infertile women group and control group was 7.5% versus 0.0% for amenorrhea; 20.8% versus 5.7% for menorrhagia; 9.4% versus 7.5% for oligomenorrhea; 7.5% versus 0.0% for hypomenorrhea; nil polymenorrhea for both groups; and 50.9% versus 86.8% for normal menstrual patterns. Ten (9.43%) patients were diagnosed with thyroid dysfunctions (80% in infertile group; 20% in control group, p=0.046). Six (18.2%) out of 33 women with menstrual anomalies were diagnosed with thyroid dysfunction. Five (83.3%) out of these 6 women with both menstrual anomalies and diagnosed thyroid dysfunction were infertile while only one (16.7%) was fertile. Thyroid gland dysfunction correlates strongly with abnormal menstrual patterns, which implies that it is vital to evaluate thyroid hormone levels in blood serum in the course of treating menstrual irregularities and female infertility. Early detection of thyroid dysfunction is important in achieving a positive treatment outcome for female infertility.

Neurodegeneration and aging are pressing issues with significant personal, economic, ethical, and social consequences. However, the underlying biological mechanisms of these conditions remain largely unknown, making the development of effective treatments challenging. The difficulty in early detection and diagnosis of neurodegenerative diseases further compounds the issue. Recent advancements in genetics, genomics, and brain imaging technology hold great promise for improving our understanding of neurodegeneration and aging, as well as the development of personalized medicine and new drugs and therapies. Addressing these challenges will require a multi-disciplinary and collaborative approach from researchers in various fields. This Special Issue offers valuable insights and perspectives on this critical area of research, which can help advance our understanding and improve the health and well-being of our aging population.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.