Low dose stimulation (LS) is emerging as an alternative regime in assisted reproductive technology (ART). This study aimed to compare the cost-effectiveness of LS to the high dose GnRH antagonist (Atg) regime.
Gestational hypertension (GH) remains one of the main causes of high maternal and perinatal morbidity and mortality worldwide with the highest incidence among primigravidae of about 10%-15%. However, it was noted that the incidence of GH in primigravidae who conceived following assisted reproductive technique (ART) or intrauterine insemination (IUI) supplemented with dydrogesterone during the first trimester was low.
Study site: Obstetrics and Gynecology
Department, Pusat Perubatan Universiti Kebangsaan Malaysia PPUKM
The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying SARS-CoV-2 in these images proves to be challenging and time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as a promising solution in medical image analysis. This article provides a meticulous and comprehensive review of imaging-based SARS-CoV-2 diagnosis using deep learning techniques up to May 2024. This article starts with an overview of imaging-based SARS-CoV-2 diagnosis, covering the basic steps of deep learning-based SARS-CoV-2 diagnosis, SARS-CoV-2 data sources, data pre-processing methods, the taxonomy of deep learning techniques, findings, research gaps and performance evaluation. We also focus on addressing current privacy issues, limitations, and challenges in the realm of SARS-CoV-2 diagnosis. According to the taxonomy, each deep learning model is discussed, encompassing its core functionality and a critical assessment of its suitability for imaging-based SARS-CoV-2 detection. A comparative analysis is included by summarizing all relevant studies to provide an overall visualization. Considering the challenges of identifying the best deep-learning model for imaging-based SARS-CoV-2 detection, the article conducts an experiment with twelve contemporary deep-learning techniques. The experimental result shows that the MobileNetV3 model outperforms other deep learning models with an accuracy of 98.11%. Finally, the article elaborates on the current challenges in deep learning-based SARS-CoV-2 diagnosis and explores potential future directions and methodological recommendations for research and advancement.
Neurodegenerative diseases are a global health issue with inadequate therapeutic options and an inability to restore the damaged nervous system. With advances in technology, health scientists continue to identify new approaches to the treatment of neurodegenerative diseases. Lost or injured neurons and glial cells can lead to the development of several neurological diseases, including Parkinson's disease, stroke, and multiple sclerosis. In recent years, neurons and glial cells have successfully been generated from stem cells in the laboratory utilizing cell culture technologies, fueling efforts to develop stem cell-based transplantation therapies for human patients. When a stem cell divides, each new cell has the potential to either remain a stem cell or differentiate into a germ cell with specialized characteristics, such as muscle cells, red blood cells, or brain cells. Although several obstacles remain before stem cells can be used for clinical applications, including some potential disadvantages that must be overcome, this cellular development represents a potential pathway through which patients may eventually achieve the ability to live more normal lives. In this review, we summarize the stem cell-based therapies that have been explored for various neurological disorders, discuss the potential advantages and drawbacks of these therapies, and examine future directions for this field.