DESIGN: Preliminary assessment of serum levels of female hormones in women with or without T1DM. Then histological and immunological examinations were carried out on the pancreas, ovaries and uteri at different stages in non-obese diabetic (NOD) and Institute of Cancer Research (ICR) mice, as well as assessment of their fertility. A protein array was carried out to detect the changes in serum inflammatory cytokines. Furthermore, RNA-sequencing was used to identify the key abnormal genes/pathways in ovarian and uterine tissues of female NOD mice, which were further verified at the protein level.
RESULTS: Testosterone levels were significantly increased (P = 0.0036) in female mice with T1DM. Increasing age in female NOD mice was accompanied by obvious lymphocyte infiltration in the pancreatic islets. Moreover, the levels of serum inflammatory factors in NOD mice were sharply increased with increasing age. The fertility of female NOD mice declined markedly, and most were capable of conceiving only once. Furthermore, ovarian and uterine morphology and function were severely impaired in NOD female mice. Additionally, ovarian and uterine tissues revealed that the differentially expressed genes were primarily enriched in metabolism, cytokine-receptor interactions and chemokine signalling pathways.
CONCLUSION: T1DM exerts a substantial impairment on female reproductive health, leading to diminished fertility, potentially associated with immune disorders and alterations in energy metabolism.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.