The prospect of artificial intelligence to personalize assisted reproductive technology.

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

Luteal phase support in assisted reproductive technology.

Infertility affects one in six couples, with in vitro fertilization (IVF) offering many the chance of conception. Compared to the solitary oocyte produced during the natural menstrual cycle, the supraphysiological ovarian stimulation needed to produce multiple oocytes during IVF results in a dysfunctional luteal phase that can be insufficient to support implantation and maintain pregnancy. Consequently, hormonal supplementation with luteal phase support, principally exogenous progesterone, is used to optimize pregnancy rates; however, luteal phase support remains largely 'black-box' with insufficient clarity regarding the optimal timing, dosing, route and duration of treatment. Herein, we review the evidence on luteal phase support and highlight remaining uncertainties and future research directions. Specifically, we outline the physiological luteal phase, which is regulated by progesterone from the corpus luteum, and evaluate how it is altered by the supraphysiological ovarian stimulation used during IVF. Additionally, we describe the effects of the hormonal triggers used to mature oocytes on the degree of luteal phase support required. We explain the histological transformation of the endometrium during the luteal phase and evaluate markers of endometrial receptivity that attempt to identify the 'window of implantation'. We also cover progesterone receptor signalling, circulating progesterone levels associated with implantation, and the pharmacokinetics of available progesterone formulations to inform the design of luteal phase support regimens.

Urinary Neutrophil Gelatinase Associated Lipocalins (NGALs) predict acute kidney injury post liver transplant.

BACKGROUND: Acute Kidney Injury, a common complication of liver transplant, is associated with a significant increase in the risk of morbidity, mortality and graft loss. Current diagnostic criteria leaves a delay in diagnosis allowing further potential irreversible damage. Early biomarkers of renal injury are of clinical importance and Neutrophil Gelatinase Associated Lipocalins (NGALs) and Syndecan-1 were investigated. METHODS: AKI was defined according to the Acute Kidney Injury Network criteria. Urine and blood samples were collected pre-operatively, immediately post-op and 24 h post reperfusion to allow measurement of NGAL and Syndecan-1 levels. RESULTS: 13 of 27 patients developed an AKI. Patients who developed AKI had significantly higher peak transaminases. Urinary NGAL, plasma NGAL and Syndecan-1 levels were significantly elevated in all patients post reperfusion. Urinary NGAL levels immediately post-op were significantly higher in patients who developed an AKI than those that didn't [1319 ng/ml vs 46.56 ng/ml, p ≤ 0.001]. ROC curves were performed and urinary NGAL levels immediately post-op were an excellent biomarker for AKI with an area under the curve of 0.948 (0.847-1.00). CONCLUSIONS: Urinary NGAL levels measured immediately post-op accurately predict the development of AKI and their incorporation into clinical practise could allow early protocols to be developed to treat post transplant AKI.

Neutrophil Gelatinase-Associated Lipocalin (NGAL) in predicting acute kidney injury following orthotopic liver transplantation: A systematic review.

BACKGROUND: Acute kidney injury (AKI) is common after orthotopic liver transplantation (OLT) usually occurring early post-transplant. Multiple causes include graft preservation injury, blood loss, hypotension but also severity of recipient liver disease. Early intervention in AKI has both short and long term patient benefits. Unfortunately there are no current clinical biomarkers of early AKI. AIM: To assess the value of NGAL in predicting AKI following OLT. METHODS: Ovid MEDLINE and EMBASE were searched between the years of 2000 and 2017 for studies using keywords: Neutrophil Gelatinase Associated Lipocalin or NGAL variants combined with synonyms for liver transplantation. RESULTS: 96 studies were identified. 11 studies including 563 patients were considered suitable for analysis. Both urinary (uNGAL) and plasma NGAL (pNGAL) measurement were found to predict AKI after liver transplantation. Optimal reported area under the receiver-operator characteristics curve (AUROC) values of 0.5-0.83 and 0.54-0.86 respectively. CONCLUSIONS: NGAL is a good predictor of early AKI post OLT although there is considerable variation in the published results. Further studies with prospectively defined cut-off values, standardized definitions of AKI and rigorous data reporting should be conducted to establish its clinical usefulness and limitations.