RISK FACTORS FOR ACUTE KIDNEY INJURY ASSOCIATED WITH SEVERE HYPOTHYROIDISM.

Objective: This study aims to investigate the factors affecting development of acute kidney injury (AKI) in patients with severe hypothyroidism. Methods: This retrospective observational study involved patients with primary hypothyroidism and thyroid stimulating hormone (TSH) levels of more than 50 mIU/L at their review in the endocrinology outpatient clinic, between January 2015 and April 2021. Factors affecting the development of AKI were examined by logistic regression analysis. Results: A total of 100 patients, 20 (11 male (M), 9 female (F)) in the AKI (case) group and 80 (23 M, 57 F) patients in control group, were included in our study. The median age of the case group (56 years, interquartile range (IQR) 44.3-68.5) was significantly higher than the control group (49 years, IQR 32.3-60; p = 0.027), and the ratio of males to females was significantly higher in the case group (p = 0.001). Multivariate logistic regression analyses showed that hypothyroidism diagnosed after the age of 60 years (odds ratio (OR) 59.674, 95% confidence intervals (CI) 5.955-598.031; p = 0.001), free triiodothyronine (FT3) < 1.3 pg/mL (OR 17.151, 95% CI 2.491-118.089; p = 0.004) and creatine kinase (CK) > 1000 U/L (OR 1.522, 95% CI 1.602-82.848; p = 0.015) were predictors for the development of AKI in patients with severe hypothyroidism. Conclusion: We recommend close follow-up and monitoring of patients with AKI caused by severe hypothyroidism if patients who are diagnosed at age > 60 years, CK > 1000 U/L or FT3 < 1.3 pg/mL.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science.

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.