Determination of the type and quantity of sialic acid in the egg jelly coat of the sea urchin Paracentrotus lividus using capillary LC-ESI-MS/MS.

Sialic acid is a terminal sugar of carbohydrate chains that participates in numerous biological events. Recent studies have explored the mechanism of carbohydrate-mediated fertilisation to understand the biochemistry of fertilisation, although the type and quantity of sialic acid and the role of sialic acid during fertilisation remain unknown. Echinoderm fertilisation in particular has been studied extensively, yet our understanding of the mechanisms of carbohydrate-mediated fertilisation and the role of sialic acid remains incomplete. In this study, we characterised the sialic acid types in the egg jelly coat of the sea urchin, Paracentrotus lividus, using the sensitive analytical system capillary liquid chromatography electro-spray ionisation tandem mass spectrometry (capLC-ESI-MS/MS). First, we isolated the egg jelly coat and released its sialic acid using acid treatment. These sialic acids were derivatised with 1,2-diamino-4,5-methylenediaoxy-benzene dihydrochloride (DMB) and injected into the capLC-ESI-MS/MS system. When compared with standards, we identified twelve different types of sialic acid according to their retention times and collision-induced dissociation fragments. The mass spectral data revealed that Neu5Gc, Neu5Ac, Neu5GcS, and Neu5Gc9Ac were the predominant types of sialic acid in the sea urchin jelly coat, with Neu5Gc being the most abundant. Other types of sialic acid detected included Neu5AcS, Neu5Gc7,9Ac2, Neu5,9Ac2, Neu5Gc8Ac, Neu5Gc7Ac, Neu5,7Ac2, Neu5Gc8,9Ac2, and Neu5,8Ac2. The types and quantities of sialic acid that we detected in the egg jelly coat will aid in the discovery of new sialic acid-specific receptors on the sperm membrane.

Artificial Intelligence in Healthcare Competition (TEKNOFEST-2021): Stroke Data Set.

OBJECTIVE: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. MATERIALS AND METHODS: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a nondisclosure agreement signed by the representative of each team. RESULTS: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. CONCLUSION: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflecting various cases and problems. Especially, annotated data set by domain experts is more valuable.

Seroprevalence of coronavirus disease 2019 (COVID-19) among health care workers from three pandemic hospitals of Turkey.

COVID-19 is a global threat with an increasing number of infections. Research on IgG seroprevalence among health care workers (HCWs) is needed to re-evaluate health policies. This study was performed in three pandemic hospitals in Istanbul and Kocaeli. Different clusters of HCWs were screened for SARS-CoV-2 infection. Seropositivity rate among participants was evaluated by chemiluminescent microparticle immunoassay. We recruited 813 non-infected and 119 PCR-confirmed infected HCWs. Of the previously undiagnosed HCWs, 22 (2.7%) were seropositive. Seropositivity rates were highest for cleaning staff (6%), physicians (4%), nurses (2.2%) and radiology technicians (1%). Non-pandemic clinic (6.4%) and ICU (4.3%) had the highest prevalence. HCWs in "high risk" group had similar seropositivity rate with "no risk" group (2.9 vs 3.5 p = 0.7). These findings might lead to the re-evaluation of infection control and transmission dynamics in hospitals.