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Abstract Background The task of writing multiple choice question examinations for medical students is complex, timely and requires significant efforts from clinical staff and faculty. Applying artificial intelligence algorithms in this field of medical education may be advisable. Methods During March to April 2023, we utilized GPT-4, an OpenAI application, to write a 210 multi choice questions-MCQs examination based on an existing exam template and thoroughly investigated the output by specialist physicians who were blinded to the source of the questions. Algorithm mistakes and inaccuracies, as identified by specialists were classified as stemming from age, gender or geographical insensitivities. Results After inputting a detailed prompt, GPT-4 produced the test rapidly and effectively. Only 1 question (0.5%) was defined as false; 15% of questions necessitated revisions. Errors in the AI-generated questions included: the use of outdated or inaccurate terminology, age-sensitive inaccuracies, gender-sensitive inaccuracies, and geographically sensitive inaccuracies. Questions that were disqualified due to flawed methodology basis included elimination-based questions and questions that did not include elements of integrating knowledge with clinical reasoning. Conclusion GPT-4 can be used as an adjunctive tool in creating multi-choice question medical examinations yet rigorous inspection by specialist physicians remains pivotal.

Tijana Drobnjak,1 Anna Margrét Jónsdóttir,2 Helga Helgadóttir,1 Margrét Soffía Runólfsdóttir,1 Hamutal Meiri,3,4 Marei Sammar,5 George Osol,6 Maurizio Mandalà,7 Berthold Huppertz,8 Sveinbjörn Gizurarson1 1Faculty of Pharmaceutical Sciences, School of Health Science, University of Iceland, Reykjavik, Iceland; 2Department of Pathology, University Hospital Iceland, Reykjavik, Iceland; 3Hy Laboratories Ltd, Rehovot, Israel; 4TeleMarpe Ltd., Tel Aviv, Israel; 5Ephraim Katzir Department of Biotechnology Engineering, ORT Braude College, Karmiel, Israel; 6Department of Obstetrics, Gynecology and Reproductive Sciences, University of Vermont College of Medicine, Burlington, VT, USA; 7Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy; 8Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria Introduction: Reduced concentrations of placental protein 13 (PP13) during the first trimester of human pregnancy are associated with elevated risk for the subsequent development of preeclampsia, which is one of the deadliest obstetrical complications of pregnancy. Previous studies by our group have shown that PP13 lowers blood pressure in pregnant rats, increases the size and weight of pups and placentas, and induces vasodilation of resistance arteries through endothelial signaling pathways involving endothelial nitric oxid synthase and prostaglandin. Methods: In the present study, the effect of PP13 was investigated in nonpregnant female Sprague Dawley rats (n=27). Osmotic pumps were surgically implanted subcutaneously that released a constant dose of PP13 or saline over 7 days. Most animals were sacrificed 6 days after the end of PP13 release (on day 13), while some were sacrificed immediately at the end of day 7 (the last PP13 release day), to compare the short- and long-term impact of PP13 on vessels’ growth and size. Results: The uterine vessels were significantly expanded in the group exposed to recombinant PP13 (rPP13) compared to the control (saline) group. Both veins and arteries were significantly expanded by rPP13 with a more pronounced effect after 13 days compared to the corresponding vessels after 7 days. Furthermore, the long-term effect of treatment by rPP13 was more pronounced in the veins compared to the corresponding arteries. The effect of a PP13 variant with a histidine-tag (His-PP13) remained the same between 7 and 13 days. Conclusion: In conclusion, PP13 might play a key role in the expansive remodeling of the uterine vessels, reflecting what would happen if the rat was pregnant, preparing the uterine vasculature for the increase in uteroplacental blood flow, which is necessary for normal pregnancy. We suggest that PP13 could act by NO signaling pathways, a hypothesis that requires future study. Keywords: preeclampsia, placenta, biomarkers, LGALS13, hypertension, pregnancy, rat vascular system, galectins, eNOS, slow release

Tijana Drobnjak,1 Hamutal Meiri,2,3 Maurizio Mandalá,4 Berthold Huppertz,5 Sveinbjörn Gizurarson1 1Faculty of Pharmaceutical Sciences, School of Health Sciences, University of Iceland, Reykjavik, Iceland; 2Hy Laboratories, Rehovot, Israel; 3TeleMarpe Ltd., Tel Aviv, Israel; 4Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy; 5Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria Introduction: Human placental protein 13 (PP13) is a galectin predominantly expressed by the placenta. Low serum concentrations of PP13 in early pregnancy indicate a higher risk of developing preeclampsia. Methods: The pharmacokinetic disposition and bioavailability of PP13 were determined by single intravenous and subcutaneous administration to 12 healthy New Zealand White rabbits. The serum pharmacokinetic values were determined by enzyme-linked immunosorbent assay, and are best described by a two-compartment model. Results: Both volume of distribution and the area under the curve were dose dependent for the intravenous group (p

Objective: Effective first‐trimester screening for pre‐eclampsia (PE) can be achieved using a competing‐risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine‐learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations. Methods: Previously, a machine‐learning model derived with the use of a fully connected neural network for first‐trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA‐PI), placental growth factor (PlGF) and pregnancy‐associated plasma protein‐A (PAPP‐A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first‐trimester PE validation (PREVAL) study, in which first‐trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing‐risks model. The performance of screening was assessed by examining the area under the receiver‐operating‐characteristics curve (AUC) and detection rate (DR) at a 10% screen‐positive rate (SPR). These indices were compared with those derived from the application of the FMF competing‐risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression. Results: The DRs at 10% SPR for early, preterm and all PE with the machine‐learning model were 84.4% (95% CI, 67.2–94.7%), 77.8% (95% CI, 66.4–86.7%) and 55.7% (95% CI, 49.0–62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864–0.975), 0.913 (95% CI, 0.882–0.944) and 0.846 (95% CI, 0.820–0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA‐PI and PlGF); inclusion of PAPP‐A did not provide significant improvement in DR. The machine‐learning model had similar performance to that achieved by the FMF competing‐risks model (DR at 10% SPR, 82.7% (95% CI, 69.6–95.8%) for early PE, 72.7% (95% CI, 62.9–82.6%) for preterm PE and 55.1% (95% CI, 48.8–61.4%) for all PE) without requiring specific adaptations to the population. Conclusions: A machine‐learning model for first‐trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

This article discusses a study that evaluated the performance of a machine learning model for predicting pre-eclampsia (PE) in a large Asian population. The study used data from 10,935 women with singleton pregnancies and applied the machine learning model for first trimester screening. The results showed that after adjusting for biochemical marker analyzers, the predictive performance of the model was comparable to that of the Fetal Medicine Foundation competing risk model in Asian populations. This study suggests that the machine learning model could be a useful tool for predicting PE in Asian populations. [Extracted from the article]

To evaluate the impact of local professional guidelines recommending second trimester ultrasound cervical length measurement, on national preterm birth (PTB) prevalence. For 170,865 deliveries in 2020, 0.21% reduction in the number of newborns 1,500 g and below, a 360K NIS government allowance for an average of 35 days at NICU per newborn, the estimated partial cost saving is at least 93.6M NIS (29.2 M US $). EP01.57: Reduced rate of preterm birth in Israel following cervical length measurements at 19-21 weeks: a national survey of 21 years. [Extracted from the article]

This article presents preliminary results from two implicit learning tasks (linguistic and nonlinguistic) aimed at ascertaining whether or not compensated adult dyslexic readers have a deficit in implicit learning. The dyslexic readers and a control group were compared on measures of accuracy and reaction time. In addition, EEG measures were obtained. Initial results suggest that dyslexic readers do in fact have a deficit in implicit learning, evidenced by differences in accuracy and the P300 component, and that this deficit is more pronounced when the task at hand is linguistic. [ABSTRACT FROM AUTHOR]