Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine.

BACKGROUND: The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula. METHODS: This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively. RESULTS: From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields. CONCLUSION: Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Anti-cancer activity of phytochemicals extracted from Urtica pilulifera on Hela cells.

Urtica pilulifera is effective against cancer cell growth but its bioactive compounds and the mechanism of action behind its effect are still to be clarified. This study evaluated the anticancer activity of compounds extracted from Urtica pilulifera leaves against cervical cancer cells. The cytotoxic effect of water, methanol and hexane extracts of Urtica pilulifera leaves was assessed by MTT assay. The most potent extract was fractionated by column chromatography and its anticancer activity was evaluated. GC-MS was used to identify the chemical components of the different fractions. Western blotting analysis was used to determine the level of specific apoptotic and cell cycle protein markers. Data showed that the hexanic extract exhibits the most potent cytotoxicity against Hela cells. Fraction 4 of the hexanic extract showed the most potent antiproliferative effect against Hela cells. GC-MS analysis showed that the most prominent compound in fraction 4 is phytol. Fraction 4 treatment induced cell cycle arrest and intrinsic apoptosis in Hela cells as evident by the increasing levels of p21, p53, PARP cleavage and active caspase 9. These findings reveal the ability of U. pilulifera hexanic extract to induce cell cycle arrest and apoptosis in cervical cancer cells.

Rare association of trisomy 13 with ectrodactyly and congenital diaphragmatic hernia.

Our findings expand the known clinical features of trisomy 13 by including ectrodactyly as a possible Trisomy 13-associated limb malformation. We highlight the importance of performing antenatal genetic test to establish more specific treatment plan.

Prenatal diagnosis of triploidy associated with holoprosencephaly: a case report and review of the literature.

We sought to identify risk factors of prenatal presentation of holoprosencephaly associated with triploidy. A case report is presented with review of the literature performed using the PubMed database. The latest search was done in June 2008. Literature review showed 11 reports with a total of 15 cases of holoprosencephaly associated with triploidy. The mean maternal age at diagnosis was 27 +/- 4.9 years, and the mean gestational age at diagnosis was 13.6 +/- 3.3 weeks. Triploidy was mainly associated with the alobar type of holoprosencephaly. Only 3 (20%) cases had associated placental abnormalities. In all cases, the pregnancy was terminated. The latest gestational age at which termination was performed was 23 weeks. Facial abnormalities were the most common associated feature (66.6%). The association of triploidy with holoprosencephaly is very rare. This association does not seem to be related to maternal age. It is mainly associated with alobar holoprosencephaly. Diagnosis is possible during the first trimester. Fetuses can survive up to the third trimester.