Aberrant SOX10 and RET expressions in patients with Hirschsprung disease.

BACKGROUND: HSCR is a complex genetic disorder characterized by the absence of ganglion cells in the intestine, leading to a functional obstruction. It is due to a disruption of complex signaling pathways within the gene regulatory network (GRN) during the development of the enteric nervous system (ENS), including SRY-Box Transcription Factor 10 (SOX10) and REarranged during Transfection (RET). This study evaluated the expressions of SOX10 and RET in HSCR patients in Indonesia. METHODS: Total RNA of 19 HSCR ganglionic and aganglionic colons and 16 control colons were analyzed using quantitative real-time polymerase chain reaction for SOX10 and RET with GAPDH as the reference gene. Livak's method (2-ΔΔCT) was used to determine the expression levels of SOX10 and RET. RESULTS: Most patients were males (68.4%), in the short aganglionosis segment (78.9%), and had undergone transanal endorectal pull-through (36.6%). There were significant upregulated SOX10 expressions in both ganglionic (2.84-fold) and aganglionic (3.72-fold) colon of HSCR patients compared to controls' colon (ΔCT 5.21 ± 2.04 vs. 6.71 ± 1.90; p = 0.032; and ΔCT 4.82 ± 1.59 vs. 6.71 ± 1.90; p = 0.003; respectively). Interestingly, the RET expressions were significantly downregulated in both ganglionic (11.71-fold) and aganglionic (29.96-fold) colon of HSCR patients compared to controls' colon (ΔCT 12.54 ± 2.21 vs. 8.99 ± 3.13; p = 0.0004; and ΔCT 13.90 ± 2.64 vs. 8.99 ± 3.13; p = 0.0001; respectively). CONCLUSIONS: Our study shows aberrant SOX10 and RET expressions in HSCR patients, implying the critical role of SOX10 and RET in the pathogenesis of HSCR, particularly in the Indonesian population. Our study further confirms the involvement of SOX10-RET within the GNR during the ENS development.

Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception.

Background: The emerging field of artificial intelligence (AI) will probably affect the practice for the next generation of doctors. However, the students' views on AI have not been largely investigated. Methods: An anonymous electronic survey on AI was designed for medical and dental students to explore: (1) sources of information about AI, (2) AI applications and concerns, (3) AI status as a topic in medicine, and (4) students' feelings and attitudes. The questionnaire was advertised on social media platforms in 2020. Security measures were employed to prevent fraudulent responses. Mann-Whitney U-test was employed for all comparisons. A sensitivity analysis was also performed by binarizing responses to express disagreement and agreement using the Chi-squared test. Results: Three thousand one hundred thirty-three respondents from 63 countries from all continents were included. Most respondents reported having at least a moderate understanding of the technologies underpinning AI and of their current application, with higher agreement associated with being male (p < 0.0001), tech-savvy (p < 0.0001), pre-clinical student (p < 0.006), and from a developed country (p < 0.04). Students perceive AI as a partner rather than a competitor (72.2%) with a higher agreement for medical students (p = 0.002). The belief that AI will revolutionize medicine and dentistry (83.9%) with greater agreement for students from a developed country (p = 0.0004) was noted. Most students agree that the AI developments will make medicine and dentistry more exciting (69.9%), that AI shall be part of the medical training (85.6%) and they are eager to incorporate AI in their future practice (99%). Conclusion: Currently, AI is a hot topic in medicine and dentistry. Students have a basic understanding of AI principles, a positive attitude toward AI and would like to have it incorporated into their training.