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Background And Aims: Inflammatory bowel disease (IBD) develops in genetically susceptible individuals exposed to certain environmental factors, of which only a few have been established. We aimed to assess whether bariatric surgery (BS) and severe obesity are associated with an increased risk of developing IBD.

Methods: Adults diagnosed with obesity or severe obesity between 2005 and 2020 were identified from the Catalan Health Surveillance System; those diagnosed with IBD prior to the diagnosis of obesity or severe obesity were excluded.

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Long-term use of low-dose aspirin has been demonstrated to reduce cancer risk, but the duration of necessary medication use remains uncertain. This study aimed to investigate the long-term chemoprotective effect of aspirin among the Chinese population. This population-based study included all aspirin users between 2000 and 2019.

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Background: Individuals with cystic fibrosis (CF; a recessive disorder) have an increased risk of colorectal cancer (CRC). Evidence suggests individuals with a single CFTR variant may also have increased CRC risk.

Methods: Using population-based studies (GECCO, CORECT, CCFR, and ARIC; 53 785 CRC cases and 58 010 controls), we tested for an association between the most common CFTR variant (Phe508del) and CRC risk.

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Purpose: This study aimed to identify prognostic factors and develop a nomogram for survival in patients with brainstem ependymoma.

Methods: Data of 652 patients diagnosed with brainstem ependymoma extracted from the Surveillance, Epidemiology, and End Results (SEER) registry from 2000 to 2020 were analyzed. Univariate and multivariable Cox regression analyses were performed to examine factors influencing overall survival (OS).

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Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.

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