RESUMEN
Resilience is associated with the degree to which post-concussion symptoms (PCS) are experienced. However, the role of resilience in the recovery trajectory of minority women, who tend to have prolonged concussion recovery, is poorly characterized. We evaluated the association between resilience and PCS, to determine if the association differed by race. A secondary data analysis was performed. Resilience was assessed using the Resilience Scale and PCS with the Rivermead questionnaire. Both variables were evaluated 6-10 weeks post-injury. Baseline demographics, spearman correlation, and multivariable linear regression models were used to determine the association between resilience and PCS. Seventy-seven women (mean age 28 ± 7.6) were included, 57% were White, and 43% were Black or Hispanic. The overall cohort had a moderate association between resilience and PCS (R = -0.304, p = 0.007). The association was present in minorities (R = -0.486, p = 0.004), and was stronger for Blacks (R = -0.745, p < 0.001). After adjusting for religion as a covariate separately, resilience (ß = -0.156, 95% confidence interval [CI]: -0.285, -0.026; p = 0.019) and mood (ß = 1.082, 95% CI: 0.847, 1.317; p < 0.001), were both independent predictors of PCS. The adjusted associations were stronger for the minority subgroup for both resilience (ß = -0.231, 95% CI: -0.413, -0.050; p = 0.014) and mood (ß = 1.122, 95% CI: 0.753, 1.491; p < 0.001). Our findings show that compared with Whites, minority individuals with higher resilience have greater resolution of PCS. However, mood is also of importance in this association. Thus resilience-based interventions must also target mood. Interventions that strengthen resilience may have promise in promoting equitable recovery in the setting of female concussions.
RESUMEN
Acute pancreatitis, marked by sudden inflammation of the pancreas, presents a complex spectrum of causative factors including gallstone obstruction, alcohol abuse, and viral infections. Recent studies have illuminated the emergence of vaccine-induced acute pancreatitis, notably associated with COVID-19 vaccinations, presenting diverse mechanisms ranging from direct viral-mediated injury to autoimmune reactions. Understanding this link is pivotal for public health, yet challenges persist in identifying and managing cases post-vaccination. Comprehensive literature reviews employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement outline the potential pathways and mechanisms leading to vaccine-induced pancreatitis, emphasizing the need for deeper investigations into underlying health conditions and modifications to vaccine components. Notably, the rare occurrences of vaccine-induced pancreatitis extend beyond COVID-19 vaccines, with reports also documenting associations with measles, mumps, and rubella (MMR), human papillomavirus (HPV), and other viral vaccinations. Mechanistically, hypotheses such as molecular mimicry and immunologic injury have been proposed, necessitating ongoing vigilance and exploration. Regulatory agencies play a crucial role in monitoring and communicating vaccine safety concerns, emphasizing transparency to address potential risks and maintain public trust. Understanding and communicating these rare adverse events with transparency remain integral for informed vaccination policies and to allay concerns surrounding vaccine safety.
RESUMEN
The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.