Annals of medicine
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Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI. ⋯ The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.
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Obesity, particularly excessive body fat, is an established risk factor and substantial prognostic determinant in breast cancer. Recent studies suggested that diet-related inflammation plays a key role in obesity. This study aimed to determine the association between energy-adjusted dietary inflammatory index (E-DII) and body composition, particularly body fat percentage, among patients with newly diagnosed breast cancer. ⋯ A higher E-DII was associated with increased body fat percentage, suggesting the potential of advocating anti-inflammatory diet to combat obesity among newly diagnosed breast cancer patients.
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To explore the crosstalk between baseline or visit hemoglobin and major adverse cardiovascular and cerebral events (MACCE) in percutaneous coronary intervention (PCI) patients and to construct risk stratification models to predict MACCE amongst these patients. ⋯ Visit hemoglobin and long-term hemoglobin changes were more predictive of MACCE risk than baseline hemoglobin levels. Our findings indicated that increasing hemoglobin levels might improve the long-term prognosis of anemia patients. We established a new risk stratification model for MACCE, which may more efficiently prioritize targeted screening for at-risk anemic patients undergoing PCI.
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Observational Study
Influencing factors of hospitalization in maintenance haemodialysis outpatients after a diagnosis of COVID-19.
The clinical manifestations of maintenance haemodialysis (MHD) outpatients diagnosed with coronavirus disease 2019 (COVID-19) are highly heterogeneous. They are prone to progress to severe conditions, and they often require hospitalization. To better guide the management of MHD outpatients, this retrospective observational study assessed risk factors for hospitalization of MHD patients after a diagnosis of COVID-19. ⋯ Older age, comorbid diabetes and lower lymphocyte count are important risk factors for hospitalization of MHD outpatients after a diagnosis of COVID-19. Focusing on these factors may help in early identification of patients who may need to be admitted due to potential disease progression.
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Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine and infectious diseases being not exempt from their rapid and exponential growth. Furthermore, the field of explainable AI and ML has gained particular relevance and is attracting increasing interest. ⋯ For example, they have been employed or proposed to better understand complex models aimed at improving the diagnosis and management of coronavirus disease 2019, in the field of antimicrobial resistance prediction and in quantum vaccine algorithms. Although some issues concerning the dichotomy between explainability and interpretability still require careful attention, an in-depth understanding of how complex AI/ML models arrive at their predictions or recommendations is becoming increasingly essential to properly face the growing challenges of infectious diseases in the present century.