Medicine
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Hodgkin lymphoma, a lymphatic system cancer, is treated by chemotherapy, radiation therapy, and hematopoietic stem cell transplantation. Posterior reversible encephalopathy syndrome (PRES) is a rare neurotoxic effect associated with several drugs and systemic conditions. This case study emphasizes the potential risks of intensive chemotherapy regimens and postulates the impact of the circle of Willis variants on the heterogeneity of hemispheric lesions in PRES. ⋯ This case underscores the importance of recognizing the severe neurological complications, including PRES, associated with chemotherapeutic treatments in Hodgkin lymphoma. PRES may also be exacerbated by coagulopathies such as thrombocytopenia in this case. The circle of Willis variants may influence cerebral blood flow, autoregulation, and other factors of hemodynamics, leading to increased susceptibility to both radiographic lesion burden and the worst clinical outcomes.
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Observational Study
Evaluation of short-term adverse events of COVID-19 vaccines: An observational study.
Coronavirus disease 2019 (COVID-19) vaccines are the most effective tools in managing the pandemic. However, the concern about these vaccines is the occurrence of unwanted adverse events (AEs). This study aimed to evaluate the short-term AEs of COVID-19 vaccines (Sputnik V, Astrazenka, and Sinopharm). ⋯ All 3 vaccines were safe and tolerable. The most commonly reported AEs were injection site pain (local) and fatigue and lethargy (systemic). These expected AEs occurred shortly after vaccination and indicated an early immune response after vaccination.
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Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. ⋯ Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.
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Peptidyl (protein) arginine deiminases (PADs) provide the transformation of peptidyl arginine to peptidyl citrulline in the presence of calcium with posttranslational modification. The dysregulated PAD activity plays an important role on too many diseases including also the cancer. In this study, it has been aimed to determine the potential cytotoxic and apoptotic activity of chlorine-amidine (Cl-amidine) which is a PAD inhibitor and whose effectiveness has been shown in vitro and in vivo studies recently on human glioblastoma cell line Uppsala 87 malignant glioma (U-87 MG) forming an in vitro model for the glioblastoma multiforme (GBM) which is the most aggressive and has the highest mortality among the brain tumors. ⋯ The findings of this study have shown that Cl-amidine exhibits significant potential as an anticancer agent in the treatment of GBM. This conclusion is based on its noteworthy antiproliferative and apoptotic effects observed in U-87 MG cells, as well as its reduced cytotoxicity toward healthy cells in comparison to existing treatments. We propose that the antineoplastic properties of Cl-amidine should be further investigated through a broader spectrum of cancer cell types. Moreover, we believe that investigating the synergistic interactions of Cl-amidine with single or combination therapies holds promise for the discovery of novel anticancer agents.
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Gallstone disease (GD) is a common gastrointestinal disease. Although traditional diagnostic techniques, such as ultrasonography, CT, and MRI, detect gallstones, they have some limitations, including high cost and potential inaccuracies in certain populations. This study proposes a machine learning-based prediction model for gallstone disease using bioimpedance and laboratory data. ⋯ State-of-the-art machine learning techniques were performed on the dataset to detect gallstones. The experimental results showed that vitamin D, C-reactive protein (CRP) level, total body water, and lean mass are crucial features, and the gradient boosting technique achieved the highest accuracy (85.42%) in predicting gallstones. The proposed technique offers a viable alternative to conventional imaging techniques for early prediction of gallstone disease.