Medicina
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Background: Erdheim Chester disease (ECD) is a rare, non-Langerhans cell histiocytosis of unknown etiology that occurs in multiple organs. The clinical characteristics of ECD are unknown, making it difficult to diagnose. Case presentation: A 61-year-old woman presented with left knee pain and contracture. ⋯ Conclusion: This report describes an unusual presentation of ECD involving the skeletal system and multiple extraskeletal organs. Owing to its non-specific nature, ECD was notably difficult to diagnose. Therefore, if a patient has knee pain and other multiorgan presentations without malignancy, clinicians should suspect ECD.
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Review
A Model of Triage of Serious Spinal Pathologies and Therapeutic Options Based on a Delphi Study.
Background and Objectives: The relevance of red flags in serious spinal pathology (SSP) has evolved throughout the last years. Recently, new considerations have been proposed to expand the consideration of red flags. The purpose of this study was to determine, approve and test a model for the triage and management process of SSPs based on the latest data available in the literature. ⋯ The use of clinical scenarios by experts brought about reflexive elements both for the determined model and for the SSPs depicted in the clinical cases. Conclusions: The validation of the model and its implementation in the clinical field could help assess the skills of first-line practitioners managing spinal pain patients. To this end, the development of additional clinical scenarios fitting the determined model should be further considered.
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Background and Objectives: Triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous subtype of breast cancer, accounts for ap-proximately 10-15% of all breast cancer cases. Currently, there is no effective therapeutic target for TNBC. Tu-mor-associated macrophages (TAMs), which can be phenotypically classified into M1 and M2 subtypes, have been shown to influence the prognosis of various cancers, including ovarian cancer. ⋯ The expression of the three significant DEGs was validated through immunohisto-chemistry. Conclusions: The study concluded that the riskScore model, based on the M1/M2 macrophage ratio, is a valid prognostic tool for TNBC. The findings underscore the importance of the TME in TNBC progression and prognosis and highlight the po-tential of the riskScore model in predicting immunotherapy response in TNBC patients.
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Background and Objectives: This study aimed to assess the dental anxiety of patients using the modified dental anxiety scale (MDAS) questionnaire along with examining the possible relationship between dental anxiety and sociodemographic factors. Materials and Methods: The MDAS questionnaire was used to assess the anxiety level of the patients which included a total of five questions and five options to respond to each question. MDAS questionnaire was filled out by all the patients before the dental treatment. ⋯ Linear regression showed that age and gender have a significant association with the pre-treatment anxiety level; however, types of treatment is not associated with the MDAS. Anxiety levels decreased for the majority of the patients after the treatment and types of treatment did not show any differences with the post-treatment anxiety level. Conclusions: Age and gender play an important role in dental anxiety; however, types of treatment are not associated with pre-treatment and post-treatment dental anxiety.
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Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PETFDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PETFDG image. This study aimed to generate PETFDG images from 18F-florbetaben PET (PETFBB) images using a generative adversarial network (GAN) and compare the generated PETFDG (PETGE-FDG) with real PETFDG (PETRE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). ⋯ The cycleGAN model generated PETGE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PETFDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.