Publications by authors named "A Martinez-Davalos"

Objective: Image quality in positron emission tomography (PET) is influenced by positron range. In this work, the effect of the magnetic field of a PET/MR Siemens Biograph mMR 3T on the quality of PET images was studied.

Approach: Experimental measurements were conducted usingF andGa-filled phantoms to quantify image uniformity, recovery coefficients (RCs), spill-over ratios and percent contrast for spherical lesions.

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Background: Lipocalin-2 (LCN-2) is an osteokine that suppresses appetite, stimulates insulin secretion, regulates bone remodeling, and is induced by proinflammatory cytokines. The aim of this work was to investigate the participation of LCN-2 in periodontitis associated with type 2 diabetes (T2D) by evaluating alveolar bone loss, glycemic control, inflammation, and femur fragility.

Methods: A murine model of periodontitis with T2D and elevated LCN-2 concentration was used.

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This study aimed to evaluate the interest in event-driven PrEP (ED-PrEP) among men who have sex with men (MSM) using daily PrEP in Mexico's PrEP demonstration project between 2019 and 2020. We compared participants interested or not in ED-PrEP during their first-month visit and identified associated factors. Of 1,021 MSM attending their first-month visit, 7% had previous knowledge of ED-PrEP, but 40% were interested in ED-PrEP.

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Background Telemedicine, which involves utilising technologies for remote health care delivery, proved useful to continue offering certain health services during the coronavirus disease 2019 (COVID-19) lockdown. However, the extent of its effectiveness in delivering pre-exposure prophylaxis services for HIV prevention remains underexplored from the viewpoint of health care providers. Therefore, this study aimed to assess the experiences of health care professionals in Mexico who utilised telemedicine for delivering pre-exposure prophylaxis services during the COVID-19 contingency.

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Purpose: X-ray scatter significantly affects the image quality of cone beam computed tomography (CBCT). Although convolutional neural networks (CNNs) have shown promise in correcting x-ray scatter, their effectiveness is hindered by two main challenges: the necessity for extensive datasets and the uncertainty regarding model generalizability. This study introduces a task-based paradigm to overcome these obstacles, enhancing the application of CNNs in scatter correction.

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