Publications by authors named "C M Serra"

Although post-transplant cyclophosphamide (PTCY)-based prophylaxis has become a widely adopted strategy for preventing graft-versus-host disease (GVHD) in 9 out of 10 HLA-mismatched unrelated donors (MMUDs), allogeneic hematopoietic cell transplants (allo-HCTs), data on the safety and efficacy of PTCY in this setting remain limited. This single-center study investigates the outcomes of 94 adults with hematological malignancies undergoing MMUD allo-HCT with PTCY and tacrolimus (Tac) (PTCY-Tac) between 2014 and 2023. The median age was 53 years, and 60.

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Objective: To examine and compare the accuracy of measurements obtained from photogrammetric models versus direct measurements taken on dry skulls, with the aim to verify the feasibility of photogrammetry for quantitative analysis in microsurgical neuroanatomy.

Methods: Two dry human skulls were used. Each was scanned using the dual camera system of a smartphone The selected photos were separately processed using 2 different softwares to create three-dimensional models.

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Obstructive Sleep Apnea (OSA) is a widespread disease, but usually is an underdiagnosed and undertreated public health problem. Nowadays its study is expensive. Collaboration and involvement of all specialties are necessary, also the implementation of simplified diagnostic methods to try to improve detection, increase the diagnosis and treatment ratio.

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Article Synopsis
  • - The research aimed to create a deep learning model for accurately assessing the extent of resection (EOR) in glioblastoma patients using postoperative MRI scans, addressing limitations of existing algorithms that only focus on preoperative images.
  • - Utilizing data from multiple sources, the model was trained to segment tumor features like contrast-enhancing tumor, edema, and surgical cavity, and was compared with other segmentation models, showing high performance in classifying resection categories.
  • - The study found that the nnU-Net framework outperformed other algorithms, achieving high accuracy in both segmentation (with median Dice scores up to 0.81) and EOR classification (96% accuracy in comparisons), making it a valuable tool for clinical use.
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Employing more than 2 million emergency department (ED) records, we combine machine learning and regression discontinuity to document novel distortions in triage nurses' assessments of patients' conditions and investigate the short- and medium-term consequences for patients. We show that triage nurses progressively become more lenient during their shifts, and identical ED patients arriving just after a shift change are thus assigned a lower priority. We show that these patients receive lower levels of care and require additional emergency care afterward.

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