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Ultra high density imaging arrays in diffuse optical tomography for human brain mapping improve image quality and decoding performance.
Sci Rep
January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4515 McKinley Ave., St. Louis, MO, 63110, USA.
Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid.
View Article and Find Full Text PDFOn the pursuit of reproducibility: the importance of large sample sizes in psychoimmunology.
Transl Psychiatry
January 2025
Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Peripheral inflammatory markers (PIMs), such as C-reactive protein (CRP) or white blood cell count (WBC), have been associated with depression severity in meta-analyses and large cohort studies. However, in typically-sized psychoimmunology studies (N < 200) that explore associations between PIMs and neurobiological/psychosocial constructs related to depression and studies that examine less-studied PIMs (e.g.
View Article and Find Full Text PDFChallenges for complement functional assays in the clinical laboratory: From test validation to clinical interpretation.
J Immunol Methods
January 2025
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States of America. Electronic address:
Complement functional assays are essential first-tier tests for a gamut of disorders spanning from inborn errors of the immune system which lead to recurrent severe infections, to angioedema attacks, presentation of autoimmune disease, thrombotic microangiopathies and rare kidney disorders. These assays evaluate the activity of the three complement pathways and specific complement components, which helps in differential diagnosis and monitoring disease progression. The rising use of complement inhibitors for treating complement-mediated thrombotic microangiopathies has heightened the demand for personalized treatment plans and laboratory assessment of complement blockage.
View Article and Find Full Text PDFCarcinosarcoma of the fallopian tube: A rare case with rapid metastases and port-site metastasis.
Int J Surg Case Rep
January 2025
Department of Obstetrics and Gynecology, Moriya Daiichi General Hospital, Moriya, Ibaraki, Japan.
Introduction And Importance: Fallopian tube cancer, particularly the carcinosarcoma subtype, is a rare malignancy posing diagnostic challenges.
Case Presentation: Our patient was an 83-year-old, nulligravida woman, presented to our outpatient clinic with one month of pelvic pain. On examination, a pelvic mass was detected.
Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.
Comput Biol Med
January 2025
Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:
- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.
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