Deep white matter hyperintensities (DWMHs) seen on magnetic resonance imaging (MRI) are thought to reflect small-vessel diseases (SVDs) and may have a background that differs from that of stenotic large-vessel diseases. We assessed risk factors for DWMHs and investigated the association between DWMHs and dilative changes in the basilar artery (BA) on MRI in nonstroke patients. We reviewed clinical information and MRI findings for 149 outpatients aged 46-90 years, excluding those with a previous symptomatic cerebrovascular event. DWMHs were graded 0-3, and the maximal BA diameter and area were measured from the flow void on axial T2-weighted MRI to assess dilatation. We divided the patients into groups with and without DWMH grade 2 or 3, and compared clinical information and BA parameters in these groups. The two groups demonstrated significant differences in age, serum low-density lipoprotein (LDL) level, estimated glomerular filtration rate (eGFR), and BA parameters. An adjusted logistic regression analysis including BA diameter found that age (odds ratio [OR], 1.974 per 10 years; 95% confidence interval [CI], 1.030-1.112; P = .0006), LDL (OR, 0.811 per 10 mg/dL; 95% CI, 0.964-0.965; P = .0085), eGFR (OR, 0.835 per 10 mL/min/1.73 m(2); 95% CI, 0.967-0.998; P = .0229), and BA diameter (OR, 2.515 per 1 mm; 95% CI, 1.191-4.098; P = .0119) were independently associated with the presence of DWMHs. An analysis including the BA area yielded similar results. DWMHs are manifestations of SVDs and show a strong association with lower serum LDL level, lower eGFR, and BA dilatation.
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Plast Reconstr Surg Glob Open
January 2025
Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Background: Given the growing demand for gender-affirming surgery (GAS) in recent years, it is essential to explore the public perceptions of GAS. Understanding the public's opinions and attitudes toward GAS will provide valuable insights for shaping educational initiatives to enhance public knowledge and awareness.
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Clin Chem Lab Med
January 2025
Department of Laboratory Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea.
Objectives: This study aimed to evaluate the performance of PBIA (UIMD, Seoul, Republic of Korea), an automated digital morphology analyzer using deep learning, for white blood cell (WBC) classification in peripheral blood smears and compare it with the widely used DI-60 (Sysmex, Kobe, Japan).
Methods: A total of 461 slides were analyzed using PBIA and DI-60. For each instrument, pre-classification performance was evaluated on the basis of post-classification results verified by users.
Front Med (Lausanne)
January 2025
Hepatobiliary Pancreatic Surgery Department, Huadu District People's Hospital of Guangzhou, Guangzhou, China.
Background: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.
Objective: To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.
Light Sci Appl
January 2025
Department of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA. Electronic address:
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white matter and cerebrospinal fluid. The presence of free water partial volume effects leads to biases in estimating diffusion properties. Additionally, the existing mathematical FWE model is a two-compartment model, which can be well posed for multi-shell data.
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