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A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.
J Neuropathol Exp Neurol
December 2024
Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, CA, United States.
Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In this study, we propose a novel application of machine learning in the automated screening of microinfarcts and microhemorrhages.
View Article and Find Full Text PDFFast intraoperative detection of primary CNS lymphoma and differentiation from common CNS tumors using stimulated Raman histology and deep learning.
Neuro Oncol
December 2024
Department of Neurosurgery, New York University Grossman School of Medicine, New York, USA.
Article Synopsis
- Accurate intraoperative diagnosis of primary CNS lymphoma (PCNSL) is vital for surgical decisions but is challenging due to similar features with other CNS diseases; a new method combines stimulated Raman histology (SRH) with deep learning to improve this process.
- The RapidLymphoma system uses a portable Raman microscope to create virtual images of tissue samples in under three minutes and employs a deep learning model trained on 54,000 images, allowing it to detect PCNSL and differentiate it from other conditions effectively.
- In testing, RapidLymphoma achieved a high accuracy rate of 97.81%, performing better than traditional methods, and demonstrated its capability to identify specific histological features crucial for diagnosis, providing quick feedback
Copy-number analysis from genome sequencing data of 11,754 rare-disease parent-child trios: A model for identifying autosomal recessive human gene knockouts including a novel gene for autosomal recessive retinopathy.
Genet Med Open
February 2024
Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Central Parkway, Newcastle upon Tyne, United Kingdom.
Purpose: In parent-child trios with genome sequencing data, we investigated inherited biallelic deletions to identify known and novel genetic disorders.
Methods: We developed a copy-number variations analysis pipeline based on autosomal genome sequencing read depth of Genomics England 100,000 Genomes Project data from 11,754 parent-child trios and additional 18,875 non-trios. A control cohort of 15,440 cancer patients provided independent deletion frequencies.
Palmitoleic acid sensitizes vancomycin-resistant to vancomycin by outpacing the expression of resistance genes.
Microbiol Spectr
December 2024
Department of Microbiology and Immunology, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina, USA.
The rise in antibiotic resistance limits the availability of antibiotics to treat bacterial infections. Despite this, antibiotic development pipelines remain sparse which makes using adjuvants to reverse antibiotic resistance a promising therapeutic strategy. The use of vancomycin, a frontline antibiotic used to treat hospitalized patients with methicillin-resistant (MRSA) infections, is complicated by high rates of treatment failure.
View Article and Find Full Text PDFDevelopment and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study.
EClinicalMedicine
December 2024
School of Mathematical Sciences, Zhejiang University, Zijingang Campus, Hangzhou, Zhejiang, China.
Article Synopsis
- Ovarian cancer has the highest death rate among women's cancers and is primarily screened using ultrasound, but accurate assessment is difficult due to complex images and anatomy.
- Researchers developed an AI system called the Ovarian Multi-Task Attention Network (OvaMTA) to automate the detection, segmentation, and classification of ovarian masses from ultrasound images.
- The AI system was trained with thousands of images, validated with real-world tests, and compared against physician assessments to evaluate its effectiveness in aiding diagnoses of ovarian cancer.
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