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38381
Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT
Published 2025-01-01“…Wenjun Liu,1 Jin Wang,2 Yiting Lei,1 Peng Liu,3 Zhenghan Han,1 Shichu Wang,1 Bo Liu1 1Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China; 2College of Medical Informatics, Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Orthopedics, Daping Hospital, Army Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Bo Liu, Department of Orthopedics, First Affiliated Hospital, Chongqing Medical University, Chongqing, People’s Republic of China, Tel +8613996065698, Email boliu@hospital.cqmu.edu.cnBackground: Early differentiation between spinal tuberculosis (STB) and acute osteoporotic vertebral compression fracture (OVCF) is crucial for determining the appropriate clinical management and treatment pathway, thereby significantly impacting patient outcomes.Objective: To evaluate the efficacy of deep learning (DL) models using reconstructed sagittal CT images in the differentiation of early STB from acute OVCF, with the aim of enhancing diagnostic precision, reducing reliance on MRI and biopsies, and minimizing the risks of misdiagnosis.Methods: Data were collected from 373 patients, with 302 patients recruited from a university-affiliated hospital serving as the training and internal validation sets, and an additional 71 patients from another university-affiliated hospital serving as the external validation set. …”
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38383
The N‐terminal domain of gasdermin D induces liver fibrosis by reprogrammed lipid metabolism
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38384
Long-term mindfulness meditation increases occurrence of sensory and attention brain states
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38386
Frost resistance and micro mechanism of metakaolin and polypropylene fiber modified coastal cement soil
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38387
Technological-Based Interventions in Cancer and Factors Associated With the Use of Mobile Digital Wellness and Health Apps Among Cancer Information Seekers: Cross-Sectional Study
Published 2025-02-01“…In the final adjusted model, participants with household incomes ≥US $50,000 had 49% higher adjusted odds of using these apps than those with incomes <US $50,000 (adjusted odds ratio [aOR]=1.49, 95% CI 1.02-2.14). College graduates and those with higher educational levels were avid users compared to those with a high school diploma or less (aOR=1.87, 95% CI 1.30-2.67). …”
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38389
Analysis of the effect of cutting depth under thick immediate roof on gob-side entry retaining stability
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38390
Reimagining Global Health: Accelerating Change for a Sustainable Future
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Advances in the detection of virulence genes of Staphylococcus aureus originate from food
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38395
Functional Verification of Differentially Expressed Genes Following DENV2 Infection in <i>Aedes aegypti</i>
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38396
Humanized Mouse Models for Immuno-Oncology Research: A Review and Implications in Lung Cancer Research
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38397
Compact source for quadripartite deterministically entangled optical fields
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Refining δ15N isotopic fingerprints of local NOx for accurate source identification of nitrate in PM2.5
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