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Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues.
J Pathol
January 2025
The Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia.
Spatial transcriptomics (ST) offers enormous potential to decipher the biological and pathological heterogeneity in precious archival cancer tissues. Traditionally, these tissues have rarely been used and only examined at a low throughput, most commonly by histopathological staining. ST adds thousands of times as many molecular features to histopathological images, but critical technical issues and limitations require more assessment of how ST performs on fixed archival tissues.
View Article and Find Full Text PDFArtificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.
Clin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Urinary lipoarabinomannan for diagnosis of Tuberculosis in an HIV-negative population: A scoping review.
Afr J Prim Health Care Fam Med
December 2024
Department of Internal Medicine, Prince Mshiyeni Memorial Hospital, Durban.
Background: Tuberculosis (TB) remains a leading cause of mortality in low-resource settings and poses a diagnostic challenge in human immunodeficiency virus (HIV)-negative populations because of limitations in traditional diagnostic methods such as sputum smear microscopy (SSM) and sputum Xpert Ultra. There is a lack of effective, non-invasive diagnostic options for TB diagnosis in HIV-negative populations. This scoping review explores the potential of urinary lipoarabinomannan (ULAM) as a point-of-care diagnostic tool for Mycobacterium tuberculosis (MTB) in HIV-negative individuals.
View Article and Find Full Text PDFAn eHealth intervention for patients with a low socioeconomic position during their waiting period preceding cardiac rehabilitation: a randomized feasibility study.
Eur Heart J Digit Health
January 2025
Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, The Netherlands.
Aims: Cardiac rehabilitation (CR) shows lower effectiveness and higher dropouts among people with a low socioeconomic position (SEP) compared to those with a high SEP. This study evaluated an eHealth intervention aimed at supporting patients with a low SEP during their waiting period preceding CR.
Methods And Results: Participants with a low SEP in their waiting period before CR were randomized into an intervention group, receiving guidance videos, patient narratives, and practical tips, or into a control group.
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
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