AIMC Topic: Adult

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Explainable machine learning model predicting neurological deterioration in Wilson's disease via MRI radiomics and clinical features.

Parkinsonism & related disorders
BACKGROUND: This study aims to build a machine learning (ML) model to predict the deterioration of neurological symptoms in Wilson's disease (WD) patients during short-term anti-copper therapy. The model combines brain T1WI MRI radiomics with clinica...

Diagnostic performances of hysteroscopy in post-remission surveillance of patients treated conservatively for endometrial cancer and atypical hyperplasia: a cohort study.

European journal of obstetrics, gynecology, and reproductive biology
OBJECTIVE: Hysteroscopy is commonly used for diagnosing benign endometrial conditions, but its diagnostic performance in malignancies post-treatment surveillance has not been evaluated. This study evaluated the correlation between hysteroscopic appea...

Machine learning-based histopathological features of histological slides and clinical characteristics as a novel prognostic indicator in diffuse large B-cell lymphoma.

Pathology, research and practice
OBJECTIVE: This study developed and validated a deep learning model based on clinical and histopathological features for predicting the outcomes of diffuse large B-cell lymphoma (DLBCL).

Identification and evaluation of biomarkers for diagnosis of chronic hepatitis B using RNA-seq.

Virus research
BACKGROUND & AIM: Chronic hepatitis B (CHB) is a global public health problem affecting hundreds of millions of people and is associated with significant morbidity and mortality of liver cancer. Exosomes originate from cells and their detection in bi...

Angular correlation-based feature selection for machine learning classification of manual automatisms using body sensor network data.

Computers in biology and medicine
Automatisms are repetitive, semi-ordered movements often observed in focal impaired awareness seizures and, less frequently, in generalized seizures with brief loss of consciousness. This study aims to improve the detection of these automatisms by op...

The urban physical environment and leisure-time physical activity in early midlife: a FinnTwin12 study.

Health & place
Under the exposome framework, this study examined the relationship between the urban physical environment and leisure-time physical activity during early midlife based on 394 participants (mean age: 37, range 34-40) from the FinnTwin12 cohort, residi...

Shotgun Metagenomics Identifies in a Cross-Sectional Setting Improved Plaque Microbiome Biomarkers for Peri-Implant Diseases.

Journal of clinical periodontology
AIM: This observational study aimed to verify and improve the predictive value of plaque microbiome of patients with dental implant for peri-implant diseases.

Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.

Science advances
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...

Machine learning-based prediction of respiratory depression during sedation for liposuction.

Scientific reports
Procedural sedation is often performed by non-anesthesiologists in various settings and can lead to respiratory depression. A tool that enables early detection of respiratory compromise could not only enhance patient safety during procedural sedation...