AIMC Topic: Humans

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Lipid metabolites as biomarkers and therapeutic targets in oral squamous cell carcinoma.

BMC oral health
This study explores the association of lipid metabolism disruption and Oral Squamous Cell Carcinoma (OSCC). We aim to identify specific lipid biomarkers and therapeutic targets for OSCC. We included 78 OSCC patients and 80 healthy controls, and appli...

Divergent biological pathways distinguish community-acquired pneumonia from COVID-19 despite similar plasma cytokine profiles.

Respiratory research
BACKGROUND: Pulmonary infections, ranging from mild respiratory issues to severe multiorgan failure, pose a major global health threat. The immune response in community-acquired pneumonia (CAP) and COVID-19 influences disease severity and outcomes, b...

Toward artificial intelligence in dental prosthesis planning - a preliminary in-silico feasibility study.

BMC oral health
BACKGROUND: Dental prosthesis planning is a multi-faceted and nuanced process of conceiving individual treatment plans based on dental findings and in line with established treatment guidelines. The aim of this study was to assess whether an artifici...

Poliomyelitis dynamics with fractional order derivatives and deep neural networks.

Scientific reports
This paper presents a comprehensive study of poliomyelitis transmission dynamics using two fractional-order models that incorporate the Atangana--Baleanu derivatives in the Caputo sense (ABC). The model includes critical epidemiological features, inc...

A fuzzy based hybrid approach for risk assessment of anesthesiologists using OPA and EDAS methods.

Scientific reports
Anesthesiologists are exposed to numerous occupational hazards due to the demanding nature of their profession and the complex environment in which they operate. Classical risk assessment approaches often fall short in addressing the multidimensional...

CXR-MultiTaskNet a unified deep learning framework for joint disease localization and classification in chest radiographs.

Scientific reports
Chest X-ray (CXR) is a challenging problem in automated medical diagnosis, where complex visual patterns of thoracic diseases must be precisely identified through multi-label classification and lesion localization. Current approaches typically consid...

Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.

Scientific reports
Intracerebral hemorrhage (ICH) is a severe form of stroke with high mortality and disability, where early hematoma expansion (HE) critically influences prognosis. Previous studies suggest that revised hematoma expansion (rHE), defined to include intr...

Major pathophysiological changes in pulmonary disease provided a molecular insight based on deep learning approach.

Scientific reports
The outburst of pulmonary disorders among the society has shown the devastating effect of undergoing a delay in diagnosis and treatment. Sometimes the traditional methods in detecting and treating the airway disease fail to cure efficiently due to a ...

Factors associated with admission to elderly medical-welfare facilities in South Korea: a cross-sectional machine-learning study.

BMJ open
OBJECTIVES: To identify the key factors associated with admission to elderly medical-welfare facilities in South Korea and to evaluate their relative importance using machine learning techniques, providing an evidence base for policy in a rapidly age...

Utilisation of artificial intelligence to enhance the detection rates of renal cancer on cross-sectional imaging: protocol for a systematic review and meta-analysis.

BMJ open
INTRODUCTION: The incidence of renal cell carcinoma has steadily been on the increase due to the increased use of imaging to identify incidental masses. Although survival has also improved because of early detection, overdiagnosis and overtreatment o...