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Related Concept Videos

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Primary care promotes wellness and prevents disease. This care includes health promotion, education, protection (such as immunizations), early disease screening, and environmental considerations. Settings providing this type of healthcare include physician offices, public health clinics, school nursing, and community health nursing.
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Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:
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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Updated: Jan 8, 2026

Determining Soil-transmitted Helminth Infection Status and Physical Fitness of School-aged Children
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Public Health.

Liwei Ma1,2, Yihan Wang1,2, Chenyin Chu1,2

  • 1University of Melbourne, Parkville, VIC, Australia.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
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Summary
This summary is machine-generated.

Hematology and biochemistry markers can help predict Alzheimer's disease (AD) biomarkers. Machine learning models show promise for detecting preclinical AD, aiding early intervention strategies.

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Area of Science:

  • Neurology
  • Biochemistry
  • Medical Diagnostics

Background:

  • Preclinical Alzheimer's disease (AD) is characterized by the silent accumulation of AD biomarkers for 15-20 years before clinical symptoms manifest.
  • Early detection and monitoring of preclinical AD are crucial for timely intervention and therapeutic development.

Purpose of the Study:

  • To develop and validate predictive models for key Alzheimer's disease biomarkers in preclinical stages.
  • To investigate the association between routine hematology and serum biochemistry markers with AD pathology.
  • To enhance the prediction of brain amyloid-PET SUVR and plasma pTau-217 levels using machine learning.

Main Methods:

  • Retrospective cohort study utilizing data from the A4 study (February 2014 - June 2023).
  • Analysis of 420 participants with preclinical AD, examining hematology/serum biochemistry markers.
  • Application of multivariable linear regression and machine learning models to predict AD biomarkers (amyloid-PET SUVR, plasma pTau-217) over 4.5 years.

Main Results:

  • Elevated monocyte count correlated with increased brain amyloid-PET SUVR.
  • Higher mean corpuscular volume (MCV) was associated with elevated plasma pTau-217.
  • Hematology and serum markers, including AST-to-ALT ratio and creatinine, improved prediction models for plasma pTau-217, but monocyte count was not a strong predictor for amyloid-PET SUVR.

Conclusions:

  • Machine learning models show significant potential for predicting Alzheimer's disease biomarkers.
  • Routine blood markers can contribute to the early detection and monitoring of preclinical AD.
  • Further research is warranted to assess the clinical and research applicability of these predictive models.