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The oral cavity, or the mouth, is a complex structure in humans that plays a vital role in our day-to-day lives. Its role is not only in chewing and swallowing food; it also plays a role in speech and facial expressions.
Teeth: The teeth are the hardest structures in our bodies. Humans have two sets of teeth throughout their lifetime: deciduous (baby) teeth and permanent teeth. Each tooth consists of several parts: the crown (visible part), the root (embedded in the jaw), enamel (hard outer...
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Oral Hypoglycemic Agents: Glinides01:06

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Repaglinide (Prandin) and Nateglinide (Starlix), known as glinides, are oral insulin secretagogues that stimulate insulin release from pancreatic β cells by closing the ATP-sensitive potassium channels (KATP channel). Repaglinide controls insulin release from pancreatic β cells by managing potassium efflux. It shares two binding sites with sulfonylureas and also has a unique site, indicating overlapping mechanisms of action. With a rapid onset and a 4-7 hour duration, it effectively...
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Psychosexual Stages of Personality: Oral01:16

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The oral stage is the initial phase of Sigmund Freud's theory of psychosexual development, occurring from birth to approximately 12 to 18 months. During this period, the infant's mouth serves as the primary source of pleasure, with actions such as sucking, chewing, biting, and drinking playing a crucial role in reducing tension. These activities are essential not only for nourishment but also for the infant's psychological and emotional satisfaction.
Weaning, typically occurring...
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Oral Hypoglycemic Agents: Sulfonylureas01:17

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Sulfonylureas are oral hypoglycemic agents utilized in treating type 2 diabetes. They are characterized by their unique sulfonylurea chemical structure. The family of sulfonylureas is divided into generations. First-generation sulfonylureas, including tolbutamide (Orinase), chlorpropamide (Diabinese), and tolazamide (Tolinase), trigger insulin release from pancreatic β cells and enhance peripheral tissues' insulin sensitivity. The second-generation members, such as glipizide...
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Assessing Body Temperature - Oral01:14

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Here are the steps to accurately measure oral temperature using an electronic thermometer:
Step 1:
Start by practicing proper hand hygiene to prevent the spread of microorganisms.
Step 2:
Take the thermometer out of the charging unit, switch it on, and wait for the ready sign.
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Gently slide the probe cover until a click is heard. This simple action prevents cross-contamination and ensures the correct placement of the probe cover.
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Instruct the patient to open their mouth and place...
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Non-oral extravascular routes, which encompass sublingual, buccal, topical, intramuscular, and inhalation methods, primarily utilize passive diffusion to transport drugs into the systemic circulation. The absorption rates and effectiveness of these routes depend on the drug's physicochemical properties, as well as the patient's anatomical and pathophysiological state.
Lipophilic drugs that are stable at salivary pH (6) and exhibit minimal binding to the oral mucosa are absorbed more...
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Video Experimental Relacionado

Updated: Jan 21, 2026

Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
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Sistema profundo de detección visual para el carcinoma de células escamosas orales

Kainat Akram1, Muhammad Aslam1, Talha Waheed1

  • 1Department of Computer Science, University of Engineering and Technology, Lahore, 54000, Pakistan.

Scientific reports
|January 19, 2026
PubMed
Resumen

Este estudio presenta un Sistema Profundo de Detección Visual (DVDS) que utiliza EfficientNetB3 para la detección automatizada de carcinoma de células escamosas orales (OSCC) a partir de imágenes histopatológicas. El sistema logró una alta precisión, demostrando potencial para un diagnóstico de OSCC más rápido y consistente para mejorar los resultados de los pacientes.

Palabras clave:
clasificación binariadetección de cáncerdiagnóstico asistido por computadoraEfficientNetB3riesgos para la saluddetección multiclasecarcinoma de células escamosas orales

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Área de la Ciencia:

  • Imágenes Médicas
  • Inteligencia Artificial
  • Patología Computacional

Sus antecedentes:

  • El carcinoma de células escamosas orales (OSCC) representa un desafío significativo para la salud, lo que requiere un diagnóstico preciso y oportuno.
  • Los métodos histopatológicos tradicionales para el diagnóstico de OSCC son subjetivos y consumen mucho tiempo.
  • Los avances en Inteligencia Artificial (IA) ofrecen potencial para el análisis objetivo y eficiente de imágenes histopatológicas.

Objetivo del estudio:

  • Desarrollar y evaluar un Sistema Profundo de Detección Visual (DVDS) automatizado para la detección de carcinoma de células escamosas orales (OSCC).
  • Comparar el rendimiento de diferentes modelos de aprendizaje profundo (EfficientNetB3, DenseNet121, ResNet50) para la clasificación de OSCC.
  • Evaluar la fiabilidad y robustez del sistema en el diagnóstico de OSCC a partir de imágenes histopatológicas.

Principales métodos:

  • Se utilizaron tres modelos de Redes Neuronales Convolucionales (CNN): EfficientNetB3, DenseNet121 y ResNet50.
  • Se entrenaron y evaluaron modelos en dos conjuntos de datos públicos: Kaggle Oral Cancer Detection y NDB-UFES.
  • Se emplearon aumento de datos, preprocesamiento de imágenes y estrategias de entrenamiento como EarlyStopping y ReduceLROnPlateau.

Principales resultados:

  • EfficientNetB3 demostró un rendimiento superior, logrando una precisión del 97,05 % en clasificación binaria y una precisión del 97,16 % en clasificación multiclase.
  • El sistema exhibió alta precisión, recuperación, puntuación F1 y especificidad en ambos conjuntos de datos.
  • DenseNet121 y ResNet50 mostraron una precisión significativamente menor en comparación con EfficientNetB3.

Conclusiones:

  • El Sistema Profundo de Detección Visual (DVDS) impulsado por EfficientNetB3 muestra alta fiabilidad para el diagnóstico de OSCC.
  • El enfoque impulsado por IA puede optimizar significativamente los flujos de trabajo de diagnóstico y ayudar a los patólogos.
  • Esta tecnología tiene un gran potencial para la implementación clínica para apoyar la intervención temprana y mejorar la atención al paciente.