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Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
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Implementing second-order low-pass filters in audio systems is crucial in refining audio signals by eliminating undesirable high-frequency noise. These filters typically involve second-order op-amp circuits configured as voltage followers, encompassing two nodes with distinct storage elements.
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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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A difference amplifier, a crucial component in numerous electronic devices, ideally amplifies only the difference-mode signal, which is the difference between two input signals. However, in practical circuits, the output voltage depends on both the differential gain and the common-mode gain.
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Within an audio system, the filter circuit plays a pivotal role in processing the amplified audio signal from an amplifier. Its primary function is significantly attenuating signal components with lower frequencies, thereby shaping the audio output. This circuit's operations are examined, focusing on the fundamental filter configuration. This configuration involves an operational amplifier arranged in an inverting setup coupled with resistors (R1 and R2) and a capacitor (C1).
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Evaluación exhaustiva de herramientas de clasificación de variantes basadas en ACMG/AMP

Tohid Ghasemnejad1, Yuheng Liang1, Khadijeh Hoda Jahanian2

  • 1UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.

Bioinformatics (Oxford, England)
|February 13, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio compara herramientas de interpretación de variantes ACMG/AMP para trastornos mendelianos. Las herramientas impulsadas por fenotipos como LIRICAL y Franklin muestran una precisión superior en la priorización de variantes.

Palabras clave:
trastornos mendelianosinterpretación de variantesACMG/AMPLIRICALFranklinpriorización de variantesmedicina genómicabioinformática clínicabiología computacional

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Área de la Ciencia:

  • Medicina Genómica
  • Bioinformática Clínica
  • Biología Computacional

Sus antecedentes:

  • Las directrices de la American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) son el estándar para la interpretación de variantes clínicas.
  • La falta de estudios comparativos sobre las herramientas de software ACMG/AMP dificulta la adopción clínica.
  • Los médicos necesitan orientación basada en evidencia para seleccionar herramientas de interpretación de variantes.

Objetivo del estudio:

  • Evaluar y comparar el rendimiento del software de interpretación de variantes basado en ACMG/AMP.
  • Identificar las herramientas de mejor rendimiento para la priorización de variantes en trastornos mendelianos.
  • Proporcionar orientación a los médicos sobre la selección de herramientas adecuadas para el análisis de variantes.

Principales métodos:

  • Se compararon cuatro herramientas ACMG/AMP (Franklin, InterVar, TAPES, Genebe) con LIRICAL utilizando 151 conjuntos de datos de trastornos mendelianos curados por expertos.
  • Se evaluaron las herramientas en función de la precisión top-N, las tasas de retención, la precisión, la recuperación, las puntuaciones F1 y el AUC.
  • Se utilizaron intervalos de confianza de bootstrap y pruebas de Friedman para la validación estadística.

Principales resultados:

  • LIRICAL (68,21%) y Franklin (61,59%) demostraron una precisión superior en la priorización de variantes top-10 en trastornos mendelianos.
  • Las herramientas con integración fenotípica avanzada superaron significativamente a las que se basaban únicamente en características genómicas.
  • El análisis estadístico confirmó la superioridad significativa de LIRICAL y Franklin (p<0,0000).

Conclusiones:

  • Las herramientas de interpretación de variantes impulsadas por fenotipos, como LIRICAL y Franklin, son más efectivas para los trastornos mendelianos.
  • El estudio proporciona datos cruciales para los médicos que seleccionan software de interpretación de variantes.
  • La disponibilidad abierta de datos y código garantiza la reproducibilidad y facilita la investigación futura.