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関連する概念動画

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

711
Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
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Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

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Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

4.6K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Facilitated Diffusion01:16

Facilitated Diffusion

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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
In this process, substrates such as organic compounds and ions interact with a transporter on one side, triggering conformational changes in proteins that enable...
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Updated: Sep 9, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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アクティブ・ラーニングによる拡散係数の予測の改善

Zeno Romero1, Kerstin Münnemann1, Hans Hasse1

  • 1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany.

The journal of physical chemistry. B
|August 29, 2025
PubMed
まとめ
この要約は機械生成です。

アクティブな学習戦略は,混合物における拡散係数の機械学習予測を改善するために実験を効率的に導きます. ターゲットを絞った測定は,最小限のデータ収集でモデルの精度を大幅に高めます.

さらに関連する動画

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
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関連する実験動画

Last Updated: Sep 9, 2025

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer
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科学分野:

  • 物理化学
  • コンピュータ化学
  • 化学工学

背景:

  • 混合物における拡散係数を予測することは極めて重要ですが,データ不足のために実験的に困難です.
  • 機械学習 (ML) モデルには可能性はあるが,膨大なトレーニングデータが必要で,その獲得にはコストがかかる.
  • アクティブ・ラーニング (AL) 戦略は,標的型データ取得のための実験設計を最適化することができます.

研究 の 目的:

  • 拡散係数の測定を計画するためのAL戦略を調査する.
  • 無限稀解 (D_ij^∞) での拡散係数のMLベースの予測を改善する.
  • マトリックスコンプリートメソッド (MCM) へのALガイドデータの影響を評価する.

主な方法:

  • 合成データに対するAL戦略の体系的なテスト
  • 不確実性サンプリングを有効なAL戦略として利用する.
  • 新しいD_ij^∞測定のためのパルスフィールドグラデント (PFG) 核磁気共振 (NMR) スペクトロスコーピーの実施.
  • ハイブリッドMCMを新たに得られた実験データで再訓練する.

主要な成果:

  • 不確実性サンプリングは,Dj^∞測定の計画に有効であることが示された.
  • 以前は特徴づけられていなかった混合物の19の新しいD_ij^∞データポイントが測定された.
  • 半経験的モデル (SEGWE) を採用したハイブリッドMCMの予測は,実質的な精度向上を示した.
  • テストセットの相対平均二乗誤差は,1つのMCMでほぼ半減した.

結論:

  • AL戦略は,最小限の実験で拡散係数のML予測を大幅に改善します.
  • ALの有効性は,特定のMLモデルと事前の情報との統合に依存します.
  • 物理的性質を予測する上で MLの価値を最大化するために 標的型実験設計が鍵となる.