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相关概念视频

Drug Classes and Categories01:25

Drug Classes and Categories

3.0K
Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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Antibody Structure and Classes01:25

Antibody Structure and Classes

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Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
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Standard Enthalpy of Formation02:37

Standard Enthalpy of Formation

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Enthalpy changes are typically tabulated for reactions in which both the reactants and products are at the same conditions. A standard state is a commonly accepted set of conditions used as a reference point for the determination of properties under other different conditions. For chemists, the IUPAC standard state refers to materials under a pressure of 1 bar and solutions at 1 M and does not specify a temperature. Many thermochemical tables list values with a standard state of 1 atm. Because...
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Standard Electrode Potentials03:02

Standard Electrode Potentials

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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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Measurement: Standard Units03:38

Measurement: Standard Units

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Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
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Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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相关实验视频

Updated: Jan 28, 2026

Author Spotlight: Expanding the Scope of Multiplex Immunoassays for Lyme Borreliosis Diagnostics and Pathogen Research
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在使用YOLOv11的标准化口内照片上检测多类缺陷.

Ani Nebiaj1, Markus Mühling2, Bernd Freisleben2

  • 1Department of Orthodontics, Johann-Wolfgang Goethe University, 60596 Frankfurt am Main, Germany.

Dentistry journal
|January 27, 2026
PubMed
概括
此摘要是机器生成的。

一个深度学习模型从口腔内照片中准确地识别牙缺陷,提高查效率. 这种人工智能工具有助于标准化记录和诊断各种咬伤问题.

关键词:
人工智能的人工智能是人工智能.临床决策支持系统 临床决策支持系统计算机视觉 计算机视觉深度学习是一种深度学习.牙科摄影的使用在口内拍摄的照片.缺陷性缺陷是指一个缺陷性缺陷.多视图成像多视图成像对象检测检测对象检测对象检测矯正牙科 矯正牙科是指矯正牙科的專業.

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科学领域:

  • 人工智能在牙科中的应用
  • 计算机视觉用于医学成像.
  • ортодонтическая 诊断 ортодонтическая 诊断 诊断 在牙上

背景情况:

  • 从临床照片中识别牙缺陷是耗时的,容易变化.
  • 自动化这个过程可以提高效率和诊断的一致性.
  • 深度学习为准确的,自动化的缺陷检测提供了一个潜在的解决方案.

研究的目的:

  • 评估基于YOLOv11的深度学习模型,用于在口内照片中自动检测缺陷.
  • 测试一个结构化协议的训练是否能够可靠地检测多重缺陷.
  • 评估模型在各种临床相关的缺陷类别中的表现.

主要方法:

  • 5854张口腔内摄影的数据集根据"正牙治疗需求指数" (IOTN) 对17个缺陷类别的边界框进行了注释.
  • 一个YOLOv11模型使用增强数据进行训练,并在一个持有测试集上进行评估.
  • 使用平均精度 (mAP50),宏精度 (macro-P) 和宏回忆 (macro-R) 来测量性能.

主要成果:

  • 在15个分析的缺陷类别中,YOLOv11模型实现了87.8%的mAP50,76.9%的宏P和86.1%的宏R.
  • 对于深咬 (98.8%),透口 (97.9%) 和二级犬 (97.5%) 进行了高的每类表现.
  • 性能在各个类别中各不相同,后侧交叉咬伤和大超喷射的检测率较低,可能是由于有限的示例和可视化约束.

结论:

  • 一个基于YOLOv11的深度学习系统可以从常规的口内照片中准确地检测出多个临床上显著的缺陷.
  • 该系统支持有效的查和标准化牙缺陷的文档.
  • 通过更大的数据集,协议标准化和多式联接输入,可以进一步提高稳定性.