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相关概念视频

Hydroboration-Oxidation of Alkenes03:08

Hydroboration-Oxidation of Alkenes

In addition to the oxymercuration–demercuration method, which converts the alkenes to alcohols with Markovnikov orientation, a complementary hydroboration-oxidation method yields the anti-Markovnikov product. The hydroboration reaction, discovered in 1959 by H.C. Brown, involves the addition of a B–H bond of borane to an alkene giving an organoborane intermediate. The oxidation of this intermediate with basic hydrogen peroxide forms an alcohol.
Preparation of Alcohols via Addition Reactions02:15

Preparation of Alcohols via Addition Reactions

Overview
The acid-catalyzed addition of water to the double bond of alkenes is a large-scale industrial method used to synthesize low-molecular-weight alcohols. An acidic atmosphere is required to allow the hydrogen in the water molecule to act as an electrophile and attack the double bond in an alkene. The addition of a proton to the double bond creates a carbocation intermediate. The proton preferentially bonds to the less substituted end of the double bond to create a more stable carbocation...
Acid-Catalyzed Dehydration of Alcohols to Alkenes02:35

Acid-Catalyzed Dehydration of Alcohols to Alkenes

In a dehydration reaction, a hydroxyl group in an alcohol is eliminated along with the hydrogen from an adjacent carbon. Here, the products are an alkene and a molecule of water. Dehydration of alcohols is generally achieved by heating in the presence of an acid catalyst. While the dehydration of primary alcohols requires high temperatures and acid concentrations, secondary and tertiary alcohols can lose a water molecule under relatively mild conditions.
Oxidation of Alcohols02:37

Oxidation of Alcohols

In this lesson, the oxidation of alcohols is discussed in depth. The various reagents used for oxidation of primary and secondary alcohols are detailed, and their mechanism of action is provided.
The process of oxidation in a chemical reaction is observed in any of the three forms:
Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes01:33

Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes

Aldehydes and ketones are prepared from alcohols, alkenes, and alkynes via different reaction pathways. Alcohols are the most commonly used substrates for synthesizing aldehydes and ketones. The conversion of alcohol to aldehyde, which involves the oxidation process, depends on the class of the alcohol used and the strength of the oxidizing agent. For instance, primary alcohol will form an aldehyde when treated with a weak oxidizing agent; however, it gets over-oxidized to a carboxylic acid in...
Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation01:22

Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation

Baeyer–Villiger oxidation converts aldehydes to carboxylic acids and ketones to esters. The reaction uses peroxy acids or peracids and is often catalyzed by acid. The reaction is named after its pioneers, Adolf von Baeyer and Victor Villiger. The reaction is achieved by a wide range of peracids such as m-chloroperoxybenzoic acid (mCPBA), perbenzoic acid (C6H5COOOH), peracetic acid (CH3COOOH), hydrogen peroxide (H2O2), and tert-butyl hydroperoxide (t-BuOOH).
The carbonyl center is activated by...

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通过高光谱成像和计算机视觉增强口腔健康诊断:临床数据集研究

Paul Römer1, Jean-Jacques Ponciano2, Katharina Kloster1

  • 1Department of Oral and Maxillofacial Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Augustusplatz 2, Mainz, 55131, Germany, 49 1747978980.

JMIR medical informatics
|September 11, 2025
PubMed
概括

这项研究引入了口腔组织内镜超谱成像 (HSI) 的新数据集,使深度学习模型能够准确区分组织类型. 这一进步支持非侵入性诊断和口腔疾病的早期检测.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.牙科 牙科 牙科 牙科这就是EHSI的意义.内镜 HSI 的内镜 HSI超光谱成像 超光谱成像缺血缺血症是因为缺血.医学成像数据集 医学成像数据集非侵入性诊断是一种非侵入性诊断.口腔健康诊断 口腔健康诊断口腔粘膜 perfusion 是一种口腔粘膜 perfusion 的方法.口腔病理学 口腔病理学口腔状细胞癌的癌症.频谱分析是一种分析.组织分类组织分类.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 口腔健康 口腔健康

背景情况:

  • 口腔疾病,包括癌症,由于检测迟到和复杂的组织分化,存在重大诊断挑战.
  • 内镜超光谱成像 (HSI) 与深度学习 (DL) 结合,为现代组织诊断提供了一个有希望的非侵入性方法.
  • 大规模的体内数据集对于开发和验证用于口腔组织分析的DL模型至关重要.

研究的目的:

  • 创建一个全面的,注释口腔内镜HSI数据集.
  • 开发和展示使用HSI和机器学习的口内组织结构的自动,可靠的差异化.
  • 为了支持口腔健康的非侵入性诊断的进步.

主要方法:

  • 从226名参与者 (500-1000纳米范围) 收集了内镜HSI数据.
  • 使用RectLabel Pro软件进行注释的口腔结构.
  • 适应DeepLabv3与ResNet-50骨干用于HSI细分,对70%的数据集进行训练.

主要成果:

  • 在DeepLabv3和U-Net模型中,F1得分分高 (分别为0.857和0.84).
  • 模型在细分粘膜 (0.915),收缩器 (0.94),牙 (0.90) 和口腔 (0.90) 方面表现出色.
  • 变量分析证实了高光谱多样性,验证了数据集的现实性.

结论:

  • 开发的数据集和DL算法解决了口腔健康成像的关键需求.
  • 现在可以准确地对口腔组织进行分类,从而实现非侵入性病理分析.
  • 这项工作为早期癌症检测和改善手术内诊断铺平了道路.