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Titlebook: Oral Biochemistry; Byung-Moo Min Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature

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樓主: 本義
31#
發(fā)表于 2025-3-27 00:05:20 | 只看該作者
Dentine,ts, which originate from the mesoderm. The odontoblastic process protrudes into the dentine, and the cell body is located on the inner surface of the pulp. Odontoblasts play roles in synthesizing or repairing dentine and are nourished from the pulp. Unlike enamel, which is static, dentine exhibits c
32#
發(fā)表于 2025-3-27 01:13:56 | 只看該作者
33#
發(fā)表于 2025-3-27 06:59:42 | 只看該作者
34#
發(fā)表于 2025-3-27 11:59:46 | 只看該作者
Oral Mucosa and Gingiva,connected to the external surface of the body. The mucosa acts as a functional unit and is thus considered to be an organ. Briefly, the function of the mucosa is as follows. (a) The membrane covering the surface protects the tissues and organs beneath it from the external environment. (b) Stimuli ou
35#
發(fā)表于 2025-3-27 13:51:38 | 只看該作者
Saliva,y glands of which there are three pairs: the parotid, submandibular, and sublingual glands (Fig. 7.1). The numerous minor salivary glands include the palatinal, buccal, labial, lingual (von Ebner’s), and retromolar glands. Minor salivary glands are located in the submucosa throughout the oral cavity
36#
發(fā)表于 2025-3-27 19:49:55 | 只看該作者
Acquired Enamel Integuments: Pellicle, Plaque, and Calculus,anic materials are removed from the enamel surface of teeth and the teeth are exposed to saliva, a colorless acellular bacteria-free thin film (1–10?μm) composed of salivary glycoproteins is formed within minutes, and this is called acquired pellicle. When bacteria deposited on acquired pellicle adh
37#
發(fā)表于 2025-3-27 22:41:28 | 只看該作者
38#
發(fā)表于 2025-3-28 05:39:33 | 只看該作者
advances in graph neural networks (GNN) have led to a promising approach for addressing this problem. However, existing methods have three major issues. First, they are incapable of modeling the transitions between inconsecutive items. Second, they are infeasible for learning the cross-feature inte
39#
發(fā)表于 2025-3-28 09:52:43 | 只看該作者
Byung-Moo Min advances in graph neural networks (GNN) have led to a promising approach for addressing this problem. However, existing methods have three major issues. First, they are incapable of modeling the transitions between inconsecutive items. Second, they are infeasible for learning the cross-feature inte
40#
發(fā)表于 2025-3-28 14:24:45 | 只看該作者
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