Nicotine Patches Patch
Product Images NDC 85333-003

View Photos of Packaging, Regulatory Labels, and Product Appearance

Product Visual Gallery

This gallery contains 2 technical images submitted to the FDA as part of the official labeling for Nicotine Patches (NDC 85333-003). Unlike standard consumer photos, these assets often include clinical data figures, molecular chemical structures, and official manufacturer packaging layouts.

As provided by Zhejiang Fta Shengzuozhi Biotechnology Co., Ltd, these visuals offer a comprehensive scientific overview of the product's physical and chemical identity, aiding pharmacists and researchers in product verification and study.

FDA Label Image

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/users/mac/desktop (21mg)
This is a detailed description of BIOVIKOTING® Nicotine Patches, which are a stop smoking aid. Each patch delivers 2mg of nicotine over 24 hours to help reduce withdrawal symptoms and cravings associated with quitting smoking. The patch should be applied to dry, clean, and hairless skin, and a new patch should be used every 24 hours. It is recommended to consult a doctor before using the nicotine patches if you have certain health conditions or are pregnant. The text also provides information on how to properly use and dispose of the patches, as well as warnings for possible side effects like increased blood pressure and symptoms of nicotine overdose.*
FDA Label Image

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This is a product description for Nicotine Patches under the brand BIOVIKOTING® used as a Stop Smoking Aid. Each patch delivers 2mg of nicotine over 24 hours to help reduce withdrawal symptoms and cravings associated with quitting smoking. The text includes instructions on proper usage, contraindications, and warnings. It also mentions the active ingredient, application process, storage conditions, and details about inactive ingredients. The product is manufactured in Zhejiang, China, and is suitable for individuals who smoke more than 10 cigarettes a day looking to quit smoking.*

* These product label images have been analyzed using experimental machine learning. Please verify findings with the primary label text.