Product Images Kindfill

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Product Label Images

The following image provide visual information about the product associated with Kindfill NDC 64796-321 by Identipak, Inc, such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.

60 in Each Box NDC: 80486-111-11 - BYHUMAN

60 in Each Box NDC: 80486-111-11 - BYHUMAN

Product Description: Kindfill™ by Humankind is a mint-flavored toothpaste in tablet form. Each package contains 60 tablets, and each tablet contains fluoride for cavity protection. The tablets are designed to be chewed until crushed, and then the wet toothbrush is used to brush for 2 minutes. The tablet should be spat out and not swallowed. This product is suitable for adults and children aged 6 and older, although supervision is recommended for children below the age of 6. The package is made from 100% compostable materials and can be composted where accepted. The tablets are manufactured in Germany by Denttabs Gmbi, and the package is made by Humankind, Inc. The product is distributed by Humankind and their contact address is 65t Johns Lane, New York, v 10013. The active ingredient in each tablet is Sodium Fluoride (62 mg/28 m fluoride ion), which helps protect against cavities. The product is used for oral hygiene and the prevention of dental cavities. Some precautions and warnings are mentioned on the packaging, including discontinuing use if irritation or sensitivity occurs, keeping the product out of reach of children, and seeking medical help if more than the recommended dose is accidentally swallowed. Directions for use are provided for both adults and children aged 6 and older. The product also includes information on storage temperature (25-30°C) and a list of inactive ingredients. Social media and website contact information for Humankind is also provided: @hyHumankind, byiumankind.com.*

* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.