Product Images Dove Clinical Protection Original Clean Antiperspirant And Deodorant
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Product Label Images
The following 3 images provide visual information about the product associated with Dove Clinical Protection Original Clean Antiperspirant And Deodorant NDC 64942-1440 by Conopco Inc. D/b/a Unilever, 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.
DVClinProtOriginalCleanrev5Carton - DVClinProtOriginalCleanrev5Carton
Dove Clinical Protection is a deodorant that offers prescription-strength wetness protection against sweat and odor. It is infused with Dove's signature moisturizers and Soothing Care technology to provide a combination of strength and beauty. It is designed for women who need clinical protection against wetness, delivered in the form of a deodorant with a pleasant scent. The product promises to offer 48 hours of odor protection and comes in a 27 oz (76g) size.*
DVClinProtOriginalCleanrev5PDP - DVClinProtOriginalCleanrev5PDP
Dove Clinical Protection is an anti-perspirant deodorant solid that provides scientifically proven wetness protection with moisturizers. The Original Clean scent is also included to leave you feeling fresh and clean.*
DVClinProtOriginalCleanrev5PDPb - DVClinProtOriginalCleanrev5PDPb
Active ingredient for external use only is Aumium Zconom Terachiontyre Y (2%). Warnings include not to use on broken skin and to ask a doctor before use if you have a kidney disease. Stop use if a rash or irritation occurs and keep out of reach of children. Directions suggest applying the product to each underarm before bed. Other information provided is to not store over 15°F. The inactive ingredients include Cydopentasloane, Dimethicone, Parffin, WicrorystalineWax Fegrance Partum), iica Hefantus Amcus Surowe) Seed il Dimethicone Crosspolymer BT. Contact 1-800-T61-3683 for questions.*
* 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.