Product Images Ulensy Corn Callus And Wart Remover
View Photos of Packaging, Labels & Appearance
Product Label Images
The following image provide visual information about the product associated with Ulensy Corn Callus And Wart Remover NDC 83818-003 by Shenzhen Xinxin Yunhai Technology Co., Ltd., 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.
1 - Label(1)
This text appears to be the description of a drug product, specifically a corn, callus, and wart remover. The active ingredient is salicylic acid at a concentration of 5.00%. The product is intended to relieve discomfort and facilitate the removal of corns, calluses, plantar warts, common warts, and flat warts. There are warnings to avoid ingestion and to avoid contact with the eyes. It is advised to ask a doctor before use if pregnant, intending to become pregnant, or breastfeeding. The product should be kept out of reach of children and if swallowed, medical help should be sought immediately. The directions for use involve cleaning and drying the affected area, applying the liquid evenly, and sealing the cap tightly after use. The recommended application is once in the morning and once in the evening, with double application each time. For foot-related issues, nighttime application is suggested. Daily application for two weeks is recommended. The product also includes a list of inactive ingredients and provides contact information for customer support. The product is marketed as pain-free, scar-free, skin-friendly, non-spreading, and non-recurring. The stated targets of the product are corns, calluses, plantar warts, common warts, and flat warts. The product is distributed by Berry Alice LLC and is packaged with 18 corn cushions. It claims to have rapid action and effective results due to advanced technology that instantly generates a shielding mask.*
* 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.