Product Images Qc Anti Dandruff T Plus

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The following 2 images provide visual information about the product associated with Qc Anti Dandruff T Plus NDC 75376-286 by Guangzhou Danting Bath&body Care Industry 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.

16 oz shampoo - QC Shampoo T Plus 16 ed

16 oz shampoo - QC Shampoo T Plus 16 ed

This product is an Anti-Dandruff T+Plus Shampoo with Coal Tar 0.5%, designed to control symptoms of Psoriasis, Seborrheic dermatitis, and Dandruff. It provides long-lasting relief of itching and flaking. The shampoo should be used at least twice a week or as directed by a doctor. The active ingredient, Coal Tar, has antidandruff properties. However, there are warnings regarding prolonged use without consulting a doctor and the potential increased sunburn risk. The product should not be used with other psoriasis therapies without medical supervision. Other information includes store away from direct sunlight and heat and freezing conditions. For inquiries or to report side effects, a contact number is provided.*

8 oz Shampoo - QC Shampoo T Plus 8 ed

8 oz Shampoo - QC Shampoo T Plus 8 ed

This text provides detailed information about T+Plus Anti-Dandruff Shampoo, which contains 0.5% coal tar and is designed to control symptoms of psoriasis, seborrheic dermatitis, and dandruff. The shampoo offers long-lasting relief from itching and flaking and is to be used at least twice a week or as directed by a doctor. The product includes drug facts, warnings, directions for use, and a list of active and inactive ingredients. Additionally, there is a caution about the product containing chemicals known to the State of California to cause cancer and birth defects. For more information, customers are advised to visit the provided website or contact the support number provided.*

* 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.