Product Images Daydream Cushion Sweet Secret

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Product Label Images

The following 2 images provide visual information about the product associated with Daydream Cushion Sweet Secret NDC 73586-106 by Em Cosmetics Llc, 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.

01b LBL_EM_Daydream Cushion_SPF50_12g_Sweet Secret_full size - 01b LBL EM Daydream Cushion SPF50 12g Sweet Secret full size

01b LBL_EM_Daydream Cushion_SPF50_12g_Sweet Secret_full size - 01b LBL EM Daydream Cushion SPF50 12g Sweet Secret full size

This is a drug fact sheet for a sunscreen product that contains various active ingredients to prevent sunburns and reduce the risk of skin cancer and skin aging. The directions include applying generously to exposed skin 15 minutes before sun exposure, using water-resistant sunscreen if sweating or swimming, and following other sun protection measures such as wearing protective clothing and limiting time in the sun. The sheet warns about not using the product on damaged or broken skin and seeking medical attention if a rash occurs. The inactive ingredients are also listed. The sheet includes the brand name, SPF rating, and other marketing information about a related product.*

01b LBL_EM_Daydream Cushion_SPF50_12g_Sweet Secret_refill - 01b LBL EM Daydream Cushion SPF50 12g Sweet Secret refill

01b LBL_EM_Daydream Cushion_SPF50_12g_Sweet Secret_refill - 01b LBL EM Daydream Cushion SPF50 12g Sweet Secret refill

An SPF 50+ sunscreen product that contains avobenzone, homosalate, octisalate, and titanium dioxide as active ingredients, and offers broad spectrum protection against sunburn and its related harms including early aging and skin cancer. It also lists directions, warnings, inactive ingredients, and additional information. The product is produced by emcosmetics and sold as a refill kit.*

* 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.