Product Images Xlear Max

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Product Label Images

The following 2 images provide visual information about the product associated with Xlear Max NDC 27017-019 by Xlear 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.

label - XLMaxLabelR1cpd

label - XLMaxLabelR1cpd

Xlear Sinus Spray is an effective congestion relief product that helps relieve symptoms of flu, upper respiratory allergies, sinus pressure headaches, hay fever, and common cold. This nasal spray contains active ingredients such as Capsicum Annuum HPUS, purified water, Aloe Vera, Grapefruit seed extract, and Capsicum that help in temporary relief of congestion. It is essential to note that the product may cause a burning sensation associated with capsicum, which is common. For best results, prime the pump before using and gently blow your nose before each use. In case of persistent symptoms, stop using and consult a doctor. Xlear Inc. manufactures this product in the USA, and the patent numbers include 6,054,143/6,258,372B1.*

priciple display panel - XlearMax1.5ozDisplaycartoncpd1

priciple display panel - XlearMax1.5ozDisplaycartoncpd1

Xlear Max Sinus Care System is a natural saline sinus spray that provides fast-acting relief from severe sinus pressure, congestion, headaches due to cold, flu, allergies, pollen, and post-nasal drip. The addition of capsicum, a natural ingredient, soothes irritated tissues in the nose and sinuses. However, you may experience a burning sensation while using the spray. The Xlear Sinus Care System is recommended worldwide by healthcare professionals to their patients. If not satisfied, the product can be returned for a full refund. Xlear recommends using the Xlear Nasal Spray for daily maintenance. Please note that Xlear Max Sinus Spray is suitable for severe sinus pressure, congestion, and headaches only.*

* 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.