Product Images Mucus Relief - Dm Maximum Strength
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Product Label Images
The following 2 images provide visual information about the product associated with Mucus Relief - Dm Maximum Strength NDC 73581-405 by Yyba Corp, 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.
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This text is a drug facts label for a medication called Mucus Relief, an expectorant and cough suppressant. It contains 60 mg of Dextromethorphan Hydrobromide and 1200 mg of Guaifenesin per extended-release tablet. The medication is used to help loosen phlegm and thin bronchial secretions to clear the airways. It is indicated for temporary relief of cough due to throat irritation, cold, or inhaled irritants. The label includes warnings not to use for children under 12 years old or if currently taking certain prescription medications. It provides directions for use and storage information.*
PRINCIPAL DISPLAY PANEL - Mucinex DM 98 ct blister UPC (4) (1)
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This text provides information about a Mucus Relief product that contains 60 mg of Dextromethorphan HBr and 1200 mg of Guaifenesin per extended-release tablet. It is indicated as an expectorant and cough suppressant, helping to loosen phlegm and thin bronchial secretions. The product is meant to temporarily relieve cough due to minor throat and bronchial irritation caused by conditions like the common cold or inhaled irritants. The packaging includes warnings about not using the product for children under 12 years of age and not taking it if on a monoamine oxidase inhibitor medication. Users are advised to consult a doctor in case of persistent cough or if certain symptoms like fever, rash, or persistent headache occur. The product is recommended to be taken with water and not to be crushed, chewed, or broken. For further questions or more detailed information, a contact phone number is 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.