Product Images Losartan Potassium
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Product Label Images
The following 6 images provide visual information about the product associated with Losartan Potassium NDC 71205-281 by Proficient Rx Lp, 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.
This appears to be a fragment of a medical research or clinical trial report. It mentions two medications - Atenolol and Losartan Potassium - and a statistical measure of risk reduction of 13%. However, without additional context and information, it is not possible to determine the purpose, methodology, or results of the study being referenced.*
This is a figure titled "Primary Endpoint Events' within Demographic Subgroups." The text includes various demographic subgroups and their corresponding results for an overall study. However, due to the errors and inconsistencies, the information is not entirely clear.*
This looks like a table with information related to a clinical trial. The table includes information on the percentage of patients with an event, risk reduction percentage, and p-value for a drug called "Losttan Potassium" and a placebo. However, without additional context and formatting, it is difficult to provide a more detailed description.*
This is a description of a medication called Losartan Potassium 50mg by ProficientRx LP. This medication comes in a 90 tablet pack, with each tablet containing USP 50mg of Losartan potassium. The tablets are oval-shaped and green-colored, with an 'E' on one side and '4' and '6' separated by a scoreline on the other. They are film-coated and are meant for oral consumption only. The medication is manufactured by Aurobindo Pharma Limited in Hyderabad, India. It is important to store this medication in a dry and cool place, between 20°-25°C (68°-77°F) and away from children. The Lot #00000 has an expiration date of 00/00/00.*
* 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.