Product Images Linglongjiuhuo Linglong Moxibustion

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Product Label Images

The following 3 images provide visual information about the product associated with Linglongjiuhuo Linglong Moxibustion NDC 73510-525 by Shanghai Jingqin Biological Technology 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.

Box

Box

Product Name: LingLongJiuHuo Linglong Moxibustion. This product is composed of different elements including heating element, vermiculite powder, medical pressure-sensitive adhesive, and non-woven fabric. Usage involves peeling off the release paper, applying essential oil on the moxibustion patch, and then applying the sticky side to the targeted part. It is advised to use 1 piece at a time, once a day. The product is for external use only and should be kept out of reach of children. Pregnant women, diabetics, skin ulcers, and acute bleeding disorders are prohibited from using the product. It should be stored in a sealed, cool, and dry place away from heat and magnetic fields. The executive standard is Q/LSJ088-2018, and the manufacturer is Hubei Lishizhen Modern Biological Medicine Group Co., Ltd., located in China.*

Pac

Pac

This is a description of the LingLongJiuHuo Lingong Moxibustion, a disposable product used for moxibustion patches. The product is composed of heating vermiculite powder, herium powder, and meoal pes. The usage involves applying the essential onto the moxibustion patch and then applying it to the desired body part. The dosage is one piece at a time and it should only be used externally. Pregnant women and people with acute bleeding disorders should avoid the product. The shelf life is 24 months and it is distributed by Shanghai Jingain Biological Technology Co., Ltd. The product is manufactured by Hubei Lishzhen Moder Biological Medicine Group Co., Ltd.*

Use

Use

* 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.