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Authored By Erik MacLaren, PhD

It is important to understand the meaning of the terms tolerance, dependence, and addiction when discussing substance abuse and the use of prescription medications such as opioid painkillers. Unfortunately, both professionals and lay people often misuse these terms, leading to the mistaken belief that tolerance, dependence, and addiction are just different names for the same thing. However, knowing the distinction between these terms can lead to a better understanding of the dangers of drug abuse.

The most important distinction between these concepts is that tolerance and dependence refer to the physical consequences of drug use. In contrast, addiction is a descriptive term that refers to a need to engage in harmful behavior such as drug use.

Drugs that result in the development of tolerance and physical dependence often have the potential to cause addiction, but not always.

What Is Tolerance?

Tolerance is defined as a person’s diminished response to a drug that is the result of repeated use . People can develop tolerance to both illicit drugs and prescription medications. As stated above, tolerance is a physical effect of repeated use of a drug, not necessarily a sign of addiction. For example, patients with chronic pain frequently develop tolerance to some effects of prescription pain medications without developing an addiction to them.

There are 3 main types of tolerance:

Chronic Learned

Finally, most drugs have more than one effect, and tolerance does not necessarily develop equally to all effects . Abusers of illicit and prescription opioids, such as heroin or oxycodone (OxyContin), quickly develop tolerance to the euphoric high these drugs produce but not to the dangerous side effect of respiratory depression (slowed breathing rate). Opioid abusers who take large doses of these drugs to overcome tolerance and get high are frequently hospitalized—or even die—because they stop breathing.

tolerance does not necessarily develop equally to all effects respiratory depression
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The words and are often used interchangeably, but there are important differences between the two. In medical terms, dependence specifically refers to a physical condition in which the body has adapted to the presence of a drug . If an individual with drug dependence stops taking that drug suddenly, that person will experience predictable and measurable symptoms, known as a withdrawal syndrome.

Although dependence is often a part of addiction, non-addictive drugs can also produce dependence in patients. A prime example is prednisone, a synthetic form of the steroid hormone cortisol that is used to treat asthma, allergic reactions, Crohn’s disease, and many other inflammatory conditions. Prednisone is not known to produce addiction. However, if a patient has taken prednisone for several weeks and then stops suddenly, they are likely to suffer from withdrawal symptoms such as fatigue, weakness, body aches, and joint pain 4 .

The pH was measured using pH meter in the suspension liquid after sample centrifugation, with a 1:5 ratio of sample to deionized water. The moisture was measured using a gravimetric approach by drying samples between 103°C–105°C for 48h after sampling. For the detection of organic acid, such as lactic acid, acetic acid, butyric acid, and caproic acid, as well as glucose and ethanol content, 5g of sample was vortex mixed with 25mL of deionized water, centrifuged and filtered through a 0.22μm MCE filter, and the metabolite contents were quantified using HPLC (Agilent 1260,USA). The operating condition of HPLC was as follows: Hi-Plex H HPLC column (300×6.5mm), refractive index detector (RID detector), 5mM HSO as mobile phase with the velocity of 0.6mL/min, and column temperature was 55°C. For the detection of esters, such as ethyl acetate, ethyl caproate, and ethyl lactate, 5g of sample was vortex mixed with 25mL of ethanol, centrifuged and filtered through a 0.22μm filter, and esters contents were determined with GC (Agilent 7890A,USA). The operating condition of GC was as following: Agilent DB-WAX column (30m×530μm×1μm), fame ionization detector (FID detector), 40mL/min of H flow rate, 300mL/min of air flow rate, and N as the carrier gas with the velocity of 15mL/min.

The genomic DNA was extracted from a total of 21 samples taken from seven fermentation stages using Soil DNA Kit (Omega Bio-tek, Inc.) following the manufacturer’s protocol. The DNA quality and quantity were determined by NanoDrop 2000 (Thermo, USA). For prokaryotes, the V4 hypervariable region of the 16S rRNA genes was amplified using universal primer 515F and 909R [ 18 ]. For eukaryotes, the ITS2 region of fungal rRNA gene was amplified using universal primer ITS4 and ITS7 [ Hush Puppies Goal Roadcrew 7j0hvAtKk
]. Primer 515F and ITS4 were added with barcodes. PCR conditions were described in detail previously [ 20 ]. The amplified PCR products were analyzed through a 1%(wt/vol) agarose gel and purified using a PCR purification kit (GE0101–50, TSINGKE). The concentrations of PCR purified products were assessed by NanoDrop 2000 (Thermo, USA). Subsequently, purified amplicons of all samples were equally pooled for constructing a PCR amplicon library, according to the protocols of the Illumina TruSeq. DNA sample preparation LT kit (San Diego, CA, USA), and then subjected to sequencing using the Illumina MiSeq platform at the Environmental Genomic Platform of the Chengdu Institute of Biology, CAS.

Sequencing data analysis was performed by QIIME Pipeline Version 1.7.0 [ Marc Joseph New York Manhasset Patent Leather Loafer c6pANcA
]. The raw sequences were sorted with their unique barcodes. Sequences with low quality, read length below 200bp as well as average base quality score less than 30, were filtered out. Chimera sequences were removed utilizing Uchime algorithm [ 22 ].

Sequences were clustered into operational taxonomic units (OTUs) at a 97% identity threshold. Each sample was rarefied to the same number of reads (10,568 reads for 16S rRNA gene and 4937 reads for ITS gene, respectively) for both alpha-diversity (chao1 estimator of richness, observed species and Shannon’s index) and beta-diversity (PCoA, UniFrac) analyses. Taxonomy was assigned using the Ribosomal Database Project classifier ( http://rdp.cme.msu.edu/ ).

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