APEH Rabbit Pab

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Leader in Biomolecular Solutions for Life Science APEH Rabbit pAb Catalog No.: A13467 Basic Information Background Catalog No. This gene encodes the enzyme acylpeptide hydrolase, which catalyzes the hydrolysis of A13467 the terminal acetylated amino acid preferentially from small acetylated peptides. The acetyl amino acid formed by this hydrolase is further processed to acetate and a free Observed MW amino acid by an aminoacylase. This gene is located within the same region of 80-108kDa chromosome 3 (3p21) as the aminoacylase gene, and deletions at this locus are also associated with a decrease in aminoacylase activity. The acylpeptide hydrolase is a Calculated MW homotetrameric protein of 300 kDa with each subunit consisting of 732 amino acid 81kDa residues. It can play an important role in destroying oxidatively damaged proteins in living cells. Deletions of this gene locus are found in various types of carcinomas, Category including small cell lung carcinoma and renal cell carcinoma. Primary antibody Applications WB, IF Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 327 P13798 IF 1:50 - 1:200 Immunogen Recombinant fusion protein containing a sequence corresponding to amino acids 1-260 of human APEH (NP_001631.3). Synonyms APEH;AARE;ACPH;APH;D3F15S2;D3S48E;DNF15S2;OPH Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts of various cell lines, using APEH antibody (A13467) at 1:1000 dilution. Secondary antibody: HRP Goat Anti-Rabbit IgG (H+L) (AS014) at 1:10000 dilution. Lysates/proteins: 25ug per lane. Blocking buffer: 3% nonfat dry milk in TBST. Detection: ECL Basic Kit (RM00020). Exposure time: 30s. Immunofluorescence analysis of HeLa cells using APEH antibody (A13467). Blue: DAPI for nuclear staining. Antibody | Protein | ELISA Kits | Enzyme | NGS | Service For research use only. Not for therapeutic or diagnostic purposes. Please visit http://abclonal.com for a complete listing of recommended products..

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Pharmacogenomic Characterization in Bipolar Spectrum Disorders
pharmaceutics Review Pharmacogenomic Characterization in Bipolar Spectrum Disorders Stefano Fortinguerra 1,2 , Vincenzo Sorrenti 1,2,3 , Pietro Giusti 2, Morena Zusso 2 and Alessandro Buriani 1,2,* 1 Maria Paola Belloni Center for Personalized Medicine, Data Medica Group (Synlab Limited), 35131 Padova, Italy; [email protected] (S.F.); [email protected] (V.S.) 2 Department of Pharmaceutical & Pharmacological Sciences, University of Padova, 35131 Padova, Italy; [email protected] (P.G.); [email protected] (M.Z.) 3 Bendessere™ Study Center, Solgar Italia Multinutrient S.p.A., 35131 Padova, Italy * Correspondence: [email protected] Received: 25 November 2019; Accepted: 19 December 2019; Published: 21 December 2019 Abstract: The holistic approach of personalized medicine, merging clinical and molecular characteristics to tailor the diagnostic and therapeutic path to each individual, is steadily spreading in clinical practice. Psychiatric disorders represent one of the most difficult diagnostic challenges, given their frequent mixed nature and intrinsic variability, as in bipolar disorders and depression. Patients misdiagnosed as depressed are often initially prescribed serotonergic antidepressants, a treatment that can exacerbate a previously unrecognized bipolar condition. Thanks to the use of the patient’s genomic profile, it is possible to recognize such risk and at the same time characterize specific genetic assets specifically associated with bipolar spectrum disorder, as well as with the individual response to the various therapeutic options. This provides the basis for molecular diagnosis and the definition of pharmacogenomic profiles, thus guiding therapeutic choices and allowing a safer and more effective use of psychotropic drugs. Here, we report the pharmacogenomics state of the art in bipolar disorders and suggest an algorithm for therapeutic regimen choice.
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