We present a combined anion photoelectron spectroscopic and quantum chemical investigation on the structures and bonding properties of CPt2−/0 and CPt2H−/0. The experimental vertical detachment energies of CPt2− and CPt2H− are measured to be 1.91 ± 0.08 and 3.54 ± 0.08 eV, respectively. CPt2− is identified as a C2v symmetric Pt—C—Pt bent structure, and CPt2 has a D∞h symmetric Pt—C—Pt linear structure. Both anionic and neutral CPt2H adopt a Pt—C—Pt—H chain-shaped structure, in which the ∠PtCPt and ∠CPtH bond angles of CPt2H− are larger than those of CPt2H. The Pt—C bonds in CPt2−/0 and CPt2H−/0 exhibit covalent double bonding characters. The Pt=C bonds are much stronger than the C—H bond that may explain why the C atom CPt2H−/0 prefers to form Pt=C bonds rather than C—H bonds. It may also explain why platinum can insert into the C—H bond to activate the C—H bond as reported in the literature.

1994-09-01·Journal of Medicinal Chemistry1区 · 医学

3-Amino-5,5-dimethylhexanoic Acid. Synthesis, Resolution, and Effects on Carnitine Acyltransferases

1区 · 医学

Article

作者: McMillin, Jeanie B. ; Wolkowicz, Paul E. ; Saeed, Ahsraf ; Brouillette, Wayne J.

The selective inhibition of individual carnitine acyltransferases may be useful in the therapy of diabetes and heart disease. Aminocarnitine (3) is a weak competitive inhibitor (K(i) = 4.0 mM) for carnitine acetyltransferase (CAT), although the N-acetyl derivative 4 is about 165 times more potent (K(i) = 0.024 mM) than 3. Compound 3 is also a potent competitive inhibitor for carnitine palmitoyltransferases 1 and 2 (CPT-1 and CPT-2) (IC50 for CPT-2 = 805 nM). We synthesized 3-amino-5,5-dimethylhexanoic acid (7) and its N-acetyl derivative (8) as isosteric analogs of 3 and 4 that lack the quaternary ammonium positive charge. Like 3 and 4, compounds 7 and 8 were competitive inhibitors of CAT with significantly different potencies, but in this case, 8 (K(i) = 25 mM) was 10 times less potent than 7 (K(i) = 2.5 mM). R-(-)-7 and S-(+)-7 were stereoselective inhibitors of CAT (K(i) = 1.9 and 9.2 mM, respectively). Racemic 7 was a weak competitive inhibitor of CPT-2 (K(i) = 20 mM) and had no effect on CPT-1. These results are consistent with differences among the carnitine-binding sites on carnitine acyl-transferases that may be useful in selective inhibitor design. Furthermore, the data suggest that the quaternary ammonium positive charge of carnitine may be important for the proper orientation of carnitine and its analogs in the binding site.

Frontiers in Psychology

Distinguishing different types of attention deficit hyperactivity disorder in children using artificial neural network with clinical intelligent test

Article

作者: Huang, Yu-Jui ; Chang, Shen-Chieh ; Lin, I-Cheng ; Kuo, Terry B J ; Chiu, Hung-Wen

BackgroundAttention deficit hyperactivity disorder (ADHD) is a well-studied topic in child and adolescent psychiatry. ADHD diagnosis relies on information from an assessment scale used by teachers and parents and psychological assessment by physicians; however, the assessment results can be inconsistent.PurposeTo construct models that automatically distinguish between children with predominantly inattentive-type ADHD (ADHD-I), with combined-type ADHD (ADHD-C), and without ADHD.MethodsClinical records with age 6–17 years-old, for January 2011–September 2020 were collected from local general hospitals in northern Taiwan; the data were based on the SNAP-IV scale, the second and third editions of Conners’ Continuous Performance Test (CPT), and various intelligence tests. This study used an artificial neural network to construct the models. In addition, k-fold cross-validation was applied to ensure the consistency of the machine learning results.ResultsWe collected 328 records using CPT-3 and 239 records using CPT-2. With regard to distinguishing between ADHD-I and ADHD-C, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 88.75 and 85.56% in the training and testing sets, respectively. The replacement of CPT-2 with CPT-3 results in this model yielded an overall accuracy of 90.46% in the training set and 89.44% in the testing set. With regard to distinguishing between ADHD-I, ADHD-C, and the absence of ADHD, a combination of demographic information, SNAP-IV scale results, and CPT-2 results yielded overall accuracies of 86.74 and 77.43% in the training and testing sets, respectively.ConclusionThis proposed model distinguished between the ADHD-I and ADHD-C groups with 85–90% accuracy, and it distinguished between the ADHD-I, ADHD-C, and control groups with 77–86% accuracy. The machine learning model helps clinicians identify patients with ADHD in a timely manner.

100 项与 CPT02 相关的药物交易

登录后查看更多信息

研发状态

10 条进展最快的记录,

后查看更多信息

适应症	最高研发状态	国家/地区	公司	日期
急性髓性白血病	药物发现	中国	苏州沃生生物医药有限公司	2022-07-20
肺癌	药物发现	中国	苏州沃生生物医药有限公司	2022-07-20
淋巴细胞白血病	药物发现	中国	苏州沃生生物医药有限公司	2022-07-20