Search

An exclusive anti‐Markovnikov hydroamination as well as hydroalkoxylation of allylic alcohols catalyzed by a simple catalyst Y(CH2SiMe3)3(THF)2 has been developed, providing a variety of γ‐amino and γ‐alkoxy alcohols in good yields under mild conditions. Moreover, this protocol is applied to the synthesis of drug molecules Propipocaine and Proroxan. The rare‐earth metal catalyst features readily available, wide substrate compatability, exclusive anti‐Markovnikov selectivity, and performs in the absence of a base or an additive. [ABSTRACT FROM AUTHOR]

C20H18Br4N2O2, monoclinic, P21/c (no. 14), a = 9.5994(7) Å, b = 11.9156(10) Å, c = 10.3508(7) Å, β = 91.025(6)°, V = 1183.76(15) Å3, Z = 2, Rgt(F) = 0.0472, wRref(F2) = 0.1008, T = 293(2) K.

C20H18Br4N2O2, monoclinic, P21/c (no. 14), a = 9.1119(6) Å, b = 17.8211(12) Å, c = 13.5228(8) Å, β = 90.809(2)°, V = 2195.7(2) Å3, Z = 4, Rgt(F) = 0.0448, wRref(F2) = 0.0963, T = 273(2) K.

Copyright of Zeitschrift für Kristallographie / New Crystal Structures is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zeitschrift für Kristallographie / New Crystal Structures is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

C20H18Br4N2O2, monoclinic, P21/c (no. 14), a = 9.1119(6) Å, b = 17.8211(12) Å, c = 13.5228(8) Å, β = 90.809(2)°, V = 2195.7(2) Å3, Z = 4, Rgt(F) = 0.0448, wRref(F2) = 0.0963, T = 273(2) K. [ABSTRACT FROM AUTHOR]

C20H18Br4N2O2, monoclinic, P21/c (no. 14), a = 9.5994(7) Å, b = 11.9156(10) Å, c = 10.3508(7) Å, β = 91.025(6)°, V = 1183.76(15) Å3, Z = 2, Rgt(F) = 0.0472, wRref(F2) = 0.1008, T = 293(2) K. [ABSTRACT FROM AUTHOR]

Background: Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD., Methods: Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD., Results: Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up., Conclusion: Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Objectives: Major depressive disorder (MDD) patients with anhedonia tend to have a poor prognosis. The underlying imaging basis for anhedonia in MDD remains largely unknown. The relationship between nodal properties and anhedonia in MDD patients need to be further investigated. Herein, this study aims to explore differences of cerebral functional node characteristics in MDD patients with severe anhedonia (MDD-SA) and MDD patients with mild anhedonia (MDD-MA) before and after the antidepressant treatment., Methods: Ninety participants with current MDD were recruited in this study. 24-Item Hamilton Depression Scale (HAMD-24) and Snaith-Hamilton Pleasure Scale (SHAPS) were used to assess the severity of depression and anhedonia at baseline and the end of 6-months treatment. The MDD patients who scored above the 25th percentile on the SHAPS were assigned to an MDD-SA group ( n= 19), while those who scored below the 25th percentile were assigned to an MDD-MA group ( n= 18). All patients in the 2 groups received antidepressant treatment. Functional magnetic resonance imaging (fMRI) images of all the patients were collected at baseline and the end of 6-months treatment. Graph theory was applied to analyze the patients' cerebral functional nodal characteristics, which were measured by efficiency (e i ) and degree (k i )., Results: Repeated measures 2-factor ANCOVA showed significant main effects on group on the e i and k i values of left superior frontal gyrus (LSFG) ( P= 0.003 and P= 0.008, respectively), and on the e i and k i values of left medial orbital-frontal gyrus (LMOFG) ( P =0.004 and P= 0.008, respectively). Compared with the MDD-MA group, the significantly higher e i and k i values of the LSFG ( P= 0.015 and P= 0.021, respectively), and the significantly higher e i and k i values of the LMOFG ( P =0.015 and P= 0.037, respectively) were observed in the MDD-SA group at baseline. Meanwhile, higher SHAPS scores could result in higher e i and k i values of LSFG ( P =0.019 and P= 0.026, respectively), and higher e i value of LMOFG ( P= 0.040) at baseline; higher SHAPS scores could result in higher e i values of LSFG ( P= 0.049) at the end of 6-months treatment. The multiple linear regression analysis revealed that sex were negatively correlated with the e i and k i values of LSFG ( r = -0.014, P =0.004; r =-1.153, P =0.001, respectively). The onset age of MDD was negatively correlated with the k i value of LSFG ( r =-0.420, P =0.034) at the end of 6-months treatment. We also found that SHAPS scores at baseline were positively correlated with the HAMD-24 scores ( r =0.387, P =0.022) at the end of 6-months treatment., Conclusions: There are obvious differences in nodal properties between the MDD-SA and the MDD-MA patients, such as the high e i of LSFG in the MDD-SA patients, which may be associated with the severity of anhedonia. These nodal properties could be potential biomarkers for the prognosis of MDD. The increased e i and k i values in the LSFG of MDD-SA patients may underlie a compensatory mechanism or protective mechanism. The mechanism may be an important component of the pathological mechanism of MDD-SA. The poor prognosis in the MDD-SA patients suggests that anhedonia may predict a worse prognosis in MDD patients. Sex and onset age of MDD may affect the nodal properties of LSFG at baseline and the end of 6-months treatment.