Publications by authors named "I A Efremov"

This research paper presents a new fundamental approach for evaluating accurate ab initio quartic, sextic, and octic centrifugal distortion parameters of A-reduced rotational effective Hamiltonians of asymmetric top molecules. In this framework, the original Watson Hamiltonian, expanded up to sextic terms of kinetic and potential energies, is subjected to a series of vibrational and rotational operator unitary transformations, leading to reduced Watson effective Hamiltonians for the equilibrium configuration, ground state, and weakly perturbed vibrationally excited states. The proposed scheme is based on a numerical-analytic implementation of the sixth-order Van Vleck operator perturbation theory with the systematic normal ordering of vibrational rising and lowering operators (a†, a) and cylindrical angular momentum operators (Jz, J+, J-).

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Herein, we describe the design and synthesis of γ-secretase modulator (GSM) clinical candidate PF-06648671 () for the treatment of Alzheimer's disease. A key component of the design involved a 2,5--tetrahydrofuran (THF) linker to impart conformational rigidity and lock the compound into a putative bioactive conformation. This effort was guided using a pharmacophore model since crystallographic information was not available for the membrane-bound γ-secretase protein complex at the time of this work.

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Article Synopsis
  • Delirium Tremens (DT) is a severe complication of alcohol withdrawal syndrome (AWS), linked to neurotransmitter issues, inflammation, and increased bodily permeability, but its biomarkers are not well understood.
  • The study compared healthy individuals and two AWS patient groups (with and without DT) to analyze various biomarkers, finding significant changes in certain biochemical markers and elevated inflammatory indicators in DT patients.
  • Results suggested a subgroup of AWS patients exhibited high inflammation, indicating the complexity of patient profiles in AWS and highlighting the need for further research into specific biomarkers related to DT.
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Article Synopsis
  • Schizophrenia is a serious mental disorder that significantly affects individuals’ lives, and early diagnosis can improve outcomes, creating a need for better diagnostic tools.
  • This review explores how machine learning can enhance the prediction and diagnosis of schizophrenia and its clinical features by analyzing various data sources and studies from 2010 to 2023.
  • Machine learning methods are applied to evaluate patients' functional status, interpret medical imaging, analyze speech and behavior, and can assist in predicting and diagnosing schizophrenia using medical history and genetic information.
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