The emergence and rapid spread of carbapenemresistant pathogens producing metallo-β-lactamases such as IMP-1 and NDM-1 have been of great concern in the global clinical setting. The X-ray crystal structures of IMP-1 from Serratia marcescens and its single mutant, D120E, in complexes with citrate were determined at resolutions of 2.00 and 1.85 Å, respectively. Two crystal structures indicate that a single mutation at position 120 caused a structural change around Zn1, where the geometry changes from a tetrahedron in the native IMP-1 to a square pyramid in D120E. Based on these two complex structures, the authors synthesized citrate monobenzyl ester 1 to evaluate the structural requirement for the inhibitory activity against IMP-1 and compared the inhibitory activities with nonsubstituted citrate. The introduction of a benzyl group into citrate enhanced the inhibitory activity in comparison to citrate (IC 50 > 5 mM).
Objectives To provide patient-centred care, psychological knowledge, and skills are necessary for pharmaceutical communication. Acquisition of these communication skills is closely related to patient comprehension. Therefore, to improve pharmacist’s communication skills, pharmacist need to learn the characteristics of their medication instructions, such as posture, facial expressions, eye contact, nodding, and more. For the analysis of medical communication, there is a rating scale, functional analysis, and others. However, these methods may not match the actual emotions due to their analysis skills and the psychological stress of the patients. In this study, we examined the methods to evaluate patient-pharmacist communication using emotion recognition AI software, which recognises emotions from facial expressions. Methods With the cooperation of six simulated patients (SP) and eight pharmacists, we recorded the SP’s facial expressions during medication instruction. The facial expression video was analysed using emotion recognition AI, which can obtain emotion values (anger, contempt, disgust, fear, joy, sadness, surprise, and engagement). We compared the emotion of the extracted peaks with the feedback and calculated the emotion match rate. Key findings As a result, 33% of the emotions matched in the peak and feedback. This result indicates that emotion recognition AI cannot capture every feedback emotion. However, in joy, the result was not affected by engagement, and the match rate between peak and feedback was high. Conclusions In the future, emotion recognition AI will allow us to assess the effects of communication skills of the pharmacists on the psychological state of the patients more objectively and noninvasively.
Background: Pharmacists must adjust their distance from patients to facilitate communication during interviews and gain their trust. The distance between the patients and the pharmacists varies depending on many factors, such as gender, posture and the patients' mood. Only a few of these papers have actually measured and validated distance with patients. In this study, we validated our method of assessing mood and measuring distance before beginning a survey with patients. Methods: We measured comfortable inter-
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