Refine
Document Type
- Article (4)
- Doctoral Thesis (2)
Has Fulltext
- yes (6)
Is part of the Bibliography
- no (6)
Keywords
- interaction (6) (remove)
Institute
Publisher
- Frontiers Media S.A. (3)
- Wiley (1)
Im Klinikalltag sind das Auftreten neuer Antibiotikaresistenzen und die steigende Anzahl an chronisch infizierten Wunden ein Problem. Um dem zu begegnen, stehen derzeit die Entwicklung neuer antimikrobieller Chemotherapeutika sowie die kalkulierte, auf Antibiogrammen begründete Antibiotikatherapie im Fokus. Auch die kombinierte Anwendung von systemisch wirksamen Antibiotika und lokal wirksamen Antiseptika auf chronisch infizierten Wunden ist bereits gängige Praxis im Klinikalltag. Untersuchungen, die Kombinationen aus Antibiotika und Antiseptika auf Wechselwirkungen bzw. Interaktionspotenziale prüfen, sind trotz ihrer häufigen Anwendung bisher aber kaum durchgeführt worden. In der vorliegenden Arbeit wurde deshalb das Interaktionsverhalten von AB/AS-Kombinationen untersucht. Getestet wurden die Antibiotika Ciprofloxacin, Gentamicin, Amikacin, Vancomycin, Mupirocin und Linezolid in Kombination mit den Antiseptika Octenidindihydrochlorid, Chlorhexidindigluconat und Polihexanid. Die Auswahl der Bakterien orientierte sich an den in chronisch infizierten Wunden häufig nachzuweisenden Erregern Staphylokokken, Enterokokken, Pseudomonaden und E. coli [17][15]. An Nährmedien kamen Isosensitest-, Müller-Hinton-, CSA- und Blut-Agar zum Einsatz. Dem Blut-Agar wurde in Hinblick auf die Problematik im Klinikalltag die größte Bedeutung bezüglich der Ergebnisinterpretation beigemessen. In Vorversuchen wurden die für die in den Hauptversuchen notwendigen MHK’s der Antiseptika mittels Mikroagardilutionsmethode bestimmt. In den Hauptversuchen wurde mit Hilfe des Agardiffusions-Dilutions-Kombitests ermittelt, wie sich die Hemmhofdurchmesser der AB/AS-Kombinationen im Vergleich zu denen der Kontrollversuche (reiner Agardiffusionstest mit Antibiotika) verändern. Anhand des Änderungsverhaltens im Hemmhofdurchmesser konnte entweder „additives/synergistisches“, „antagonistisches“ oder „indifferentes“ Interaktionsverhalten nachgewiesen werden. Aus insgesamt 372 getesteten Kombinationen (aus Antibiotika, Antiseptika, Erreger und Agar) konnten 65 als (über-)additiv und 20 als antagonistisch bewertet werden. Die hohe Anzahl sich indifferent verhaltender AB/AS- Kombinationen begründet sich in der Größe des geschätzten Standardfehlers. Additives/synergistisches Interaktionsverhalten zeigte sich am häufigsten bei der Testung gegen Staphylokokken, gefolgt von Pseudomonaden. 68 Antagonistisches Interaktionsverhalten trat weder bei Staphylokokken noch bei Enterokokken auf. Gegen Pseudomonaden zeigten die Kombinationen CIP 5/Octenidin, CIP 5/Chlorhexidin und AK 30/Chlorhexidin ein hohes Interaktionspotenzial. Die Kombination CIP 5/Octenidin wirkte maßgeblich antagonistisch, CIP 5/ Chlorhexidin und AK 30/Chlorhexidin dagegen rein additiv/synergistisch. Bei der Testung gegen Enterokokken und E. coli traten kaum Interaktionen auf. Auf Blut-Agar fielen insbesondere die AB/AS-Kombinationen CIP 5/ Chlorhexidin, LZD 30/Chlorhexidin, LZD 30/Octenidin, LZD 30/Polihexanid und VA 30/Chlorhexidin auf. Alle fünf Kombinationen zeigten bei Staphylokokken ein additives/synergistisches Interaktionsverhalten. Kann ihr Interaktionsverhalten in größeren Studien bestätigt werden, könnten diese Kombinationen als neue Therapieoptionen in der Behandlung chronisch infizierter Wunden an Bedeutung erlangen. Antagonismus wurde nur durch eine sehr geringe Anzahl an AB/AS-Kombinationen auf Blut-Agar hervorgerufen. Das stärkste Potenzial wies CIP 5 in Verbindung mit Octenidin bei Pseudomonaden auf. Der Einsatz dieser Kombination sollte daher im Klinikalltag möglichst vermieden werden. Die Ergebnisse verdeutlichen, dass die Interaktionen nicht allein von der AB/AS-Kombination, sondern auch von der Art des Agars und Erregers abhängen. Dennoch sind sie richtungsweisend und sollten Anlass dazu geben, weitere Untersuchungen in der AB/AS-Kombinationstestung durchzuführen, um Fortschritte erzielen zu können.
Im Rahmen des Verbund-Forschungsprojektes KOKON wurde nach systematischer Literaturrecherche eine Datenbank entwickelt; in der KOKONbase sind sowohl die Interaktionsprofile als auch die Interaktionsmatrix die wesentlichen Elemente der Vorhaltung von primärem und bewertetem Wissen. In der Interaktionsmatrix wird mit Hilfe eines Ampelschemas die Möglichkeit der Beeinflussung der Pharmakokinetik von Arzneistoffen durch ausgewählte Drogen dargestellt. Die Droge und die Arzneistoffe werden paarweise abgebildet. Die Interaktionsmatrix wird durch praktisch tätige Onkologen als sehr wertvolles Instrument in der onkologischen Beratungspraxis angesehen, um schnell einen Überblick über das von einer Droge ausgehende Gefahrenpotential bzgl. der Beeinflussung der Wirksamkeit eines in der Onkologie genutzten Arzneistoffs zu bekommen.
Abstract
Background
Heparins are usually produced from animal tissues. It is now possible to synthesize heparins. This provides the abilities to overcome shortages of heparin, to optimize biological effects, and to reduce adverse drug effects. Heparins interact with platelet factor 4 (PF4), which can induce an immune response causing thrombocytopenia. This side effect is called heparin‐induced thrombocytopenia (HIT). We characterized the interaction of PF4 and HIT antibodies with oligosaccharides of 6‐, 8‐, 10‐, and 12‐mer size and a hypersulfated 12‐mer (S12‐mer).
Methods
We utilized multiple methodologies including isothermal calorimetry, circular dichroism spectroscopy, single molecule force spectroscopy (SMFS), enzyme immunosorbent assay (EIA), and platelet aggregation test to characterize the interaction of synthetic heparin analogs with PF4 and anti‐PF4/heparin antibodies.
Results
The synthetic heparin‐like compounds display stronger binding characteristics to PF4 than animal‐derived heparins of corresponding lengths. Upon complexation with PF4, 6‐mer and S12‐mer heparins showed much lower enthalpy, induced less conformational changes in PF4, and interacted with weaker forces than 8‐, 10‐, and 12‐mer heparins. Anti‐PF4/heparin antibodies bind more weakly to complexes formed between PF4 and heparins ≤ 8‐mer than with complexes formed between PF4 and heparins ≥ 10‐mer. Addition of one sulfate group to the 12‐mer resulted in a S12‐mer, which showed substantial changes in its binding characteristics to PF4.
Conclusions
We provide a template for characterizing interactions of newly developed heparin‐based anticoagulant drugs with proteins, especially PF4 and the resulting potential antigenicity.
Objective: The instrument THERapy-related InterACTion (THER-I-ACT) was developed to document therapeutic interactions comprehensively in the human therapist–patient setting. Here, we investigate whether the instrument can also reliably be used to characterise therapeutic interactions when a digital system with a humanoid robot as a therapeutic assistant is used.
Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis [n = 9] or arm ability training (AAT) for mild arm paresis [n = 8]) using the digital therapy system E-BRAiN over a course of nine sessions. Analysis of the therapeutic interaction: A total of 34 therapy sessions were videotaped. All therapeutic interactions provided by the humanoid robot during the first and the last (9th) session of daily training were documented both in terms of their frequency and time used for that type of interaction using THER-I-ACT. Any additional therapeutic interaction spontaneously given by the supervising staff or a human helper providing physical assistance (ABT only) was also documented. All ratings were performed by two trained independent raters.
Statistical analyses: Intraclass correlation coefficients (ICCs) were calculated for the frequency of occurrence and time used for each category of interaction observed.
Results: Therapeutic interactions could comprehensively be documented and were observed across the dimensions provision of information, feedback, and bond-related interactions. ICCs for therapeutic interaction category assessments from 34 therapy sessions by two independent raters were high (ICC ≥0.90) for almost all categories of the therapeutic interaction observed, both for the occurrence frequency and time used for categories of therapeutic interactions, and both for the therapeutic interaction performed by the robot and, even though much less frequently observed, additional spontaneous therapeutic interactions by the supervisory staff and a helper being present. The ICC was similarly high for an overall subjective rating of the concentration and engagement of patients (0.87).
Conclusion: Therapeutic interactions can comprehensively and reliably be documented by trained raters using the instrument THER-I-ACT not only in the traditional patient–therapist setting, as previously shown, but also in a digital therapy setting with a humanoid robot as the therapeutic agent and for more complex therapeutic settings with more than one therapeutic agent being present.
Objective: To characterize a socially active humanoid robot’s therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists’ interaction.
Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist–patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA.
Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists’ interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients’ training behavior.
Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.
Objective: To develop an instrument for the observation of therapeutic communication interactions during rehabilitation sessions and test its inter-rater reliability.
Methods: The new instrument THER-I-ACT (THERapy–related Inter-ACTion) has been designed to assess both the frequency and timing of therapeutic interactions in the thematic fields information provision, feedback, other motivational interaction, and bonding. For this inter-rater reliability study, a sample of stroke survivors received arm rehabilitation as either arm ability training, arm basis training, or mirror therapy, or neglect training as individually indicated. Therapy sessions were video-recorded (one for each participant) and therapeutic interactions rated by two independent raters using THER-I-ACT.
Results: With regard to the instrument's comprehensiveness to document therapeutic interactions with pre-defined categories the data from 29 sessions suggested almost complete coverage. Inter-rater reliability was very high both for individual categories of therapeutic interaction (frequency and time used for interaction) (intraclass correlation coefficient, ICC 0.91–1.00) and summary scores for the thematic fields of interaction (again for frequency and time used for interaction) (ICC 0.98–1.00).
The inter-rater reliability for rating engagement and being focussed for both the therapist and patient was substantial (ICC 0.71 and 0.86).
Conclusions: The observational study documented that by use of the newly designed THER-I-ACT various types of therapy-related communication interactions performed by therapists can be assessed with a very high inter-rater reliability. In addition, the thematic fields and categories of therapeutic interaction as defined by the instrument comprehensively covered the type of interaction that occurred in the therapeutic sessions observed.