Best Clinical Practice in Botulinum Toxin Treatment for Children with Cerebral Palsy

Botulinum toxin A (BoNT-A) is considered a safe and effective therapy for children with cerebral palsy (CP), especially in the hands of experienced injectors and for the majority of children. Recently, some risks have been noted for children with Gross Motor Classification Scale (GMFCS) of IV and the risks are substantial for level V. Recommendations for treatment with BoNT-A have been published since 1993, with continuous optimisation and development of new treatment concepts. This leads to modifications in the clinical decision making process, indications, injection techniques, assessments, and evaluations. This article summarises the state of the art of BoNT-A treatment in children with CP, based mainly on the literature and expert opinions by an international paediatric orthopaedic user group. BoNT-A is an important part of multimodal management, to support motor development and improve function when the targeted management of spasticity in specific muscle groups is clinically indicated. Individualised assessment and treatment are essential, and should be part of an integrated approach chosen to support the achievement of motor milestones. To this end, goals should be set for both the long term and for each injection cycle. The correct choice of target muscles is also important; not all spastic muscles need to be injected. A more focused approach needs to be established to improve function and motor development, and to prevent adverse compensations and contractures. Furthermore, the timeline of BoNT-A treatment extends from infancy to adulthood, and treatment should take into account the change in indications with age.     

Synthesis and biological evaluation of phloroglucinol derivatives possessing α‐glycosidase,acetylcholinesterase, butyrylcholinesterase,carbonic anhydrase inhibitory activity

A series of novel phloroglucinol derivatives were designed, synthesized, characterized spectroscopically and tested for their inhibitory activity against selected metabolic enzymes, including α‐glycosidase, acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and human carbonic anhydrase I and II (hCA I and II). These compounds displayed nanomolar inhibition levels and showed K_i values of 1.14–3.92 nM against AChE, 0.24–1.64 nM against BChE, 6.73–51.10 nM against α‐glycosidase, 1.80–5.10 nM against hCA I, and 1.14–5.45 nM against hCA II.

     

Glossokinetic potential based tongue–machine interface for 1-D extraction

The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue–human computer interface or tongue–machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue–machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22–34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain–computer interfaces which have significant inadequacies arisen from the EEG signals.