AuNP-peptide probe for caspase-3 detection in living cells by SERS.

Colloidal nanoparticles can be used as surface-enhanced Raman scattering (SERS) substrates because the very close spacing between particles existing in these colloidal systems is beneficial for the generation of extremely strong and highly spatially localized electric field enhancements. Herein, a caspase-3-specified peptide was used as a molecular cross-linker to engineer gold nanoparticle (AuNP) junctions in a controllable manner. The peptide was designed with a sequence of CCALNNPFFDVED (Cys-Cys-Ala-Leu-Asn-Asn-Pro-Phe-Phe-Asp-Val-Glu-Asp) or CCALNNKYDDVED (Cys-Cys-Ala-Leu-Asn-Asn-Lys-Tyr-Asp-Asp-Val-Glu-Asp), where the CALNN (Cys-Ala-Leu-Asn-Asn) fragment helps to stabilize AuNP suspension in aqueous media and the sequence of DVED (Asp-Glu-Val-Asp) can be cleaved by caspase-3. In addition, the PFF (Pro-Phe-Phe) or KYD (Lys-Tyr-Asp) was exposed and interacted via the hydrophobic or alternate negative and positive electro-interaction in the presence of caspase-3, inducing the aggregation of colloidal Au-peptides accompanied with the enhancement of SERS. It can be observed that the SERS-enhanced signals were correlated with the caspase-3 concentrations and the limit of detection can reach 1.5 ng mL-1. Finally, this caspase-3-specified AuNP-peptide probe has been found to be a promising candidate for its application in the analysis of caspase-3 in living cells.

The research on 3D printing fingerboard and the initial application on cerebral stroke patient's hand spasm.

PURPOSE: To research the possibility of designing customized 3D printing fingerboard to apply to the limb rehabilitation of cerebral stroke patients as well as the prevention and treatment of finger spasm, through 3D printing technology. METHODS: Taking 18 hospitalized cerebral stroke patients for example, through scanning, molding and printing, to make and wear 3D printing fingerboard for them, and then observe the compliance, main complaint, muscular tension of affected hand and changes on range of motion after they wear the fingerboard for 3 weeks and 3 months. RESULTS: Have acquired completed data from 13 patients. The time of them wearing the fingerboard every day varied from 1 to 8 h, and most of them reflected that they felt comfortable and there was no feeling of worsened pain or finger skin allergy. In addition, the patients' grip strength, hand function and range of motion improved by varying degrees while their muscular tensions declined by varying degrees. The tension and bending resistance of the fingerboard all met the patients' treatment requirements. CONCLUSIONS: With the advantages of being accurate and customized, 3D printing fingerboard can benefit patients fixing and orthopedic treatment, and even prevent and treat cerebral stroke patient's finger spasm. Trial registration The research topic has been registered in Chinese Clinic Trial Registry. Registration time: January 15, 2016. Registration topic: The Use of 3D Printing Technology in the Orthotic of Extremity Rehabilitation of Stroke Patient. Registration Number: ChiCTR-INR-16007774.

[Onset detection of surface diaphragmatic electromyography based on sample entropy and individualized threshold].

The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters w and r0, the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error τ was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.

[E Glove Evaluation & Training System Based on ARAT and Fusion of Visual and Tactile Information].

Aiming to solve the problem of complex structure, low flexibility and practicability for a wearable sensing data glove device of hand function evaluation system, this paper presented a wearable pressure-sensing data glove device based on visual and tactile fusion, which can stimulate the active motor function of patients. Firstly, it introduced the upper limb action research test theory, the basic test flow and the grading rules that used to evaluate the hand function. Secondly, it described the processing flow of visual and tactile information, and the hand function evaluation test method of visual and tactile fusion, which was used to achieve digital score and evaluation of the patient training process. Finally, ten patients with stroke were enrolled into the EGET system for hand function test and evaluation. The results were compared with the doctors'. The maximum relative error is 8%, and the average relative error is 4%, which means that EGET system can achieve the expected goal.

[Motion signal extraction method based on sEMG energy Gauss distribution characteristics].

Motion segment and extraction from continuous signals is the premise of surface electromyography (sEMG) analysis. For the problem that sEMG energy threshold required repeated manual testing to set, this the paper established a this mathematical model of continuous actions based on Gaussian sEMG energy curve, in which the energy threshold was set according to the distribution of Gaussian signal section, and differentiated the action signals from no-action signals combined with energy comparison method. The experiment results showed the method can achieve continuous repetitive action segmentation, and compared with manual segmentation of the same signal, has a higher degree of coincidence.

Relationship between EMG features and force in orbicularis oris muscle.

BACKGROUND: Lip incompetence resulting from mouth breathing is a common clinical manifestation, while there are no definite indicators of amplitude and intensity of muscle functional training in clinical practice, which leads to unsatisfactory training results. OBJECTIVE: The aim was to quantify the relationship between electromyography (EMG) and force in orbicularis oris muscle, so that the indicators of muscle functional training can be evaluated using EMG signals, so as to improve the training effects. METHODS: The EMG and the force signals of orbicularis oris muscle from 0% to 100% MVC within 5 s in twelve healthy subjects (six males and six females; age, 25 ± 2 years; mass, 60 ± 15 kg) were recorded simultaneously for three trials. Four EMG features consisting of RMS, WAMP, SampEn and FuzzyEn were analyzed. The regression analyses were performed using first-order and third-order polynomial model. RESULTS: There were high correlations between the four EMG features and muscle force with the two models. The third-order model yielded a higher coefficient of determination (R2) than the linear model (p< 0.001) and the result of FuzzyEn (R2: 0.884 ± 0.059) was the highest in the four features. CONCLUSION: The third-order model with FuzzyEn of EMG signals may be used to guide the muscle functional training.

Research and development of 3D printing orthotic insoles and preliminary treatment of leg length discrepancy patients.

BACKGROUND: Leg length discrepancy (LLD) can cause abnormal posture, muscle and/or joint pains, which leads to walking difficulties. The common treatment is to use a shoe lift on the shorter leg side, but this has unsatisfying results. OBJECTIVE: Through research and development, we created 3D printing orthotic insoles for LLD patients and aimed to improve their symptoms. METHODS: 1. Research and development of 3D printing orthotic insole: (1) foot scan and data acquisition; (2) insole software modeling; (3) 3D printing insole using TPU materials, and (4) post-processing. 2. CLINICAL OBSERVATION: we customized insoles for LLD patients and required them to wear them while walking. We conducted general inquiries and a functional evaluation before and after 12 weeks of wearing insoles. RESULTS: There are seven complete cases. The joint and lower back pains were alleviated. The stride frequency, stride and pace were improved in all seven cases. Patients' overall health condition improved significantly. CONCLUSIONS: The 3D printing orthotic insoles are made with clear procedures and practical operability. By wearing insoles, patients' muscle and/or joint pains were relieved and their gaits were improved.

Study on sEMG-based exercise therapy for upper limb of severe hemiplegic patients.

sEMG, as a kind of bioelectrical signal reflecting muscle motion state, generally applies to motion recognition and human interface. Healthy subjects are selected in most studies, while for hemiplegic patients, especially patients with severe hemiplegia, high accuracy motion recognition is difficult to acquire due to the non-ideal sEMG signal from dysfunction muscles. Therefore, this paper presents an upper limb exercise therapy, based on 5 defined motions and 6 Muscle-Units, for patients with severe hemiplegia. Through the sampling and analysis of sEMG signals from 8 subjects, including 4 healthy and 4 hemiplegic patients, we draw a conclusion of the relevance between specific motions and Muscle-Units, which can be used as a reference for paralyzed arm training. According to this relevance, six Muscle-Units can be classified into two categories: major Muscle-Units and minor Muscle-Units. In order to improve the interest and positivity of patients, a PC based virtual interactive platform is established. The sEMG signal from major Muscle-Units is processed with a moving average algorithm, and the result is used as the control signal for training interaction.