A novel copper (II) binding peptide for a colorimetric biosensor system design.

Filamentous bacteriophages are viruses infecting only bacteria. In this study, phage display technique was applied to identify highly selective Cu(II) binding peptides. After five rounds of positive screening against Cu(II) and various rounds of negative screenings against competitive metal ions (Al(III), Co(II), Fe(III), Ni(II) and Zn(II)), bacteriophages were enriched. Selective Cu(II) binding of final phages was confirmed by Enzyme Linked Immunosorbent Assay (ELISA), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray spectroscopy (EDX) analyses. 15 phage plaques were randomly selected and sequenced. Cu-5 peptide (HGFANVA) with the highest frequency of occurrence and the strongest Cu(II) affinity was chosen for further Cu(II) detection and removal tests. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) confirmed the strong Cu(II) binding potential of engineered viruses. Cu-5 peptides were synthetically synthesized with three Cysteine units at C-terminal and a AuNP-peptide biosensor system was developed based on aggregation behavior of AuNPs upon Cu(II) ion treatment. AuNP-based Cu(II) sensor was selective for Cu(II) and the LOD was 91.15 nM (ca. 5.8 × 10-3 mg/L; 3σ/k, n = 5, R2 = 0.992) for the case study which is considerably lower than the WHO's accepted guideline of 1.3 mg/L. This study provides an interdisciplinary approach to apply short peptides as recognition units for biosensor studies which are user friendly, not bulky and cost-effective.

System Accuracy Assessment of a Combined Invasive and Noninvasive Glucometer.

BACKGROUND: The pain associated with pricking the fingertip for blood glucose self-testing is considered to be a major burden in diabetes treatment. This study was performed to evaluate the system accuracy of the invasive TensorTip Combo Glucometer (CoG) device component in accordance with ISO15197:2015 requirements and to explore the accuracy of the noninvasive tissue glucose prediction component. METHODS: One hundred samples were obtained from people with type 1 and type 2 diabetes and healthy volunteers (43 females, 57 males; age: 53 ± 16 years), with glucose distribution as requested by the ISO standard. Three strip lots were tested twice by healthcare professionals in comparison to YSI 2300 Stat Plus reference method followed by a noninvasive tissue glucose reading (NI-CoG). Mean Absolute (Relative) Difference (MARD) was calculated and a consensus error grid (CEG) analysis was performed. RESULTS: The ISO system accuracy criteria were met with the invasive strip technology by 586/600 of the data points (97.1%) and for each strip lot separately. All invasive results (100%) were within CEG-zone A and total MARD was calculated to be 7.1%. With the noninvasive reading, 99% of raw data points were in A + B (91.1% and 7.8%), and the total MARD was calculated to be 18.1%. DISCUSSION: The invasive component of the CoG device was shown to be in full compliance with the current ISO15197 criteria. Good results were also obtained with the NI-CoG tissue glucose prediction. This noninvasive technology would potentially be suitable for frequent pain-free glucose monitoring in many people with diabetes.