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The aim of this study was to explore the influence of amphiphilic and zwitterionic structures on the resistance of protein adsorption to peptide self-assembled monolayers (SAMs) and gain insight into the associated antifouling mechanism. Two kinds of cysteine-terminated heptapeptides were studied. One peptide had alternating hydrophobic and hydrophilic residues with an amphiphilic sequence of CYSYSYS. The other peptide (CRERERE) was zwitterionic. Both peptides were covalently attached onto gold substrates via gold-thiol bond formation. Surface plasmon resonance analysis results showed that both peptide SAMs had ultralow or low protein adsorption amounts of 1.97-11.78 ng/cm2 in the presence of single proteins. The zwitterionic peptide showed relatively higher antifouling ability with single proteins and natural complex protein media. We performed molecular dynamics simulations to understand their respective antifouling behaviors. The results indicated that strong surface hydration of peptide SAMs contributes to fouling resistance by impeding interactions with proteins. Compared to the CYSYSYS peptide, more water molecules were predicted to form hydrogen-bonding interactions with the zwitterionic CRERERE peptide, which is in agreement with the antifouling test results. These findings reveal a clear relation between peptide structures and resistance to protein adsorption, facilitating the development of novel peptide-containing antifouling materials.

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Ochratoxin A (OTA) is a type of mycotoxin generated from the metabolism of Aspergillus and Penicillium, and is extremely toxic to humans, livestock, and poultry. However, traditional assays for the detection of OTA are expensive and complicated. Other than OTA aptamer, OTA itself at high concentration can also adsorb on the surface of gold nanoparticles (AuNPs), and further inhibit AuNPs salt aggregation. We herein report a new OTA assay by applying the localized surface plasmon resonance effect of AuNPs and their aggregates. The result obtained from only one single linear calibration curve is not reliable, and so we developed a “double calibration curve” method to address this issue and widen the OTA detection range. A number of other analytes were also examined, and the structural properties of analytes that bind with the AuNPs were further discussed. We found that various considerations must be taken into account in the detection of these analytes when applying AuNP aggregation-based methods due to their different binding strengths.

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AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.