Identifying diabetes from conjunctival images using a novel hierarchical multi-task network

Diabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.

Colorimetric sensor array for rapid discrimination of edible oil species based on halogen ion exchange reaction between CsPbBr3 and iodide

Peroxides in edible oils, measured by peroxidation value for their amount, are closely related to human health. Long-term consumption of edible oils with high peroxidation values can lead to a...

Discrimination of 11 Malaysian Durian Cultivars Based on Sulfur-Containing Volatiles and Esters Using Multivariate Data Analysis

There are reports documenting the volatile oils of several durian cultivars in Malaysia. However, there is limited information on the rapid discrimination of the durian cultivars based on the composition of the total volatiles and individual volatile compounds. Thus, the present work aims to discriminate 11 Malaysian durian cultivars based on their volatile compositions using multivariate data analysis. Sulfur-containing volatiles are the major volatiles in D175 (Udang Merah), D88 (Darling), D13 (Golden Bun), DXO (D24 Special), D17 (Green Bamboo), D2 (Dato Nina), and D168 (Hajah Hasmah) durian cultivars, while esters are predominant in D99 (Kop Kecil), D24 (Bukit Merah), and D160 (Musang Queen) durian cultivars. D197 (Musang King) cultivar has an almost equal composition of sulfur-containing volatiles and esters. In the ester predominated volatile durian oil, ethyl 2-methylbutanoate and propyl 2-methylbutanoate are the major volatile compounds, while the durian cultivars with predominant sulfur-containing volatiles mainly contain diethyl disulfide, diethyl trisulfide, and 3,5-dimethyl-1,2,4-trithiolane. The durian cultivars were clustered into 8 clusters using principal component analysis, with 3 clusters consisting of 2 cultivars, and with the remaining cultivars clustered individually. The highly sought-after durian cultivars, D160 and D197, were clustered into one. Hierarchal clustering analysis identified the distinct compounds which discriminate every durian cultivar.

Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China’s Consumer Market

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R2 value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.