Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K + , Na + and Ca 2+ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% ( R 2 = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors’ algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% ( R 2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.

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Cytomegalovirus disease is a frequent infection of the early posttransplant period. Late-onset disease is common in patients receiving chemoprophylaxis. Here, we report 2 cases of cytomegalovirus gastritis, occurring late in the posttransplant course, with undetectable viral load in the patients' blood, in patients not receiving valganciclovir prophylaxis and not having the classic risk factors described herein. Patients presented with chronic diarrhea and gastric bleeding late in the posttransplant course. Diagnoses were based on endoscopic gastric biopsy. Patients were treated successfully with ganciclovir/valganciclovir. Because preventive strategies are unclear in late-onset disease in persons at low risk, early diagnosis and treatment are crucial.

BackgroundThe ability to engineer zinc finger proteins binding to a DNA sequence of choice is essential for targeted genome editing to be possible. Experimental techniques and molecular docking have been successful in predicting protein-DNA interactions, however, they are highly time and resource intensive. Here, we present a novel algorithm designed for high throughput prediction of optimal zinc finger protein for 9 bp DNA sequences of choice. In accordance with the principles of information theory, a subset identified by using K-means clustering was used as a representative for the space of all possible 9 bp DNA sequences. The modeling and simulation results assuming synergistic mode of binding obtained from this subset were used to train an ensemble micro neural network. Synergistic mode of binding is the closest to the DNA-protein binding seen in nature, and gives much higher quality predictions, while the time and resources increase exponentially in the trade off. Our algorithm is inspired from an ensemble machine learning approach, and incorporates the predictions made by 100 parallel neural networks, each with a different hidden layer architecture designed to pick up different features from the training dataset to predict optimal zinc finger proteins for any 9 bp target DNA.ResultsThe model gave an accuracy of an average 83% sequence identity for the testing dataset. The BLAST e-value are well within the statistical confidence interval of E-05 for 100% of the testing samples. The geometric mean and median value for the BLAST e-values were found to be 1.70E-12 and 7.00E-12 respectively. For final validation of approach, we compared our predictions against optimal ZFPs reported in literature for a set of experimentally studied DNA sequences. The accuracy, as measured by the average string identity between our predictions and the optimal zinc finger protein reported in literature for a 9 bp DNA target was found to be as high as 81% for DNA targets with a consensus sequence GCNGNNGCN reported in literature. Moreover, the average string identity of our predictions for a catalogue of over 100 9 bp DNA for which the optimal zinc finger protein has been reported in literature was found to be 71%.ConclusionsValidation with experimental data shows that our tool is capable of domain adaptation and thus scales well to datasets other than the training set with high accuracy. As synergistic binding comes the closest to the ideal mode of binding, our algorithm predicts biologically relevant results in sync with the experimental data present in the literature. While there have been disjointed attempts to approach this problem synergistically reported in literature, there is no work covering the whole sample space. Our algorithm allows designing zinc finger proteins for DNA targets of the user’s choice, opening up new frontiers in the field of targeted genome editing. This algorithm is also available as an easy to use web server, ZifNN, at http://web.iitd.ac.in/~sundar/ZifNN/.Electronic supplementary materialThe online ver...

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