Deep networks and network representation in bioinformatics

Nonenzymatic Flexible Wearable Biosensors for Vitamin C Monitoring in Sweat

Nutritional status monitoring plays an important role in the maintenance of human health and disease prevention. Monitoring the intake of vitamins can support the improvement of diet behavior. In this work, a polyaniline (PANI) film-based nonenzymatic electrochemical sensor was prepared to track the vitamin C level in sweat. The PANI film was modified with organic acids (ethylformic acid, malic acid, tartaric acid, and phytic acid). The phytic acid-modified PANI film based on sensor has a wide detection range (0.5-500 μmol·L^-1), high sensitivity (665.5 and 326.2 μA·(mmol·L^-1)^-1·cm^-2), and low detection limit (0.17 μmol·L^-1) toward vitamin C in sweat. The phytate enhances the band transport between PANI chains, which increases the electrical conductivity of the film to improve the electrochemical properties of the sensor. In addition, we monitored changes of vitamin C levels in human body after taking vitamin C pills by detecting sweat or saliva. The ability to track the pharmacological profile demonstrates the potential of PANI film-based sensors for applications in personalized nutritional intake and tracking. And a simple and portable vitamin C detection system was developed to improve the practicability of the sensor. This work provides an idea for the application of wearable electrochemical sensing devices in nutrition guidance.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

Cellular transcriptional alterations of peripheral blood in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD.

METHODS

To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion.

RESULTS

We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B. Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains.

CONCLUSIONS

We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology.