Spin-Enhanced Lateral Flow Immunoassay for High-Sensitivity Detection of Nonstructural Protein NS1 Serotypes of the Dengue Virus.

Dengue fever is a global mosquito-borne viral infectious disease that has, in recent years, rapidly spread to almost all regions of the world. Lack of vaccination and directed treatment makes detection at the infection's early stages extremely important for disease prevention and clinical care. In this paper, we developed a rapid and highly sensitive dengue detection tool using a novel platform of diagnosis, called spin-enhanced lateral flow immunoassay (SELFIA) with a fluorescent nanodiamond (FND) as a reporter. Taking advantage of the unique magneto-optical properties of negatively charged nitrogen-vacancy centers in the FND, the SELFIA platform utilizes alternating electromagnetic fields to modulate signals from FND's fluorescence to provide sensitive and specific results. With sandwich SELFIA, we could efficiently detect all four dengue non-structural protein (NS1) serotypes (DV1, DV2, DV3, and DV4). The lowest detection concentration of the dengue NS1 antigens varied from 0.1 to 1.3 ng/mL, which is among the lowest limits of detection to date. The FND-based SELFIA technique is up to 500 and 5000 times more sensitive than carbon black and conventional gold nanoparticles, respectively. By using different anti-NS1 antibodies, we could differentiate the NS1 antigen serotypes contained in the tested samples via three simultaneous assays. Proposed SELFIA allows for both qualitative and quantitative differentiation between different NS1 protein serotypes, which will assist in the development of a highly sensitive and specific detection platform for dengue screening that has the potential to detect the disease at its early stages, especially in high-risk and limited-resource areas.

An Extensive Examination of Discovering 5-Methylcytosine Sites in Genome-Wide DNA Promoters Using Machine Learning Based Approaches.

It is well-known that the major reason for the rapid proliferation of cancer cells are the hypomethylation of the whole cancer genome and the hypermethylation of the promoter of particular tumor suppressor genes. Locating 5-methylcytosine (5mC) sites in promoters is therefore a crucial step in further understanding of the relationship between promoter methylation and the regulation of mRNA gene expression. High throughput identification of DNA 5mC in wet lab is still time-consuming and labor-extensive. Thus, finding the 5mC site of genome-wide DNA promoters is still an important task. We compared the effectiveness of the most popular and strong machine learning techniques namely XGBoost, Random Forest, Deep Forest, and Deep Feedforward Neural Network in predicting the 5mC sites of genome-wide DNA promoters. A feature extraction method based on k-mers embeddings learned from a language model were also applied. Overall, the performance of all the surveyed models surpassed deep learning models of the latest studies on the same dataset employing other encoding scheme. Furthermore, the best model achieved AUC scores of 0.962 on both cross-validation and independent test data. We concluded that our approach was efficient for identifying 5mC sites of promoters with high performance.

Nanodiamond Solid-Phase Extraction and Triton X-114 Cloud Point Separation for Robust Fractionation and Shotgun Proteomics Analysis of the Human Serum Proteome.

Human serum is one of the most attractive specimens in biomarker research. However, its overcomplicated properties have hindered the analysis of low-abundance proteins by conventional mass spectrometry techniques. This work proposes an innovative strategy for utilizing nanodiamonds (NDs) in combination with Triton X-114 protein extraction to fractionate the crude serum to six pH-tuned fractions, simplifying the overall proteome and facilitating protein profiling with high efficiency. A total of 663 proteins are identified and evenly distributed among the fractions along with 39 FDA-approved biomarkers─a remarkable increase from the 230 proteins found in unfractionated crude serum. In the low-abundance protein section, 88 proteins with 7 FDA-approved biomarkers are detected─a marked increase from the 15 proteins (2 biomarkers) observed in the untreated sample. Notably, fractions at pH 11, derived from the aqueous phase of detergent separation, suggest potential applications in rapid and robust serum proteome analysis. Notably, by outlining the excellent properties of NDs for proteomic research, this work suggests a promising extraction protocol utilizing the great compatibility of NDs with streamlined serum proteomics and identifies potential avenues for future developments. Finally, we believe that this work not just improves shotgun proteomics but also opens up studies on the interaction between NDs and the human proteome. Data are available via ProteomeXchange with the identifier PXD029710.

Recent Advances in Novel Lateral Flow Technologies for Detection of COVID-19.

The development of reliable and robust diagnostic tests is one of the most efficient methods to limit the spread of coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, most laboratory diagnostics for COVID-19, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcriptase-polymerase chain reaction (RT-PCR), are expensive, time-consuming, and require highly trained professional operators. On the other hand, the lateral flow immunoassay (LFIA) is a simpler, cheaper device that can be operated by unskilled personnel easily. Unfortunately, the current technique has some limitations, mainly inaccuracy in detection. This review article aims to highlight recent advances in novel lateral flow technologies for detecting SARS-CoV-2 as well as innovative approaches to achieve highly sensitive and specific point-of-care testing. Lastly, we discuss future perspectives on how smartphones and Artificial Intelligence (AI) can be integrated to revolutionize disease detection as well as disease control and surveillance.

Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels.

Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions. This study used a computational method to predict N-linked glycosylation sites, the most common type, in ion channel proteins. From segments of ion channel proteins centered around N-linked glycosylation sites, the amino acid embedding vectors of each residue were concatenated to create features for prediction. We experimented with two different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other with a large dataset composed of more than one million protein sequences. The latter model stemmed from the idea of transfer learning technique and emerged as a more efficient feature extractor. Our best model was obtained from this transfer learning approach and a hyperparameter tuning process with a random search on 5-fold cross-validation data. It achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4%, 92.8%, 98.6%, and 0.726, respectively. Corresponding scores on an independent test were 92.9%, 92.2%, 99%, and 0.717. These results outperform the position-specific scoring matrix features that are predominantly employed in post-translational modification site predictions. Furthermore, compared to N-GlyDE, GlycoEP, SPRINT-Gly, the most recent N-linked glycosylation site predictors, our model yields higher scores on the above 4 metrics, thus further demonstrating the efficiency of our approach.

Efficient Isolation and Long-term Red Fluorescent Nanodia-mond Labeling of Umbilical Cord Mesenchymal Stem Cells for the Effective Differentiation into Hepatocyte-like Cells

Abstract Fluorescent nanodiamond (FND) has been used for long-term cell labeling and in vivo cell tracking because they have good at photostability and biocompatibility. In this study, we evaluate the effect of fluorescent nanodiamond labeling on in vitro culture and differentiation of human umbilical cord mesenchymal stem cells (hUCMSCs) into hepatocyte-like cells (HLCs). For hepatic differentiation of hUCMSCs, cells were induced with human hepatocyte growth factor, nicotinamide and Dexamethasone. FND was supplied in two experimental groups with 20 μg/mL and 100 μg/mL in 2 hours. The cell was assessed for FND uptake by laser scan microscopy and flow cytometry methods. The effect of FND on hUCMSCs was evaluated by the cell viability and growth assays as well as the differentiation throughout of morphology alterations or gene expression of anfa-fetoprotein, albumin, and hepatocyte nuclear factor 4α. The results showed that the labeling of hUCMSCs is efficient and easy and there was significant cellular uptake of FND. We did not observe any negative impacts of FND to the cell viability and growth. FND can be utilized for the long-term labeling and tracking of hUCSCs and HLCs in vivo studies.