Rapid and multi-target genotyping of Helicobacter pylori with digital microfluidics.

Helicobacter pylori (H. pylori) infection correlates closely with gastric diseases such as gastritis, ulcers, and cancer, influencing more than half of the world's population. Establishing a rapid, precise, and automated platform for H. pylori diagnosis is an urgent clinical need and would significantly benefit therapeutic intervention. Recombinase polymerase amplification (RPA)-CRISPR recently emerged as a promising molecular diagnostic assay due to its rapid detection capability, high specificity, and mild reaction conditions. In this work, we adapted the RPA-CRISPR assay on a digital microfluidics (DMF) system for automated H. pylori detection and genotyping. The system can achieve multi-target parallel detection of H. pylori nucleotide conservative genes (ureB) and virulence genes (cagA and vacA) across different samples within 30 min, exhibiting a detection limit of 10 copies/rxn and no false positives. We further conducted tests on 80 clinical saliva samples and compared the results with those derived from real-time quantitative polymerase chain reaction, demonstrating 100% diagnostic sensitivity and specificity for the RPA-CRISPR/DMF method. By automating the assay process on a single chip, the DMF system can significantly reduce the usage of reagents and samples, minimize the cross-contamination effect, and shorten the reaction time, with the additional benefit of losing the chance of experiment failure/inconsistency due to manual operations. The DMF system together with the RPA-CRISPR assay can be used for early detection and genotyping of H. pylori with high sensitivity and specificity, and has the potential to become a universal molecular diagnostic platform.

Discrepancies in breast cancer guideline recommendations despite similar Cochrane systematic review conclusions.

AIM: This study aims to describe the citation patterns of Cochrane systematic reviews (CSR) in guidelines for managing breast cancer. METHODS: We searched for systematic reviews on breast cancer in The Cochrane Library from the date of inception to November 15, 2023, and identified guidelines that cited them. We described how systematic reviews were cited by the guidelines in each database and each year. Additionally, we presented the relationships between the conclusions of the systematic reviews and guideline recommendations and compared the consistency of the recommendations on the same topic across different guidelines. RESULTS: A total of 64 systematic reviews and 228 guidelines were included in this study. The average number of the 64 systematic reviews cited by the guidelines was 5.91. We found that the guideline recommendations were irrelevant or inconsistent with the conclusions of the systematic reviews in 56 (38.36%) cited entries. We grouped recommendations on the same topic across different guidelines into one group, of which only 5 groups (15.15%) had completely consistent recommendations, and the other 28 groups (84.85%) had inconsistent recommendations. CONCLUSION: The average number of citations for CSR on breast cancer in the guidelines was 5.91. There were also situations in which the guideline recommendations were inconsistent with the conclusions of the included systematic reviews, and recommendations on the same topic across different guidelines were inconsistent.

[Ferroptosis inducer IKE ameliorate pulmonary fibrosis in collagen-induced arthritis (CIA) mice via decreasing the expression of IL-6, CCL5 and CXCL9].

Objective To investigate the impact of imidazole ketone erastin (IKE), a ferroptosis inducer, on pulmonary fibrosis progression in mice with collagen-induced arthritis (CIA), and to understand its potential mechanism. Methods Chick type II collagen emulsified in complete Freund's adjuvant (CFA) was injected into DBA/1 mice, aged 8 to 10 weeks, to induce CIA. Fourteen days later, type II collagen emulsified in incomplete Freund's adjuvant (IFA) was administered to the mice. The mice were randomly divided into a control group, a CIA group and a CIA combined IKE group. The development of arthritis was monitored by evaluating the arthritis scores every two days until day 39 and then the mice were sacrificed for organ collection. The histopathological changes of joints were evaluated by HE staining, Safranin O-fast green staining and toluidine blue staining. The histopathological changes of organs including heart, liver, spleen, lung, and kidney were evaluated by HE staining, and Masson's trichrome staining was used to assess pulmonary fibrosis. The expression levels of smooth muscle actin α (α-SMA), fibroblast activating protein α (FAPα), transforming growth factor ß (TGF-ß), type I collagen (Col1), interleukin 1(IL-1), IL-6, IL-17 and tumor necrosis factor α (TNF-α) were detected by immunohistochemical staining. The expression levels of serum cytokines including IL-17α, IL-17F, TGF-ß1, ITG-ß6, TNF receptor superfamily menber 11B(TNFRSF11B), TNFRSF12A, IL-6, IL-1α, IL-1ß, IL-10, TNF-α, CCL5, CCL2, CXCL9, CXCL1, NADK, EPO, CSF2, TGF-α, CCL20 and CCL3 in serum were detected by Olink mouse exploratory panel. Results Histological staining in the CIA mice administered with IKE model demonstrated that IKE treatment reduced bone absorption and the degree of synovial inflammation when active inflammation was present. CIA mice administered with IKE showed lower expression levels of α-SMA, FAPα, TGF-ß, Col1, IL-1, IL-6, IL-17 and TNF-α, according to the immunohistochemical staining of the lung. In addition, the expression levels of CCL5, CXCL9 and IL-6 were also decreased in serum of CIA mice treated with IKE. Conclusion IKE not only ameliorates joint inflammation and bone damage, but also alleviates the inflammation and the progression of pulmonary fibrosis in CIA mice.

iPiDA-SWGCN: Identification of piRNA-disease associations based on Supplementarily Weighted Graph Convolutional Network.

Accurately identifying potential piRNA-disease associations is of great importance in uncovering the pathogenesis of diseases. Recently, several machine-learning-based methods have been proposed for piRNA-disease association detection. However, they are suffering from the high sparsity of piRNA-disease association network and the Boolean representation of piRNA-disease associations ignoring the confidence coefficients. In this study, we propose a supplementarily weighted strategy to solve these disadvantages. Combined with Graph Convolutional Networks (GCNs), a novel predictor called iPiDA-SWGCN is proposed for piRNA-disease association prediction. There are three main contributions of iPiDA-SWGCN: (i) Potential piRNA-disease associations are preliminarily supplemented in the sparse piRNA-disease network by integrating various basic predictors to enrich network structure information. (ii) The original Boolean piRNA-disease associations are assigned with different relevance confidence to learn node representations from neighbour nodes in varying degrees. (iii) The experimental results show that iPiDA-SWGCN achieves the best performance compared with the other state-of-the-art methods, and can predict new piRNA-disease associations.

iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network.

MOTIVATION: Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various diseases. Accurately identifying the associations between piRNAs and diseases is important for diagnosing and prognosticating diseases. Although some computational methods have been proposed to detect piRNA-disease associations, it is challenging for these methods to effectively capture nonlinear and complex relationships between piRNAs and diseases because of the limited training data and insufficient association representation. RESULTS: With the growth of piRNA-disease association data, it is possible to design a more complex machine learning method to solve this problem. In this study, we propose a computational method called iPiDA-GCN for piRNA-disease association identification based on graph convolutional networks (GCNs). The iPiDA-GCN predictor constructs the graphs based on piRNA sequence information, disease semantic information and known piRNA-disease associations. Two GCNs (Asso-GCN and Sim-GCN) are used to extract the features of both piRNAs and diseases by capturing the association patterns from piRNA-disease interaction network and two similarity networks. GCNs can capture complex network structure information from these networks, and learn discriminative features. Finally, the full connection networks and inner production are utilized as the output module to predict piRNA-disease association scores. Experimental results demonstrate that iPiDA-GCN achieves better performance than the other state-of-the-art methods, benefitted from the discriminative features extracted by Asso-GCN and Sim-GCN. The iPiDA-GCN predictor is able to detect new piRNA-disease associations to reveal the potential pathogenesis at the RNA level. The data and source code are available at http://bliulab.net/iPiDA-GCN/.

iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank.

Piwi-interacting RNAs (piRNAs) are regarded as drug targets and biomarkers for the diagnosis and therapy of diseases. However, biological experiments cost substantial time and resources, and the existing computational methods only focus on identifying missing associations between known piRNAs and diseases. With the fast development of biological experiments, more and more piRNAs are detected. Therefore, the identification of piRNA-disease associations of newly detected piRNAs has significant theoretical value and practical significance on pathogenesis of diseases. In this study, the iPiDA-LTR predictor is proposed to identify associations between piRNAs and diseases based on Learning to Rank. The iPiDA-LTR predictor not only identifies the missing associations between known piRNAs and diseases, but also detects diseases associated with newly detected piRNAs. Experimental results demonstrate that iPiDA-LTR effectively predicts piRNA-disease associations outperforming the other related methods.