SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Long-term efficacy of a recombinant hepatitis E vaccine in adults: 10-year results from a randomised, double-blind, placebo-controlled, phase 3 trial.

BACKGROUND: Hepatitis E virus (HEV) is a frequently overlooked causative agent of acute hepatitis. Evaluating the long-term durability of hepatitis E vaccine efficacy holds crucial importance. METHODS: This study was an extension to a randomised, double-blind, placebo-controlled, phase-3 clinical trial of the hepatitis E vaccine conducted in Dontai County, Jiangsu, China. Participants were recruited from 11 townships in Dongtai County. In the initial trial, a total of 112 604 healthy adults aged 16-65 years were enrolled, stratified according to age and sex, and randomly assigned in a 1:1 ratio to receive three doses of hepatitis E vaccine or placebo intramuscularly at month 0, month 1, and month 6. A sensitive hepatitis E surveillance system including 205 clinical sentinels, covering the entire study region, was established and maintained for 10 years after vaccination. The primary outcome was the per-protocol efficacy of hepatitis E virus vaccine to prevent confirmed hepatitis E occurring at least 30 days after administration of the third dose. Throughout the study, the participants, site investigators, and laboratory staff remained blinded to the treatment assignments. This study is registered with ClinicalTrials.gov (NCT01014845). FINDINGS: During the 10-year study period from Aug 22, 2007, to Oct 31, 2017, 90 people with hepatitis E were identified; 13 in the vaccine group (0·2 per 10 000 person-years) and 77 in the placebo group (1·4 per 10 000 person-years), corresponding to a vaccine efficacy of 83·1% (95% CI 69·4-91·4) in the modified intention-to-treat analysis and 86·6% (73·0 to 94·1) in the per-protocol analysis. In the subsets of participants assessed for immunogenicity persistence, of those who were seronegative at baseline and received three doses of hepatitis E vaccine, 254 (87·3%) of 291 vaccinees in Qindong at the 8·5-year mark and 1270 (73·0%) of 1740 vaccinees in Anfeng at the 7·5-year mark maintained detectable concentrations of antibodies. INTERPRETATION: Immunisation with this hepatitis E vaccine offers durable protection against hepatitis E for up to 10 years, with vaccine-induced antibodies against HEV persisting for at least 8·5 years. FUNDING: National Natural Science Foundation of China, Fujian Provincial Natural Science Foundation, Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, and the Fundamental Research Funds for the Central Universities.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Re-expression of epigenetically silenced PTPRR by histone acetylation sensitizes RAS-mutant lung adenocarcinoma to SHP2 inhibition.

Silenced protein tyrosine phosphatase receptor type R (PTPRR) participates in mitogen-activated protein kinase (MAPK) signaling cascades during the genesis and development of tumors. Rat sarcoma virus (Ras) genes are frequently mutated in lung adenocarcinoma, thereby resulting in hyperactivation of downstream MAPK signaling. However, the molecular mechanism manipulating the regulation and function of PTPRR in RAS-mutant lung adenocarcinoma is not known. Patient records collected from the Cancer Genome Atlas and Gene Expression Omnibus showed that silenced PTPRR was positively correlated with the prognosis. Exogenous expression of PTPRR suppressed the proliferation and migration of lung cancer cells. PTPRR expression and Src homology 2 containing protein tyrosine phosphatase 2 (SHP2) inhibition acted synergistically to control ERK1/2 phosphorylation in RAS-driven lung cancer cells. Chromatin immunoprecipitation assay revealed that HDAC inhibition induced enriched histone acetylation in the promoter region of PTPRR and recovered PTPRR transcription. The combination of the HDAC inhibitor SAHA and SHP2 inhibitor SHP099 suppressed the progression of lung cancer markedly in vitro and in vivo. Therefore, we revealed the epigenetic silencing mechanism of PTPRR and demonstrated that combination therapy targeting HDAC and SHP2 might represent a novel strategy to treat RAS-mutant lung cancer.

Color-Tunable Lead Halide Perovskite Single-Mode Chiral Microlasers with Exceptionally High glum.

Chiral microlasers hold great promise for optoelectronics from integrated photonic devices to high-density quantum information processing. Despite significant progress in lead-halide perovskite emitters, chiral lasing with high dissymmetry factors (glum) has not yet been realized. Here, we demonstrate chiral single-mode microlasers with exceptional stability and tunable emission across the visible range by combining CsPbClxBr3-x perovskite microrods (MRs) with a cholesteric liquid crystal (CLC) layer. The MRs lase via a whispering gallery mode (WGM) microcavity and confer chirality through the encapsulated CLC layer, thus exhibiting circularly polarized lasing with dissymmetry factors reaching 1.62. Importantly, we demonstrate wavelength-tunable high dissymmetry chiral lasers in a broad spectral range by tuning the halide composition and using CLC layers with the desired photonic bandgap (PBG). This facile approach to generate chiral lasing not only is applicable to semiconductor nano- and microcrystals but also paves the way for potential integration into nanoscale photonic devices.

Harnessing Smectic Ordering for Electric-Field-Driven Guided-Growth of Surface Topography in a Liquid Crystal Polymer.

The guided-growth strategy has been widely explored and proved its efficacy in fabricating surface micro/nanostructures in a variety of systems. However, soft materials like polymers are much less investigated partly due to the lack of strong internal driving mechanisms. Herein, the possibility of utilizing liquid crystal (LC) ordering of smectic liquid crystal polymers (LCPs) to induce guided growth of surface topography during the formation of electrohydrodynamic (EHD) patterns is demonstrated. In a two-stage growth, regular stripes are first found to selectively emerge from the homogeneously aligned region of an initially flat LCP film, and then extend neatly along the normal direction of the boundary line between homogeneous and homeotropic alignments. The stripes can maintain their directions for quite a distance before deviating. Coupled with the advanced tools for controlling LC alignment, intricate surface topographies can be produced in LCP films starting from relatively simple designs. The regularity of grown pattern is determined by the LC ordering of the polymer material, and influenced by conditions of EHD growth. The proposed approach provides new opportunities to employ LCPs in optical and electrical applications.

A Self-Assembled 3D/0D Quasi-Core-Shell Structure as Internal Encapsulation Layer for Stable and Efficient FAPbI3 Perovskite Solar Cells and Modules.

FAPbI3 perovskites have garnered considerable interest owing to their outstanding thermal stability, along with near-theoretical bandgap and efficiency. However, their inherent phase instability presents a substantial challenge to the long-term stability of devices. Herein, this issue through a dual-strategy of self-assembly 3D/0D quasi-core-shell structure is tackled as an internal encapsulation layer, and in situ introduction of excess PbI2 for surface and grain boundary defects passivating, therefore preventing moisture intrusion into FAPbI3 perovskite films. By utilizing this method alone, not only enhances the stability of the FAPbI3 film but also effectively passivates defects and minimizes non-radiative recombination, ultimately yielding a champion device efficiency of 23.23%. Furthermore, the devices own better moisture resistance, exhibiting a T80 lifetime exceeding 3500 h at 40% relative humidity (RH). Meanwhile, a 19.51% PCE of mini-module (5 × 5 cm2) is demonstrated. This research offers valuable insights and directions for the advancement of stable and highly efficient FAPbI3 perovskite solar cells.

A long-term experiment reveals no trade-off between seed persistence and seedling emergence.

Information on seed persistence and seedling emergence from the soil seed bank is critical for understanding species coexistence and predicting community dynamics. However, quantifying seed persistence in the soil is challenging; thus, its association with other life-history traits is poorly known on a broad scale. Using germination phenology for 349 species in a 42-yr experiment, we quantified the persistence-emergence correlations and their associations with intrinsic regeneration traits using Bayesian phylogenetic multilevel models. We showed no trade-off between seed persistence and seedling emergence. Physically dormant seeds were more persistent but exhibited lower emergence than nondormant seeds. Monocarpic species had both higher persistence and emergence than polycarpic species. Seed mass posed a marginal proxy for persistence, while emergence almost doubled from the smallest to the largest seeds. This study challenges the traditional assumption and is the first demonstration of noncorrelation between persistence and emergence, probably owing to the complexity of regenerative strategies. Species with short persistence and low emergence would be the most vulnerable for in situ conservation. Our analyses of this unique, long-term dataset provide a strong incentive for further experimental studies and a rich data resource for future syntheses.

Association between adult food insecurity and mortality among adults aged 20-79 years with diabetes: A population-based retrospective cohort study.

AIMS: There is limited research on the relationship between food insecurity and mortality among individuals with diabetes. This study aims to investigate the impact of food insecurity on all-cause and cause-specific mortality in adults with diabetes. RESEARCH DESIGN AND METHODS: This study included 5749 adults with diabetes from the National Health and Nutrition Examination Survey cycles 2003-2018 and followed up until 31 December 2019. Food insecurity was measured by the Food Security Survey Module. Cox proportional hazard models were employed to estimate hazard ratios (HRs) and 95% confidence intervals for both all-cause mortality and cause-specific mortality. RESULTS: The weighted prevalence of full food security, marginal food security, low food security, and very low food security was 70.8%, 11.0%, 10.4%, and 7.8%, respectively. Food insecurity demonstrated a significant correlation with diminished diet quality and reduced consumption of healthy foods. Over the course of 42,272.0 person-years of follow-up, we documented 1091 deaths, of which 370 were attributed to cardiovascular disease and 180 to cancer. After adjusting for multiple variables, food insecurity scores were significantly and linearly associated with increased all-cause mortality. Comparing to full food security, participants experiencing very low food security had a multivariate-adjusted HR of 1.48 (1.12, 1.95) for all-cause mortality (ptrend = 0.010). CONCLUSIONS: Food insecurity was associated with increased all-cause mortality and compromised diet quality, especially in individuals experiencing very low food security. Future strategies may necessitate the monitoring of and interventions for food insecurity among individuals with diabetes.