Profiling analysis of long non-coding RNAs in early postnatal mouse hearts

Mammalian cardiomyocytes undergo a critical hyperplastic-to-hypertrophic growth transition at early postnatal age, which is important in establishing normal physiological function of postnatal hearts. In the current study, we intended to explore the role of long non-coding (lnc) RNAs in this transitional stage. We analyzed lncRNA expression profiles in mouse hearts at postnatal day (P) 1, P7 and P28 via microarray. We identified 1,146 differentially expressed lncRNAs with more than 2.0-fold change when compared the expression profiles of P1 to P7, P1 to P28, and P7 to P28. The neighboring genes of these differentially expressed lncRNAs were mainly involved in DNA replication-associated biological processes. We were particularly interested in one novel cardiac-enriched lncRNA, ENSMUST00000117266, whose expression was dramatically down-regulated from P1 to P28 and was also sensitive to hypoxia, paraquat, and myocardial infarction. Knockdown ENSMUST00000117266 led to a significant increase of neonatal mouse cardiomyocytes in G0/G1 phase and reduction in G2/M phase, suggesting that ENSMUST00000117266 is involved in regulating cardiomyocyte proliferative activity and is likely associated with hyperplastic-to-hypertrophic growth transition. In conclusion, our data have identified a large group of lncRNAs presented in the early postnatal mouse heart. Some of these lncRNAs may have important functions in cardiac hyperplastic-to-hypertrophic growth transition

Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale for Diagnosing Alzheimer’s Disease

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases

Ocorrência e alternância de cytorhabdovirus em trigo no norte da China

Northern cereal mosaic cytorhabdovirus (NCMV) and Barley yellow striate mosaic cytorhabdovirus (BYSMV) are two of the most important viral pathogens of wheat. Northern China is the main wheat-producing region in the country. Wheat growing regions pertaining to four provinces, located in northern China, were surveyed for occurrence of NCMV and BYSMV during the growing seasons of the years 2010 and 2016. Wheat leaf samples were collected randomly from symptomatic plants displaying stunting, chlorotic stripes or mosaic. Roughly 73 samples were collected in the year 2010 from 13 fields, and 154 samples were collected in 2016 from 41 fields. Samples were tested for the presence of NCMV or BYSMV using multiplex reverse transcription-polymerase chain reaction (mRT-PCR). The results suggested that BYSMV (49.32% in 2010, 82.47% in 2016) is gradually replacing NCMV (87.67% in 2010, 13.64% in 2016) and becoming the main cytorhabdovirus in different wheat growing regions in northern China.O cytorhabdovirus do mosaico do cereal do norte (NCMV) e o cytorhabdovirus do mosaico estriado amarelo da cevada (BYSMV) são dois dos mais importantes patógenos virais do trigo. O norte da China é a principalregião produtora de trigo do país. As regiões produtoras de trigo pertencentes a quatro províncias do norte da China foram pesquisadas quanto à ocorrência de NCMV e BYSMV durante as safras dos anos de 2010 e 2016. Amostras de folhas de trigo foram coletadas aleatoriamente de plantas sintomáticas, exibindo listras ou mosaico clorótico com baixo crescimento. Cerca de 73 amostras foram coletadas no ano de 2010 a partir de 13 campos, e 154 amostras foram coletadas em 2016 de 41 campos. As amostras foram testadas quanto à presença de NCMV ou BYSMV usando reação em cadeia da polimerase de transcrição reversa multiplex (mRT-PCR). Os resultados sugerem que o BYSMV (49,32% em 2010, 82,47% em 2016) está gradualmente substituindo o NCMV (87,67% em 2010, 13,64% em 2016) e se tornando o principal cytorhabdovirus em diferentes regiões produtoras de trigo no norte da China.&nbsp

Effects of different application ratios of biochar-organic compound fertilizers and chemical fertilizers on soil nutrition content and yield of maize

Overuse of traditional chemical fertilizers may result in environmental pollution and a decrease in the quality of farm produce. By contrast, applying biochar-organic compound fertilizers can enhance soil structure, increase soil fertility, and mitigate pollution levels. This study explores the intricate mechanisms of the combined application of biochar-organic compound fertilizers and chemical fertilizers on soil chemical properties and corn growth. The aim is to elucidate the theoretical foundations supporting the widespread adoption of biochar-organic compound fertilizers. A total of 6 treatments were set up, among which the CK treatment did not apply fertilizer, the CF treatment used bovine excrement organic fertilizer combined with chemical fertilizer, the T1 to T4 treatments used biochar-organic compound fertilizers and replaced 40%, 60%, 80%, and 100% bovine excrement organic fertilizer combined with chemical fertilizer. The results showed that applying biochar-organic compound fertilizers enhanced the slow-release properties of soil available nutrients, increased corn yield, and improved grain quality. Notably, when biochar-organic compound fertilizers were employed instead of 100% bovine excrement organic fertilizer, the yield surpassed that of other treatments, exhibiting a remarkable 9.30% increase compared to the CF treatment. Through comprehensive analysis, it was determined that using biochar-organic compound fertilizer to replace 60% of bovine excrement organic fertilizer is a scheme that can balance both fertilizer efficacy and cost and is recommended to farmers. This research can contribute to promoting the green transformation of agriculture and help achieve the goal of "carbon neutrality"

Translational computerized clinical decision support systems for Alzheimer's disease: A systematic review

Background Alzheimer's disease (AD), marked by progressive memory loss and cognitive decline, poses diagnostic challenges due to its multifactorial nature. Therefore, researchers are increasingly leveraging artificial intelligence and data-driven approaches to develop computerized clinical decision support systems (CCDSS), aiming to enhance early detection, improve treatment, and slow disease progression. Objective This study seeks to conduct a systematic review of the most recently developed AD-CCDSS, delving into their progress and the challenges to guide future development and implementation of CCDSS for AD-related decision-making and intervention strategies. Methods We follow the PRISMA 2020 guideline to search for articles published within the past seven years across PubMed, ScienceDirect, IEEE Xplore Digital Library, Web of Science, and Scopus, with Google Scholar as a supplementary source. Key components are then extracted from the selected studies for qualitative analysis, including data modalities, computational modeling approaches, system explainability and interpretability, research priorities, and graphical user interfaces designed for non-technical stakeholders. Results After searching and removing duplicates, we meticulously selected 55 studies. After reviewing key components of CCDSS, we highlight advancements and potential clinical applications, demonstrating their promise in enhancing decision support. However, despite growing attention to explainability in AD-CCDSS, its clinical applicability remains limited. Moreover, challenges such as multi-center system interoperability and data security remain underexplored, hindering real-world implementation. Conclusions This study analyzes recent translational AD-CCDSS, identifying key challenges in advancing CCDSS for clinical applications. It offers insights for researchers to enhance CCDSS development and facilitate their integration into clinical practice

Regulation of Exosomes in the Pathogenesis of Breast Cancer

Extracellular vesicles (EVs) are a heterogeneous group of endogenous nanoscale vesicles that are secreted by various cell types. Based on their biogenesis and size distribution, EVs can be broadly classified as exosomes and microvesicles. Exosomes are enveloped by lipid bilayers with a size of 30–150 nm in diameter, which contain diverse biomolecules, including lipids, proteins and nucleic acids. Exosomes transport their bioactive cargoes from original cells to recipient cells, thus play crucial roles in mediating intercellular communication. Breast cancer is the most common malignancy among women and remains a major health problem worldwide, diagnostic strategies and therapies aimed at breast cancer are still limited. Growing evidence shows that exosomes are involved in the pathogenesis of breast cancer, including tumorigenesis, invasion and metastasis. Here, we provide a straightforward overview of exosomes and highlight the role of exosomes in the pathogenesis of breast cancer, moreover, we discuss the potential application of exosomes as biomarkers and therapeutic tools in breast cancer diagnostics and therapeutics

Probing into the Interaction of Nicotine and Bovine Submaxillary Mucin: NMR, Fluorescence, and FTIR Approaches

Nicotine, the important component of cigarette products, may have an impact on the oral environment after inhalation. The research of interaction between nicotine and bovine submaxillary mucin (BSM) contributes to understand the binding mechanism of nicotine and BSM, and the effects of nicotine on the structure and function of the mucin. NMR data demonstrated that the interaction between nicotine and BSM did exist, and it was pyrrolidyl ring of nicotine playing the major role in the binding. The quenching mechanisms of nicotine and BSM in different pH were different: for acidic environment, the quenching was dynamic; while it became static in the alkaline circumstance. Synchronous fluorescence spectra indicated that nicotine had effect on the microenvironment of the Trp rather than Tyr residue. Meanwhile, the impact of nicotine on the conformation of BSM was also confirmed by 3D fluorescence and FTIR spectra

A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers

Introduction: Inadequate primary care infrastructure and training in China and misconceptions about aging lead to high mis−/under-diagnoses and serious time delays for dementia patients, imposing significant burdens on family members and medical carers.Main body: A flowchart integrating rural and urban areas of China dementia care pathway is proposed, especially spotting the obstacles of mis/under- diagnoses and time delays that can be alleviated by data-driven computational strategies. Artificial intelligence (AI) and machine learning models built on dementia data are succinctly reviewed in terms of the roadmap of dementia care from home, community to hospital settings. Challenges and corresponding recommendations to clinical transformation are then reported from the viewpoint of diverse dementia data integrity and accessibility, as well as models’ interpretability, reliability, and transparency.Discussion: Dementia cohort study along with developing a center-crossed dementia data platform in China should be strongly encouraged, also data should be publicly accessible where appropriate. Only be doing so can the challenges be overcome and can AI-enabled dementia research be enhanced, leading to an optimized pathway of dementia care in China. Future policy- guided cooperation between researchers and multi-stakeholders are urgently called for dementia 4E (early-screening, early-assessment, early-diagnosis, and early-intervention)

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD