PseU-KeMRF: A novel method for identifying RNA pseudouridine sites.

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models. On the three species' training datasets, the testing accuracy of PseU-KeMRF was 0.66%, 3.66%, and 2.76% higher, respectively, than the best available methods. Moreover, PseU-KeMRF's accuracy on independent testing datasets was 15.15% and 11.0% higher, respectively, than the best available methods. The above results can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

CircRNA identification and feature interpretability analysis.

BACKGROUND: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. RESULTS: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. CONCLUSIONS: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations.

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.

A new method for handling heterogeneous data in bioinformatics.

Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.

Bacterial and Fungal Biocontrol Agents for Plant Disease Protection: Journey from Lab to Field, Current Status, Challenges, and Global Perspectives.

Plants are constantly exposed to various phytopathogens such as fungi, Oomycetes, nematodes, bacteria, and viruses. These pathogens can significantly reduce the productivity of important crops worldwide, with annual crop yield losses ranging from 20% to 40% caused by various pathogenic diseases. While the use of chemical pesticides has been effective at controlling multiple diseases in major crops, excessive use of synthetic chemicals has detrimental effects on the environment and human health, which discourages pesticide application in the agriculture sector. As a result, researchers worldwide have shifted their focus towards alternative eco-friendly strategies to prevent plant diseases. Biocontrol of phytopathogens is a less toxic and safer method that reduces the severity of various crop diseases. A variety of biological control agents (BCAs) are available for use, but further research is needed to identify potential microbes and their natural products with a broad-spectrum antagonistic activity to control crop diseases. This review aims to highlight the importance of biocontrol strategies for managing crop diseases. Furthermore, the role of beneficial microbes in controlling plant diseases and the current status of their biocontrol mechanisms will be summarized. The review will also cover the challenges and the need for the future development of biocontrol methods to ensure efficient crop disease management for sustainable agriculture.

Special Protein or RNA Molecules Computational Identification.

The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases [...].

scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means.

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://github.com/YuBinLab-QUST/scBKAP/ and https://doi.org/10.24433/CO.4592131.v1.

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.

Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

Association of the occurrence of reticular macular diseases with chronic kidney disease based on estimated glomerular filtration rate / 中华实验眼科杂志

Objective:To analyze the relationship between reticular macular disease (RMD) and chronic kidney disease (CKD) by estimated glomerular filtration rate (eGFR).Methods:A cross-sectional study was conducted.Thirty-six consecutive patients (71 eyes) with subretinal drusenoid deposits in at least one eye in optical coherence tomography (OCT) images were enrolled as the RMD group, and 29 consecutive patients (50 eyes) with age-related macular degeneration (AMD) in at least one eye were identified as the non-RMD group at the First Affiliated Hospital of Guangzhou Medical University from February to September 2019.In the same period, 32 healthy volunteers (64 eyes) without eye disease were included as the healthy control group.Serum was collected to calculate the estimated creatinine clearance (eCcr) and the eGFR.The choroidal thickness of macular fovea and the flow density of choroidal capillary layer were measured by OCT.The related factors of RMD and the correlation between CKD and RMD were analyzed by multiple logistic regression analysis.The relationship between eGFR and choroidal capillary blood flow density and foveal choroidal thickness in RMD patients was analyzed by Pearson linear correlation analysis.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No.2022-50)Results:The eGFR value of the RMD group was (66.40±27.58)ml/(min·1.73 m 2), which was significantly lower than (84.40±20.91)ml/(min·1.73 m 2) of the non-RMD group and (87.64±22.32)ml/(min·1.73 m 2) of the healthy control group (both at P<0.01). eGFR was significantly correlated with the occurrence of RMD ([odds ratio, OR]=0.973, 95%[confidence interval, CI]: 0.954-0.992, P=0.005). Subgroup analysis showed that this correlation was significant in the CKD stage (eGFR<60 ml/[min·1.73 m 2]) ( OR=6.482, 95% CI: 1.543-27.236, P=0.011). The choroidal thickness of the macular fovea in the RMD group was significantly lower than that of the non-RMD grup and healthy control group (both at P<0.01). In the RMD group, no significant correlation was found between the choroidal thickness of the macular fovea and eGFR ( r=0.138, P>0.05), and the flow density of choroidal capillary layer was moderately positively correlated with eGFR ( r=0.457, P<0.05). Conclusions:There is a correlation between the occurrence of CKD and RMD, which may be due to the confounding effect of the systemic microcirculation disorder.

Study on RHD and RHCE gene mRNA based on three generation sequencing technology / 中国输血杂志

【Objective】 To establish RH gene mRNA sequencing method based on nanopores sequencing and to explore the RHD and RHCE mRNA transcripts in D positive and Del individuals. 【Methods】 From March 2021 to May 2022, 5 RhD positive samples and 5 Del samples screened out by hospitals in Chengdu were sent to our laboratory for futher examination. The erythrocytes and buff coat were isolated, then DNA and RNA were extracted.All 10 samples were genotyped by PCR-SSP. After the mRNA was reversely transcribed into cDNA, the full-length mRNA of RHD and RHCE genes were simultaneously amplified by a pair of primers. Sanger sequencing and third-generation sequencing technology based on Nanopore were used to sequence the amplified products, and the types and expressions of different splices of RHD and RHCE gene mRNA transcripts were analyzed. 【Results】 The method established in this study can simultaneously amplify the full length transcripts of RHD and RHCE. Ten different RHD gene mRNA transcripts and nine RHCE gene mRNA transcripts were detected in 10 samples. RHD full-length transcript (RHD-201) can be detected in RhD Del type, but the expression amount was significantly lower than that in RhD positive samples. The expression amount of transcript RHD-207 (Del789) in Del samples was significantly higher than that in RhD positive samples. The transcript RHD-208 (Del8910+ 213) was only detected in RhD Del type individuals, and no significant difference was found between other RHD transcripts and all RHCE transcripts in the two phenotypes. 【Conclusion】 In this study, an analytical method for sequencing full-length transcript isomers of RHD and RHCE mRNA via the third generation was successfully established, and complex alternative splicing patterns were found in RHD and RHCE genes, providing a new method for the study of alternative splicing of blood group gene variants mRNA.

Value of high-flow oxygen therapy after weaning for successful extubation of critical ill patients with mechanical ventilation / 中华危重病急救医学

Objective:To investigate the value of high-flow oxygen therapy after weaning in successful extubation of critically ill patients with mechanical ventilation.Methods:A retrospective study was conducted. The weaned patients who were older than 18 years old and underwent mechanical ventilation for the first time due to cerebrovascular accidents, surgical operations, cardiovascular diseases, and pneumonia admitted to the department of critical care medicine of Zhejiang Hospital from January 2018 to June 2020 were enrolled. Among the patients, 40 cases received high-flow oxygen therapy after weaning, and 37 cases received Venturi combined with the humidifier. The patient's gender, age, primary disease, severity score, duration of mechanical ventilation before weaning, heart rate (HR), blood pressure, pulse oxygen saturation (SpO 2) at 0, 6, 12, 18, and 24 hours after weaning, and pH value, arterial partial pressure of oxygen (PaO 2), arterial partial pressure of carbon dioxide (PaCO 2) at 6, 12, 18, and 24 hours after weaning, the rate of performing mechanical ventilation after weaning, extubation time after weaning, and the rate of reintubation after extubation for 72 hours were collected. Results:There was no significant difference in baseline data such as gender, age, primary disease, severity score, and duration of mechanical ventilation before weaning between the two groups. After weaning, the vital signs of the two groups were stable, and there was no significant difference in HR, systolic blood pressure (SBP), diastolic blood pressure (DBP) or SpO 2 at each time point between the two groups. After weaning, the pH of arterial blood gas analysis in the two groups and the fluctuations of PaO 2 and PaCO 2 in the high-flow group were not obvious. In the Venturi group, PaO 2 gradually decreased after weaning, PaCO 2 increased significantly at 12 hours, and slowly decreased after 12 hours. The PaO 2 from 6 hours and PaCO 2 from 12 hours in the high-flow group were significantly lower than those in the Venturi group, and continued to 24 hours [PaO 2 (mmHg, 1 mmHg≈0.133 kPa): 112.34±38.25 vs. 156.76±68.44 at 6 hours, 110.92±38.66 vs. 150.64±59.07 at 12 hours, 111.12±36.77 vs. 141.30±39.05 at 18 hours, 110.82±39.37 vs. 139.65±41.50 at 24 hours; PaCO 2 (mmHg): 41.30±7.51 vs. 47.42±7.54 at 12 hours, 40.97±6.98 vs. 45.83±8.63 at 18 hours, 40.10±7.06 vs. 46.14±9.15 at 24 hours, all P < 0.01]. The rate of performed mechanical ventilation after weaning and the rate of reintubation after extubation for 72 hours in the high-flow group were significantly lower than those in the Venturi group [17.5% (7/40) vs. 40.5% (15/37), 6.2% (2/32) vs. 31.8% (7/22), both P < 0.05], and the extubation time after weaning was significantly shorter than that in the Venturi group (hours: 22.43±11.72 vs. 28.07±10.42, P < 0.05). Conclusion:Using high-flow oxygen therapy to the extubation process of critically ill mechanical ventilation patients can reduce the incidence of carbon dioxide retention and the rate of performed mechanical ventilation after weaning, shorten the extubation time after weaning, and reduce the rate of reintubation after extubation for 72 hours.

Effect of prophylactic plasma transfusion on postoperative bleeding rate in ICU patients after different invasive procedures / 中国输血杂志

【Objective】 To evaluate the association between prophylactic plasma transfusion and postoperative bleeding rate in critically ill patients undergoing different invasive procedures. 【Methods】 The information of ICU patients who received different invasive procedures from January 2019 to December 2019 in 6 tertiary hospitals in China were retrospectively investigated. The inclusion criteria of patients were as follows: age ≥ 18 years; received invasive procedures; INR ≥ 1.5 within 72 hours before surgery. Exclusion criteria were patients with incomplete case records. The patients finally included in the study were divided into prophylactic plasma transfusion group and non-prophylactic plasma transfusion group according to their plasma transfusion status. The outcome variable was the incidence of invasive procedure-related bleeding within 48 hours after different invasive procedures. 【Results】 A total of 407 patients underwent invasive procedures, and 362 patients were finally included in this study after excluding 45 patients with incomplete case records. The proportions of prophylactic plasma transfusion in different types of invasive procedures were central venous catheterization (46/146, 31.5%), thoracentesis (13/37, 35.1%), bronchoscopy (8/31, 25.8%), tracheal intubation (9/38, 23.7%), arterial catheterization (9/50, 18.0%) and others (13/60, 21.7%). The bleeding rates showed that different invasive procedures presented no statistical difference between the groups received plasma transfusion or not. In the prophylactic plasma transfusion group, the bleeding rate of arterial catheterization (4/9, 44.4%) was the highest, but all were potential bleeding, followed by tracheal intubation (4/10, 40.0%) and central venous intubation (16/46, 34.8%), with a higher rate of significant bleeding. 【Conclusion】 Prophylactic infusion of plasma did not reduce the bleeding rate after different invasive procedures, but prospective studies are needed to further confirm the conclusion; this study also provides a certain data basis for later prospective studies.

Genetic background of RhD negative blood donors / 中国输血杂志

【Objective】 To analyze the genetic background of RhD-negative blood donors by detecting RHD and RHCE genes of those donors. 【Methods】 From March 2021 to May 2022, the blood samples of RhD-negative blood donors, who had been screened out by RhD primary screening and confirmatory experiments in the Yaan Blood Center, were firstly identified whether the RHD allele was completely deleted, then whether there were deletions in 10 exons of non-RHD allele complete deletion samples, finally, the remaining samples without RHD alleles and exon deletions were further analyzed by DNA sequencing. RHCE gene was detected by SSP-PCR method. 【Results】 Among the RHD gene test results of 104 RhD-negative samples, 65 cases were completely deleted (d/d), 33 were RHD partially deleted (one allele deletion), and 6 were without RHD gene deletion. The RHD alleles of 33 samples with partial deletion were detected by 10 exons, 13 had partial exon deletion, with genotype as RHD*D-CE(3-9)-D/d and phenotype as RhD negativity, and the remaining 20 samples had no exon deletion. The exon sequencing results of the non-deletion samples showed RHD*1227A/RHD*1227A in 6 samples, RHD*1227A/d in 19, RHD*3A/d in 1; both of the last two were considered Del by ISBT. The RHCE gene test results showed that all cc genotype blood donors were RhD true negative, while Del blood donors had no cc genotype. 【Conclusion】 Through the genetic background study of RhD negative blood donors, it is found that there is a high proportion of Del with weak expression of RhD antigen, whether this blood type affects clinical blood safety needs further researches.

Research progress of evidence-based medicine in clinical practice of plasmatransfusion / 中国输血杂志

Plasma is widely used in clinical, but the reliable evidence-based medical evidences that can guide clinicians to properly use plasma are limited, and inappropriate use may even cause deterioration of the disease and serious adverse reactions. Based on the relevant international blood transfusion guidelines and published clinical trial studies, this paper aims to summarize the evidence-based basis of plasma in clinical applications, discuss the safety and efficacy of plasma applications under different conditions, and provide assistance to clinical practice and scientific research of plasma in the future.

Role of Foxo6 in insulin resistance of vascular endothelial cells induced by high glucose and high fat and its interaction with NF-κB pathway / 中国药理学通报

Aim To investigate the expression of Foxos in human umbilical vein endothelial cells(HUVECs)with insulin resistance(IR)induced by high glucose and high fat(HG/HF)stress and its significance.Methods First, the IR model of endothelial cells was established by HG /HF stress.The differential expression of Foxos gene in normal(Ctrl )group and HG /HF group was observed, and the subtypes with the most significant changes in Foxos were screened out, such as Foxo6.Next, Foxo6 was silenced to observe its role in endothelial cell with IR.Finally, whether the mechanism of Foxo6-mediated IR was related to the interaction of NF-κB signaling was investigated.Results The expression increase of Foxo6 was the most significant among Foxos under the IR condition induced by HG/HF.Using a small RNA interference and plasmid transfection technique, we found that the silence effect of the siRNA3 fragments targeting Foxo6 was the most significant among the siRNAs.Moreover, the further study showed that silencing the Foxo6 gene could significantly reverse the endothelial IR induced by HG/HF, and the mechanism of the reversal effect was related to the interaction between the Foxo6 and NF-κB signal.Conclusions Foxo6 mediates the endothelial cell IR induced by the HG /HF stress.The underlying mechanism is that Foxo6 can interact with NF-κBp65 and activate NF-κB signaling pathway.Silencing Foxo6 can improve the IR of vascular endothelial cells induced by HG /HF stress.

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.

Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer's disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a powerful framework that can be applied to general scRNA-Seq analyses.

A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data.

Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.