DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

N4-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Traditional laboratory methods for detecting 4mC DNA methylation have limitations, but the rise of artificial intelligence has introduced efficient computational strategies for 4mC site prediction. Despite this progress, challenges persist in terms of model performance and interpretability. To tackle these challenges, we propose DeepSF-4mC, a deep learning model specifically designed for predicting DNA cytosine 4mC methylation sites by leveraging sequence features. Our approach incorporates multiple encoding techniques to enhance prediction accuracy, increase model stability, and reduce the computational resources needed. Leveraging transfer learning, we harness existing models to enhance performance through learned representations or fine-tuning. Ensemble learning techniques combine predictions from multiple models, boosting robustness and accuracy. This research contributes to DNA methylation analysis and lays the groundwork for understanding 4mC's multifaceted role in biological processes. The web server for DeepSF-4mC is accessible at: http://deepsf-4mc.top/and the original code can be found at: https://github.com/754131799/DeepSF-4mC.

Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images.

Alzheimer's disease, a primary neurodegenerative condition, predominantly impacts the elderly and pre-elderly population. This progressive neurological disorder is characterized by an array of symptoms including memory loss, cognitive decline, and various physiological and psychological disturbances, significantly compromising the quality of life of patients and their caregivers. Recent advancements in Magnetic Resonance Imaging (MRI) technology have catalyzed research in AI-enhanced diagnostics for Alzheimer's disease, fostering optimism for early detection and timely interventions. This progress has paved the way for the development of sophisticated algorithms and models adept at analyzing complex brain imaging data, thereby augmenting diagnostic accuracy and efficiency. This advancement fuels optimism regarding the transformative potential of AI-driven diagnostics in revolutionizing Alzheimer's disease management, with the prospect of facilitating more effective treatment strategies and improved patient outcomes. The objective of this review is to provide a comprehensive overview of recent developments in deep learning methodologies applied to brain MRI images for the classification of various stages of Alzheimer's disease, with a particular emphasis on early diagnosis. Furthermore, this review underscores the limitations of current research, discussing potential challenges and future research directions in this dynamic field.

EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species.

Methylation is a major DNA epigenetic modification for regulating the biological processes without altering the DNA sequence, and multiple types of DNA methylations have been discovered, including 6mA, 5hmC, and 4mC. Multiple computational approaches were developed to automatically identify the DNA methylation residues using machine learning or deep learning algorithms. The machine learning (ML) based methods are difficult to be transferred to the other predicting tasks of the DNA methylation sites using additional knowledge. Deep learning (DL) may facilitate the transfer learning of knowledge from similar tasks, but they are often ineffective on small datasets. This study proposes an integrated feature representation framework EpiTEAmDNA based on the strategies of transfer learning and ensemble learning, which is evaluated on multiple DNA methylation types across 15 species. EpiTEAmDNA integrates convolutional neural network (CNN) and conventional machine learning methods, and shows improved performances than the existing DL-based methods on small datasets when no additional knowledge is available. The experimental data suggests that the EpiTEAmDNA models may be further improved via transfer learning based on additional knowledge. The evaluation experiments on the independent test datasets also suggest that the proposed EpiTEAmDNA framework outperforms the existing models in most prediction tasks of the 3 DNA methylation types across 15 species. The source code, pre-trained global model, and the EpiTEAmDNA feature representation framework are freely available at http://www.healthinformaticslab.org/supp/.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications.

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features.

BACKGROUND: Fundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature. METHODS: This study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases. RESULTS: In fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy. CONCLUSIONS: This study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.

FunSwin: A deep learning method to analysis diabetic retinopathy grade and macular edema risk based on fundus images.

Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient's quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.

Allyl isothiocyanate increases MRP1 expression in cigarette smoke extract-stimulated human bronchial epithelial cells via the JNK/Nrf2 pathway.

Multidrug resistance-related protein 1 (MRP1) is involved in the biological transport of several molecules with diverse structural characteristics outside of the cell. In addition to its transport activity, MRP1 exhibits multiple defense mechanisms in vivo. MRP1 is highly expressed in normal lung tissues and plays a protective role in the process of chronic obstructive pulmonary disease. In the present study, human bronchial epithelial cells (16HBE14o-cells) were stimulated by cigarette smoke extract (CSE) in vitro to simulate a smoking environment. On this basis, the mechanism of Allyl isothiocyanate (AITC) administration on the expression of MRP1 in CSE-stimulated 16HBE14o-cells was investigated. The effects of CSE on the viability of 16 HBE14o-cells were investigated by an MTT assay. The changes in the mRNA expression levels of nuclear erythroid factor 2 (Nrf2) and MRP1 were investigated in CSE-stimulated 16HBE14o-cells using western blotting and reverse transcription quantitative PCR (RT-qPCR). Immunofluorescence analysis was used to detect Nrf2 nuclear translocation. Incubation of the cells with 5% CSE for 24 h had minor effects on cell viability and resulted in the activation of the JNK and p38MAPK signaling pathways. AITC activated the JNK pathway, inhibited the activation of the p38MAPK pathway in 16HBE14o-cells stimulated by 5% CSE and upregulated the expression levels of Nrf2 and MRP1 in a time-dependent manner. The upregulation of Nrf2, MRP1 and of Nrf2, and MRP1 mRNA expression levels in CSE-stimulated cells was inhibited by pretreatment with SP600125 (a JNK pathway inhibitor). Furthermore, the fluorescence intensity in the nucleus was significantly enhanced following AITC pretreatment and the analysis indicated nuclear translocation of Nrf2 in the cells. These results indicated that Nrf2 and MRP1 expression levels in CSE-stimulated cells were altered following AITC pretreatment. Thus demonstrating that the primary mechanism may be associated with activation of the JNK pathway, while the p38MAPK pathway may not be involved.

Identification and Characterization of Chemical Constituents in HuaTanJiangQi Capsules by UPLC-QTOF-MS Method.

BACKGROUND: HuaTanJiangQi (HTJQ) is a classical Chinese medicine compound preparation, mainly used for clinically treating and improving chronic obstructive pulmonary disease (COPD) in China. OBJECTIVE: To establish a rapid and efficient analytical method for the identification and characterization of chemical constituents in HTJQ based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). METHOD: UPLC-QTOF-MS was used to rapidly separate and identify the chemical constituents of HTJQ via a gradient elution system. The accurate mass data of the protonated and deprotonated molecules and fragment ions were detected in positive and negative ion modes. Compounds of HTJQ can be identified and assigned by analyzing accurate mass measurements and ion fragmentation mechanisms and comparing them with a chemical compositions database. RESULTS: A total of 61 compounds in HTJQ were separated and identified, including 14 flavonoids, 16 organic acids, four isothiocyanic acids, eight butyl phthalides, two alkaloids, 10 terpenoids, four methoxyphenols and furanocoumarins, and three other compounds. The chemical compounds of HTJQ were identified and elucidated comprehensively for the first time. CONCLUSIONS: A rapid, accurate, and efficient UPLC-QTOF-MS method has been developed for the identification of chemical components and applied to simultaneously evaluate the quality and effectiveness of HTJQ. HIGHLIGHTS: Systematic identification of chemical constituents in HTJQ can provide a scientific and reasonable basis for the application of HTJQ in the clinical treatment of COPD.

Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches.

OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce pulmonary failures, and chest X-ray imaging becomes one of the major confirmed diagnostic technologies. The very limited number of publicly available samples has rendered the training of the deep neural networks unstable and inaccurate. This study proposed a two-step transfer learning pipeline and a deep residual network framework COVID19XrayNet for the COVID-19 detection problem based on chest X-ray images. COVID19XrayNet firstly tunes the transferred model on a large dataset of chest X-ray images, which is further tuned using a small dataset of annotated chest X-ray images. The final model achieved 0.9108 accuracy. The experimental data also suggested that the model may be improved with more training samples being released. COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

Metabolic profiling of chronic obstructive pulmonary disease model rats and the interventional effects of HuaTanJiangQi decoction using UHPLC-Q-TOF/MSE.

The occurrence of chronic obstructive pulmonary disease (COPD) will lead to physiological and pathological variations and endogenous metabolic disorders. A traditional Chinese medicine formula, HuaTanJiangQi decoction (HTJQ), exhibits an unambiguous therapeutic effect on COPD in China. Nevertheless, the mechanism of its therapeutic effect on COPD is not clear. With this purpose, pulmonary function, histopathological and the inflammatory factors in bronchoalveolar lavage fluid (BALF) in rats model of COPD were investigated. Then, ultra high-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) analysis and multivariate statistical analysis were used to further reveal the mechanism of HTJQ therapeutic effect on COPD via metabolomics study. The results showed that the characteristics of lung tissues were significantly reversed, the concentration of LTB4 and LTC4 were gradually decreased, and the lung function began to recover after HTJQ treatment. These typical indicators of COPD in HTJQ intervention group were reversed similar to the control group, suggested that HTJQ has a therapeutic effect on COPD. Moreover, 32 dysregulated metabolites, including Thromboxane a2, Sphingosine 1-phosphate, PC(18:2(9Z,12Z)/18:1(11Z)), Leukotriene B4, Glutathione, Arachidonic acid, Sphingosylphosphocholine acid, N-Acetyl-leukotriene e4, Lysopc(18:1(11Z)), L-Cysteine, and Guanosine diphosphate. All the altered metabolites were associated with the onset and development of COPD, and involved in glycerophospholipid metabolism, sphingolipid metabolism, glutathione metabolism, and arachidonic acid metabolism, which were significantly changed in rats model with COPD. Generally, these findings provide a systematic view of metabolic changes linked to the onset and development of COPD, also indicated that HTJQ could provide satisfactory therapeutic effects on COPD and metabolomics study can be utilized to further understand the molecular mechanisms.

MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI.

The neurological disorder mild cognitive impairment (MCI) demonstrates minor impacts on the patient's daily activities and may be ignored as the status of normal aging. But some of the MCI patients may further develop into severe statuses like Alzheimer's disease (AD). The brain functional connectivity network (BFCN) was usually constructed from the resting-state functional magnetic resonance imaging (rs-fMRI) data. This technology has been widely used to detect the neurodegenerative dementia and to reveal the intrinsic mechanism of neural activities. The BFCN edge was usually determined by the pairwise correlation between the brain regions. This study proposed a weighted voting model of multi-source connectivity networks (MuscNet) by integrating multiple BFCNs of different correlation coefficients. Our model was further improved by removing redundant features. The experimental data demonstrated that different BFCNs contributed complementary information to each other and MuscNet outperformed the existing models on detecting MCI patients. The previous study suggested the existence of multiple solutions with similarly good performance for a machine learning problem. The proposed model MuscNet utilized a weighted voting strategy to slightly outperform the existing studies, suggesting an effective way to fuse multiple base models. The reason may need further theoretical investigations about why different base models contribute to each other for the MCI prediction.

Allyl isothiocyanate may reverse the expression of MRP1 in COPD rats via the Notch1 signaling pathway.

In the present study, the roles of AITC in up-regulating the MRP1 expression and its relationship with the activation of the Notch1 signaling pathway were investigated by combining the in vivo and in vitro experiments. AITC was administered to the COPD model rats and normal rats to explore the association between Notch1 and MRP1. The human bronchial epithelial cells were treated with DAPT, the Notch1 signaling pathway inhibitor, to verify the effect of Notch1 on the expression of AITC-induced MRP1. Compared with the control group, the expressions of Notch1, Hes1 (the target gene of Notch1) and MRP1 in the lung tissue of the COPD model group were significantly inhibited. In contrast to the COPD model group, the expressions of MRP1, Hes1 and Notch1 dramatically up-regulated following the treatment with Low/High doses of AITC. The expression of MRP1 in the 16 HBE cells was down-regulated by the inhibition of Notch in a DAPT concentration-dependent manner. Additionally, the AITC-induced up-regulation of the MRP1 expression was markedly impaired following the inhibition of Notch1. The above results indicated that the pulmonary function and the expression of MRP1 in COPD rats could be improved by AITC, which was partly dependent on the Notch1 signaling pathway. Therefore, targeting the Notch signaling pathway may present as an effective therapeutic strategy for COPD treatment.

Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images.

Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.