Cancer is one of the most challenging health problems facing humankind. Conventional treatments such as surgery, chemotherapy and radiotherapy can usually have a positive patient outcome in the early-stage tumours without metastasis. However, late-stage cancers can have high mortality; therefore, urging scientists and clinicians to investigate novel treatment approaches. Novel cancer treatment methods provide specific treatment for patients with different cancer types and substantially elevate the survival rate, prognosis, and quality of life, commonly with a much lower immune-related side effect. Monoclonal antibodies are one of the fastest growing novel treatments with high target specificity and high therapeutic selectivity, enabling the treatment of advanced cancers with a low autoimmune inflammatory reaction. Chimeric antigen receptor (CAR)-T is another novel treatment which edits target T lymphocytes for high target specificity and high therapeutic selectivity. However, challenges exist, such as cytokine release syndrome resulting in immunotoxicity problems, including neurotoxicity and brain oedema. Cancer is a genetic disease, and gene-editing technologies, such as CRISPR/Cas9, enable gene modification to cure the disease completely in gene therapy. Although limitations, such as off-target effect and DNA-damaged toxicity can cause immunotoxicity problems, multiple derivatives of the CRISPR system can provide long-term cancer treatment. This review discusses conventional and novel cancer treatment methods and explains challenges which need to be addressed before such treatments are widely available in cancer treatment.
Autism Spectrum Disorder (ASD) is a neurological developmental disorder characterized by repetitive behaviors accompanied by communication and social interaction impairment.In this paper, the effect of prenatal stress factors on ASD was investigated by collecting single-cell RNA sequencing and calculation. The results suggest that stress responses, such as obesity-related stress and oxidative stress, may be related to neuronal death and contribute to the development of ASD. In addition, studies have shown that stress response genes decline over time under low temperature conditions.
Pseudomonas aeruginosa is an abundant bacterium in nature. Several P. aeruginosa strains are capable of producing a wide range of secondary metabolites with broad-spectrum high antimicrobial activity. This study aims to identify antimicrobial activity genes of P. aeruginosa SWUC02 by whole-genome sequencing analysis. Whole genome sequencing of P. aeruginosa SWUC02 was proceeded with the BGISEQ-500 sequencing system. The genome of P. aeruginosa SWUC02 is a single circular chromosome with 6,404,055 bp. 6,214 coding sequences were identified by RAST. We compared P. aeruginosa SWUC02 against two reference strains: P. aeruginosa LV and P. aeruginosa PAO1, according to their antimicrobial activity. Our result shows that several antimicrobial gene clusters including bacteriocins, secondary metabolites, and siderophores are shared among P. aeruginosa SWUC02 and the two reference strains. The notable difference arises in secondary metabolite gene clusters where we identified pyocyanine and thanamycin in P. aeruginosa SWUC02 genome, which are absent from the reference strains. P. aeruginosa SWUC02 shows the potential for being a prospective antagonist and warrants a follow up study for antimicrobial compound production.
Coarctation of aorta (CoA) is a critical congenital malformation which can lead to serious complications such as hypertension, heart failure and even shock in severe case. The effective diagnosis and surgical management of CoA require accurate aortic inner diameter measurement mainly based on cardiovascular computed tomography (CT). Due to the traditional manual aortic inner diameter measurement is labor-intensive and susceptible to observer’s expertise, the deep learning (DL) enabled aortic segmentation based aortic inner diameter measurement methods have been investigated. However, most existing DL based aortic segmentation methods ignore the edge information and spatial consistency, which lead to poor segmentation performances. To solve this problem, we propose an edge enhancement and contextual fusion network, called ECN, which can enhance edge information and utilize the contextual relationships of CT slices so as to improve aortic segmentation. Simulation results show that the proposed algorithm outperforms the compared DL algorithms in dice score (0.9370) and 95% Hausdorff distance (1.3383mm) in our private patient-specific CoA dataset. Moreover, the proposed ECN based aortic inner diameter measurement achieves low bias and high correlation with the results measured by doctors.
Reconstructing gene regulatory networks based on time-series gene expression data is a huge challenge in the field of systems biology. However, the accuracy of traditional methods can be further improved. In practical situations, the gene regulatory network topology and the dynamic rules of genes are not observable. Therefore, reconstructing the underlying network structure and dynamics from observed time-series gene expression data is an important task. In this work, we introduce a new framework, Gumbel Graph Network (GGN), a model-free, data-driven deep learning framework for accomplishing the reconstruction of gene regulatory networks and the reconstruction of gene dynamics. Our model consists of two co-trained parts: a network generator, which generates a discrete network using Gumbel Softmax technique; and a dynamic learner, which uses the generated network and single-step trajectory values to predict the state at the next moment. According to the experimental results on multiple simulated and real datasets, GGN has better performance and stability compared with other state-of-the-art algorithms.
As the major cause of deaths worldwide, cardiovascular diseases are responsible for about 17.9 million deaths per year 1. Research on new technologies and methodologies allowed the acquisition of reliable data in several high income countries, however, in various developing countries, due to poverty and common scarcity of resources, this has not been reached yet. In this work, cardiovascular data acquired using cardiac auscultation is going to be used to detect cardiac murmurs through an innovative deep learning approach. The proposed screening algorithm was built using pre-trained models comprising Residual Neural Networks, namely Resnet50, and Visual Geometry Groups, such as VGG16 and VGG19. Furthermore, and up to our knowledge, our proposal is the first one that characterizes heart murmurs based on their frequency components, i.e. the murmur pitch. Such analysis may be used to augment the system’s capability on detecting heart diseases. A novel decision-making function was also proposed regarding the murmur’s pitch. From our experiments, low-pitch murmurs were more difficult to detect, with final f1-score values nearing the 0.40 value mark for all three models, while high-pitch murmurs presented an higher f1-score value of about 0.80. This might be due to the fact that the low-pitch share their respective frequency range with the normal and fundamental heart sounds, therefore making it harder for the model to correctly detect their presence whereas high-pitch murmurs’ frequencies distance from the latter.
The leading cause of cancer-related deaths among women is breast cancer. Breast cancer indicators can be blocked by natural plant chemicals with anti-cancer potential, but they must be carefully chosen to prevent negative side effects. In this study, the molecular interaction between the breast cancer biomarkers and phytocompound from Dimocarpus longan; and its stability were studied using the molecular docking and dynamic simulation approaches. α-terpineol (ID:442501), corilagin (ID:73568), isoscopoletin (ID:69894), protocatechuic acid (ID:72) and rutin (ID: 5280805) (plant compounds) from longan and estrogen receptor (1ERR) and progesterone receptor (3D90) (target proteins) that were involved in breast cancer were retrieved from the PubChem database and RCSB Protein Data Bank (PDB) respectively. The pharmacokinetics and toxicity analysis of natural compounds were also performed to find a safe and suitable drug for breast cancer treatment using SwissADME and admetSAR tools. Then, The SwissDock server was used to dock them. A molecular dynamics simulation approach was used to determine the stability of the target protein interaction between phytocompound using Gromacs version 4.6.3 software. Corilagin and rutin violated Lipinski's Rule of 5. Moreover, toxicity prediction analysis has demonstrated that α-terpineol did not show any properties that may cause harmful effects to humans when compared with the other four phytocompounds. The docking results show that 1ERR and 3D90 had a negative binding affinity with the α-terpineol at the value of -6.0 and -6.1 kcal/mol respectively. α- terpineol had a stable interaction with 1ERR and 3D90 with the root mean square deviation (RMSD) value of 0.20nm. Therefore, α- terpineol was chosen to use as a safe drug and potential to be a lead compound. It also could be a multi-target inhibitor for estrogen and progesterone receptors. The results of this investigation should help pharmaceutical researchers locate drugs containing longan.
Due to the exhausted usage of non-renewable petroleum-derived fuels, an increasing global demand for a biologically produced energy source has emerged. Microalgae biomass has become one of the most promising alternatives. Arthrospira platensis is one of the easiest species to cultivate due to their inherent resistance to contamination and environmental changes. In this study, we examined the genomic sequence of the Arthrospira platensis strain SBC through whole genome sequencing and annotated a total of 6419 genes. These genes were further mapped to different biological processes with KEGG results. Phylogenomic analysis revealed that Arthrospira platensis strain is closest to A. platensis NIES-39 and comparative genomic analysis revealed that more lipid biosynthesis genes were unique to the A. platensis strain when compared to other Arthrospira genomes found on NCBI's GenBank, indicating the potential role of these candidate genes in improving lipid growth in the A. platensis SBC strain via genetic modification.
Acute myeloid leukemia (AML) is an aggressive hematologic malignancy composed of a mixture of genotypically, phenotypically and functionally diverse cell populations including wild-type (WT) cells. The generation of high throughput single cell gene expression and mutational profiles in AML enables the deployment of deep learning frameworks for gaining insights on how genotypic changes are associated with disease phenotypes. However, the question if the single cell gene expression patterns together with the computational power of neural networks have the capacity to predict a cell's genotype remains unclear. In this study, we train two supervised deep learning models to predict the cell's malignant or wild-type (WT) status as well as the mutational status of specific genomic abnormalities in a binary and multi-class multi-label setting respectively, based on single cell RNA sequencing data from 6 AML patients and 4 healthy individuals. In the independent test sets, the binary classification model achieved an accuracy of 98% while the multi-class multi-label model achieved a macro-average AUC ROC of 0.84. Moreover, applying black box feature selection on the trained networks identified genes involved in biological processes and pathways of reported significance in AML, such as the IL-2/STAT5 and NF-kB signaling pathways. Overall, this study proposes two deep learning tasks for the prediction of single cell genotypic profiles from single cell expression data and showcases how the trained models can be used for the derivation of biologically related signals.
Tendon functions in a complex multi-scale physiological environment to give flexibility and force to the wrist, and hence, it helps to the wrist to perform the daily motion as flexion, extension, adduction and abduction. Tendons often might suffer injuries that produce several types of damages on wrist motion abilities, such as tendon degeneration, pain, and they also can alter joint kinematics and affect other tendons as rotator cuff, achilles, flexor and extensor. This work aims to model and simulate the pre and post-surgical tendon transfer on the wrist using OpenSim 3.3. Tendon transfer is a useful approach focused on the restoring and improving the function of a damaged tendon in order to enhance motion abilities of the wrist. Besides a description of how the tendon force increases afterwards a tendon transfer, the impact of stiffness on the wrist motion is discussed through calculations based on inverse kinematics and dynamics. This experimental study provides a point-of-view to formulate hypotheses for future experimental studies associated to tendon transfer and provide insight to develop an assisting device design that complements experimental techniques.
SARS-Cov-2, a type of coronavirus, caused the global pandemic, and the era has yet ended. The coronavirus has a complicated life cycle. Many researchers have investigated the targeting steps to prevent the coronavirus from replicating in human cells, aiming to find an efficient treatment. Here, I focus on the co-translational targeting and insertion of coronavirus structure proteins. I calculated the Kyte-Doolittle hydrophobicity of the coronavirus structure proteins from over 61 species. Based on this, I developed a simple algorism to search the signal sequence and transmembrane helix which is responsible for the co-translational synthesis of the viral proteins. I further calculated the apparent free energy of of membrane insertion and investigated the consensus sequences by multiple sequence alignment using the ClustalOmega algorism. Based on the computational calculations, I found that the transmembrane helixes of S protein and E proteins show extremely high hydrophobicity and low apparent free energy of . I also found that the signal sequence of S protein shows a non-charged characteristic at the N-terminus, which is typical as a signal sequence for membrane targeting. After that, I predicted the second structure, did the molecular docking and found a possible protein to inhibit the protein. This research is important for the understanding of coronavirus assembly on the membrane. Therefore, I hope it provides a new targeting site for the inhibition of coronavirus replication in the host cells. CCS CONCEPTS: Applied computing∼Life and medical sciences∼Genomics∼Computational genomics
The goal of this paper is to propose a new feature selection model which enhances the discrimination power of the selected feature subsets and minimizes their size of them. The proposed algorithm is capable of exploring the essential factors of classification problems for molecular datasets comprising a tremendous amount of input variables. Our model devotes to two accomplishments of multi-class classification tasks. Feature discretization using fuzzy clustering analysis for the improvement of feature discrimination is the first. Multivariate analysis for the investigation of information relevance and redundancy is the second achievement in this study. Experimental results convince our model acquires significant discrimination improvement for microarray classification problems.
In modern neuroscience and clinical research, functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique that uses magnetic resonance imaging to measure hemodynamic changes caused by neuronal activity. This technique is used to study human brain function and cognition in healthy individuals and groups with abnormal brain states. And it is one of the most commonly used imaging modalities. Because of its characteristics of containing temporal information, it is widely used in research in cognitive neuroscience, clinical psychiatry/psychology, and preoperative planning. Advances in artificial intelligence, especially the advent of deep learning techniques have shown promising results for better interpretation of fMRI data. This paper focuses on fMRI data and summarizes the current state of application of deep learning methods and models on resting-state and Task-evoked data. In the future, deep learning combined with advanced feature selection methods or task-state fMRI data has the potential to become a powerful tool for exploring the state and function of the human brain.
Extracellular action potentials (EAP) are one of the most important features in biological study. Many researchers have studied the classification of EAP by their differences in voltage and magnitude. However, most research ignored the fundamental origin of the EAP variation around the neurons in their classification and treated waveforms of different shapes as signals recorded from different neurons. In our research, we theoretically investigated the shapes of EAP by clustering the spatially-varied EAP around the neuron. We use an unsupervised machine-learning algorithm to classify all EAPs measured around the same neuron. To eliminate the influence of the non-characteristic part of the EAP curve, we also compared the classification results by eliminating the unchanged part at the front and end of the curve in the second group of our study. Our results illustrate the previously overlooked relationship between different shaped EAP and the biological structure of the neuron. The results show that EAP measured is closer to classical theory prediction in the axon while more eccentric, even with a shape similar to an intracellular action potential in the dendrite. Our research has important implications for further device design to record accurate electric signals and extracting biological related information from extracellular recordings.
Lung nodule detection remains one of the most common and painstaking tasks in radiology. Efforts to aid overworked radiologists are made using artificial intelligence (AI), although computed tomography (CT) makes it a hard computational task in practical scenarios. This study analyses the translation of a weight-averaging ensemble technique, from natural image classification to small object detection on CT. A dataset of 1050 patients is used to fine-tune models under diverse configurations to compare different types of ensembles. The model soup boosts the FROC score from 0.872 to 0.886, with no computational downsides. Next, two radiologists test their detection performance with and without the ensemble assistance on 20 CT studies. The AI improves the physicians’ mean sensitivity from 91.2 ± 5.1% to 94.2 ± 4.6%, while preserving a non-inferior specificity (P < 0.001). These results further pave the way to translating general computer vision advancements into the medical domain, supporting AI’s place in the physician’s toolbox.
Gliomas are lethal cancers that originate in the central nervous system. Glioblastoma multiforme (GBM) is the most aggressive and commonly occurring malignant brain glioma, accounting for just under 50% of all cases of malignant brain tumours in adults. In this paper, glioblastoma tumour cell single-cell RNA sequencing data were analyzed with Seurat, and cell groups were identified by employing various marker genes. Diverse populations of cell types were revealed, including tumour-associated macrophages, microglia, monocytes, t-cells, oligodendrocytes, glioblastoma stem cells, and other progenitor cells. Glioblastoma heterogeneity was also observed, as different samples of glioblastoma possessed distinct cellular compositions. Analysis of phagocytic cell clusters revealed the presence of microglia-like cells that resulted from monocyte differentiation. The upregulation of TIGIT and STAT3 in t-cell clusters was observed in cases with especially low t-cell counts, which demonstrates glioblastoma's immunosuppressive abilities. Furthermore, stem cell count was shown to be exceedingly low in cases of recurrent glioblastoma in comparison to cases of newly-diagnosed glioblastoma. Presumably, tumour recurrence should be caused by stem cells, but the exceptionally low stem cell count in cases of recurrent glioblastoma proves otherwise. This reveals that treatment or surgery should target stem cells in cases of newly developed glioblastoma but should target other factors in cases of recurrent glioblastoma—examples of which include the tumour microenvironment. These results can be used to help create more innovative and effective treatments for glioblastoma multiforme.
In this study, we show that DNA methylation markers can be used for the early detection of breast cancer subtypes based on the status of Estrogen, Progesterone and HER2 receptors. A machine learning approach is used to predict breast cancer patients’ receptor status. DNA methylation datasets created using Illumina Hypermethylation 450K platform from four different studies from NCBI GEO and TCGA-BRCA are collated to create a dataset of 1514 samples. Of these, 1260 samples passed quality control and had receptor status information available, and these 1260 samples were used for the training and testing of the SVM model. We analysed the prognostic value of DNA methylation-based receptor status prediction by comparing the survival outcomes with the traditional methods. We observe that patients whose classification status does not match with those of traditional methods exhibit lower survival probability. This could possibly be due to incorrect classification and treatment plans by the traditional IHC method. This suggests that the proposed DNA methylation-based classifier is reliable for receptor status prediction.
Overproduction of nitric oxide free radical (•NO) potentially causes the reduction in antioxidant defense system, damaging cell components and leading to physiological disorders in postharvest horticultural crops. In this study, the effects of sodium nitroprusside (SNP; NO donor) or 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO; NO scavenger) on antioxidant enzyme activities, antioxidant capacity and pericarp browning development of harvested longan fruit were investigated. Longan fruits were dipped in distilled water (control), SNP (50 and 100 mM) or cPTIO (100 and 500 μM) for 10 min, then stored at 25 ± 1 °C for 7 d. It was shown that antioxidant enzyme activities including superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) and glutathione peroxidase (GPX) and total antioxidant capacity including ABTS and DPPH decreased during storage. These reductions coincided with an increase in pericarp browning (browning index). Enzyme activity and antioxidant capacity assays confirmed our results that NO involved in longan pericarp browning. The activity of SOD, CAT, APX and GPX were reduced by 2.5-71.5%, 30.6-66.1%, 26.7-75.5% and 34.3-68.7%, respectively, after SNP treatment for 7 d but were increased by 2.4-109.6%, 3.8-153.8%, 6.3-47.1% and 31.9-156.5%, respectively, after treatment with cPTIO. In addition, the total antioxidant capacity ABTS and DPPH were reduced by 13.3-66.8% and 17.6-49.4%, respectively, after SNP treatment for 7 d but were increased by 3.6-58.8% and 2.0-128.7%, respectively, after treatment with cPTIO. Treatment with 100 mM of SNP was more efficient in inhibited the activity and capacity of antioxidant than that of 50 mM SNP. In contrast, the cPTIO-treated fruit exhibited opposite results by maintaining antioxidant enzyme activities and antioxidant capacity, corresponding to the reduction in pericarp browning. Treatment with 500 μM of cPTIO was more efficient in increased the activity and capacity of antioxidant than that of 100 μM SNP. The antioxidant potential and development of pericarp browning depended on the applied concentration of SNP or cPTIO. These results revealed that exposure to excessive NO resulted in the reduction in antioxidant potential and the increment of pericarp browning of harvested longan fruit.
Objective: Investigate the mechanism of Coptis chinensis in the treatment of Gastric Ulcer based on network pharmacology and computer technology. Methods: The chemical constituts were collected based on the TCMSP database by using Coptis chinensis as the key word; GeneCards were used to select the target genes related to Gastric Ulcer from the human gene database, and the Gastric Ulcer's related target genes were screened by Genemap in the OMIM database, Disgenet database and Genecards database. We used R language VennDiagram package to crosse the gene targets of drugs and diseases, and screenthe target of the main components of Coptis chinensis in the treatment of Gastric Ulcer, and to use Cytoscape software to map the gene regulatory network of galangal in the treatment of Gastric Ulcer, and to use the String database to construct the gene-protein of Coptis chinensis. The interaction visualization network map for protein-protein interaction (PPI) were screened out the core genes. On this basis, gene ontology process analysis and Kyoto Encyclopedia of genes and genomes pathway enrichment analysis were performed using Bioconductor database. Results: Fourteen main active compounds of Coptis chinensis were screened, and 138 gene targets may be involved in the treatment of Gastric Ulcer. The main function of Coptis in the treatment of gastric ulcer is to combine various proteins with different functions such as Akt1, EGFR, MAPK3, TNF etc. The results of GO and KEGG pathway analysis indicated that the pathways involved include P13K/Akt, VEGF and TNF signaling pathway and a variety of cancer pathways. Conclusions: Coptis in the treatment of gastric ulcer in the process of the use of multi-channel joint collaborative approach, laid the foundation for further research.
Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normalization (LN) U-Net architecture, while IBU-Net is derived from the instance-batch normalized (IB) U-Net for medical image segmentation. The architectures of LNU-Net and IBU-Net have a down-sampling path for feature extraction and an up-sampling path for precise localization. We use the original U-Net as the basic segmentation approach and compared it with our proposed architectures. Both LNU-Net and IBU-Net have left ventricle segmentation methods: LNU-Net applies layer normalization in each convolutional block, while IBU-Net incorporates instance and batch normalization together in the first convolutional block and passes its result to the next layer. Our method incorporates affine transformations and elastic deformations for image data processing. Our dataset that contains 805 MRI images regarding the left ventricle from 45 patients is used for evaluation. We experimentally evaluate the results of the proposed approaches outperforming the dice coefficient and the average perpendicular distance than other state-of-the-art approaches.
