PLA2G2D fosters angiogenesis in non-small cell lung cancer through aerobic glycolysis.

Non-small cell lung cancer (NSCLC) stands prominent among the prevailing and formidable oncological entities. The immune and metabolic-related molecule Phospholipase A2, group IID (PLA2G2D) exerts promotional effects on tumor progression. However, its involvement in cancer angiogenesis remains elusive. Therefore, this investigation delved into the functional significance of PLA2G2D concerning angiogenesis in NSCLC. This study analyzed the expression and enriched pathways of PLA2G2D in NSCLC tissues through bioinformatics analysis, and measured the expression of PLA2G2D in NSCLC cells using qRT-PCR and western blot (WB). Subsequently, the viability and angiogenic potential of NSCLC cells were assessed employing CCK-8 and angiogenesis assays, respectively. The expression profile of angiogenic factors was analyzed through WB. Finally, the expression of glycolysis pathway-related genes, extracellular acidification rate and oxygen consumption rate, and the levels of pyruvate, lactate, citrate, and malate were analyzed in NSCLC cells using qRT-PCR, Seahorse XF 96, and related kits. Bioinformatics analysis revealed the upregulation of PLA2G2D in NSCLC tissues and its association with VEGF and glycolysis signaling pathways. Molecular and cellular experiments demonstrated that upregulated PLA2G2D promoted the viability, angiogenic ability, and glycolysis pathway of NSCLC cells. Rescue assays revealed that the effects of high expression of PLA2G2D on the viability, angiogenic ability, and glycolysis of NSCLC cells were weakened after the addition of the glycolysis inhibitor 2-DG. In summary, PLA2G2D plays a key role in NSCLC angiogenesis through aerobic glycolysis, displaying great potential as a target for anti-angiogenesis therapy.

miR-126 regulates the proliferation, migration, invasion, and apoptosis of non-small lung cancer cells via AKT2/HK2 axis.

This study tended to clarify the role of miR-126 in non-small cell lung cancer (NSCLC) cell biological behaviors in vitro, containing cell proliferation, migration, invasion, and apoptosis. miRNA expression microarray related to NSCLC was accessed from gene expression omnibus (GEO) database and subjected to differential analysis using the "limma" package. Real-time quantitative PCR was conducted to assess the expression of miR-126 in NSCLC cell lines. wIn vitro experiments including 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT), wound healing assay, Transwell, and flow cytometry assay were used for evaluating the effect of miR-126 on cell proliferation, migration, invasion, and apoptosis. Additionally, target mRNA for miR-126 was predicted and further validated by bioinformatics analysis and dual-luciferase reporter assay, respectively. It suggested that miR-126 was significantly down-regulated in NSCLS based on the expression microarray, and similar expression trend was exhibited in cancer cell lines. In the meantime, overexpression of miR-126 was found to result in inhibition of cell proliferation, migration, and invasion while promotion of cell apoptosis, with reductions in protein expression of AKT2 and phosphorylated HK2 (p-HK2) as well. AKT2, identified to be a direct target of miR-126 in NSCLC as judged by dual-luciferase reporter assay. Additionally, overexpression of AKT2 was observed to have the ability of elevating p-HK2 protein expression and reversing the effect of miR-126 on NSCLC cell proliferation, migration, and invasion. Given the above findings, we can see that miR-126 exerts its role in NSCLC cell proliferation, migration, invasion, and apoptosis with the aid of AKT2/HK2 axis.

Prevalence of and factors associated with alexithymia among patients with chronic obstructive pulmonary disease in China: a cross-sectional study.

BACKGROUND: Alexithymia is a common psychological disorder. However, few studies have investigated its prevalence and predictors in patients with chronic obstructive pulmonary disease (COPD). Therefore, we aimed to determine the prevalence and predictors of alexithymia in Chinese patients. METHODS: This cross-sectional study included 842 COPD patients to assess the prevalence and predictors of alexithymia using the 20-item Toronto Alexithymia Scale (TAS-20). We used the Hospital Anxiety and Depression Scale (HADS) to assess anxiety and depression, the modified British Medical Research Council dyspnea Rating Scale (mMRC) to assess dyspnea, St. George's Respiratory Questionnaire (SGRQ) to assess quality of life, and the age-adjusted Charlson comorbidity index (ACCI) to assess comorbidities. Alexithymia-related predictors were identified using univariate and multivariate logistic regression analyses. RESULTS: The prevalence of alexithymia in COPD patients was 23.6% (199/842). Multivariate analysis showed that age [odds ratio (OR) 0.886; 95% confidence interval (CI) 0.794-0.998], body mass index (OR 0.879; 95% CI 0.781-0.989), HADS-anxiety (OR 1.238; 95% CI 1.097-1.396), HADS-depression (OR 1.178; 95% CI 1.034-1.340), mMRC (OR 1.297; 95% CI 1.274-1.320), SGRQ (OR 1.627; 95% CI 1.401-1.890), ACCI (OR 1.165; 95% CI 1.051-1.280), and GOLD grade (OR 1.296; 95% CI 1.256-1.337) were independent predictors for alexithymia in patients with COPD. CONCLUSIONS: The prevalence of alexithymia was high in Chinese COPD patients. Anxiety, depression, dyspnea, quality of life, comorbidities, and disease severity are independent risk factors, and age and BMI are predictive factors for alexithymia in COPD patients.

The mzqLibrary--An open source Java library supporting the HUPO-PSI quantitative proteomics standard.

The mzQuantML standard has been developed by the Proteomics Standards Initiative for capturing, archiving and exchanging quantitative proteomic data, derived from mass spectrometry. It is a rich XML-based format, capable of representing data about two-dimensional features from LC-MS data, and peptides, proteins or groups of proteins that have been quantified from multiple samples. In this article we report the development of an open source Java-based library of routines for mzQuantML, called the mzqLibrary, and associated software for visualising data called the mzqViewer. The mzqLibrary contains routines for mapping (peptide) identifications on quantified features, inference of protein (group)-level quantification values from peptide-level values, normalisation and basic statistics for differential expression. These routines can be accessed via the command line, via a Java programming interface access or a basic graphical user interface. The mzqLibrary also contains several file format converters, including import converters (to mzQuantML) from OpenMS, Progenesis LC-MS and MaxQuant, and exporters (from mzQuantML) to other standards or useful formats (mzTab, HTML, csv). The mzqViewer contains in-built routines for viewing the tables of data (about features, peptides or proteins), and connects to the R statistical library for more advanced plotting options. The mzqLibrary and mzqViewer packages are available from https://code.google.com/p/mzq-lib/.

Structural classification of Ag and Cu nanocrystals with machine learning.

We use machine learning (ML) to classify the structures of mono-metallic Cu and Ag nanoparticles. Our datasets comprise a broad range of structures - both crystalline and amorphous - derived from parallel-tempering molecular dynamics simulations of nanoparticles in the 100-200 atom size range. We construct nanoparticle features using common neighbor analysis (CNA) signatures, and we utilize principal component analysis to reduce the dimensionality of the CNA feature set. To sort the nanoparticles into structural classes, we employed both K-means clustering and the Gaussian mixture model (GMM). We evaluated the performance of the clustering algorithms through the gap statistic and silhouette score, as well as by analysis of the CNA signatures. For Ag, we found five structural classes, with 14 detailed sub-classes, while for Cu, we found two broad classes (crystalline and amorphous), with the same five classes as for Ag, and 15 detailed sub-classes. Our results demonstrate that these ML methods are effective in identifying and categorizing nanoparticle structures to different levels of complexity, enabling us to classify nanoparticles into distinct and physically relevant structural classes with high accuracy. This capability is important for understanding nanoparticle properties and potential applications.

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.

BACKGROUND: Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. METHODS: To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. RESULTS: Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. CONCLUSIONS: Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

Size and temperature dependent shapes of copper nanocrystals using parallel tempering molecular dynamics.

We performed parallel-tempering molecular dynamics simulations to predict the temperature- and size-dependent equilibrium shapes of a series of Cu nanocrystals in the 100- to 200-atom size range. Our study indicates that temperature-dependent, solid-solid shape transitions occur frequently for Cu nanocrystals in this size range. Complementary calculations with electronic density functional theory indicate that vibrational entropy favors nanocrystals with a shape intermediate between a decahedron and an icosahedron. Overall, we find that entropy plays a significant role in determining the shapes Cu nanocrystals, so studies aimed at determining minimum-energy shapes may fail to correctly predict shapes observed at experimental temperatures. We also observe significant shape changes with nanocrystal size - sometimes with changes in a single atom. The information from this study could be useful in efforts to devise processing routes to achieve selective nanocrystal shapes.

Minimum Free-Energy Shapes of Ag Nanocrystals: Vacuum vs Solution.

We use two variants of replica-exchange molecular dynamics (MD) simulations, parallel tempering MD and partial replica exchange MD, to probe the minimum free-energy shapes of Ag nanocrystals containing 100-200 atoms in a vacuum, ethylene glycol (EG) solvent, and EG solvent with a PVP polymer containing 100 repeat units. Our simulations reveal a shape intermediate between a Dh and an Ih, a Dh-Ih, that has distinct structural signatures and magic sizes. We find several prominent features associated with entropy: pure FCC nanocrystals are less common than FCC crystals containing stacking faults, and crystals with the minimum potential energy are not always preferred over the range of relevant temperatures. The shapes of the nanocrystals in solution are influenced by the chemical identities of the solution-phase molecules. Comparing Ag nanocrystal shapes in EG to those in an EG+PVP solution, we find more icosahedra in EG and more decahedra in EG+PVP across all of the nanocrystal sizes probed in this study. At certain critical sizes, nanocrystal shapes can change dramatically with the addition and removal of a single atom or with a change in temperature at a fixed size. The information in our study could be useful in efforts to devise processing routes to achieve selective nanocrystal shapes.

MiR-129-2-3p Inhibits Esophageal Carcinoma Cell Proliferation, Migration, and Invasion via Targeting DNMT3B.

PURPOSE: The study aims to explore the regulatory mechanism of miR-129-2-3p underlying esophageal carcinoma (EC) cell progression and generate new ideas for targeted treatment of EC. METHODS: Mature miRNA expression data and total RNA sequencing data of EC in the TCGAESCA dataset were utilized to explore differentially expressed miRNAs (DEmiRNAs). StarBase database was then utilized to predict targets of miRNA. MiR-129-2-3p and DNMT3B expression in EC cell lines was assayed through qRT-PCR and Western blot. CCK-8, scratch healing, and transwell assays were conducted to assess the impact of miR-129-2-3p on EC cell phenotypes. In addition, a dual-luciferase assay was completed to identify the binding relationship between DNMT3B and miR-129-2-3p. RESULTS: MiR-129-2-3p was noticeably less expressed in EC cell lines, while DNMT3B was highly expressed. MiR-129-2-3p could bind to DNMT3B. Furthermore, in vitro functional experiments uncovered that overexpressed miR-129-2-3p repressed EC cell progression while further overexpressing DNMT3B would restore the above inhibitory effect. CONCLUSION: MiR-129-2-3p is a cancer repressor in EC cells, and it could target DNMT3B, thus hampering the progression of EC cells.

Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images.

This study aimed to explore the effect of deep learning models on lung CT image lung parenchymal segmentation (LPS) and the application value of CT image texture features in the diagnosis of peripheral non-small-cell lung cancer (NSCLC). Data of peripheral lung cancer (PLC) patients was collected retrospectively and was divided into peripheral SCLC group and peripheral NSCLC group according to the pathological examination results, ResNet50 model and feature pyramid network (FPN) algorithm were undertaken to improve the Mask-RCNN model, and after the MaZda software extracted the texture features of the CT images of PLC patients, the Fisher coefficient was used to reduce the dimensionality, and the texture features of the CT images were analyzed and compared. The results showed that the average Dice coefficients of the 2D CH algorithm, Faster-RCNN, Mask-RCNN, and the algorithm proposed in the validation set were 0.882, 0.953, 0.961, and 0.986, respectively. The accuracy rates were 88.3%, 93.5%, 94.4%, and 97.2%. The average segmentation speeds in lung CT images were 0.289 s/sheet, 0.115 s/sheet, 0.108 s/sheet, and 0.089 s/sheet. The improved deep learning model showed higher accuracy, better robustness, and faster speed than other algorithms in the LPS of CT images. In summary, deep learning can achieve the LPS of CT images and show excellent segmentation efficiency. The texture parameters of GLCM in CT images have excellent differential diagnosis performance for NSCLC and SCLC and potential clinical application value.

EZH2 affects malignant progression and DNA damage repair of lung adenocarcinoma cells by regulating RAI2 expression.

BACKGROUND: Lung adenocarcinoma (LUAD) is featured in high morbidity and mortality. Aberrant activation of the histone methyltransferase EZH2 has close association with cancer progression. This research aimed to deeply dive into the role and possible molecular mechanisms of EZH2 and its downstream genes in malignant progression and DNA damage repair of LUAD cells. METHODS: Expression of EZH2 in LUAD cells was analyzed by qRT-PCR, and the effects of EZH2 on proliferation, and apoptosis of LUAD cells were examined by CCK-8, colony formation and flow cytometry assays. The downstream targets of EZH2 were predicted by bioinformatics analysis. Then, the targeting relationship between EZH2 and RAI2 was examined by CHIP and luciferase reporter assays. Rescue assay were used to further validate the effect of EZH2/RAI2 on the malignant progression of LUAD cells. The expression levels of EZH2, RAI2 and p53 were examined by Western blot. RESULTS: Upregulation of EZH2 was identified in LUAD tissues and cells. RAI2 was a downstream target gene of EZH2, and the two were negatively correlated. Silencing EZH2 suppressed proliferation of LUAD cells, promoted expression of p53, cell cycle arrest and apoptosis. While silencing RAI2 could reverse the above-mentioned effects caused by EZH2 silencing. CONCLUSION: These results demonstrated that EZH2 promoted malignant progression and DNA damage repair of LUAD cells by targeting and negatively regulating RAI2.

Identification of a dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in lung squamous cell carcinoma.

Lung cancer is primarily responsive for cancer death, and its progression is aggressively affected by copy number variation (CNV). Through bioinformatics approach, a ceRNA network of CNV-driven lncRNAs in lung squamous cell carcinoma (LUSC) patients was constructed. Data on normal and LUSC tumor tissue from The Cancer Genome Atlas (TCGA)-LUSC dataset were subjected to differential analysis, and differentially expressed lncRNAs (DElncRNAs), DEmiRNAs, and DEmRNAs were obtained. Based on TCGA-LUSC, CNVs of normal and tumor tissue samples were then compared using a Chi-square test, and lncRNAs were intersected based on their CNVs and expression alternation. In combination with the Kruskal-Wallis test, CNV-driven lncRNAs were acquired. Afterwards, miRNAs and mRNAs that interacted with CNV-driven lncRNAs were obtained based on databases (LncBase, starBase, miRDB, mirDIP and TargetScan), DElncRNAs, DEmiRNAs and DEmRNAs, and correlation analysis. The acquired lncRNAs, miRNAs and mRNAs were subjected to Cytoscape software to construct a CNV-driven ceRNA network, which involved 5 lncRNAs (MIR143HG, LINC00702, MIR22HG, RP11-180 N14.1, RP11-473 M20.9), 6 miRNAs (miR-3200-3p, miR-1301-3p, miR-93-3p, miR-96-5p, miR-96-5p, miR-130b-5p, miR-205-5p) and 80 mRNAs. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses indicated that downstream mRNAs were mainly correlated with blood vessel development and T cell-mediated immunity. In summary, we devoted to analyzing CNV-related lncRNAs, mRNAs, and miRNAs in LUSC, thus clarifying 5 lncRNAs that may influence the malignant progression of LUSC. The ceRNA network regulated by these lncRNAs may be the novel pathogenesis of LUSC.

MiR-196b Promotes the Invasion and Migration of Lung Adenocarcinoma Cells by Targeting AQP4.

OBJECTIVE: We aimed to investigate the mechanism of the regulatory axis of miR-196b/AQP4 underlying the invasion and migration of lung adenocarcinoma (LUAD) cells. METHODS: LUAD miRNA and mRNA expression profiles were downloaded from TCGA database and then differential analysis was used to identify the target miRNA. Target gene for the miRNA was obtained via prediction using 3 bioinformatics databases and intersection with the differentially expressed mRNAs searched from TCGA-LUAD. Then, qRT-PCR and western blot were used to validate the expression of miR-196b and AQP4. Dual-luciferase reporter assay was performed to confirm the targeting relationship between miR-196b and AQP4. Transwell assay was used to investigate the migration and invasion of LUAD cells. RESULTS: MiR-196b was screened out by differential and survival analyses, and the downstream target gene AQP4 was identified. In LUAD, miR-196b was highly expressed while AQP4 was poorly expressed. Besides, overexpression of miR-196b promoted cell invasion and migration, while overexpression of AQP4 had negative effects. Moreover, the results of the dual-luciferase reporter assay suggested that AQP4 was a direct target of miR-196b. In addition, we also found that overexpressing AQP4 could suppress the promotive effect of miR-196b on cancer cell invasion and migration. CONCLUSION: MiR-196b promotes the invasion and migration of LUAD cells by down-regulating AQP4, which helps us find new molecular targeted therapies for LUAD.

Effectiveness of cognitive behavior therapy for sleep disturbance and glycemic control in persons with type 2 diabetes mellitus: A community-based randomized controlled trial in China.

BACKGROUND: Poor sleep quality is a common clinical feature in patients with type 2 diabetes mellitus (T2DM), and often negatively related with glycemic control. Cognitive behavioral therapy (CBT) may improve sleep quality and reduce blood sugar levels in patients with T2DM. However, it is not entirely clear whether CBT delivered by general practitioners is effective for poor sleep quality in T2DM patients in community settings. AIM: To test the effect of CBT delivered by general practitioners in improving sleep quality and reducing glycemic levels in patients with T2DM in community. METHODS: A cluster randomized controlled trial was conducted from September 2018 to October 2019 in communities of China. Overall 1033 persons with T2DM and poor sleep quality received CBT plus usual care or usual care. Glycosylated hemoglobin A1c (HbAlc) and sleep quality [Pittsburgh Sleep Quality Index (PSQI)] were assessed. Repeated measures analysis of variance and generalized linear mixed effects models were used to estimate the intervention effects on hemoglobin A1c and sleep quality. RESULTS: The CBT group had 0.64, 0.50, and 0.9 lower PSQI scores than the control group at 2 mo, 6 mo, and 12 mo, respectively. The CBT group showed 0.17 and 0.43 lower HbAlc values than the control group at 6 mo and 12 mo. The intervention on mean ΔHbAlc values was significant at 12 mo (t = 3.68, P < 0.01) and that mean ΔPSQI scores were closely related to ΔHbAlc values (t = 7.02, P < 0.01). Intention-to-treat analysis for primary and secondary outcomes showed identical results with completed samples. No adverse events were reported. CONCLUSION: CBT delivered by general practitioners, as an effective and practical method, could reduce glycemic levels and improve sleep quality for patients with T2DM in community.

Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering.

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast, and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization, and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, namely INSPIRE, IOSTAR, VICAVR, DRIVE, and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community.

Divergence of Gradient Convolution: Deformable Segmentation With Arbitrary Initializations.

In this paper, we propose a unified approach to deformable model-based segmentation. The fundamental force field of the proposed method is based on computing the divergence of a gradient convolution field (GCF), which makes the full use of directional information of the image gradient vectors and their interactions across image domain. However, instead of directly using such a vector field for deformable segmentation as in the conventional approaches, we derive a more salient representation for contour evolution, and very importantly, we demonstrate that this representation of image force field not only leads to global minimum through convex relaxation but also can achieve the same result using the conventional gradient descent with an intrinsic regularization. Thus, the proposed method can handle arbitrary initializations. The proposed external force field for deformable segmentation has both edge-based properties in that the GCF is computed from image gradients, and the region-based attributes since its divergence can be treated as a region indication function. Moreover, nonlinear diffusion can be conveniently applied to GCF to improve its performance in dealing with noise interference. We also show the extension of GCF from 2D to 3D. In comparison to the state-of-the-art deformable segmentation techniques, the proposed method shows greater flexibility in model initialization and optimization realization, as well as better performance toward noise interference and appearance variation.

[Fluorescence property of OBBE and its application].

The effects of acidity and temperature on the fluorescent characteristics of N-N-bis-(O-Carboxylphenyl)-Oxamide (OBBE) have been studied in details. And the quantum efficiency of fluorescence has been measured. In a pH 8-9.5 buffer medium of H3BO3-NaOH, OBBE has a maximum excitation wavelength at 210 nm and a maximum emission wavelength at 395 nm. The nitrate reacts with OBBE and quenches the fluorescent intensity of OBBE. Thus a new sensitive method using the quenching of synchronous fluorimetry has been developed for the direct determination of nitrate in water. The linear calibration curve was obtained in the range of 0.0028 and 0.16 mg.L-1, the detection limit was 0.0028 mg.L-1. The proposed method has been applied to the determination of nitrate in water with satisfactory results.

Enhancement of fiber orientation distribution reconstruction in diffusion-weighted imaging by single channel blind source separation.

In diffusion-weighted imaging (DWI), reliable fiber tracking results rely on the accurate reconstruction of the fiber orientation distribution function (fODF) in each individual voxel. For high angular resolution diffusion imaging (HARDI), deconvolution-based approaches can reconstruct the complex fODF and have advantages in terms of computational efficiency and no need to estimate the number of distinct fiber populations. However, HARDI-based methods usually require relatively high b-values and a large number of gradient directions to produce good results. Such requirements are not always easy to meet in common clinical studies due to limitations in MRI facilities. Moreover, most of these approaches are sensitive to noise. In this study, we propose a new framework to enhance the performance of the spherical deconvolution (SD) approach in low angular resolution DWI by employing a single channel blind source separation (BSS) technique to decompose the fODF initially estimated by SD such that the desired fODF can be extracted from the noisy background. The results based on numerical simulations and two phantom datasets demonstrate that the proposed method achieves better performance than SD in terms of robustness to noise and variation in b-values. In addition, the results show that the proposed method has the potential to be applied to low angular resolution DWI which is commonly used in clinical studies.