Bactericidal activity of biosynthesized silver nanoparticles against human pathogenic bacteria

Green synthesis is an attractive and eco-friendly approach to generate potent antibacterial silver nanoparticles (Ag-NPs). Such particles have long been used to fight bacteria and represent a promising tool to overcome the emergence of antibiotic-resistant bacteria. In this study, green synthesis of Ag-NPs was attempted using plant extracts of Aloe vera, Portulaca oleracea and Cynodon dactylon. The identity and size of Ag-NPs was characterized by ultraviolet–visible spectrophotometer and scanning electron microscopy. Monodispersed Ag-NPs were produced with a range of different sizes based on the plant extract used. The bactericidal activity of Ag-NPs against a number of human pathogenic bacteria was determined using the disc diffusion method. The results showed that Gram positive bacteria were more susceptible than Gram negative ones to these antibacterial agents. The minimum inhibitory concentration was determined using the 96-well plate method. Finally, the mechanism by which Ag-NPs affect bacteria was investigated by SEM analysis. Bacteria treated with Ag-NPs were seen to undergo shrinkage and to lose their viability. This study provides evidence for a cheap and effective method for synthesizing potent bactericidal Ag-NPs and demonstrates their effectiveness against human pathogenic bacteria

Pitfalls and recommended strategies and metrics for suppressing motion artifacts in functional MRI

AbstractIn resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson’s Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust motion correction metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.</jats:p

The BLENDS Method for Data Augmentation of 4-Dimensional Brain Images

ABSTRACTPurposeData augmentation improves the accuracy of deep learning models when training data is scarce by synthesizing additional samples. This work addresses the lack of validated augmentation methods specific for synthesizing anatomically realistic 4D (3D+time) images for neuroimaging, such as fMRI, by proposing a new augmentation method.Materials and MethodsThe proposed method, BLENDS, generates new nonlinear warp fields by combining intersubject coregistration maps, computed using symmetric normalization, through spatial blending. These new warp fields can be applied to existing 4D fMRI to create new augmented images. BLENDS is tested on two neuroimaging problems using de-identified datasets: 1) the prediction of antidepressant response from task-based fMRI in the EMBARC dataset (n = 163), and 2) the prediction of Parkinson’s Disease symptom trajectory from baseline resting-state fMRI regional homogeneity in the PPMI dataset (n = 43).ResultsBLENDS readily generates hundreds of new fMRI from existing images, with unique anatomical variations from the source images, that significantly improve prediction performance. For antidepressant response prediction, augmenting each original image once (2x the original training data) significantly increased prediction R2 from 0.055 to 0.098 (p &lt; 1e-6), while at 10x augmentation R2 increased to 0.103. For the prediction of Parkinson’s Disease trajectory, 10x augmentation R2 increased from 0.294 to 0.548 (p &lt; 1e-6).ConclusionAugmentation of fMRI through nonlinear transformations with BLENDS significantly improves the performance of deep learning models on clinically relevant predictive tasks. This method will help neuroimaging researchers overcome dataset size limitations and achieve more accurate predictive models.</jats:sec

Predicting Longitudinal Disease Severity for Individuals with Parkinson’s Disease using Functional MRI and Machine Learning Prognostic Models

AbstractParkinson’s disease is the second most common neurodegenerative disorder and is characterized by the loss of ability to control voluntary movements. Predictive biomarkers of progression in Parkinson’s Disease are urgently needed to expedite the development of neuroprotective treatments and facilitate discussions about disease prognosis between clinicians and patients. Resting-state functional magnetic resonance imaging (rs-fMRI) shows promise in predicting progression, with derived measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), having been previously been associated withcurrentdisease severity. In this work, ReHo and fALFF features from 82 Parkinson’s Disease subjects are used to train machine learning predictors of baseline clinical severity and progression at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Depression Rating Scale (MDS-UPDRS) score. This is the first time that rs-fMRI and machine learning have been combined to predictfuturedisease progression. The machine learning models explain up to 30.4% (R2= 0.304) of the variance in baseline MDS-UPDRS scores, 55.8% (R2= 0.558) of the variance in year 1 scores, and 47.1% (R2= 0.471) of the variance in year 2 scores with high statistical significance (p &lt;0.0001). For distinguishing high- and low-progression individuals (MDS-UPDRS score above or below the median), the models achieve positive predictive values of up to 71% and negative predictive values of up to 84%. The models learn patterns of ReHo and fALFF measures that predict better and worse prognoses. Higher ReHo and fALFF in regions of the default motor network predicted lower current severity and lower future progression. The rs-fMRI features in the temporal lobe, limbic system, and motor cortex were also identified as predictors. These results present a potential neuroimaging biomarker that accurately predicts progression, which may be useful as a clinical decision-making tool and in future trials of neuroprotective treatments.</jats:p