Forbesmiller1252

The scheme provides an average Accuracy, Recall (Sensitivity), Precision, F1 score, Specificity, and AUC of 95.48%, 84.54%, 97.3%, 90.47%, 86.81% and 93.43% respectively.COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely "Author total activity", "Author total activity per year", and "Author tweets per year". The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.The vitality of commercial entities reflects the business condition of their surrounding area, the prediction of which helps identify the trend of regional development and make investment decisions. The indicators of business conditions, like revenues and profits, can be employed to make a prediction beyond any doubt. Unfortunately, such figures constitute business secrets and are usually publicly unavailable. Thanks to the rapid growing of location based social networks such as Yelp and Foursquare, massive amount of online data has become available for predicting the vitality of commercial entities. In this paper, a Spatio-Temporal Convolutional Residual Neural Network (STCRNN) is proposed for regional commercial vitality prediction, based on public online data, such as reviews and check-ins from mobile apps. Firstly, a commercial vitality map is built to indicate the popularity of business entities. Afterwards, a local convolutional neural network is employed to capture the spatial relationship of surrounding commercial districts on the vitality map. Then, a 3-dimension convolution is applied to deal with both recent and periodic variations, i.e., the sequential and seasonal changes of commercial vitality. Finally, long short-term memory is introduced to synthesize these two variations. In particular, a residual network is used to eliminate gradient vanishing and exploding, caused by the increase of depth of neural networks. Experiments on public Yelp datasets from 2013 to 2018 demonstrate that STCRNN outperforms the current methods in terms of mean square error.Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.Porphyromonas gingivalis is a keystone pathogen in periodontitis, a biofilm-mediated infection disease. This research aimed to investigate the effect of coumarin on P. gingivalis biofilm formation. We detected the antimicrobial effect on P. gingivalis planktonic growth, observed membrane structure and morphological change by TEM, and quantified membrane permeability by calcein-AM staining. The cell surface hydrophobicity, aggregation, and attachment were assessed. We also investigated different sub-MIC concentrations of coumarin on biofilm formation, and observed biofilm structureby confocal laser scanning microscopy. The biofilm-related gene expression was evaluated using real-time PCR. The results showed that coumarin inhibited P. gingivalis growth and damaged the cell morphology above 400 μM concentration. Coumarin did not affect cell surface hydrophobicity, aggregation, attachment, and the early stage of biofilm formation at sub-MIC concentrations. Still, it exhibited anti-biofilm effects for the late-stage and pre-formed biofilms dispersion. The biofilms after coumarin treatment became interspersed, and biofilm-related gene expression was downregulated. Coumarin also inhibited AI-2 activity and interacted with the HmuY protein by molecular docking analysis. Our research demonstrated that coumarin inhibited P. gingivalis biofilm formation through a quorum sensing system.

Our objectives were to assess the prevalence of specific side-effects experienced by current and recent contraceptive users, describe patterns of side-effects that users were concerned about, and share measurement lessons learned.

Data come from the PMA Ethiopia 2019 nationally-representative, cross-sectional survey. Our analytic sample included women who were current (weighted

=2190; unweighted

=2020) or recent (past 24 months; weighted

=627; unweighted

=622) users of a hormonal method or IUD. We provide descriptive statistics of the percentage of current/recent users who report currently/ever experiencing specific side-effects, not experiencing but being concerned about experiencing specific side-effects, and both currently experiencing and being concerned about experiencing specific side-effects. All analyses are stratified by method type (implant, injectable, pill) to explore variation by method.

Among current users, 648/2190 women (30%) reported experiencing any side-effect, while 252/644 (articularly headaches, is high among hormonal contraceptive and IUD users in Ethiopia. counseling that addresses an array of side-effects is needed. Additional research is also needed to disentangle the effect of experiencing versus fearing side-effects on contraceptive use.

Experiencing and fearing contraceptive-induced menstrual bleeding changes and physical discomfort, particularly headaches, is high among hormonal contraceptive and IUD users in Ethiopia. read more counseling that addresses an array of side-effects is needed. Additional research is also needed to disentangle the effect of experiencing versus fearing side-effects on contraceptive use.Background?PD-1 ablation or PD-L1 specific monoclonal antibody against PD-1 can recruit the accumulation of functional T cells, leading to tumor rejection in the microenvironment and significantly improving the prognosis of various cancers. Despite these unprecedented clinical successes, intervention remission rates remain low after treatment, rarely exceeding 40%. The observation of PD-1/L1 blocking in patients is undoubtedly multifactorial, but the infiltrating degree of CD8+T cell may be an important factor for immunotherapeutic resistance. MethodsWe proposed two computational algorithms to reveal the immune cell infiltration (ICI) landscape of 1646 lung adenocarcinoma patients. Three immune cell infiltration patterns were defined and the relative ICI scoring depended on principal-component analysis. ResultsA high ICI score was associated with the increased tumor mutation burden and cell proliferation-related signaling pathways. Different cellular signaling pathways were observed in low ICI score subtypes, indicating active cell proliferation, and may be associated with poor prognosis. ConclusionOur research identified that the ICI scores worked as an effective immunotherapy index, which may provide promising therapeutic strategies on immune therapeutics for lung adenocarcinoma.We present a 67-year-old male patient who presented with insidious worsening of right hip pain over a 6-month period with clinical and radiographic evidence of severe osteoarthritis. The patient underwent a primary total hip arthroplasty where the femoral head specimen was sent to pathology as a routine specimen. Pathology results demonstrated metastatic adenocarcinoma of prostate origin. The present case emphasizes the importance of routine pathologic examination of femoral head specimens retrieved during total hip arthroplasty, particularly since this was a clinically unsuspected finding. Although cases like these are rare and the process of routine pathologic examination raises a concern for economic implications, a timely diagnosis of adenocarcinoma provides benefits for the patient, for which cost-benefit ratios are difficult to quantify.