•World Health Organization (WHO) reported globally that adult diabetes patients have nearly doubled since 1980, rising from 4.7% to 8.5%. In 2012, 1.5 million people died due to diabetes.•The early ...identification of disease serves to recognize and stay away from its complications. Machine learning models help to obtain an initial stage recognition about diabetes disease based on physical data and it has shown their abilities to efficiently and strongly deal with high numbers of variables while making strong predictive models.•Therefore, authors have proposed an ensemble of machine learning algorithms viz. random forest, logistic regression, and Naïve Bayes with soft voting classifier for the binary classification of disease into positive and negative.•Accuracy, Precision, Recall, F1-score, AUC value has been taken as the evaluation criteria.•Two datasets have been used for experimentation i.e. PIMA diabetes dataset and breast cancer dataset. The performance of the proposed methodology has been compared and analysed with conventional machine learning algorithms using both the datasets.
Diabetes is a dreadful disease identified by escalated levels of glucose in the blood. Machine learning algorithms help in identification and prediction of diabetes at an early stage. The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms. The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes. The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz. random forest, logistic regression, and Naive Bayes for the classification. Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naïve Bayes, Bagging, GradientBoost, XGBoost, CatBoost. by taking accuracy, precision, recall, F1-score as the evaluation criteria. The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 79.04%, 73.48%, 71.45% and 80.6% respectively on the PIMA diabetes dataset. Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset. The proposed ensemble soft voting classifier has given 97.02% accuracy on the breast cancer dataset.
An entity's existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be ...state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models-Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)-detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.
•Fake News has been a concern all over the world and social media has only amplified this phenomenon and it has been affecting the world on a large scale as these are targeted to sway the decisions ...of the crowd in a particular direction.•The manual verification of the legitimacy of news is a tedious process and takes time. Thus, using machine learning and deep learning techniques allows the classification easily and efficiently.•The research thus proposes the same approach to fake news classification using the BERT model with an LSTM for binary classification of news article into fake or legitimate.•Accuracy, Precision, Recall, and F1 Score have been used as the evaluation criteria for evaluating the results produced by the model.•The FakeNewsNet Dataset was used for training and testing the model and was then compared to the baseline models using the evaluation criteria mentioned above.
Fake News has been a concern all over the world and social media has only amplified this phenomenon. Fake News has been affecting the world on a large scale as these are targeted to sway the decisions of the crowd in a particular direction. Since manually verifying the legitimacy of news is very hard and costly, there has been a great interest of researchers in this field. Different approaches to identifying fake news were examined, such as content-based classification, social context-based classification, image-based classification, sentiment-based classification, and hybrid context-based classification. This paper aims to propose a model for fake news classification based on news titles, following the content-based classification approach. The model uses a BERT model with its outputs connected to an LSTM layer. Training and evaluation of the model were done on the FakeNewsNet dataset which contains two sub-datasets, PolitiFact and GossipCop. A comparison of the model with base classification models has been done. A vanilla BERT model has also been trained on the dataset under similar constraints as the proposed model has to evaluate the impact same using an LSTM layer. The results obtained showed a 2.50% and 1.10% increase in accuracy on PolitiFact and GossipCop datasets respectively over the vanilla pre-trained BERT model.
Lymphoma microenvironment is a complex system composed of stromal cells, blood vessels, immune cells as well as extracellular matrix, cytokines, exosomes, and chemokines. In this review, we describe ...the function, localization, and interactions between various cellular components. We also summarize their contribution to lymphoma immunity in the era of immunotherapy. Publications were identified from searching Pubmed. Primary literature was carefully evaluated for replicability before incorporating into the review. We describe the roles of mesenchymal stem/stromal cells (MSCs), lymphoma-associated macrophages (LAMs), dendritic cells, cytotoxic T cells, PD-1 expressing CD4+ tumor infiltrating lymphocytes (TILs), T-cells expressing markers of exhaustion such as TIM-3 and LAG-3, regulatory T cells, and natural killer cells. While it is not in itself a cell, we also include a brief overview of the lymphoma exosome and how it contributes to anti-tumor effect as well as immune dysfunction. Understanding the cellular players that comprise the lymphoma microenvironment is critical to developing novel therapeutics that can help block the signals for immune escape and promote tumor surveillance. It may also be the key to understanding mechanisms of resistance to immune checkpoint blockade and immune-related adverse events due to certain types of immunotherapy.
Leukocytes, produced in the bone marrow, make up around one percent of all blood cells. Uncontrolled growth of these white blood cells leads to the birth of blood cancer. Out of the three different ...types of cancers, the proposed study provides a robust mechanism for the classification of Acute Lymphoblastic Leukemia (ALL) and Multiple Myeloma (MM) using the SN-AM dataset. Acute lymphoblastic leukemia (ALL) is a type of cancer where the bone marrow forms too many lymphocytes. On the other hand, Multiple myeloma (MM), a different kind of cancer, causes cancer cells to accumulate in the bone marrow rather than releasing them into the bloodstream. Therefore, they crowd out and prevent the production of healthy blood cells. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. The proposed model eradicates the probability of errors in the manual process by employing deep learning techniques, namely convolutional neural networks. The model, trained on cells' images, first pre-processes the images and extracts the best features. This is followed by training the model with the optimized Dense Convolutional neural network framework (termed DCNN here) and finally predicting the type of cancer present in the cells. The model was able to reproduce all the measurements correctly while it recollected the samples exactly 94 times out of 100. The overall accuracy was recorded to be 97.2%, which is better than the conventional machine learning methods like Support Vector Machine (SVMs), Decision Trees, Random Forests, Naive Bayes, etc. This study indicates that the DCNN model's performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset. Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.
Abiotic stresses adversely affect rice yield and productivity, especially under the changing climatic scenario. Exposure to multiple abiotic stresses acting together aggravates these effects. The ...projected increase in global temperatures, rainfall variability, and salinity will increase the frequency and intensity of multiple abiotic stresses. These abiotic stresses affect paddy physiology and deteriorate grain quality, especially milling quality and cooking characteristics. Understanding the molecular and physiological mechanisms behind grain quality reduction under multiple abiotic stresses is needed to breed cultivars that can tolerate multiple abiotic stresses. This review summarizes the combined effect of various stresses on rice physiology, focusing on grain quality parameters and yield traits, and discusses strategies for improving grain quality parameters using high-throughput phenotyping with
approaches.
The International Space Station (ISS) is a unique built environment due to the effects of microgravity, space radiation, elevated carbon dioxide levels, and especially continuous human habitation. ...Understanding the composition of the ISS microbial community will facilitate further development of safety and maintenance practices. The primary goal of this study was to characterize the viable microbiome of the ISS-built environment. A second objective was to determine if the built environments of Earth-based cleanrooms associated with space exploration are an appropriate model of the ISS environment.
Samples collected from the ISS and two cleanrooms at the Jet Propulsion Laboratory (JPL, Pasadena, CA) were analyzed by traditional cultivation, adenosine triphosphate (ATP), and propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR) assays to estimate viable microbial populations. The 16S rRNA gene Illumina iTag sequencing was used to elucidate microbial diversity and explore differences between ISS and cleanroom microbiomes. Statistical analyses showed that members of the phyla Actinobacteria, Firmicutes, and Proteobacteria were dominant in the samples examined but varied in abundance. Actinobacteria were predominant in the ISS samples whereas Proteobacteria, least abundant in the ISS, dominated in the cleanroom samples. The viable bacterial populations seen by PMA treatment were greatly decreased. However, the treatment did not appear to have an effect on the bacterial composition (diversity) associated with each sampling site.
The results of this study provide strong evidence that specific human skin-associated microorganisms make a substantial contribution to the ISS microbiome, which is not the case in Earth-based cleanrooms. For example, Corynebacterium and Propionibacterium (Actinobacteria) but not Staphylococcus (Firmicutes) species are dominant on the ISS in terms of viable and total bacterial community composition. The results obtained will facilitate future studies to determine how stable the ISS environment is over time. The present results also demonstrate the value of measuring viable cell diversity and population size at any sampling site. This information can be used to identify sites that can be targeted for more stringent cleaning. Finally, the results will allow comparisons with other built sites and facilitate future improvements on the ISS that will ensure astronaut health.
Emergence of atypical enteropathogenic Escherichia coli (EPEC) and hybrid E. coli (harboring genes of more than one DEC pathotypes) strains have complicated the issue of growing antibiotic resistance ...in diarrhoeagenic Escherichia coli (DEC). This ongoing evolution occurs in nature predominantly via horizontal gene transfers involving the mobile genetic elements like integrons notably class 1 integron. This study was undertaken to determine the virulence pattern and antibiotic resistance among the circulating DEC strains in a tertiary care center in south of India.
Diarrhoeal stool specimens were obtained from 120 children (< 5 years) and 100 adults (> 18 years), subjected to culture and isolation of diarrhoeal pathogens. Conventional PCR was performed to detect 10 virulence and 27 antimicrobial resistance (AMR) genes among the E. coli isolated.
DEC infection was observed in 45 (37.5%) children and 18 (18%) adults, among which 18 (40%), 10 (10%) atypical EPEC was most commonly detected followed by 6 (13.3%), 4 (4%) ETEC, 5 (11.1%) 2 (2%) EAEC, (3 (6.6%), 0 (0%) EIEC, 3 (6.6%), 0 (0% typical EPEC, and 4 (8.8%), 1 (1%) STEC, and no NTEC and CDEC was detected. DEC co-infection in 3 (6.6%) children, and 1(1%) adult and sole hybrid DEC infection in 3 (6.6%) children was detected. The distribution of sulphonamide resistance genes (sulI, sulII, and sulIII were 83.3 and 21%, 60.41 and 42.1%, and 12.5 and 26.3%, respectively) and class 1 integron (int1) genes (41.6 and 26.31%) was higher in DEC strains isolated from children and adults, respectively. Other AMR genes detected were qnrS, qnrB, aac(6')Ib-cr, dhfr1, aadB, aac(3)-IV, tetA, tetB, tetD, catI, blaCTX, blaSHV, and blaTEM. None harbored qnrA, qnrC, qepA, tetE, tetC, tetY, ermA, mcr1, int2, and int3 genes.
Atypical EPEC was a primary etiological agent of diarrhea in children and adults among the DEC pathotypes. Detection of high numbers of AMR genes and class 1 integron genes indicate the importance of mobile genetic elements in spreading of multidrug resistance genes among these strains.
INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis. ...OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis results. METHODS: AiCNNs model integrates 5 already trained models including simple convolutional neural networks (one uses a simple CNN while the other utilizes data augmentation) and three pre-trained networks whose weights are transferred from ImageNet dataset. RESULTS: AiCNNs model was trained on 3501 augmented T1-weighted contrast enhanced MRI (CE-MRI) brain images. Validation results of 99.49% (loss=0.0303) had been achieved by AiCNNs on a set of 1167 images, which outperform its contemporaries which have got results upto 97.81% (loss=0.1794) and 97.79% (loss=0.1787). CONCLUSION: AiCNNs has been shown to obtained a test accuracy of 98.89 % on a set of 1167 images
We present this case of coronavirus disease 2019-associated acute kidney injury with rhabdomyolysis-with noteworthy renal biopsy findings demonstrating myoglobin cast nephropathy-to add to the ...limited literature on coronavirus disease 2019-related acute kidney injury and rhabdomyolysis.
A 67-year-old Caucasian man presented to our hospital with 3 weeks of malaise and decreased oral intake and several days of abnormal taste, poor appetite, decrease urine output, gastrointestinal symptoms, and myalgias, and was ultimately diagnosed with coronavirus disease 2019. His hospital course was complicated by acute kidney injury and, upon workup of his renal failure, was diagnosed with myoglobin cast nephropathy due to coronavirus disease 2019-mediated rhabdomyolysis. Ultimately, his renal function improved following hydration back to his baseline 6 weeks after his initial diagnosis of coronavirus disease 2019.
Given our limited knowledge of manifestations of coronavirus disease 2019, it is important to have a more in-depth understanding of the spectrum of disease of coronavirus disease 2019, which can affect various organ systems, including the kidney, and the manifestations of end-organ damage associated with it. We present this case to highlight a rarely reported finding of myoglobin cast nephropathy due to coronavirus disease 2019-mediated rhabdomyolysis.
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