Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma.

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Partial purification and characterization of protease extracted from kinema.

Proteases are large group of highly demanded enzymes having huge application in food and pharmaceutical industries. Numerous sources, including plants, microorganisms, and animals, can be used to obtain protease. Due to its affordability and safety consideration, fermented foods have recently attracted more attention as a source of microbial protease. The present study aimed to extract protease from kinema, partially purify the extracted protease following dialysis after precipitation with ammonium sulfate, and determine general characteristics of protease. The kinema having highest proteolysis activity after three days of control fermentation (Temperature 30±2 °C, RH 66 ± 2%) was taken for the study. About 2.45 fold of purification with overall recovery of 63.21% was achieved after precipitation with ammonium sulfate at 30-70% saturation level followed by dialysis of crude extracted protease. The dialysed kinema protease had specific activity of 7.90 U/mg. The enzyme remained actively functional across a wider pH (5-9) and temperature (40-60 °C) range. SDS-PAGE and Zymogram confirmed the presence of three major active bands respectively of 29.04 kDa, 36.09 kDa and 46.35 kDa in the kinema protease extract. The enzyme kinetics data on casein, fitted to Mechaelis Mentens' plots showed the protease had Vmax of 1.001 U/ml with corresponding Km value of 0.825 mg/ml. Metal ions such as iron, mercury and aluminium showed the inhibition effect whereas presence of sodium, zinc, and calcium shows the activation effect on protease performance. The enzyme was active over various natural substrates; showing maximal activity on casein, and subsequent to bovine serum albumin, gelatin, hemoglobin and whey protein respectively. Furthermore, molecular weight distribution of the protease extract and activity inhibition with ethylenediaminetetraacetic acid and phenylmethylsulfonyl fluoride, suggesting the protease from kinema could be a metal dependent serine protease or mixture of them.

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application.

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML-CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/.

Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture.

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.

Insecticidal toxicity of essential oil of Nepalese Acorus calamus (Acorales:Acoraceae) against Sitophilus zeamais (Coleoptera:Curculionidae).

Maize weevil (Sitophilus zeamais) (Coleoptera:Curculionidae) is an economic stored grain pest that causes significant damage to various stored products, including maize (Zea mays). In this study, we extracted essential oil from the rhizome of sweet flag (Acorus calamus) (Acorales:Acoraceae) by hydro-distillation and tested insecticidal property of the oil at 7 concentrations (10, 5, 2.5, 1.25, 0.625, 0.3125, 0.15625 and control) against maize weevil (Sitophilus zeamais) at the National Entomology Research Center, Nepal Agricultural Research Council in the year 2020/2021. Three different experiments were conducted: scintillating vial bioassay, repellency test, and exposing weevils to oil treated maize grains. Scintillating vial bioassay showed that higher the concentration of essential oil, lower the time required to cause 50 % maize weevil mortality. Median lethal concentration (LC50) at 3 and 24 h was calculated as 2.29 and 0.16 % of oil concentration in scintillating vial bioassay. When oil is treated to maize grain, LC50 for 10 and 16 days was calculated as 2.77 and 0.23 % of oil concentrations. In the same way, at 10 % concentration maize weevil showed highest repellent activity (98.75 %) as compared to 5, 2.5 and 1.25 % concentrations after 24 h of treatment. Weight loss and grain damage were significantly less in the oil treatments than the control. However, from the perspective of health benefits, Acorus calamus treated maize is still questionable for feed and food purpose. As ß asarone has carcinogenic effects at certain level, it needs further residue tests of treated maize to know allowable maximum residue limit (MRL) before consumption as food or feed.

4mC-CGRU: Identification of N4-Methylcytosine (4mC) sites using convolution gated recurrent unit in Rosaceae genome.

An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs. In our research work, we propose a 4mC-CGRU which is a deep learning-based computational model with a standard encoding method to identify the 4mC sites from DNA sequences that learned autonomous feature selection in the Rosaceae genome, particularly in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca). The proposed model consists of a convolutional neural network (CNN) and a gated recurrent unit network (GRU)-based model for identifying 4mC sites from Fragaria vesca and Rosa chinensis in the genomes. The CNN model extracts useful features from the datasets and the GRU classifies the DNA sequences. Thus, our approach can automatically extract important features to detect relative sites from DNA sequence. The performance analysis shows that the proposed model consistently outperforms over the state-of-the-art works in detecting 4mC sites.

Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture.

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data.

Biomarker-based cancer identification and classification tools are widely used in bioinformatics and machine learning fields. However, the high dimensionality of microarray gene expression data poses a challenge for identifying important genes in cancer diagnosis. Many feature selection algorithms optimize cancer diagnosis by selecting optimal features. This article proposes an ensemble rank-based feature selection method (EFSM) and an ensemble weighted average voting classifier (VT) to overcome this challenge. The EFSM uses a ranking method that aggregates features from individual selection methods to efficiently discover the most relevant and useful features. The VT combines support vector machine, k-nearest neighbor, and decision tree algorithms to create an ensemble model. The proposed method was tested on three benchmark datasets and compared to existing built-in ensemble models. The results show that our model achieved higher accuracy, with 100% for leukaemia, 94.74% for colon cancer, and 94.34% for the 11-tumor dataset. This study concludes by identifying a subset of the most important cancer-causing genes and demonstrating their significance compared to the original data. The proposed approach surpasses existing strategies in accuracy and stability, significantly impacting the development of ML-based gene analysis. It detects vital genes with higher precision and stability than other existing methods.

A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is one of the leading causes of dementia among older people. In addition, a considerable portion of the world's population suffers from metabolic problems, such as Alzheimer's disease and diabetes. Alzheimer's disease affects the brain in a degenerative manner. As the elderly population grows, this illness can cause more people to become inactive by impairing their memory and physical functionality. This might impact their family members and the financial, economic, and social spheres. Researchers have recently investigated different machine learning and deep learning approaches to detect such diseases at an earlier stage. Early diagnosis and treatment of AD help patients to recover from it successfully and with the least harm. This paper proposes a machine learning model that comprises GaussianNB, Decision Tree, Random Forest, XGBoost, Voting Classifier, and GradientBoost to predict Alzheimer's disease. The model is trained using the open access series of imaging studies (OASIS) dataset to evaluate the performance in terms of accuracy, precision, recall, and F1 score. Our findings showed that the voting classifier attained the highest validation accuracy of 96% for the AD dataset. Therefore, ML algorithms have the potential to drastically lower Alzheimer's disease annual mortality rates through accurate detection.

Detection and explanation of anomalies in healthcare data.

The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field; however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4-Methylcytosine Using Deep Learning Approach.

DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction-modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method "word2vec" and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model's efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.

A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?

For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds. Therefore, in this article, we review state-of-the-art works in both years 2020 and 2021 by considering AI-guided tools to analyze cough sound for COVID-19 screening primarily based on machine learning algorithms. In our study, we used PubMed central repository and Web of Science with key words: (Cough OR Cough Sounds OR Speech) AND (Machine learning OR Deep learning OR Artificial intelligence) AND (COVID-19 OR Coronavirus). For better meta-analysis, we screened for appropriate dataset (size and source), algorithmic factors (both shallow learning and deep learning models) and corresponding performance scores. Further, in order not to miss up-to-date experimental research-based articles, we also included articles outside of PubMed and Web of Science, but pre-print articles were strictly avoided as they are not peer-reviewed.

Origin of novel coronavirus causing COVID-19: A computational biology study using artificial intelligence.

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI)-based unsupervised learning algorithms and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Cushing Syndrome in a Pediatric Patient with Topical Steroid Overuse.

Cushing syndrome is a state of hypercortisolism from exogenous or endogenous exposure to glucocorticoids resulting in various clinical manifestations. In this case report, we present a case of a 15-month-old child who presented with cushingoid facies due to over-the-counter misuse of a very potent topical steroid (clobetasol 0.05%) for suspected scabies. Laboratory measurement of urinary free cortisol level was low, and 8 : 00 am basal cortisol level was measured, which was decreased, which confirmed the diagnosis of Cushing syndrome due to exogenous source. Over-the-counter topical steroids should not be used, and one should always consult a registered medical practitioner before using such products. Physicians when prescribing topical steroids should warn patients about the potential side effects of prolonged use of topical steroids.

Fusion of multi-scale bag of deep visual words features of chest X-ray images to detect COVID-19 infection.

Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).

New bag of deep visual words based features to classify chest x-ray images for COVID-19 diagnosis.

PURPOSE: Because the infection by Severe Acute Respiratory Syndrome Coronavirus 2 (COVID-19) causes the Pneumonia-like effect in the lung, the examination of Chest X-Rays (CXR) can help diagnose the disease. For automatic analysis of images, they are represented in machines by a set of semantic features. Deep Learning (DL) models are widely used to extract features from images. General deep features extracted from intermediate layers may not be appropriate to represent CXR images as they have a few semantic regions. Though the Bag of Visual Words (BoVW)-based features are shown to be more appropriate for different types of images, existing BoVW features may not capture enough information to differentiate COVID-19 infection from other Pneumonia-related infections. METHODS: In this paper, we propose a new BoVW method over deep features, called Bag of Deep Visual Words (BoDVW), by removing the feature map normalization step and adding the deep features normalization step on the raw feature maps. This helps to preserve the semantics of each feature map that may have important clues to differentiate COVID-19 from Pneumonia. RESULTS: We evaluate the effectiveness of our proposed BoDVW features in CXR image classification using Support Vector Machine (SVM) to diagnose COVID-19. Our results on four publicly available COVID-19 CXR image datasets (D1, D2, D3, and D4) reveal that our features produce stable and prominent classification accuracy (82.00% on D1, 87.86% on D2, 87.92% on D3, and 83.22% on D4), particularly differentiating COVID-19 infection from other Pneumonia. CONCLUSION: Our method could be a very useful tool for the quick diagnosis of COVID-19 patients on a large scale.

Vector representation based on a supervised codebook for Nepali documents classification.

Document representation with outlier tokens exacerbates the classification performance due to the uncertain orientation of such tokens. Most existing document representation methods in different languages including Nepali mostly ignore the strategies to filter them out from documents before learning their representations. In this article, we propose a novel document representation method based on a supervised codebook to represent the Nepali documents, where our codebook contains only semantic tokens without outliers. Our codebook is domain-specific as it is based on tokens in a given corpus that have higher similarities with the class labels in the corpus. Our method adopts a simple yet prominent representation method for each word, called probability-based word embedding. To show the efficacy of our method, we evaluate its performance in the document classification task using Support Vector Machine and validate against widely used document representation methods such as Bag of Words, Latent Dirichlet allocation, Long Short-Term Memory, Word2Vec, Bidirectional Encoder Representations from Transformers and so on, using four Nepali text datasets (we denote them shortly as A1, A2, A3 and A4). The experimental results show that our method produces state-of-the-art classification performance (77.46% accuracy on A1, 67.53% accuracy on A2, 80.54% accuracy on A3 and 89.58% accuracy on A4) compared to the widely used existing document representation methods. It yields the best classification accuracy on three datasets (A1, A2 and A3) and a comparable accuracy on the fourth dataset (A4). Furthermore, we introduce the largest Nepali document dataset (A4), called NepaliLinguistic dataset, to the linguistic community.

Fusion of whole and part features for the classification of histopathological image of breast tissue.

PURPOSE: Nowadays Computer-Aided Diagnosis (CAD) models, particularly those based on deep learning, have been widely used to analyze histopathological images in breast cancer diagnosis. However, due to the limited availability of such images, it is always tedious to train deep learning models that require a huge amount of training data. In this paper, we propose a new deep learning-based CAD framework that can work with less amount of training data. METHODS: We use pre-trained models to extract image features that can then be used with any classifier. Our proposed features are extracted by the fusion of two different types of features (foreground and background) at two levels (whole-level and part-level). Foreground and background feature to capture information about different structures and their layout in microscopic images of breast tissues. Similarly, part-level and whole-level features capture are useful in detecting interesting regions scattered in high-resolution histopathological images at local and whole image levels. At each level, we use VGG16 models pre-trained on ImageNet and Places datasets to extract foreground and background features, respectively. All features are extracted from mid-level pooling layers of such models. RESULTS: We show that proposed fused features with a Support Vector Classifier (SVM) produce better classification accuracy than recent methods on BACH dataset and our approach is orders of magnitude faster than the best performing recent method (EMS-Net). CONCLUSION: We believe that our method would be another alternative in the diagnosis of breast cancer because of performance and prediction time.