RSAFormer: A method of polyp segmentation with region self-attention transformer.

Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention enhancement network (RSAFormer) with a transformer encoder to capture more robust features. Different from other excellent methods, RSAFormer uniquely employs a dual decoder structure to generate various feature maps. Contrasting with traditional methods that typically employ a single decoder, it offers more flexibility and detail in feature extraction. RSAFormer also introduces a region self-attention enhancement module (RSA) to acquire more accurate feature information and foster a stronger interplay between low-level and high-level features. This module enhances uncertain areas to extract more precise boundary information, these areas being signified by regional context. Extensive experiments were conducted on five prevalent polyp datasets to demonstrate RSAFormer's proficiency. It achieves 92.2% and 83.5% mean Dice on Kvasir and ETIS, respectively, which outperformed most of the state-of-the-art models.

Novel UV-spectrophotometric method for quantitative estimation of furazolidone using mixed hydrotropic agent.

A novel, eco friendly, accurate, sensitive, economic and safe spectrophotometric method was developed by application of mixed hydrotropy using 2 M sodium acetate, 8 M urea, 2 M niacinamide and 2 M sodium benzoate solution (25:25:25:25% V/V) as hydrotropic agent, for the solubalizing of poorly water-soluble Furazolidone (FZ) (solubility:- 3.64e-01 mg/mL in water). There were more than 32 times enhancements in the solubility of FZ were found in mixed hydrotropic solution as compared to solubilities in distilled water. FZ shows maximum absorbance at 360 nm where sodium acetate, urea, niacinamide, sodium benzoate and other tablets excipients did not show any absorbance above 300 nm, and thus no interference in the estimation was seen. FZ was obeyed Beers law in the concentration range of 10 to 50 µg/ml (r(2)=0.9992) in mixed hydrotropic solvent with mean recovery ranging from 97.32% to 98.9%. Proposed method is new, simple, economic, safe, rapid, accurate and reproducible and was validated according to ICH guidelines and values of accuracy, precision and other statistical analysis were found to be in good accordance with the prescribed values.

CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams.

The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from online intimidating and trolling to lynching and riots in real- life. It has therefore become vital to use computational techniques that can detect rumours, do fact-checking and inhibit its amplification. In this paper, we put forward a model for rumour detection in streaming data on social platforms. The proposed CanarDeep model is a hybrid deep neural model that combines the predictions of a hierarchical attention network (HAN) and a multi-layer perceptron (MLP) learned using context-based (text + meta-features) and user-based features, respectively. The concatenated context feature vector is generated using feature-level fusion strategy to train HAN. Eventually, a decision-level late fusion strategy using logical OR combines the individual classifier prediction and outputs the final label as rumour or non-rumour. The results demonstrate improved performance to the existing state-of-the-art approach on the benchmark PHEME dataset with a 4.45% gain in F1-score. The model can facilitate well-time intervention and curtail the risk of widespread rumours in streaming social media by raising an alert to the moderators.

Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources.

In lung cancer, tumor histology is a significant predictor of treatment response and prognosis. Although tissue samples for pathologist view are the most pertinent approach for histology classification, current advances in DL for medical image analysis point to the importance of radiologic data in further characterization of disease characteristics as well as risk stratification. Cancer is a complex global health problem that has seen an increase in death rates in recent years. Progress in cancer disease detection based on subset traits has enabled awareness of significant as well as exact disease diagnosis, thanks to the rapid flowering of high-throughput technology as well as numerous ML techniques that have emerged in recent years. As a result, advanced ML approaches that can successfully distinguish lung cancer patients from healthy people are of major importance. This paper proposed lung tumor detection based on histopathological image analysis using deep learning architectures. Here, the input image is taken as a histopathological image, and it has also been processed for removing noise, image resizing, and enhancing the image. Then the image features are extracted using Kernel PCA integrated with a convolutional neural network (KPCA-CNN), in which KPCA has been used in the feature extraction layer of CNN. The classification of extracted features has been put into effect using a Fast Deep Belief Neural Network (FDBNN). Finally, the classified output will give the tumorous cell and nontumorous cell of the lung from the input histopathological image. The experimental analysis has been carried out for various histopathological image datasets, and the obtained parameters are accuracy, precision, recall, and F-measure. Confusion matrix gives the actual class and predicted class of tumor in an input image. From the comparative analysis, the proposed technique obtains enhanced output in detecting the tumor once compared with an existing methodology for the various datasets.

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.

On the Design of Secured and Reliable Dynamic Access Control Scheme of Patient E-Healthcare Records in Cloud Environment.

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance. All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients' medical records due to cloud computing. The cloud's services are very cost-effective and scalable, and provide various mobile access options for a patient's electronic health records (EHRs). EHR privacy and security are critical concerns despite the many benefits of the cloud. Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards. This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider. The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security. There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them. End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (KCF), and access to cloud services is granted based on a person's identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme. The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness. Using different Amazon EC2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases. The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant. The proposed secure access scheme for cloud-based web applications was compared to the Ethereum blockchain platform, which uses internet of things (IoT) devices in terms of execution time, throughput, and latency.

Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA.

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.

The Safety, Efficacy and Therapeutic Potential of Histone Deacetylase Inhibitors with Special Reference to Panobinostat in Gastrointestinal Tumors: A Review of Preclinical and Clinical Studies.

Histone deacetylase inhibitors (HDACi) have been demonstrated as an emerging class of anticancer drugs involved in regulation of gene expression and chromatin remodeling thus indicating valid targets for different types of cancer therapeutics. The pan-deacetylase inhibitor panobinostat (Farydac®, LBH589) is developed by Novartis Pharmaceuticals and a newly US FDA approved drug for the multiple myeloma. It is under clinical investigation for a range of hematological and solid tumors worldwide in both oral and intravenous formulations. Panobinostat inhibits tumor cell growth by interacting with acetylation of histones and nonhistone proteins as well as various apoptotic, autophagy-mediated targets and various tumorigenesis pathways involved in the development of cancer. The current article summarizes the status of panobinostat in gastrointestinal cancers. Preclinical and clinical data suggest that panobinostat has potential inhibitory activity in hepatocellular, pancreatic, colorectal, gastric and gastrointestinal stromal tumors. Clinical evaluations of panobinostat are currently underway. Herein, we have also reviewed the rationale behind the combination therapy under the trials and possible future prospective for the treatment of GI tumors.

A Novel Approach using Hydrotropic Solubalization Technique for Quantitative Estimation of Entacapone in Bulk Drug and Dosage Form.

PURPOSE: Analysis of drug utilized the organic solvent which are costlier, toxic and causing environment pollution. Hydrotropic solution may be a proper choice to preclude the use of organic solvents so that a simple, accurate, novel, safe and precise method has been developed for estimation of poorly water soluble drug Entacapone (Water Solubility-7.97e-(02) g/l). METHODS: Solubility of entacapone is increased by using 8M Urea as hydrotropic agent. There was more than 67 fold solubility enhanced in hydrotropic solution as compare with distilled water. The entacapone (ENT) shows the maximum absorbance at 378 nm. At this wavelength hydrotropic agent and other tablet excipients do not shows any significant interference in the spectrophotometric assay. RESULTS: The developed method was found to be linear in the range of 4-20 µg/ml with correlation coefficient (r(2)) of 0.9998. The mean percent label claims of tablets of ENT in tablet dosage form estimated by the proposed method were found to be 99.17±0.63. The developed methods were validated according to ICH guidelines and values of accuracy, precision and other statistical analysis were found to be in good accordance with the prescribed values. CONCLUSION: As hydrotropic agent used in the proposed method so this method is Ecofriendly and it can be used in routine quantitative analysis of drug in bulk drug and dosage form in industries.

Design of combretastatin A-4 analogs as tubulin targeted vascular disrupting agent with special emphasis on their cis-restricted isomers.

Tubulin protein is a highly imperative and feasible goal for anticancer drug discovery. Hundreds of naturally occurring, semi synthetic and synthetic antitubulin agents have been reported till now. Among these, Combretastatin A - 4 (CA - 4) is effective antimitotic agent possessing potent cytotoxicity against a panel of cancer cells, including multi-drug resistant cancer cell lines. The inadequate water solubility and inactivation of these analogs during storage limit their use as clinical anticancer agents. To overcome these shortcomings, numerous water soluble amino analogs, amino acid derivative, phosphate prodrug (CA - 4P) and cis-locked CA - 4 have been developed with distinctive attributes of antitubulin and antivascular properties in a wide variety of preclinical tumor models. Subsequently, several heterocycle based cis restricted CA - 4 analogs are being reported for antitumor activity against collection of cancer cell lines. This review recapitulates the rational design, structure activity relationship, pharmacokinetic and pharmacodynamic profile of synthesized cis restricted CA - 4 analogs.

The RP-HPLC method for simultaneous estimation of esomeprazole and naproxen in binary combination.

OBJECTIVE: A simple, precise, reliable, rapid, sensitive and validated RP-HPLC method has been developed to determine esomeprazole magnesium trihydrate (ESO) and naproxen (NAP) in synthetic mixture form. MATERIALS AND METHODS: Chromatographic separation achieved isocratically on Phenomenex, Luna C18 column (5 µm, 150mm × 4.60mm) and acetonitrile: phosphate buffer (pH 7.0) in the ratio of 50:50 (v/v) as the mobile phase, at a flow rate of 0.5 ml/min. Detection was carried out at 300 nm. The retention times for NAP and ESO was found to be 2.67 ±0.014 and 5.65 ±0.09 min respectively. Parameters such as linearity, precision, accuracy, recovery, specificity and ruggedness are studied as reported in the ICH guidelines. RESULTS: The method was linear in the concentration range of 50-250 µg/ml for NAP and 2-10 µg/ml for ESO with correlation coefficient of 0.999 and 0.998 respectively. The mean recoveries obtained for NAP and ESO were 100.01% and 97.76 % respectively and RSD was less than 2. The correlation coefficients for all components are close to 1. CONCLUSIONS: Developed method was found to be accurate, precise, selective and rapid for simultaneous estimation of NAP and ESO.

Development and validation of reversed phase-high-performance liquid chromatography method for determination of paracetamol and lornoxicam in tablet dosage form.

A simple, precise, reliable, rapid and reproducible reversed phase-high-performance liquid chromatography method was developed and validated for the simultaneous estimation of Paracetamol (PCM) and Lornoxicam (LOX) present in tablet dosage forms. Chromatographic separation achieved isocratically on Luna C18 column (5 µm, 150 × 4.60 mm) and methanol/phosphate buffer (60:40, v/v, pH 7.0) as mobile phase, at a flow rate of 1 ml/min. Detection was carried out at 260 nm. Parameters such as linearity, precision, accuracy, recovery, specificity and ruggedness are studied as reported in the ICH guidelines. The retention times for PCM and LOX was found to be 2.06±0.013and 4.38±0.07 min, respectively. Linearity for PCM and LOX was in the range of 10-50 mg/ml and 8-40 mg/ml, respectively. The mean recoveries obtained for LOX and PCM were 100± 0.16 and 99.50± 0.43%, respectively, and relative standard deviation (RSD) was less than 2. The correlation coefficients for all components are close to 1. The RSDs for three replicate measurements in three concentrations of samples in tablets are always less than 2%. Developed method was found to be accurate, precise, selective and rapid for simultaneous estimation of PCM and LOX in tablets.

Evaluation of anti-fertility activity of Tabernaemontana divaricata (Linn) R.Br. leaves in rats.

The aim of the present study was to assess the anti-fertility activity of ethanolic extracts of Tabernaemontana divaricata (TD) leaves in oestrogenic activity models in immature female rats. Mature green leaves of TD were collected and authenticated. Extractions of the dried leaves were carried out with ethanol in a Soxhlet's apparatus. For oestrogenic activity, the extracts were administered orally once daily at a dose of 200 and 400 mg kg(-1), and the activity was compared with the standard drug ethinyl oestradiol (0.02 mg). The extracts caused significant increase in uterine weight compared to the control. The ethanolic extract exhibited oestrogenic activity. The histological study of epithelium tissues with the 400 mg of TD extract-treated animals showed increases in the height of the luminal epithelium and loose edematous stroma when compared with the 200 mg of TD extract-treated group of animals. However, this was better than the control group of animals. Enhanced uterine weight and increase in the height of luminal epithelium and histological characteristics suggest that TD extract may be useful in anti-fertility therapy.

Evaluation of wound-healing activity of Acorus calamus Linn.

The aim of the present study was to assess the wound-healing activity of ethanolic extracts of Acorus calamus leaves. A wound was induced by an excision- and incision-based wound model in rats of either sex. The mature green leaves of A. calamus were collected and authenticated. Extractions of dried leaves were carried out with 80% ethanol in a soxhlet apparatus. For wound-healing activity, the extracts were applied topically once daily in conc. of 40% w/w and 20% w/w in the form of ointment and compared with a standard drug (povidion-iodine). The healing of the wound was assessed by the rate of wound closure, period of epithelialisation, tensile strength and weight of the granulation tissue, hydroxyproline content and histopathology of the granulation tissue. The ethanolic extract of A. calamus promoted wound-healing activity significantly in both the wound models studied. The histological study of the granulation tissue with 20% A. calamus extract ointment-treated animals showed a larger number of inflammatory cells and lesser collagen when compared with the 40% A. calamus extract ointment-treated animals. However, this was better than the control group of animals. Enhanced wound contraction, decreased epithelialisation time, increased hydroxyproline content and histological characteristics suggest that A. calamus extract may have therapeutic benefits in wound healing.