Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.

BACKGROUND: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Nevertheless, accurately identifying various lesion features, such as irregular sizes, shapes, colors, and textures, remains challenging in this field. OBJECTIVE: Several computer vision algorithms have been introduced to tackle these challenges, but many relied on handcrafted features, resulting in inaccuracies in various instances. METHODS: In this work, a novel Deep SS-Hexa model is proposed which is a combination two different deep learning structures for extracting two different features from the WCE images to detect various GIT ailment. The gathered images are denoised by weighted median filter to remove the noisy distortions and augment the images for enhancing the training data. The structural and statistical (SS) feature extraction process is sectioned into two phases for the analysis of distinct regions of gastrointestinal. In the first stage, statistical features of the image are retrieved using MobileNet with the support of SiLU activation function to retrieve the relevant features. In the second phase, the segmented intestine images are transformed into structural features to learn the local information. These SS features are parallelly fused for selecting the best relevant features with walrus optimization algorithm. Finally, Deep belief network (DBN) is used classified the GIT diseases into hexa classes namely normal, ulcer, pylorus, cecum, esophagitis and polyps on the basis of the selected features. RESULTS: The proposed Deep SS-Hexa model attains an overall average accuracy of 99.16% in GIT disease detection based on KVASIR and KID datasets. The proposed Deep SS-Hexa model achieves high level of accuracy with minimal computational cost in the recognition of GIT illness. CONCLUSIONS: The proposed Deep SS-Hexa Model progresses the overall accuracy range of 0.04%, 0.80% better than GastroVision, Genetic algorithm based on KVASIR dataset and 0.60%, 1.21% better than Modified U-Net, WCENet based on KID dataset respectively.

Paradigm Shift in the Arena of Sample Preparation and Bioanalytical Approaches Involving Liquid Chromatography Mass Spectroscopic Technique.

Sample preparation is a highly important and integral part of bioanalysis for cleaning up the complex biological matrices and thereby minimizing matrix effect. Matrix effect can jeopardize the precise quantification and adversely affect the reliability of liquid chromatography-mass spectrometry-based analytical results by alteration of analyte ionization. Matrix components result in suppression or enhancement of the intensity of analyte response. In spite of the high specificity and selectivity of tandem mass spectrometry, a relatively higher concentration of coeluted matrix elements present in biofluids may alter the efficiency of quantification of a bioanalytical method. Numerous literature reports different types of sample preparation techniques employed in bioanalysis. In this review, the strategies for selection of the appropriate sample clean-up technique in bioanalysis are discussed extensively. A paradigm shift in the arena of sample preparation and bioanalytical approaches involving the liquid chromatography-mass spectroscopic technique has been scrutinized. Current trends and possible future advancements in the field of biological sample extraction methods, including instrumental techniques are analyzed in detail.

Removal of toxic heavy metal lead (II) using chitosan oligosaccharide-graft-maleic anhydride/polyvinyl alcohol/silk fibroin composite.

The present work was aimed to investigate the efficiency of novel chitosan oligosaccharide-graft-maleic anhydride(COS-g-MAH)/Polyvinyl alcohol (PVA)/silk fibroin (SF) composite for removing the toxic heavy metal lead (II) ion from aqueous solution by batch adsorption studies. Initially the chitosan oligosaccharide-graft-maleic anhydride copolymer has been prepared by utilizing ceric ammonium nitrate as an initiator and the optimised graft copolymer was then used for synthesizing COS-g-MAH/PVA/SF composite. The prepared samples were analyzed through FTIR and XRD studies. The FTIR results indicate that the grafted chitosan oligosaccharide copolymer was mixed homogeneously with silk fibroin and polyvinyl alcohol through intermolecular hydrogen bonding. The XRD results elucidate the changes in the crystalline behaviour of the prepared COS-g-MAH/PVA/silk fibroin composite. Both FTIR and XRD results revealed a strong interaction among COS-g-MAH, PVA and silk fibroin components. To evaluate the adsorption potential of the synthesized composite, the parameters such as pH, adsorbent dosage, contact time and initial Pb(II)ion concentration was investigated. The adsorption isotherms of Pb(II) could be described very well by Langmuir model and the kinetic results revealed that pseudo second order kinetics shows a better fit. This work provides a practical and high-efficient method for water treatment at moderate concentration of toxic heavy metals.

Comparative evaluation of tensile bond strengths of total-etch adhesives and self-etch adhesives with single and multiple consecutive applications: An in vitro study.

AIM: This study evaluates the effect of single and multiple consecutive applications of adhesives on the tensile bond strength. The currently available adhesives follow either the total-etch or the self-etch concept. However, in both techniques the uniformity and thickness of the adhesive layer plays a significant role in the development of a good bond. MATERIALS AND METHODS: Sixty composite-dentin bonded specimens were prepared using a total-etch adhesive (Gluma) and another 60 using a self-etch adhesive (AdheSE). Each group was further divided into six subgroups based on the number of applications, i.e., single application and multiple (2, 3, 4, 6, and 8) applications. The tensile bond strength was tested with the Instron universal testing machine. The values were analyzed with one-way ANOVA and multiple range tests by Tukey's HSD procedure to identify those subgroups that had significantly higher bond strength. RESULTS: The results indicate that with total-etch adhesive the bond strength increases significantly as the number of applications are increased from one to two or from two to three", for self-etch adhesive the bond strength obtained with two applications is significantly higher than that with one application. However, for both adhesive systems, there was a decrease in the tensile bond strength values with further applications. CONCLUSION: We conclude that, in the clinical setting, the application of multiple coats of total etch adhesive improves bonding.