A systematic review on deep learning-based automated cancer diagnosis models.

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs.

Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

Nanoemulsion Formulation of Ocimum gratissimum Essential Oil and Its Antifungal Activity Against Penicillium digitatum.

Plant-derived essential oils and extracts are known to be effective against many microorganisms. The essential oil obtained from fresh leaves of Ocimum gratissimum possessed promising antifungal activity against Penicillium digitatum. The antifungal potential of O. gratissimum essential oil can be markedly improved with the use of nanotechnology. O. gratissimum essential oil based nanoformulations were prepared using non-ionic surfactant and water by ultrasonication. The process was optimized for parameters such as surfactant concentration and sonication time to achieve minimum droplet diameter with high physical stability. Stable O. gratissimum essential oil nanoemulsions were obtained for 1:1 ratio (v/v) of essential oil and surfactant with mean droplet diameter of 259.4 nm for 10 min sonication time. Essential oil and all nanoemulsions were evaluated for their antifungal activity against P. digitatum of kinnow fruit by poisoned food technique. The nanoemulsion (1:1, 10 min) showed the highest growth inhibition (1 × 104 CFU ml-1, 96%) against P. digitatum followed by pure oil (13 × 104 CFU ml-1, 85%) on 15th day of incubation. Scanning electron and optical microscopy results further revealed stronger suppressive activity of O. gratissimum essential oil nanoemulsions for spore germination and hyphal elongation of P. digitatum than pure oil.

Response surface methodology-based optimisation of agarose gel electrophoresis for screening and electropherotyping of rotavirus.

Management of rotavirus diarrhoea cases and prevention of nosocomial infection require rapid diagnostic method at the patient care level. Diagnostic tests currently available are not routinely used due to economic or sensitivity/specificity constraints. Agarose-based sieving media and running conditions were modulated by using central composite design and response surface methodology for screening and electropherotyping of rotaviruses. The electrophoretic resolution of rotavirus genome was calculated from input parameters characterising the gel matrix structure and running conditions. Resolution of rotavirus genome was calculated by densitometric analysis of the gel. The parameters at critical values were able to resolve 11 segmented rotavirus genome. Better resolution and electropherotypic variation in 11 segmented double-stranded RNA genome of rotavirus was detected at 1.96% (w/v) agarose concentration, 0.073 mol l(-1) ionic strength of Tris base-boric acid-ethylenediamine tetraacetic acid buffer (1.4x) and 4.31 h of electrophoresis at 4.6 V cm(-1) electric field strength. Modified agarose gel electrophoresis can replace other methods as a simplified alternative for routine detection of rotavirus where it is not in practice.

Identification, purification and characterization of a receptor for dengue virus-induced macrophage cytotoxin (CF2) from murine T cells.

Dengue type-2 virus infection in mice induces a subpopulation of T lymphocytes to produce a cytokine cytotoxic factor, which induces macrophages (Mphi) to produce a biologically active cytotoxic cytokine, the Mphi cytotoxin (CF2). Previously we have identified the presence of intermediate-affinity receptors for CF2 on mouse peritoneal Mphi. The present study was undertaken to identify the CF2-receptors (CF2-R) on murine T cells followed by their purification and characterization. Receptor binding assay and Scatchard analysis revealed single, high-affinity (1.0309 nM) receptors for CF2 on T cells (22000 receptors per cell). The binding of [125I]CF2 on murine T cells was saturable and specific. Furthermore, CF2-R was purified from normal mouse T cell plasma membrane by affinity chromatography followed by reversed-phase high-pressure liquid chromatography. The presence of CF2-R was confirmed by indirect dot-blot assay and its binding with [125I]CF2. The purified CF2-R is a 90-95-kDa protein as characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and immunoblot analysis. The chemical crosslinking of [125I]CF2 and its receptor complex showed a product of 100-110 kDa on different subpopulations of murine T cells. The pretreatment of target cells with anti-CF2-R antisera inhibited the cytotoxic activity of CF2 in a dose-dependent manner and thus confirmed the biological significance of CF2-R. Moreover, the presence of CF2-R was also identified on normal human peripheral blood mononuclear cells and T and B cells by crosslinking with [125I]CF2, thus revealing the possible role of CF2 and CF2-R in the immunopathogenesis of dengue virus disease.