Showing papers in "Microscopy Research and Technique in 2021"
TL;DR: A new deep learning‐based method is proposed for microscopic brain tumor detection and tumor type classification and a comparison with existing techniques shows the proposed design yields comparable accuracy.
Abstract: Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.
160 citations
TL;DR: In this article, the authors presented a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM).
Abstract: Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.
55 citations
TL;DR: In this article, the authors presented segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU).
Abstract: A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.
52 citations
TL;DR: In this paper, a deep learning approach was proposed to classify brain tumors using an MRI data analysis to assist practitioners, which comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (ie, 19 layered Visual Geometric Group) model Moreover, the synthetic data augmentation concept was introduced to increase available data size for classifier training.
Abstract: Image processing plays a major role in neurologists' clinical diagnosis in the medical field Several types of imagery are used for diagnostics, tumor segmentation, and classification Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information Indeed, early diagnosis may increase the chances of being lifesaving However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time-consuming, and formidable task Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (ie, 19 layered Visual Geometric Group) model Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques
45 citations
TL;DR: A new method is presented for WBC classification using feature selection and extreme learning machine (ELM) and it is clearly shown that the proposed method results are improved as compared to other implemented techniques.
Abstract: In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time-consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best-selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.
35 citations
TL;DR: In this article, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19.
Abstract: COVID-19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation COVID-19 was emerged due to the SARS-CoV-2 that is highly infectious pandemic Every country tried to control the COVID-19 spread by imposing different types of lockdowns Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals Currently are imposed three types of lockdown (partial, herd, complete) in different countries In this study, three countries from every type of lockdown were studied by applying time-series and machine learning models, named as random forests, K-nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID-19 The models' accuracy and effectiveness were evaluated by error based on three performance criteria Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types Three top-ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out-of-sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID-19 This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID-19
34 citations
TL;DR: In this article, the tunable cobalt oxide nanoparticles (CoONPs) are produced due to the phytochemicals present in Rhamnus virgata (RhV) leaf extract which functions as reducing and stabilization agents.
Abstract: The tunable cobalt oxide nanoparticles (CoONPs) are produced due to the phytochemicals present in Rhamnus virgata (RhV) leaf extract which functions as reducing and stabilization agents. The synthesis of CoONPs was confirmed using different analytical techniques: UV-Vis spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), dynamics light scatterings (DLS), Fourier-transform infrared spectroscopy (FTIR), energy dispersive X-ray, and Raman spectroscopy analyses. Furthermore, multiple biological activities were performed. Significant antifungal and antibacterial potentials have been reported. The in vitro cytotoxic assays of CoONPs revealed strong anticancer activity against human hepatoma HUH-7 (IC50 : 33.25 μg/ml) and hepatocellular carcinoma HepG2 (IC50 : 11.62 μg/ml) cancer cells. Dose-dependent cytotoxicity potency was confirmed against Leishmania tropica (KMH23 ); amastigotes (IC50 : 58.63 μg/ml) and promastigotes (IC50 : 32.64 μg/ml). The biocompatibility assay using red blood cells (RBCs; IC50 : 4,636 μg/ml) has confirmed the bio-safe nature of CoONPs. On the whole, results revealed nontoxic nature of RhV-CoONPs with promising biological potentials.
30 citations
TL;DR: In this article, a convolutional neural network (CNN) was used to classify normal and abnormal cases of diabetic maculopathy from retinal images, and the results were used for retinal image classification.
Abstract: Automatic detection of maculopathy disease is a very important step to achieve high-accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.
29 citations
TL;DR: In this article, a nickel oxide nanoparticles (NiONPs) were synthesized using leaf extract of Berberis balochistanica (BB) an endemic medicinal plant.
Abstract: In current report, nickel oxide nanoparticles (NiONPs) were synthesized using leaf extract of Berberis balochistanica (BB) an endemic medicinal plant. The BB leaves extract act as a strong reducing, stabilizing, and capping agent in the synthesis of BB@NiONPs. Further, BB@NiONPs were characterized using Uv-visible spectroscopy (UV-vis), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Energy dispersive spectroscopy (EDS), scanning electron microscopy (SEM), and average size was calculated ~21.7 nm). Multiple in vitro biological activities were performed to determine their therapeutic potentials. The BB@NiONPs showed strong antioxidant activities in term of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and total antioxidant capacity (TAC) with scavenging potential of 69.98 and 59.59% at 200 μg/ml, respectively. The antibacterial and antifungal testes were examined using different bacterial and fungal strains and dose-dependent inhibition response was reported. Laterally, cytotoxic and phytotoxic activities were studied using brine shrimp and radish seeds. The result determined potential cytotoxic activity with LD50 value (49.10 μg/ml) and outstanding stimulatory effect of BB@NiONPs on seed germination at lower concentrations as compared to control. Overall, result concluded that biosynthesis of NiONPs using leaf extracts of Berberis balochistanica is cheap, easy, and safe method and could be used in biomedical and agriculture field as nanomedicine and nano fertilizer.
27 citations
TL;DR: In this paper, a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures is presented, which has two main stages.
Abstract: Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.
27 citations
TL;DR: In this article, a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features is presented, and several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity are compared on benchmark data sets to assess reported techniques.
Abstract: Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non-handcrafted features. Additionally, clinical features such as Menzies method, seven-point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.
TL;DR: The aim of the present research was to elucidate the micromorphological characters to distinguish the studied taxa for taxonomic purposes.
Abstract: The family Scrophulariaceae consists of taxonomically complex genera and species. The delimitation of the taxa within this family is always challenging. In this paper, we studied leaf epidermis anatomical characteristics and its taxonomic significance of four species belonging to four genera of the family Scrophulariaceae collected from northern Pakistan. The species were examined under light and scanning electron microscopes (LM and SEM). Qualitative and quantitative foliar epidermal anatomical features were examined for both adaxial and abaxial surfaces. Qualitative characters like epidermal cell shape, epidermal cell cover, anticlinal wall, trichomes type, stomata type and stomata position were examined. Quantitative characters like the length and width of leaf epidermis, stomata, stomatal pore, subsidiary cell and trichomes for both adaxial and abaxial surfaces were studied and measured. Stomatal index within the species and between the species was found to be different on adaxial and abaxial surfaces. Diacytic stomata and glandular trichomes on epidermis were only found in Anticharis glandulosa while rest of the taxa has anomocytic type stomata and dendroid trichomes on both surfaces. Based on the micromorphological characters, we did principal component analysis (PCA), and cluster analysis for the species delimitation and identification. A taxonomic key has been provided to delimit and identify the studied taxa based on foliar epidermal characters. The aim of the present research was to elucidate the micromorphological characters to distinguish the studied taxa for taxonomic purposes.
TL;DR: In this paper, the authors used Emblica officinalis (EO) fruit extract to mitigate the MG induced nephrotoxicity, which resulted in the restoration of renal cytoarchitecture and significantly enhanced activity of antioxidant enzymes.
Abstract: Malachite green (MG) is a multi-application dye with raised concern as aquatic toxicant. Cyprinus carpio fingerlings were exposed to MG and simultaneously fed with Emblica officinalis (EO) fruit extract to mitigate the MG induced nephrotoxicity. MG exposure developed depressed activity of antioxidant enzymes such as catalase, superoxide dismutase, glutathione-s-transferase, and reduced glutathione, while levels of malondialdehyde got significantly (p < .05) elevated after 60 days MG exposure. HE glomerulus exhibited erythrocyte infiltration and fused pedicels of podocyte. While, EO extract supplemented diet culminated in the restoration of the renal cytoarchitecture and significantly (p < .05) enhanced activity of antioxidant enzymes.
TL;DR: The pollen morphology of some selected taxa of the subfamily Lamioideae from Pakistan is documented and strengthens the taxonomic identification of subfamily based on pollen characters, which helps in the correct identification, discrimination of the species of Lamioidesae at generic and species level.
Abstract: Lamioideae comprised the second-largest subfamily in Lamiaceae. Although considerable progress has recently been made in the taxonomic study of Lamioideae, the subfamily remains one of the most poorly investigated subfamily in Lamiaceae. Therefore, the present study was designed with the aim to document the pollen micromorphology of some selected Lamioideae taxa and its taxonomic significance from Pakistan. Pollen micromorphological features were observed using scanning electron microscopy. The pollen grains are monad, tricolpate, radially/bilateral symmetrical. The pollen grains were small to medium-sized having oblate, oblate/subspheroidal, and subspheroidal shape. Exine sculpturing was observed as reticulate, microreticulate, and bireticulate. The colpus surface ornamentation was found as verrucate, gemmate, scabrate, and psilate. There was a considerable variation between the species in the micromorphology, that is, the coarseness of the reticulum, thickness of the muri comprising the reticulum and the number of secondary lumina per primary lumen. Hence, this study documented the pollen morphology of some selected taxa of the subfamily Lamioideae from Pakistan and strengthens the taxonomic identification of subfamily based on pollen characters, which helps in the correct identification, discrimination of the species of Lamioideae at generic and species level.
TL;DR: In this paper, low-density polyethylene (LDPE) was exposed to an ultraviolet (UV) fluorescence lamp in simulated aging and degradation experiments and the results were compared with their Fourier Transform Infrared-Attenuated Total Reflectance (FTIR-ATR) and ultraviolet visible (UV-Vis) spectra.
Abstract: Polyethylene plastics are widely used in daily life in the packaging of foodstuffs, pharmaceuticals, cosmetics, detergents, and chemicals. In this study, low-density polyethylene (LDPE) was exposed to an ultraviolet (UV) fluorescence lamp in simulated aging and degradation experiments. Ultraviolet degradation mechanisms were investigated on the surface after sunlight and UV lamp exposure. The plastic surfaces' molecular and surface degradation results were compared with their Fourier Transform Infrared-Attenuated Total Reflectance (FTIR-ATR) and ultraviolet visible (UV-Vis) spectra. By growing the length of exposure time increased stages of degradation were observed. After UV lamp and sunlight exposure, changing degradation levels were also determined with spectroscopic evaluations and the results were compared. LDPE was selected since it has a simple structure and a number of branched polymer structures that facilitate easily disruption of the chemical bond. Breaks in the polymer chain were easily seen in the plastics at the end of degradation and a fragile structure was formed throughout the polymer chain after accelerating UV light aging. The FTIR spectrum clarified the changed and fractured molecular bond structures of UV-exposed polyethylene. The change in the molecular structure of the plastic caused small changes in its color and small variations in this color change were detected by recording the Ultraviolet-Visible (UV-Vis) spectrum. The Philips UV lamp's light intensity and the wavelength spectrum range were measured. The UV lamp and sun UV light doses were calculated and compared.
TL;DR: In this paper, cerium oxide nanoparticles (CeO2 -NPs) were synthesized using Acorus calamus aqueous extract and tested for the antibiofilm activity both against Gram + ve and Gram -ve bacteria.
Abstract: The emergence of multidrug resistance in bacterial pathogens has increased drastically and it has become prevalent in clinical infections. In last few decades, there is a large gap in the discovery of new antibiotics with novel mode of action. The situation of antimicrobial resistance has become so alarming that if not action is taken, infectious diseases will become major cause of global mortality and morbidity by 2050. The growing interest of researchers in nanotechnology and their possible application in healthcare is being seen as a new hope in discovery of novel antimicrobial agents. Among various approaches employed for the nanoparticle synthesis, biological methods are considered more advantageous and environment friendly. Biofilms are considered as novel target for the development of new antimicrobial entities. In this study, cerium oxide nanoparticles (CeO2 -NPs) were synthesized using Acorus calamus aqueous extract and tested for the antibiofilm activity both against Gram +ve and Gram -ve bacteria. The average size of synthesized CeO2 -NPs was found to be 22.03 nm. The biofilms of the test bacteria were inhibited by more than 75% by the treatment with CeO2 -NPs. The quantitative biofilm data were further verified by light microscopy, electron microscopy, and confocal microscopy. The confocal and electron microscopic analysis confirmed that treatment with CeO2-NPs reduced the development and colonization of the bacteria on solid support. Moreover, it was found that the colonization and biofilm development by test bacteria were fairly reduced on the glass surface. Moreover, a dose-dependent inhibition of preformed biofilms was also found. The exopolysaccharides (EPS) production by the test bacteria were substantially reduced by the supplementation of CeO2 -NPs in culture media. The findings of this study highlight the efficacy of cerium oxide nanoparticles against bacterial pathogens that may be exploited for the development of new alternative antimicrobial agent.
TL;DR: It could be concluded that Fe2O3 nanoparticles, synthesized in the leaf extract of A. indica, can be successfully used for the control of brown rot of sweet oranges in Pakistan.
Abstract: Citrus is the leading fruit crop of Pakistan and exported to different parts of the world. Due to suitable weather condition, this crop is affected by different biotic factors which seriously deteriorate its quality and quantity. During the months of November 2018 to January 2019, citrus brown rot symptoms were recurrently observed on sweet oranges in National Agricultural Research Centre (NARC), Islamabad. Causal agent of citrus brown rot was isolated, characterized, and identified as Fusarium oxysporum. For environment-friendly control of this disease, leaf extract of Azadirachta indica was used for the green synthesis of iron oxide (Fe2 O3 ) nanoparticles. These nanoparticles were characterized before their application for disease control. Fourier transform infrared spectroscopy (FTIR) of these synthesized nanoparticles described the presence of stabilizing and reducing compounds like alcohol, phenol, carboxylic acid, and alkaline and aromatic compounds. X-Ray diffraction (XRD) analysis revealed the crystalline nature and size (24 nm) of these nanoparticles. Energy dispersive X-Ray (EDX) analysis elaborated the presence of major elements in the samples. Scanning electron microscopy (SEM) confirmed the spinal shaped morphology of prepared nanoparticles. Successfully synthesized nanoparticles were evaluated for their antifungal potential. Different concentrations of Fe2 O3 nanoparticles were used and maximum mycelial inhibition was observed at 1.0 mg/ml concentration. On the basis of these findings, it could be concluded that Fe2 O3 nanoparticles, synthesized in the leaf extract of A. indica, can be successfully used for the control of brown rot of sweet oranges.
TL;DR: In this article, the authors proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks (GANs).
Abstract: With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
TL;DR: In this paper, a three-phase model is proposed for COVID-19 detection using denoise convolutional neural network (DnCNN) and stack sparse autoencoder (SSAE) deep learning model.
Abstract: The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.
TL;DR: In this paper, the pollen micro-morphological features were observed using scanning electron microscopy-SEM (SEM) to document the pollen micromorphology of closely related Polygonatum taxa and its taxonomic significance.
Abstract: Although considerable progress has recently been made in the taxonomic study of Asparagaceae, but the Polygonatum remains one of the most poorly investigated genus. Therefore, the present study was designed with the aim, to document the pollen micromorphology of closely related Polygonatum taxa and its taxonomic significance. Pollen micro-morphological features were observed using scanning electron microscopy-SEM). The pollen grains are monad, navicular, monocolpate, and radially symmetrical. A significant variation was observed in the exine sculpturing. Moreover, most of the Polygonatum taxa have perforate pollen while some of them were mixed with psilate to perforate, perforate to microreticulate, sometimes scabrate, gemmate with baculate. Hence, this study documented the pollen morphology of Polygonatum taxa and strengthens the taxonomic identification of the genus Polygonatum based on pollen characters, which helps and can be used as an additional tool for the correct identification and discrimination of the species of Polygonatum at generic and species level.
TL;DR: In this paper, an ecofriendly approach utilizing silver nanoparticles were synthesized using sheep blood serum, which proved strong antibacterial activity against different bacterial species isolated from the Beishiku Cave Temple.
Abstract: Possible high biodeterioration of the microorganisms due to their metabolic pathway and activities on stone materials causes solemn problems in cultural heritage. Different kinds of laboratory-scale methods have been used for the reduction of microbial growth, that is, chemical, mechanical, and physical, which are cost-effective and not ecofriendly. In the current study, an ecofriendly approach utilizing silver nanoparticles were synthesized using sheep blood serum. Transmission electron microscopy results have confirmed the spherical and well dispersed silver nanoparticles with an average size of 32.49 nm, while energy dispersive X-ray has shown the abundance of silver nanoparticles. The efficiency against bacterial species was verified through laboratory-scale testing. The strong antibacterial activity was confirmed when B-AgNPs was tested against different bacterial species isolated from the Beishiku Cave Temple. The largest zone of inhibition was measured 26.48 ± 0.14 mm against Sphingomonas sp. while the smallest zone of inhibition measured was 9.70 ± 0.27 mm against Massilia sp. Moreover, these ecofriendly B-AgNPs were tested for daily based dose in different concentrations (0.03, 0.06, and 0.09 mg/L) against common carp fish for a long exposure (20 days) and 6.5% fatality was found. The highest lethal concentration (LC50 ) for fish (0.61 ± 0.09 mg/L). No doubt, the laboratory scale applications have revealed the best results with minute toxicity in fish. Therefore, sheep serum should be continued to synthesize silver nanoparticles on a large scale. A strict monitoring system should be developed for the synthesis and application of AgNPs.
TL;DR: The nontoxicity of TiO2NPs on A. salina nauplii is highlighted by low percentages of immobilization and on cysts because TiO 2NPs do not affect their hatching and despite AuNPs exerted toxic effects on hatching, they did not affect larvae development as well as TiO1NPs.
Abstract: The focus of this work was to investigate the toxicity of different metal nanoparticles (gold nanoparticles [AuNPs], silver nanoparticles [AgNPs], titanium dioxide nanoparticles [TiO2 NPs]) on brine shrimp Artemia salina. We investigated if nanoparticles could have an influence on hatching of cysts and on mortality of larvae. Larvae (also called nauplii) and cysts were exposed to NPs for 24 hr in artificial seawater on microplates. At the end of the test, we assessed the endpoint (immobility/death) for the larvae by a stereomicroscope. Nauplii that appeared completely motionless, were counted as dead, and the percentages of mortality were calculated for each treatment. Instead for the cysts, the percentages of not-hatched nauplii for each concentration considered were calculated by counting the number of whole cysts. Currently, nanoparticles toxicity has been investigated in several research; in our study we highlighted the nontoxicity of TiO2 NPs on A. salina nauplii as shown by low percentages of immobilization and on cysts because TiO2 NPs do not affect their hatching. Despite AuNPs exerted toxic effects on hatching, they did not affect larvae development as well as TiO2 NPs. Otherwise, AgNPs induced mortality of the larvae and inhibited cysts hatching.
TL;DR: In this article, the authors evaluated the palynological features of family Asteraceae and Lamiaceae from flora of District Bannu, Khyber Pakhtunkhwa, Pakistan using both scanning electron microscopy (SEM) and light microscope (LM) for their taxonomic importance.
Abstract: Pollen micro-morphological features have proven to be helpful for the plant taxonomists in the identification and classification of plants The aim of this study was to evaluate the palynological features of family Asteraceae and Lamiaceae from flora of District Bannu, Khyber Pakhtunkhwa, Pakistan using both scanning electron microscopy (SEM) and light microscope (LM) for their taxonomic importance Pollen of seven Asteraceous species belonging to four genera and four Lamiaceae species categorized into four genera were collected from different localities of research area The present research work provides detailed information of diverse morpho-palynological characters both qualitatively and quantitatively including pollen shape, type, diameter, P/E ratio, exine sculpturing and thickness Type of pollen in Asteraceae and Lamiaceae was ranged from tricolporate, tricolpate, trizonocolpate and hexazonocolpate The maximum polar diameter (4005 μm) and equatorial diameter (3766 μm) was observed in the Ajuga bracteoosa while minimum polar and equatorial diameter was noted in Isodon rugosus (1110 μm) and Erigeron canadensis (1320 μm) respectively Sculpturing of exine include; echinate, reticulate scabrate, aerolate, reticulate-verrucate, reticulate-scabrate, perforate and reticulate to perforate Exine thickness was examined maximum 150 μm in Helianthus tuberosus, whereas minimum in Conyza Canadensis (016 μm) The pollen fertility was found highest in C Canadensis (8333%) and lowest in Ajuga bracteosa (5806%) The observed pollen morphology has many valuable qualitative and quantitative attributes for the better understanding of their taxonomy and play significant role in correct identification
TL;DR: The type of fluorophore influences the calcium silicate sealers' tubular penetration but not of epoxy resin‐based ones using CLSM, and bioceramic sealers should not be used associated with Rhodamine for CLSM evaluation.
Abstract: Root canal filling aims at eliminating empty spaces into the root canal system using biologically compatible materials. Three-dimensional root canal obturation must prevent or minimize the reinfection caused by microorganisms' leakage. This study aimed at evaluating whether fluorophore (Rhodamine or Fluo-3) influences the CLSM images of intratubular penetration of four endodontic sealers. Eighty bovine teeth were prepared using K files up to a size #70 and irrigated with 2.5% sodium hypochlorite. All roots were divided into eight groups (n = 10) according to the sealer and fluorophore used: AH Plus/Rhodamine, AH Plus/Fluo-3, Sealer Plus/Rhodamine, Sealer Plus/Fluo-3, Sealer Plus BC/Rhodamine, Sealer Plus BC/Fluo-3, Endosequence/Rhodamine, and Endosequence/Fluo-3. All roots were filled using cold lateral compaction technique. After 7 days, the roots were transversely sectioned, and three slices, one of each canal third, were obtained. Intratubular penetration was evaluated using CLSM. Sealer Plus BC/Rhodamine and Endosequence BC/Rhodamine presented higher intratubular penetration than AH Plus/Fluo-3 and Sealer Plus/Fluo-3 (p ˂ .05). The intragroup analysis showed similar intratubular penetration, regardless of the root third, except for the apical third in AH Plus/Fluo-3 and Sealer Plus BC/Fluo-3 groups. The type of fluorophore influences the calcium silicate sealers' tubular penetration but not of epoxy resin-based ones using CLSM. Bioceramic sealers should not be used associated with Rhodamine for CLSM evaluation. RESEARCH HIGHLIGHTS: The type of fluorophore influences the calcium silicate sealers' tubular penetration but not of epoxy resin-based ones when CLSM is used for assessment. Bioceramic sealers should not be used associated with Rhodamine.
TL;DR: Endocrowns were manufactured using different restorative materials to evaluate the marginal adaptation and fracture strength and Clinically acceptable marginal gaps were seen in both endocrown types.
Abstract: Endocrowns were manufactured using different restorative materials to evaluate the marginal adaptation and fracture strength. Fifty endodontically treated mandibular first molar teeth were divided into five groups (n = 10). Endocrowns were obtained from lithium disilicate glass ceramic ingots by heat-press technique (Group e.max Press: GEP), and from feldspathic blocks (Group Cerec: GC), polymer infiltrated ceramic network blocks (Group Enamic: GE), lithium disilicate glass ceramic blocks (Group e.max CAD: GEC), and zirconia-reinforced glass ceramic blocks (Group Suprinity: GS) by CAD/CAM technique. After thermocycling, marginal adaptation was evaluated under scanning electron microscope at ×200 magnification. The specimens' fracture strengths were tested in universal test machine, and fracture types were evaluated. Statistical analyses were performed with Kruskal-Wallis test. The highest marginal gap value was found in GEP, but no significant differences were determined among the other four groups (p > .05). Significant differences were observed among the groups in terms of fracture strength (p = .019). The fracture strength values of GEC were significantly higher than GE, GC, and GS (p .05). CAD/CAM endocrowns showed better marginal adaptation than heat-pressed endocrowns. Clinically acceptable marginal gaps were seen in both endocrown types. Both CAD/CAM and heat-pressed lithium disilicate glass ceramic endocrowns showed higher fracture strength.
TL;DR: In this article, the authors investigated the pollen morphology of melliferous plant taxa of Southern Khyber Pakhtunkhwa-Pakistan using light microscope (LM) and scanning electron microscope (SEM), the palynological study of 18 species of Melliferous plants namely Calendula arvensis, Cenchrus pennisetiformis, Citrullus colocynthis, Cucumis melo subsp. agrestis, CUCurbita maxima, Cymbopogon jwarancusa, Cynodon dect
Abstract: The aim of the present study is to investigate the pollen morphology of melliferous plant taxa of Southern Khyber Pakhtunkhwa-Pakistan. Using light microscope (LM) and scanning electron microscope (SEM), the palynological study of 18 species of melliferous plants namely Calendula arvensis, Cenchrus pennisetiformis, Citrullus colocynthis, Cucumis melo subsp. agrestis var. agrestis, Cucurbita maxima, Cymbopogon jwarancusa, Cynodon dectylon, Dactyloctenium aegyptium, Helianthus annus, Lagenaria siceraria, Launaea procumbens, Luffa cylindrica, Pennisetum glaucum, Saccharum spontaneum, Sonchus asper, Verbesina encelioides, Xanthium strumarium, and Zea mays was carried out. Both qualitative and quantitative characteristics of pollen were studied. Variations were observed in pollen morphology. The dominant pollen shape was prolate-spheroidal (11 species). All the pollen units were monad. The highest exine thickness was found in Citrullus colocynthis (8.45 μm). The maximum polar and equatorial diameter (102 and 97.55 μm) was found in Luffa cylindrica. Similarly, the highest P/E ratio was found in Cucurbita maxima (1.46). Most of the species showed tricolpate and monoporate type of pollen. The exine sculpturing, number of spines per pollen and between colpi and the pollen fertility and sterility provided significant results for the documentation of melliferous plants. Thus, the information listed in this article will prove helpful to identify the potential melliferous plants in the area, geographical origin of the honey, and the availability of pure honey in the local and international market.
TL;DR: In this paper, the authors assess pollen morphological attributes of selected Asteraceous and Brassicaceous species from tehsil Esa Khel (Mianwali), Punjab using scanning electron microscopy and light microscopy (LM) techniques for its systematic and taxonomic significance for correct identification.
Abstract: The present study was intended to assess pollen morphological attributes of selected Asteraceous and Brassicaceous species from tehsil Esa Khel (Mianwali), Punjab using scanning electron microscopy (SEM) and light microscopy (LM) techniques for its systematic and taxonomic significance for correct identification. Pollen from 12 different species belongs to two plant families from various distributional localities were collected, acetolyzed and measured. Different palynomorphological features were investigated using LM and SEM techniques. In Asteraceous species, three types of pollen (tricolporate, trizonocolporate, and tetracolporate) were observed. Pollen shape was observed prolate-spheroidal in three species while oblate and oblate-spheroidal were detected in Parthenium hysterophorus and Erigeron bonariensis. While sculpturing pattern of exine were echinate, echinate fenestrate, echinate perforate and scabrate echinate. Mesocolpium measurement was calculated maximum for Sonchus oleraceous (16.6 μm). Brassicaceae pollen were circular, lobate, tricolpate and exine show reticulate peculiarities. Whereas dominant shape was oblate-spheroidal followed by prolate-spheroidal and sub-prolate in Lepidium didymum and Sisymbrium irio, respectively. Mesocolpium distance was noted highest in Raphanus raphanistrum (14.4 μm). Exine thickness was noted maximum in Erigeron bonariensis (2.9 μm) in Asteraceous species and in Brassicaceae; Lepedium didymum exine measurement was 2.7 μm. The study showed that pollen micromorphology has important role to accurately identify and classify diverse plants genera belong to different families. Based on these taxonomic palynomorph features, the accurate identification of species from flora of tehsil Esa Khel, Mianwali were elaborated.
TL;DR: A simple and reliable method based on argon ion polishing that is able to remove hydrocarbon contamination and oxide layers, thereby significantly improving APT specimen yield, particularly after EM is presented.
Abstract: Many materials science phenomena require joint structural and chemical characterization at the nanometer scale to be understood. This can be achieved by correlating electron microscopy (EM) and atom probe tomography (APT) subsequently on the same specimen. For this approach, specimen yield during APT is of particular importance, as significantly more instrument time per specimen is invested as compared to conventional APT measurements. However, electron microscopy causes hydrocarbon contamination on the surface of atom probe specimens. Also, oxide layers grow during specimen transport between instruments and storage. Both effects lower the chances for long and smooth runs in the ensuing APT experiment. This represents a crucial bottleneck of the method correlative EM/APT. Here, we present a simple and reliable method based on argon ion polishing that is able to remove hydrocarbon contamination and oxide layers, thereby significantly improving APT specimen yield, particularly after EM.
TL;DR: In this article, a review of recent technical advances in simulated emission depletion (STED) microscopy with emphasis on resolution and measurement range of XYZt four dimensions is presented.
Abstract: Stimulated emission depletion (STED) microscopy allows high lateral and axial resolution, long term imaging in living cells. Here we review recent technical advances in STED microscopy, with emphasis on resolution and measurement range of XYZt four dimensions. Different STED technical advances and novel STED probes are discussed with their respective application in biological subcellular imaging. This review may serve as a practical guide for choosing a suitable approach to the advanced STED super-resolution imaging.
TL;DR: In this article, six different solvents were used to prepare L. orientalis (LO) seed extracts and the phytochemical and antioxidant activities were determined calorimetrically.
Abstract: Lactuca orientalis (Boiss.) Boiss. is one of the most frequently used ethnomedicinal plant. This research study was designed to decipher the phytochemical screening, pharmacological potential and implementation of scanning electron microscope (SEM). Six different solvents were used to prepare L. orientalis (LO) seed extracts. Phytochemical and antioxidant activities were determined calorimetrically. To investigate antidiabetic, α-amylase inhibition assay was performed. Brine shrimp assay was performed for cytotoxicity and anti-leishmanial via MTT assay. Disc-diffusion assay was performed to detect protein kinase inhibitory, antibacterial and antifungal activities. SEM was used as identification tool. Significant amount of phenolic and flavonoid content were identified in methanol extract (LOSM) (95.76 ± 3.71 GAE/mg) and (77 ± 3.60 QE/mg). Highest DPPH scavenging potential (82%) was reported for LOSM. Significant total antioxidant capacity (90.60 ± 1.55 AAE/mg) and total reducing power (94.44 ± 1.38 AAE/mg) were determined for LOSM. Highest α-amylase inhibition was found in LOSM (78.20 ± 1.58%). The highest LD50 of brine shrimp was found for n-Hexane extract (LOSH) 13.03 𝜇g/ml. All extracts showed strong anti-leishmanial activity except LOSH. L. orientalis seeds showed significant protein kinase inhibition, antibacterial and antifungal activities. The seeds of L. orientalis were seen to be oblong to obovate, projections, wavy slightly straight, anticlinal wall was raised with apex acuminate. The outer-periclinal wall convex with fine texture. In conclusion, our findings scientifically support ethnomedicinal and biological potentials of L. orientalis seeds. In future, L. orientalis seeds need to be explored for identification and isolation of bioactive compounds. The results obtained necessitate further in vivo studies to evaluate their pharmacological potentials.