Showing papers in "Microscopy Research and Technique in 2019"

Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.

Abstract: Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker-based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker-based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi-square max conditional priority features approach. In the later step, selected features are fused using a serial-based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

Synthesis, characterization and use of iron oxide nano particles for antibacterial activity

Abstract: In this decade, the use of nano particles (NPs) against bacterial growth is increasing day by day due to remarkable alternative properties compared to molecular antibiotics. Thus, the use of iron oxide nanoparticles (IONPs) has proven one of the most important transition metals oxide-based remedy in nanotechnological advances and biological applications due to enriched biocompatibility of iron. In this study synthesis of IONPs was carried out via co-precipitation method. The crystallographic morphology of the synthesized particles was studied via X-ray diffraction which revealed cubic structure of the particles, whereas, the spinal shaped morphology of the prepared NPs was confirmed from scanning electron microscopy. Likewise, the presence of the major elements in the sample was determined through energy dispersive X-ray analysis characterization. Bactericidal effect of the NPs was assessed at pre-defined concentrations (50 and 100 μg/ml) against Gram +ve bacteria Staphylococcus aureus, Gram -ve bacteria Shigella dysentry and Escherichia coli. Bacterial strains, which demonstrate the potential of NPs. The purpose of this study was assessing the structure of the synthesized NPs for protective effect against harmful bacterial activity.

Feature enhancement framework for brain tumor segmentation and classification

Abstract: Automatic medical image analysis is one of the key tasks being used by the medical community for disease diagnosis and treatment planning. Statistical methods are the major algorithms used and consist of few steps including preprocessing, feature extraction, segmentation, and classification. Performance of such statistical methods is an important factor for their successful adaptation. The results of these algorithms depend on the quality of images fed to the processing pipeline: better the images, higher the results. Preprocessing is the pipeline phase that attempts to improve the quality of images before applying the chosen statistical method. In this work, popular preprocessing techniques are investigated from different perspectives where these preprocessing techniques are grouped into three main categories: noise removal, contrast enhancement, and edge detection. All possible combinations of these techniques are formed and applied on different image sets which are then passed to a predefined pipeline of feature extraction, segmentation, and classification. Classification results are calculated using three different measures: accuracy, sensitivity, and specificity while segmentation results are calculated using dice similarity score. Statistics of five high scoring combinations are reported for each data set. Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent. However, the combinations of these techniques depend on the characteristics and type of data set used.

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Abstract: Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.

Development of electrically conductive hybrid nanofibers based on CNT‐polyurethane nanocomposite for cardiac tissue engineering

Abstract: Conductive nanofibers have been considered as one of the most interesting and promising candidate scaffolds for cardiac patch applications with capability to improve cell-cell communication. Here, we successfully fabricated electroconductive nanofibrous patches by simultaneous electrospray of multiwalled carbon nanotubes (MWCNTs) on polyurethane nanofibers. A series of CNT/PU nanocomposites with different weight ratios (2:10, 3:10, and 6:10wt%) were obtained. Scanning electron microscopy, conductivity analysis, water contact angle measurements, and tensile tests were used to characterize the scaffolds. FESEM showed that CNTs were adhered on PU nanofibers and created an interconnected web-like structures. The SEM images also revealed that the diameters of nanofibers were decreased by increasing CNTs. The electrical conductivity, tensile strength, Young's modulus, and hydrophilicity of CNT/PU nanocomposites also enhanced after adding CNTs. The scaffolds revealed suitable cytocompatibility for H9c2 cells and human umbilical vein endothelial cells (HUVECs). This study indicated that simultaneous electrospinning and electrospray can be used to fabricate conductive CNT/PUnanofibers, resulting in better cytocompatibility and improved interactions between the scaffold and cardiomyoblasts.

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Abstract: Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK-1, ISIC MSK-2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.

Lungs nodule detection framework from computed tomography images using support vector machine.

Abstract: The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps-playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well-known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Evaluation of X-ray tomography contrast agents: A review of production, protocols, and biological applications

Abstract: X-ray computed tomography is a strong tool that finds many applications both in medical applications and in the investigation of biological and nonbiological samples. In the clinics, X-ray tomography is widely used for diagnostic purposes whose three-dimensional imaging in high resolution helps physicians to obtain detailed image of investigated regions. Researchers in biological sciences and engineering use X-ray tomography because it is a nondestructive method to assess the structure of their samples. In both medical and biological applications, visualization of soft tissues and structures requires special treatment, in which special contrast agents are used. In this detailed report, molecule-based and nanoparticle-based contrast agents used in biological applications to enhance the image quality were compiled and reported. Special contrast agent applications and protocols to enhance the contrast for the biological applications and works to develop nanoparticle contrast agents to enhance the contrast for targeted drug delivery and general imaging applications were also assessed and listed.

Effect of long-term exposure of silver nanoparticles on growth indices, hematological and biochemical parameters and gonad histology of male goldfish (Carassius auratus gibelio).

Abstract: Studying the impact of emerging pollutants such as nanoparticles is necessary to reveal the adverse effect. In this study, the effects of silver nanoparticles (Ag-NPs) on hematological, biochemical, and gonad histopathological indices of male goldfish were examined. Sublethal toxicity were calculated based on acute toxicity and three dosages were selected. Live specimen of Carassius auratus gibelio larval were treated in 1, 2, and 3 ppm Ag-NP with one control group. Blood and tissue samples were extracted after 6 months exposure to sublethal concentrations. Results showed that Ag-NPs have reduced growth rate and effected on all blood indices significantly. Biochemical analysis revealed that Ag-NPs significantly reduced blood glucose and total protein than in comparison to the control group and caused significantly differences in the concentrations of serum cholesterol (p < .05). Furthermore, histological observation of intestine after 6 months exposure showed definite alterations in tissue and maximum hypertrophy injuries were found after long-term exposure to 3 ppm Ag-NPs concentration. In addition, indicated that long-term exposure to Ag-NPs postponed sexual maturity in male gibel carp.

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Abstract: Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co-occurrences matrices features are extracted and covariance-based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.

Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images.

Abstract: The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

Automated lung nodule detection and classification based on multiple classifiers voting.

Abstract: Lung cancer is the most common cause of cancer-related death globally. Currently, lung nodule detection and classification are performed by radiologist-assisted computer-aided diagnosis systems. However, emerged artificially intelligent techniques such as neural network, support vector machine, and HMM have improved the detection and classification process of cancer in any part of the human body. Such automated methods and their possible combinations could be used to assist radiologists at early detection of lung nodules that could reduce treatment cost, death rate. Literature reveals that classification based on voting of classifiers exhibited better performance in the detection and classification process. Accordingly, this article presents an automated approach for lung nodule detection and classification that consists of multiple steps including lesion enhancement, segmentation, and features extraction from each candidate's lesion. Moreover, multiple classifiers logistic regression, multilayer perceptron, and voted perceptron are tested for the lung nodule classification using k-fold cross-validation process. The proposed approach is evaluated on the publically available Lung Image Database Consortium benchmark data set. Based on the performance evaluation, it is observed that the proposed method performed better in the stateof the art and achieved an overall accuracy rate of 100%.

Taxonomic study of subfamily Nepetoideae (Lamiaceae) by polynomorphological approach

Abstract: The present study is insight into pollen morphology for characterizing species and their utility in the taxonomic separation of certain taxa of subfamily Nepetoideae (Lamiaceae) from Pakistan. The pollen micromorphology of 11 species of the Nepetoideae was analyzed and documented using light microscopy and scanning electron microscopy (SEM) for both qualitative and quantitative characteristics. Most species have hexazonocolpate pollen grains but trizonocolpate and tetrazonocolpate pollen with circular and oval amb were also rarely observed in Mentha spicata. The basic pollen shape in most of the studied species was subspheroidal but prolate grains were also observed in M. spicata, S. coccinea, and S. plebeia. The exine sculpturing of Nepetoideae pollen was taxonomically very informative particularly at subfamily level. Observations of exine sculpturing with SEM revealed various types of pollen grains: reticulate, bireticulate, microreticulate, perforate, aerolate, and gammate. The bireticulate type further subdivided into three subtypes based on the number of secondary lumina in each primary lumen and is characterized by varying characteristics of the secondary reticulum and primary muri. A significant variation was observed in colpus surface ornamentation. The maximum polar diameter was found in O. americanum (58 ± 5.8 μm) and the maximum equatorial diameter observed in O. basilicum (50.25 ± 1.37 μm). Pollen features of the studied species were discussed and compared based on the current taxonomical concepts. The results showed that pollen traits of the subfamily Nepetoideae was found significant to classify the taxa. Furthermore, pollen features provide additional evidence to distinguish macromorphologically similar taxa from each other.

Taxonomic significance of foliar epidermal morphology in Lamiaceae from Pakistan

Abstract: Foliar micromorphological features are useful to elucidate the taxonomy and systematics of the Lamiaceae species. Leaf epidermal morphology using scanning electron microscopy and light microscopy of 22 Lamiaceae species from 15 genera have been investigated with an aim to solve its taxonomic problem in the correct identification. Various foliar micromorphological features were observed to explain their importance in resolving the correct identification of Lamiaceae taxa. Two main types of trichomes were observed; glandular trichomes (GTs) and nonglandular trichomes (NGTs). GTs were further divided into seven subtypes including the capitate, subsessile capitate, sessile capitate, sunken, barrel, peltate, and clavate. Similarly, NGTs were also divided into simple unicellular and multicellular including conical, falcate, cylindrical, dendrite, papillose, and short hook shape. Quantitative measurement includes the length and width of the trichomes, stomatal complex, epidermal cells, stomata, and trichomes index. Based on the foliar micromorphological characters, a taxonomic key was developed to delimit and correctly identify studied taxa. Further molecular, other anatomical and phylogenetic studies are recommended to strengthen the systematics of Lamiaceae.

Systematic approach to the correct identification of Asplenium dalhousiae (Aspleniaceae) with their medicinal uses.

Abstract: In present study, multiple microscope techniques were used for the systematics identification of the species Asplenium dalhousiae. The plant was collected from different phytogeographical and its natural habitat of Pakistan, where it shows higher diversity. Morphology, foliar epidermal anatomy, and spore morphological characters of the species were studied in detailed using multiple microscopic techniques through light microscopy (LM) and scanning electron microscopy (SEM). LM and SEM were used for the systematics identification of the species. Traditionally, the species is used in the ailment of many diseases, so the spore morphology, anatomical features, and morphological characters are relevant to describe the species taxonomy. The importance of multiple methods of taxonomic study (e.g., documentation and morphological characteristics) for characterizing herbs are important step in systematic certification to maintain the efficacy of herbal medicines. The aim of the present study is to examine the morphological, anatomical, and spore morphology of the species A. dalhousiae in more detailed for the correct taxonomic identification and their medicinal validation from Pakistan.

Foliar epidermal anatomy of Lamiaceae with special emphasis on their trichomes diversity using scanning electron microscopy

Abstract: Foliar epidermal features were based on the micromorphology of trichomes types, epidermal cells and stomatal complex. Even though each feature has its own limited taxonomic value but collectively these characteristics may be systematically important especially for the discrimination and identification of complex and problematic taxa. The systematics significance of nonglandular (NGTs) and glandular trichomes (GTs), stomatal complex and epidermal cells of Lamiaceous flora were analyzed by using the light microscopy (LM) and scanning electron microscopy (SEM). Variations on the observed epidermal appendages were divided into two basic types: glandular and nonglandular. GTs can be divided into subtypes: sessile capitate, subsessile capitate, and barrel and sunken. NGTs were also divided into subtypes: dendritic, stellate, conical, falcate, simple and 1-6 cells long having granulate and smooth surface ornamentation. NGTs were the most dominant features of both adaxial and abaxial surfaces of all observed taxa. Vitex negundo, Isodon rugosus, Colebrookea oppositifolia, and Marrubium vulgare could be demarked because of their twisted like appearance of NGTs at the abaxial surface. The Lamiaceae had both hypostomatic and amphistomatic leaf. Stomata were observed as diacytic, anisocytic, and anomocytic. Epidermal cells were found to be irregular, isodiametric, and rectangular. Based on these characters a taxonomic key was developed to delimit the closely related taxa. Distribution and morphology of the foliar epidermal trichomes through SEM highlight an important taxonomic tool used by the taxonomists as an aid to the correct identification of problematic Lamiaceae taxa.

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection.

Abstract: Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity. Similarly, three well-known classifiers that is, Naive Bayes classifier, Support Vector Machine, and Artificial Neural Network are used for the classification of EXs. Moreover, an ensemble-based classifier is used for the selection of best classifier on the basis of majority voting technique. Experiments are performed on three well-known benchmark datasets and a real dataset developed at local Hospital. It has been observed that the proposed technique achieved an accuracy of 98% in the detection and classification of EXs in color fundus images.

Seed micromorphology and its taxonomic evidence in subfamily Alsinoideae (Caryophyllaceae)

Abstract: Seed micromorphology of 13 species, belonging to four genera of subfamily Alsinoideae (Caryophyllaceae) were investigated with scanning electron microscopy (SEM), in order to assess their diagnostic significance at generic level and provide additional evidence on species delimitation, as well as correct identification and phylogenetic position. Genera and species of subfamily Alsinoideae exhibit great variation in ultrastructure and a high diversity of novel micromorphological characters were observed. Variation in seed shape, color, hilum, anticlinal wall, epidermal cell, cell surface, margins, and quantitative characters as length and width were studied in detail, compared, illustrated, and their taxonomic significant were discussed. Seed shapes of the species were classified as reniform, round, angular, subcircular, subreniform, and elliptical pyriform, with sub-central, central, basal, and nearly basal hilum. Wavy, irregular, tetragonal, and elongated epidermal cells structure has been observed as an exomorphological character. The present findings show that the micromorphology of subfamily Alsinoideae provides taxonomic information and is helpful to distinguish different species. The results also explained that SEM morphology of seeds provide important data about affinity among taxa and give potential characters in delimitation of members of subfamily Alsinoideae at generic and species level. A principal component analysis allowed to highlight the most outsiders among seed micromorphology with a possible explanation. Taxonomic keys were developed based on micromorphological characters to delimit the species and useful for their quick identification within subfamily Alsinoideae.

Surface roughness and filler particles characterization of resin-based composites.

Abstract: The purpose of this study was to evaluate the surface roughness (Ra), and the morphology and composition of filler particles of different composites submitted to toothbrushing and water storage. Disc-shaped specimens (15 mm × 2 mm) were made from five composites: two conventional (Z100™, and Filtek™ Supreme Ultra Universal, 3M), one "quick-cure" (Estelite ∑ Quick, Tokuyama), one fluoride-releasing (Beautiful II, Shofu), and one self-adhering (Vertise Flow, Kerr) composite. Samples were finished/polished using aluminum oxide discs (Sof-Lex, 3M), and their surfaces were analyzed by profilometry (n = 5) and scanning electron microscopy (SEM; n = 3) at 1 week and after 30,000 toothbrushing cycles and 6-month water storage. Ra data were analyzed by two-way analysis of variance and Tukey's test (α = 0.05). Filler particles morphology and composition were analyzed by SEM and X-ray dispersive energy spectroscopy, respectively. Finishing/polishing resulted in similar Ra for all the composites, while toothbrushing and water storage increased the Ra of all the tested materials, also changing their surface morphology. Beautifil II and Vertise Flow presented the highest Ra after toothbrushing and water storage. Filler particles were mainly composed of silicon, zirconium, aluminum, barium, and ytterbium. Size and morphology of fillers, and composition of the tested composites influenced their Ra when samples were submitted to toothbrushing and water storage.

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Abstract: Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k-nearest neighbor, Naive Bayes and multi-class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non-infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.

Using multiple microscopic techniques for the comparative systematic of Spergula fallax and Spergula arvensis (Caryophyllaceae)

Abstract: In this study, comparative morphology, foliar anatomy and palynology of Spergula fallax and Spergula arvensis (Caryophyllaceae) were studied using multiple microscopic techniques. Genus Spergula includes worldwide five species, while in Flora of Pakistan the genus has two species. In this research, the comparative morphological, anatomical, and palynological characters of the two Pakistani Spergula species were studied. We examined some distinguishing morphological features, in both species, such as plant size, habitat, leaf morphological characters, inflorescences, flowers outer whorls, sepals and petals, and flowers number. These characters species were studied analyzing their comparative systematic significant. The foliar anatomical features also provided distinctive characters as the epidermal cell shape, the wall of the epidermal cell, lobes per cell. The differences in quantitative characters were also examined. The palynological characters showed difference in echini arrangement, echini density, and numbers of pore. Quantitative characters were variations in size of polar, equatorial, exine thickness, pore length, and width and P/E ratio. The multiple microscopic techniques provided sufficient evidence about the systematics of the genus Spergula. Based on morphological, anatomical, and palynological characters, analytical keys were developed for the identification and distinction of the species S. fallax and S. arvensis.

Leaf epidermal micromorphology and its implications in systematics of certain taxa of the fern family Pteridaceae from Northern Pakistan

Abstract: The present study is insights into foliar epidermal anatomy for characterizing clades, and their utility in taxonomic segregation of certain species of Pteridaceae from Northern Pakistan. The leaf epidermal anatomy of 10 species of Pteridaceae representing four genera were examined using light and scanning electron microscope. A micromorphological matrix was constructed for eight qualitative and 12 quantitative characters. unweighted pair group method with arithmetic means and principal components analysis statistical analysis were performed to test the validity of foliar epidermal anatomical features as method of separating species and genera, and phylogenetic clusters among species are constructed using qualitative and quantitative traits. The qualitative characters described here are shape of epidermal cells, stomata, guard cell and subsidiary cells, anticlinal wall pattern, and trichomes types which is helpful in defining groups within Pteridaceae. In addition, the size of stomata, guard cells, subsidiary cells, stomatal pore epidermal cells, and trichomes are quantitatively analyzed. All species have hypostomatic leaves. Two types of stomata were observed in studied species, anomocytic and polocytic. Anomocytic stomata were observed in three genera namely: Adiantum, Onychium, and Chielanthes whereas Pteris can be discriminated from other genera by its polocytic stomata. On the basis of multivariate analysis present study does provides sufficient information on the taxonomic importance of foliar anatomy which validate its efficacy in species and genera discrimination. From result obtained here it is further possible to use leaf micromorphologic data in ferns phylogeny and providing basis for future taxonomic delimitation in other taxa.

Palyno-anatomical studies of monocot taxa and its taxonomic implications using light and scanning electron microscopy.

Abstract: Palyno-anatomical study of monocots taxa using Light and Scanning Electron Microscopy (SEM) was first time conducted with a view to evaluating their taxonomic significance. Studied plants were collected from different eco-climatic zones of Pakistan ranges from tropical, sub-tropical, and moist habitats. The aim of this study is to use palyno-anatomical features for the correct identification, systematic comparison, and investigation to elucidate the taxonomic significance of these features, which are useful to taxonomists for identifying monocot taxa. A signification variation was observed in quantitative and qualitative characters by using the standard protocol of light microscopy (LM) and SEM. Epidermal cell length varied from maximum in Allium griffthianum (480 ± 35.9) μm at the adaxial surface to minimum in Canna indica (33.6 ± 8.53) μm on abaxial surface. Maximum exine thickness was observed in Canna indica (4.46) μm and minimum in Allium grifthianum (0.8) μm. Variation was observed in shape and exine ornamentation of the pollen, shape of the epidermal cell, number, size, and type of stomata, guard cell shape, and anticlinal wall pattern. Based on these palyno-anatomical features a taxonomic key was developed, which help in the discrimination of studied taxa. In conclusion, LM and SEM pollen and epidermal morphology is explanatory, significant, and can be of special interest for the plant taxonomist in the correct identification of monocots taxa.

Indoxyl sulfate-induced calcification of vascular smooth muscle cells via the PI3K/Akt/NF-κB signaling pathway.

Abstract: Vascular calcification (VC) is highly prevalent in patients with chronic kidney disease (CKD) and contributes to their high rate of cardiovascular mortality. Indoxyl sulfate (IS) is a representative protein-bound uremic toxin in CKD patients, which has been recognized as a major risk factor for VC. Recent studies have demonstrated that nuclear factor-kappa B (NK-κB) is highly activated in the chronic inflammation conditions of CKD patients and participated in the pathogenesis of VC. However, whether NK-κB is involved in the progression of IS-induced VC remains without elucidation. Here, we showed that NK-κB activity was increased in the IS-induced calcification of human aortic smooth muscle cells (HASMCs). Blocking the NK-κB with a selective inhibitor (Bay-11-7082) significantly relieved the osteogenic transdifferentiation of HASMCs, characterized by the downregulation of early osteogenic-specific marker, core-binding factor alpha subunit 1 (Cbfα1), and upregulation of smooth muscle α-actin (α-SMA), a specific vascular smooth muscle cell marker. Besides, IS stimulated the activation of PI3K/Akt signaling. Furthermore, LY294002, a specific inhibitor of PI3K/Akt pathway, attenuated the activation of NK-κB and osteogenic differentiation of HASMCs. Together, these results suggest that PI3K/Akt/NK-κB signaling plays an important role in the pathogenesis of osteogenic transdifferentiation induced by IS.

Foliar and stem epidermal anatomy of the tribe Cynoglosseae (Boraginaceae) and their taxonomic significance

Abstract: Foliar and stem epidermal anatomical features of the tribe Cynoglosseae have been studied in detail for the taxonomic identification using light microscopy (LM) and scanning electron microscopic (SEM) techniques. A comparative study was conducted on different plant parts (leaf and stem epidermal anatomy) of 25 species belonging to eight genera of the tribe Cynoglosseae (Boraginaceae) collected from different phytogeographical regions of Iran for the first time. Different qualitative and quantitative characteristics were observed in detail using LM and SEM. Results showed that although generally the stem and leaf anatomical traits were similar, but some diagnostic features were examined for distinguishing the closely related genera in the tribe. The ratio of cortex/diameter of stem and phloem/xylem, the average row number of collenchyma, palisade and spongy cells, structure of trichomes, type of indumentum and palisade arrangement were found taxonomically important. The anatomical characters were statistically analyzed using cluster analysis and principal component analysis. The study found that stem and leaf eccentrics are variable in the genus but constant within species of the same genus. Most species had typical isobilateral leaves, but some showed an incipient dorsoventrally symmetry with a layer of abaxial palisade tissue. Eglandular trichomes were observed found in all the studied species, which were recognized based on structure and function. In present study some novel characters have been observed which are of great interest to the taxonomist for the correct identification some genera delimitations. The characters studied here are of less taxonomic value and delimitating at species level.

Taxonomic importance of spore morphology in Thelypteridaceae from Northern Pakistan.

Abstract: Spore morphology of Thelypteridaceae species growing in Malakand Division, Northern Pakistan, was studied using both light microscopy and scanning electron microscopy. The taxa are Christella dentata and Glaphyropteridopsis erubescens in the subfamily Thelypteridoideae, and Phegopteris connectilis, Pseudophegopteris pyrrhorhachis, and Pseudophegopteris levingei in the subfamily Phegopteridoideae. The studied species exhibit differences in spore size, exospore thickness, color, and ornamentation. Spores of the studied species are monolete and medium-sized, and shape is ellipsoidal in both polar and equatorial views. The average measurement of the polar diameter ranges from 27 μm to 31 μm, whereas in the equatorial direction it varied from 20 μm to 40 μm. The exospore thickness ranges from 1.2 μm to 2.4 μm. Reticulate, laevigate with microgranules, cristate, and coarsely echinate surface ornamentation are observed among the species. Multivariate analysis including unweighted pair group method with arithmetic mean and principal component analysis was used for the grouping and discrimination of species and genera.

Antimicrobial activity of intracanal medications against both Enterococcus faecalis and Candida albicans biofilm

Abstract: Enterococcus faecalis and Candida albicans have been associated with cases of secondary and persistent root canal infections, been resistant to calcium hydroxide. So, the evaluation of the susceptibility of these microorganisms biofilms to new drugs is an important practice for establishing the best drug and consequently success of treatment. For this, in vitro biofilm formation of E. faecalis and C. albicans was induced separately on blocks obtained from bovine teeth. After the period of specimen incubation for biofilm maturation, the samples were immersed in the pastes: 1 - calcium hydroxide (CH), 2 - chlorhexidine (C), 3 - ciprofloxacin (CP), 4 - metronidazole (MT), 5 - ketoconazole (KE), 6 - double antibiotic (DB), 7 - triple antibiotic (TA), 8 - ciprofloxacin + ketoconazole (CPKE); 9 - ciprofloxacin + metronidazole + ketoconazole (CPMTKE), 10 - metronidazole + ketoconazole (MTKE), and 11 - control (CO) for 7 days. Next, the specimens were live/dead stained for analysis by confocal microscopy. By means of the Bioimage program, the biovolume and percentage of live cells were measured. The data were statistically compared (p = .05). For the C. albicans biofilm, the best antimicrobial action was found for MTKE, CPKE, and MT groups. Whereas for E. faecalis biofilm, the lowest percentage of live bacteria was found in TA, DB, and CP groups; however, KE, CPKE, CPMTKE, and MTKE groups shown to be effective. The authors concluded calcium hydroxide paste and chlorhexidine was not effective for both biofilms. The MTKE and CPKE pastes presented effectiveness for both biofilms. TA and DB pastes were effective just in the E. faecalis biofilms.

The effects of human menstrual blood stem cells-derived granulosa cells on ovarian follicle formation in a rat model of premature ovarian failure.

Abstract: Many studies have reported that human endometrial mesenchymal stem cells (HuMenSCs) are capable of repairing damaged tissues. The aim of the present study was to investigate the effects of HuMenSCs transplantation as a treatment modality in premature ovarian failure (POF) associated with chemotherapy-induced ovarian damage. HuMenSCs were isolated from menstrual blood samples of five women. After the in vitro culture of HuMenSCs, purity of the cells was assessed by cytometry using CD44, CD90, CD34, and CD45 FITC conjugate antibody. Twenty-four female Wistar rats were randomly divided into four groups: negative control, positive control, sham, and treatment groups. The rat models of POF used in our study were established by injecting busulfan intraperitoneally into the rats during the first estrus cycle. HuMenSCs were transplanted by injection via the tail vein into the POF-induced rats. Four weeks after POF induction, ovaries were collected and the levels of Amh, Fst, and Fshr expression in the granulosa cell (GC) layer, as well as plasma estradiol (E2) and progesterone (P4) levels were evaluated. Moreover, migration and localization of DiI-labeled HuMenSCs were detected, and the labeled cells were found to be localized in GCs layer of immature follicles. In addition to DiI-labelled HuMenSCs tracking, increased levels of expression of Amh and Fshr and Fst, and the high plasma levels of E2 and P4 confirmed that HuMenSC transplantation had a significant effect on follicle formation and ovulation in the treatment group compared with the negative control (POF) group.

Foliar epidermal anatomical characteristics of taxa of Iris subg. Scorpiris Spach (Iridaceae) from Turkey

Abstract: Iris L. is one of the important genus of family Iridaceae, consist of 56 taxa naturally occurred in Turkey. The similarities and variations in the subgenus overlapping the taxonomic positions of the species in the subgenera and needs anatomical assessment especially by microscopic techniques. In this study, the taxonomic significance of leaf anatomical characters of 10 Iris subgenus Scorpiris taxa were studied in detail and the relationship among these taxa were evaluated using microscopy techniques. Fresh leaf samples of species were fixed in 70% alcohol solution for anatomical observation under microscope. Eleven different micromorphological features were statistically analyzed to delimit the species in subgenus. Based on morphological and anatomical similarities, we studied relationships among; (1) ssp. turcica, ssp. caucasica, I. nezahatiae and I. pseudocaucasica; (2) correlation between ssp. turcica and ssp. caucasica; (3) association of I. galatica, I. persica, ssp. margaretiae and ssp. stenophylla with each other; (4) relationship between ssp. stenophylla and ssp. margaretiae; and (5) relevance between I. aucheri and I. peshmeniana. Moreover, the taxonomy of subgenus Scorpiris has been discussed in detail with novel and diagnostic features based on micromorphological physiognomies. We found that four species in this study are endemic to Turkey, while seven are critically endangered geophytes in the country. The leaf anatomical characteristics of 10 taxa were divided into three groups. Main aim of this research was to study the taxonomy of the complex subgenus Scorpiris through microscopic techniques.

Palyno‐morphological investigations of halophytic taxa of Amaranthaceae through SEM from Salt range of Northern Punjab, Pakistan

Abstract: The pollen morphology of 11 salt tolerant plant species of family Amaranthaceae from the salt range of Northern Punjab, Pakistan has been studied. The palyno-morphological characters were examined using light and scanning electron microscope. The examined all salt tolerant species have a slight difference in size but have similarity in shape, pore ornamentation, and polarity. The observed morphological characters of pollen grains were pollen symmetry, size, shape, pore ornamentation, pore size, number of pores, exine thickness, polar and equatorial diameter and, P/E ratio. Apolar type of pollens has been observed in all species. Shape of pollens was spheroidal. Exine sculpturing of pollen grains was scabrate (six spp), microechinate (four spp), and microechinate-scabrate (one spp). Different pori numbers were observed in different species. The pantoporate aperturate and sunken pore ornamentation have been reported in all species. A pollen taxonomic key was developed using examined morphological characters for the accurate identification of halophytic taxa. The high fertility and low sterility of pollens confirmed that the selected halophytes are well-established in the salt region. The findings highlight the taxonomic significance of pollen morphology in correct identification and differentiation of salt tolerant plant species.