Evaluation of the teratogenic potency of bulk zinc oxide and its nanoparticles on embryos of the freshwater snail, Helisoma duryi.

Bulk zinc oxide (ZnO-BPs) and its nanoparticles (ZnO-NPs) are frequently used in various products for humans. Helisoma duryi embryos can serve as effective model organisms for studying the toxicity of NPs. This study aimed to compare the teratogenic potency of ZnO-BPs and ZnO NPs in the embryonic stages of H. duryi to evaluate the utility of this snail as a bioindicator for ZnO-NPs in the aquatic environment. The mechanisms of teratogenesis were evaluated by determination of the LC50, studying the effect of sub-lethal concentrations of both ZnO forms on the embryos, and studying their enzyme activity, oxidative stress, and biochemical analysis. The SDS-PAGE electrophoresis was undertaken to assess the effect of ZnO-BPs and ZnO NPs on protein synthesis. The results revealed that the veliger stage of H. duryi is the specific stage for bulk and nano ZnO. ZnO-NPs proved to be more toxic to snails' embryos than ZnO-BPs. Exposure to ZnO influences specific types of defects in development, which in the case of BPs are far less drastic than those caused by NPs. Thus, the toxicity of ZnO-NPs in embryonic development is due to their unique physicochemical properties. The observed malformations include mainly hydropic malformation, exogastrulation, monophthalmia, shell misshapen, and cell lyses. Almost all tested oxidative biomarkers significantly changed, revealing that ZnONPs display more oxidative stress than ZnO-BPs. Also, the low concentration of ZnO induces many disturbances in the organic substances of veliger larvae, such as a decrease in the total protein and total lipid levels and an increase in the glycogen level. The results indicated that ZnO-BPs increase the number of protein bands. Conversely, ZnO-NPs concealed one band from treated egg masses, which was found in the control group. Embryos of snail are an appropriate model to control freshwater snails. This study demonstrates that H. duryi embryos can serve as effective model organisms to study the toxicity of ZnO-NPs.

Reproductive performance of freshwater snail, Helisoma duryi under the effect of bulk and nano zinc oxide.

Nanotechnology has been used to apply nanoparticle essential elements to enhance the ability of animals to absorb these elements and consequently improve their reproductive performance. High concentrations of nanoparticles (NPs) can directly harm a range of aquatic life forms, ultimately contributing to a decline in biodiversity. Helisoma duryi snails are a good model for studying the toxicological effects of bulk zinc oxide (ZnO-BPs) and nano zinc oxide (ZnO-NPs) on freshwater gastropods. This study aimed to compare the toxic effects of ZnO-BPs and ZnO-NPs on H. duryi snails and explore how waterborne and dietary exposure influenced the reproductive performance of this snail. ZnO-BPs and ZnO-NPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray powder (XRD). This study revealed that the size of ZnO-BPs and ZnO-NPs were 154 nm and 11-31 nm, respectively. The results showed that exposure of adult snails to sub-lethal concentrations of both ZnO forms (bulk and nano) for 24 h/week for 4 weeks markedly changed their reproductive performance in a concentration-dependent manner, where fecundity was negatively affected by high concentrations. It was concluded that dietary exposure to the lowest tested concentration of ZnO-NPs (1 ppm) has a positive effect as the number of eggs and egg masses/snails increased and the incubation period decreased. Also, poly-vitelline eggs (The formation of twins) were observed. ZnO-NPs at low concentrations positively affect the reproductive performance of snails, especially after dietary exposure. The results revealed that 1 ppm ZnO-NPs could be supplementary provided to snails to improve their fertility, reduce the developmental time course, increase hatchability percentage, and produce poly-vitelline eggs.

A novel approach toward skin cancer classification through fused deep features and neutrosophic environment.

Variations in the size and texture of melanoma make the classification procedure more complex in a computer-aided diagnostic (CAD) system. The research proposes an innovative hybrid deep learning-based layer-fusion and neutrosophic-set technique for identifying skin lesions. The off-the-shelf networks are examined to categorize eight types of skin lesions using transfer learning on International Skin Imaging Collaboration (ISIC) 2019 skin lesion datasets. The top two networks, which are GoogleNet and DarkNet, achieved an accuracy of 77.41 and 82.42%, respectively. The proposed method works in two successive stages: first, boosting the classification accuracy of the trained networks individually. A suggested feature fusion methodology is applied to enrich the extracted features' descriptive power, which promotes the accuracy to 79.2 and 84.5%, respectively. The second stage explores how to combine these networks for further improvement. The error-correcting output codes (ECOC) paradigm is utilized for constructing a set of well-trained true and false support vector machine (SVM) classifiers via fused DarkNet and GoogleNet feature maps, respectively. The ECOC's coding matrices are designed to train each true classifier and its opponent in a one-versus-other fashion. Consequently, contradictions between true and false classifiers in terms of their classification scores create an ambiguity zone quantified by the indeterminacy set. Recent neutrosophic techniques resolve this ambiguity to tilt the balance toward the correct skin cancer class. As a result, the classification score is increased to 85.74%, outperforming the recent proposals by an obvious step. The trained models alongside the implementation of the proposed single-valued neutrosophic sets (SVNSs) will be publicly available for aiding relevant research fields.

Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects.

The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

A Deep Learning Computer-Aided Diagnosis Approach for Breast Cancer.

Breast cancer is a gigantic burden on humanity, causing the loss of enormous numbers of lives and amounts of money. It is the world's leading type of cancer among women and a leading cause of mortality and morbidity. The histopathological examination of breast tissue biopsies is the gold standard for diagnosis. In this paper, a computer-aided diagnosis (CAD) system based on deep learning is developed to ease the pathologist's mission. For this target, five pre-trained convolutional neural network (CNN) models are analyzed and tested-Xception, DenseNet201, InceptionResNetV2, VGG19, and ResNet152-with the help of data augmentation techniques, and a new approach is introduced for transfer learning. These models are trained and tested with histopathological images obtained from the BreakHis dataset. Multiple experiments are performed to analyze the performance of these models through carrying out magnification-dependent and magnification-independent binary and eight-class classifications. The Xception model has shown promising performance through achieving the highest classification accuracies for all the experiments. It has achieved a range of classification accuracies from 93.32% to 98.99% for magnification-independent experiments and from 90.22% to 100% for magnification-dependent experiments.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Does level of sedation impact detection of advanced neoplasia?

BACKGROUND AND AIMS: Two of the foremost issues in screening colonoscopy involve delivering quality and maximizing adenoma detection rates (ADR). Little is known about the impact of deep sedation on ADR. This study aims to compare the detection of advanced lesions during screening colonoscopy performed with moderate conscious sedation (MCS) versus deep sedation (DS). METHODS: A retrospective cohort study was performed using the Clinical Outcomes Research Initiative database. Average risk screening colonoscopies performed January 2000 to December 2005 were examined for practice setting, patient demographics, and findings, including detection of a polyp >9 mm and suspected malignant lesions. RESULTS: A total of 104,868 colonoscopies were examined, 97% of which were performed with MCS. Univariate analysis demonstrated that more polyps of any size were detected with MCS (38 vs. 34%, p < 0.0001) and more advanced lesions were found with DS compared with MCS (7 vs. 6%, p = 0.01). When exclusively examining sites that performed DS > 10% for all procedures, a more significant increase in advanced lesion detection when using DS was observed (7.5 vs. 5.7%, p = 0.003). When adjusted for age, gender, race/ethnicity, site, prep quality, and ASA group, DS was 25% more likely to detect an advanced lesion. CONCLUSIONS: Our data suggest that use of DS may be associated with a higher rate of advanced lesion detection. However, this retrospective design has limitations that necessitate follow-up with prospective studies. These follow-up studies would be essential to support any change in the standard practices of sedation.

Structural analysis of a set of proteins resulting from a bacterial genomics project.

The targets of the Structural GenomiX (SGX) bacterial genomics project were proteins conserved in multiple prokaryotic organisms with no obvious sequence homolog in the Protein Data Bank of known structures. The outcome of this work was 80 structures, covering 60 unique sequences and 49 different genes. Experimental phase determination from proteins incorporating Se-Met was carried out for 45 structures with most of the remainder solved by molecular replacement using members of the experimentally phased set as search models. An automated tool was developed to deposit these structures in the Protein Data Bank, along with the associated X-ray diffraction data (including refined experimental phases) and experimentally confirmed sequences. BLAST comparisons of the SGX structures with structures that had appeared in the Protein Data Bank over the intervening 3.5 years since the SGX target list had been compiled identified homologs for 49 of the 60 unique sequences represented by the SGX structures. This result indicates that, for bacterial structures that are relatively easy to express, purify, and crystallize, the structural coverage of gene space is proceeding rapidly. More distant sequence-structure relationships between the SGX and PDB structures were investigated using PDB-BLAST and Combinatorial Extension (CE). Only one structure, SufD, has a truly unique topology compared to all folds in the PDB.

Mutation in rugose gene locus affects the pigment cell number and alters the optic lobe phenotype in the Drosophila visual system.

In the Drosophila melanogaster, developmental signals orchestrate cell differentiation in a sequential manner which lead to precise neural and cellular patterns. The previous work demonstrated that the rugose (rg) gene is required for proper differentiation of the cone and pigment cells in the Drosophila compound eye. In this study, the pigment cell number in several ommatidia in rugose mutant eyes was analyzed and showed multiplication of secondary pigment cells in the null mutation. In addition, rugose mutant-flies exhibit optic lobe defects. The lamina, medulla, lobula and lobula plate are affected by mutation. The results suggest that rugose gene encodes a secondary pigment cell specific function, and plays an essential role in determining the optic lobe's structure.

rugose (rg), a Drosophila A kinase anchor protein, is required for retinal pattern formation and interacts genetically with multiple signaling pathways.

In the developing Drosophila eye, cell fate determination and pattern formation are directed by cell-cell interactions mediated by signal transduction cascades. Mutations at the rugose locus (rg) result in a rough eye phenotype due to a disorganized retina and aberrant cone cell differentiation, which leads to reduction or complete loss of cone cells. The cone cell phenotype is sensitive to the level of rugose gene function. Molecular analyses show that rugose encodes a Drosophila A kinase anchor protein (DAKAP 550). Genetic interaction studies show that rugose interacts with the components of the EGFR- and Notch-mediated signaling pathways. Our results suggest that rg is required for correct retinal pattern formation and may function in cell fate determination through its interactions with the EGFR and Notch signaling pathways.

Gastric acid secretion in aquaporin-4 knockout mice.

The aquaporin-4 (AQP4) water channel has been proposed to play a role in gastric acid secretion. Immunocytochemistry using anti-AQP4 antibodies showed strong AQP4 protein expression at the basolateral membrane of gastric parietal cells in wild-type (+/+) mice. AQP4 involvement in gastric acid secretion was studied using transgenic null (-/-) mice deficient in AQP4 protein. -/- Mice had grossly normal growth and appearance and showed no differences in gastric morphology by light microscopy. Gastric acid secretion was measured in anesthetized mice in which the stomach was luminally perfused (0. 3 ml/min) with 0.9% NaCl containing [(14)C]polyethylene glycol ([(14)C]PEG) as a volume marker. Collected effluent was assayed for titratable acid content and [(14)C]PEG radioactivity. After 45-min baseline perfusion, acid secretion was stimulated by pentagastrin (200 microg. kg(-1). h(-1) iv) for 1 h or histamine (0.23 mg/kg iv) + intraluminal carbachol (20 mg/l). Baseline gastric acid secretion (means +/- SE, n = 25) was 0.06 +/- 0.03 and 0.03 +/- 0.02 microeq/15 min in +/+ and -/- mice, respectively. Pentagastrin-stimulated acid secretion was 0.59 +/- 0.14 and 0.70 +/- 0.15 microeq/15 min in +/+ and -/- mice, respectively. Histamine plus carbachol-stimulated acid secretion was 7.0 +/- 1.9 and 8.0 +/- 1.8 microeq/15 min in +/+ and -/- mice, respectively. In addition, AQP4 deletion did not affect gastric fluid secretion, gastric pH, or fasting serum gastrin concentrations. These results provide direct evidence against a role of AQP4 in gastric acid secretion.

Treatment of intraventricular hemorrhage using urokinase.

Four patients with severe intraventricular hemorrhage (IVH) were treated using urokinase administered into the lateral ventricles via a ventricular drainage catheter. All patients were female and of ages ranging from 24 to 53 years. The primary diseases were hemorrhagic infarction, moyamoya disease, sinus thrombosis, and thalamic bleeding. Urokinase administration was initiated at 1.3 days average after occurrence of IVH and continued for 3.3 days average in doses of 12,000-96,000 IU per day. Average clot lysis times from IVH, as assessed by computed tomography, were 5.0 +/- 0.8, 5.0 +/- 1.4, and 6.0 +/- 0.8 days for the fourth, the third, and the lateral ventricles, respectively. All patients suffered from meningitis which was probably caused by urokinase administration through a ventricular catheter. However, this was successfully treated by changing the antibiotics. There was no general bleeding tendency or intraventricular rebleeding due to urokinase administration, and none of the ventricular catheters were obstructed by clots throughout the course. The final outcome was good recovery in two patients, severe disability in one, and persistent vegetative state in one. These results correlated well with the consciousness level seen before ventricular drainage in each patient. Consequently, we are convinced that urokinase administration can prevent the harmful effects of IVH and that urokinase is useful not only for lysing ventricular clots but also for maintaining the patency of the ventricular catheter, which is important for control of intracranial pressure in the acute stage of severe IVH.