Insights into nanoparticles-induced neurotoxicity and cope up strategies

Nanoparticle applications are becoming increasingly popular in fields such as photonics, catalysis, magnetics, biotechnology, manufacturing of cosmetics, pharmaceuticals, and medicines. There is still a huge pile of undermining information about the potential toxicity of these products to humans, which can be encountered by neuroprotective antioxidants and anti-inflammatory compounds. Nanoparticles can be administered using a variety of methods, including oronasal, topical applications, and enteral and parenteral routes of administration. There are different properties of these nanomaterials that characterize different pathways. Crossing of the blood-brain barrier, a direct sensory nerve-to-brain pathway whose barriers are bypassed, these checks otherwise prevent the nanoparticles from entering the brain. This inflicts damage to sensory neurons and receptors by nanoparticles that lead to neurotoxicity of the central nervous system. A number of routes make nanoparticles able to penetrate through the skin. Exposure by various routes to these nanoparticles can result in oxidative stress, and immune suppression triggers inflammatory cascades and genome-level mutations after they are introduced into the body. To out-power, these complications, plant-based antioxidants, essential oils, and dietary supplements can be put into use. Direct nanoparticle transport pathways from sensory nerves to the brain via blood have been studied grossly. Recent findings regarding the direct pathways through which nanoparticles cross the blood-brain barriers, how nanoparticles elicit different responses on sensory receptors and nerves, how they cause central neurotoxicity and neurodegeneration through sensory nerve routes, and the possible mechanisms that outcast these effects are discussed

Breast Cancer Diagnosis Using Lightweight Deep Convolution Neural Network Model

In the past few decades, breast cancer has rapidly increased the death rate among women worldwide. An early diagnosis of such fatal disease is important for the best treatment and death rate reduction. Automatic diagnosis of breast cancer from histopathological images using artificial intelligence (AI) based methods is a top-priority research area in the biomedical field. However, automatic detection is challenging due to high resolution of histopathology images and the tremendous amount of parameters required by deep AI models. Due to high computational complexity and bulky memory usage, deep models suffer from inefficient inference that limits their application in resource-constrained platforms. To address this problem, a fast cancer detection strategy has been proposed to overcome the computational cost issue of deep automatic systems. Instead of directly using input images the wavelet transform (WT) is applied to decompose the images into different frequency bands and then only low frequency bands are subjected to our proposed lightweight deep convolutional neural network (CNN). The lightweight deep model is designed using invertible residual block module. The incorporation of invertible residual block module in the deep CNN model and the use of WT considerably reduces the computational cost of the proposed model, without a noticeable accuracy downgrade Further, the effect of various machine vision classifiers i.e. support vector machine (SVM), softmax, and K nearest neighbor classifier (KNN) on model performance is analyzed. Experiments are performed using three publicly available benchmark histopathology image datasets. The proposed model has shown multi-class classification accuracy of 96.25%, and 99.8% and 72.2%, on the international conference on image analysis and recognition (ICIAR 2018), BreakHis and Bracs datasets, respectively. The reported inference time per image of the proposed model is 0.67s, and 0.21s for ICIAR 2018 and BreakHis and Bracs images, respectively

Psychological Effect on children at Secondary Level after Flood Disaster District RajanPur

The aim of this study was to investigate the psychological effects of flooding on secondary school children in the Rajanpur, Jampur, Taunsa, Dera Ghazi Khan, and Fazalpur districts. A total of 150 students from public schools in flood-affected areas participated in the study, which used a non-probability convenience sampling approach and a self-administered questionnaire to collect data. The collected information was then analyzed using descriptive statistics, such as percentage and mean scores, with the Statistical Package for Social Science (SPSS). The study found that the majority of students experienced negative psychological impacts from the flooding, including impacts on their emotional, mental, and academic development. Although both male and female students were affected, females experienced higher levels of psychological effects. This study highlights the importance of reducing flood risk before disasters occur, which can greatly reduce the devastating effects of floods. To effectively manage risks, it is essential to have a thorough understanding of the causes and risks associated with flooding

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data

Design and Analysis of an O+E-Band Hybrid Optical Amplifier for CWDM Systems

Broadband amplification in the O+E-band is very desirable nowadays as a way of coping with increasing bandwidth demands. The main issue with doped fiber amplifiers working in this band such as the bismuth-doped fiber amplifier is that they are costly and not widely available. Therefore, a wideband and flat-gain hybrid optical amplifier (HOA) covering the O+E-band based on a parallel combination of a praseodymium-doped fiber amplifier (PDFA) and a semiconductor optical amplifier (SOA) is proposed and demonstrated through numerical simulations. The praseodymium-doped fiber (PDF) core is pumped using a laser diode with a power of 500 mW that is centered at a wavelength of 1030 nm. The SOA is driven by an injection current of 60 mA. The performance of the HOA is analyzed by the optimization of various parameters such as the PDF length, Pr3+ concentration, pump wavelength, and injection current. A flat average gain of 24 dB with a flatness of 1 dB and an output power of 9.6 dBm is observed over a wavelength range of 1270–1450 nm. The noise figure (NF) varies from a minimum of 4 dB to a maximum of 5.9 dB for a signal power of 0 dBm. A gain reduction of around 4 dB is observed for an O-band signal at a wavelength of 1290 nm by considering the up-conversion effect. The transmission performance of the designed HOA as a pre-amplifier is evaluated based on the bit-error rate (BER) analysis for a coarse wavelength-division multiplexing (CWDM) system of eight on-off keying (OOK)-modulated channels, each having a data rate of 10 Gbps. An error-free transmission over 60 km of standard single-mode fiber (SMF) is achieved for different data rates of 5 Gbps, 7.5 Gbps, and 10 Gbps

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals

Molecular Genetic Studies and Delineation of the Oculocutaneous Albinism Phenotype in the Pakistani Population

Background: Oculocutaneous albinism (OCA) is caused by a group of genetically heterogeneous inherited defects that result in the loss of pigmentation in the eyes, skin and hair. Mutations in the TYR, OCA2, TYRP1 and SLC45A2 genes have been shown to cause isolated OCA. No comprehensive analysis has been conducted to study the spectrum of OCA alleles prevailing in Pakistani albino populations. Methods: We enrolled 40 large Pakistani families and screened them for OCA genes and a candidate gene, SLC24A5. Protein function effects were evaluated using in silico prediction algorithms and ex vivo studies in human melanocytes. The effects of splice-site mutations were determined using an exon-trapping assay. Results: Screening of the TYR gene revealed four known (p.Arg299His, p.Pro406Leu, p.Gly419Arg, p.Arg278*) and three novel mutations (p.Pro21Leu, p.Cys35Arg, p.Tyr411His) in ten families. Ex vivo studies revealed the retention of an EGFP-tagged mutant (p.Pro21Leu, p.Cys35Arg or p.Tyr411His) tyrosinase in the endoplasmic reticulum (ER) at 37°C, but a significant fraction of p.Cys35Arg and p.Tyr411His left the ER in cells grown at a permissive temperature (31°C). Three novel (p.Asp486Tyr, p.Leu527Arg, c.1045-15 T \u3e G) and two known mutations (p.Pro743Leu, p. Ala787Thr) of OCA2 were found in fourteen families. Exon-trapping assays with a construct containing a novel c.1045-15 T \u3e G mutation revealed an error in splicing. No mutation in TYRP1, SLC45A2, and SLC24A5 was found in the remaining 16 families. Clinical evaluation of the families segregating either TYR or OCA2 mutations showed nystagmus, photophobia, and loss of pigmentation in the skin or hair follicles. Most of the affected individuals had grayish-blue colored eyes. Conclusions: Our results show that ten and fourteen families harbored mutations in the TYR and OCA2 genes, respectively. Our findings, along with the results of previous studies, indicate that the p.Cys35Arg, p.Arg278* and p.Gly419Arg alleles of TYR and the p.Asp486Tyr and c.1045-15 T \u3e G alleles of OCA2 are the most common causes of OCA in Pakistani families. To the best of our knowledge, this study represents the first documentation of OCA2 alleles in the Pakistani population. A significant proportion of our cohort did not have mutations in known OCA genes. Overall, our study contributes to the development of genetic testing protocols and genetic counseling for OCA in Pakistani families

Resistance Modulation of Individual and Polymicrobial Culture of <i>S. aureus</i> and <i>E. coli</i> through Nanoparticle-Coupled Antibiotics

Polymicrobial mastitis is now becoming very common in dairy animals, resulting in exaggerated resistance to multiple antibiotics. The current study was executed to find drug responses in individual and mixed Culture of Staphylococcus aureus and Escherichia coli isolated from milk samples, as well as to evaluate the antibacterial potential of tungsten oxide nanoparticles. These isolates (alone and in mixed culture) were further processed for their responses to antibiotics using the disc diffusion method. On the other hand, tungsten oxide WO3 (W) nanoparticles coupled with antibiotics (ampicillin, A, and oxytetracycline, O) were prepared through the chemical method and characterized by X-ray diffraction, scanning electron microscopy (SEM), and UV-visible techniques. The preparations consisting of nanoparticles alone (W) and coupled with ampicillin (WA) and oxytetracycline (WO) were tested against individual and mixed Culture through the well diffusion and broth microdilution methods. The findings of the current study showed the highest resistance in E. coli was against penicillin (60%) and ampicillin (50%), while amikacin, erythromycin, ciprofloxacin, and oxytetracycline were the most effective antibiotics. S. aureus showed the highest resistance against penicillin (50%), oxytetracycline (40%), and ciprofloxacin (40%), while, except for ampicillin, the sensitive strains of S. aureus were in the range of 40–60% against the rest of antibiotics. The highest zones of inhibition (ZOI) against mixed Culture were shown by imipenem and ampicillin, whereas the highest percentage decrease in ZOI was noted in cases of ciprofloxacin (−240%) and gentamicin (−119.4%) in comparison to individual Culture of S. aureus and E. coli. It was noteworthy that the increase in ZOI was not more than 38% against mixed Culture as compared to the individual Culture. On the other hand, there was a significant reduction in the minimum inhibitory concentration (MIC) of nanoparticle-coupled antibiotics compared to nanoparticles alone for individual and mixed-culture bacteria, while MICs in the case of mixed Culture remained consistently high throughout the trial. This study therefore concluded that diverse drug resistance was present in both individual and mixed-culture bacteria, whereas the application of tungsten oxide nanoparticle-coupled antibiotics proved to be an effective candidate in reversing the drug resistance in bacterial strains

Delineating the Molecular and Phenotypic Spectrum of the CNGA3-Related Cone Photoreceptor Disorder in Pakistani Families

Cone photoreceptor dysfunction represents a clinically heterogenous group of disorders characterized by nystagmus, photophobia, reduced central or color vision, and macular dystrophy. Here, we described the molecular findings and clinical manifestations of achromatopsia, a partial or total absence of color vision, co-segregating with three known missense variants of CNGA3 in three large consanguineous Pakistani families. Fundus examination and optical coherence tomography (OCT) imaging revealed myopia, thin retina, retinal pigment epithelial cells loss at fovea/perifovea, and macular atrophy. Combination of Sanger and whole exome sequencing revealed three known homozygous missense variants (c.827A&gt;G, p.(Asn276Ser); c.847C&gt;T, p.(Arg283Trp); c.1279C&gt;T, p.(Arg427Cys)) in CNGA3, the &alpha;-subunit of the cyclic nucleotide-gated cation channel in cone photoreceptor cells. All three variants are predicted to replace evolutionary conserved amino acids, and to be pathogenic by specific in silico programs, consistent with the observed altered membrane targeting of CNGA3 in heterologous cells. Insights from our study will facilitate counseling regarding the molecular and phenotypic landscape of CNGA3-related cone dystrophies