Enhanced toughness and strength of 3D printed carbide-oxide composite for biomedical applications

Natural materials derived/extracted Ceramics is an excellent material for developing ceramic-based orthopedic implants. Recently, we have demonstrated an easily scalable, energy-efficient green method to extract ceramic particles from bio-waste i.e. chicken bone. Though the chicken bone extract (CBE) has good biocompatibility, it lacks good mechanical properties in the 3D printed condition as that of human bones. Here, we have reinforced CBE with different weight proportions of silicon carbide to improve the mechanical characteristics of the composite. The hybrid of CBE (oxide) and carbide (SiC) is sintered at different temperatures to understand the effect of the interface of the two ceramics. It is observed that temperature has minimal effect and composition has a noticeable effect on mechanical strength as well as bio-toxicity. The toughness (∼3.58 MJ/m3) and compressive strength (∼64.64 MPa) of the 90:10 composition sintered at 1250 °C show the maximum optimum values. A mathematical model has also been developed to predict and correlate the toughness with porosity, volumetric loading, and elastic modulus of the 3D-printed ceramic composite.

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

Probiotic Escherichia coli Nissle 1917 alleviates the neurotoxicity caused by acrylamide in zebrafish

Neurotoxicity is caused by damage to the brain tissue by neurotoxic agents present in the environment and artificial substances produced by human beings. Acrylamide (ACR) is one such chemical substance that causes neurotoxicity, affecting the brain cells. This neurotoxicity causes damage to the sensory and metabolic functions. The current research investigates the favourable effect of probiotic EcN (Escherichia coli Nissle 1917) on ACR-induced neurotoxicity in zebrafish. The protective role of EcN against ACR induced toxicity was assessed based on behaviour, biochemical, and gene expression analysis. Initially, the colonisation period of EcN in the zebrafish gut was determined and EcN was given orally to the zebrafish only once prior to the ACR treatment. Very interestingly, this dosage was able to ameliorate the adverse effects of ACR significantly in the brain cells. Quantification of oxidative stress and neuronal cell death clearly vindicate the efficiency of probiotic EcN in reversing the damages caused by ACR. EcN is being explored largely in recent days for its therapeutic applications. This study strongly supports the view that EcN can be developed as a supplement to the patients diagnosed with neuronal cell toxicity.

Anthropogenic marine litter: An approach to environmental quality for India's southeastern Arabian Sea coast

Anthropogenic marine litter (AML), mainly plastic, is a global concern that is persistent and widespread. To prevent and mitigate this threat, we need to understand the magnitude and source of AML. There is limited knowledge about AML pollution on the Indian Coast. In this context, the present study examined the distribution, abundance, typology, and beach quality based on AML along 22 beaches on the southeastern coast of the Arabian Sea. A total of 4911 AML items were classified into 9 categories, weighing 16.79 kg, and retrieved from a total area of 8000 m². The mean abundance and weight of AML in the current study were 0.45 ± 0.34 items/m² and 1.53 ± 0.92 g/m², respectively. Thottapally showed the most abundant AML among the studied beaches with 0.96 items/m², followed by Azheekkal with 0.73 items/m². Plastic, being the most common item, accounts for 77.6 % of all items and has a mean density of 0.35 items/m² comprising hard plastic (22 %), thermocol (13 %), food wrappers (7 %), cigarette butts (7 %), plastic rope (6 %), and plastic cutlery (6 %). Hazardous anthropogenic litter (HAL) was maximum at Thottapally (17.71 %; 85 out of 480 items collected). Based on the cleanliness of beaches, they are graded "moderately clean" (63 %) by the General Index (GI), "clean" (54 %), and "moderately clean" (40 %) as calculated by the Clean Coast Index (CCI). Hazardous Anthropogenic Beach Litter Index (HABLI) classifies 72 % of beaches as "moderately safe", while the Environmental Status Index (ESI) rates 68 % of beaches as "mediocre". Besides, model simulations demonstrated the pathways of AML propagation, which correlate to the littoral and coastal current flow patterns over the region. Land-based activities were the crucial factors influencing AML distribution. The study highlighted the need for effective regional litter management strategies, policy instruments for the litter impact pathways, economic, regulatory, and behavioural management tools, which were also discussed.

Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives

Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the radiologists in producing accurate results toward effective disease diagnosis. The objective of this research is to highlight the importance of deep learning models in the detection of Alzheimer's Disease (AD). The main objective of this research is to analyze different deep learning methods used for detecting AD. This study examines 103 research articles published in various research databases. These articles have been selected based on specific criteria to find the most relevant findings in the field of AD detection. The review was carried out based on deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL). To propose accurate methods for the detection, segmentation, and severity grading of AD, the radiological features need to be examined in greater depth. This review attempts to analyze different deep learning methods applied for AD detection using neuroimaging modalities like Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), etc. The focus of this review is restricted to deep learning works based on radiological imaging data for AD detection. There are a few works that have utilized other biomarkers to understand the effect of AD. Also, articles published in English were alone considered for analysis. This work concludes by highlighting the key research issues towards effective AD detection. Though several methods have yielded promising results in AD detection, the progression from Mild Cognitive Impairment (MCI) to AD need to be analyzed in greater depth using DL models.

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Background and objective

In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field.

Methods

The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection.

Results

The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well.

Conclusion

This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques.

Microplastic pollution in fragile coastal ecosystems with special reference to the X-Press Pearl maritime disaster, southeast coast of India

Microplastics (MPs) are a global environmental concern and pose a serious threat to marine ecosystems. This study aimed to determine the abundance and distribution of MPs in beach sediments (12 beaches), marine biota (6 beaches) and the influence of microbes on MPs degradation in eco-sensitive Palk Bay and Gulf of Mannar coast. The mean MP abundance 65.4 ± 39.8 particles/m² in beach sediments; 0.19 ± 1.3 particles/individual fish and 0.22 ± 0.11 particles g^-1 wet weight in barnacles. Polyethylene fragments (33.4%) and fibres (48%) were the most abundant MPs identified in sediments and finfish, respectively. Histopathological examination of fish has revealed health consequences such as respiratory system damage, epithelial degradation and enterocyte vacuolization. In addition, eight bacterial and seventeen fungal strains were isolated from the beached MPs. The results also indicated weathering of MPs due to microbial interactions. Model simulations helped in tracking the fate and transboundary landfall of spilled MPs across the Indian Ocean coastline after the X-Press Pearl disaster. Due to regional circulations induced by the monsoonal wind fields, a potential dispersal of pellets has occurred along the coast of Sri Lanka, but no landfall and ecological damage are predicted along the coast of India.

CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

Evaluating patient criticality is the foremost step in administering appropriate COVID-19 treatment protocols. Learning an Artificial Intelligence (AI) model from clinical data for automatic risk-stratification enables accelerated response to patients displaying critical indicators. Chest CT manifestations including ground-glass opacities and consolidations are a reliable indicator for prognostic studies and show variability with patient condition. To this end, we propose a novel attention framework to estimate COVID-19 severity as a regression score from a weakly annotated CT scan dataset. It takes a non-locality approach that correlates features across different parts and spatial scales of the 3D scan. An explicit guidance mechanism from limited infection labeling drives attention refinement and feature modulation. The resulting encoded representation is further enriched through cross-channel attention. The attention model also infuses global contextual awareness into the deep voxel features by querying the base CT scan to mine relevant features. Consequently, it learns to effectively localize its focus region and chisel out the infection precisely. Experimental validation on the MosMed dataset shows that the proposed architecture has significant potential in augmenting existing methods as it achieved a 0.84 R-squared score and 0.133 mean absolute difference.

Contour-enhanced attention CNN for CT-based COVID-19 segmentation

Accurate detection of COVID-19 is one of the challenging research topics in today's healthcare sector to control the coronavirus pandemic. Automatic data-powered insights for COVID-19 localization from medical imaging modality like chest CT scan tremendously augment clinical care assistance. In this research, a Contour-aware Attention Decoder CNN has been proposed to precisely segment COVID-19 infected tissues in a very effective way. It introduces a novel attention scheme to extract boundary, shape cues from CT contours and leverage these features in refining the infected areas. For every decoded pixel, the attention module harvests contextual information in its spatial neighborhood from the contour feature maps. As a result of incorporating such rich structural details into decoding via dense attention, the CNN is able to capture even intricate morphological details. The decoder is also augmented with a Cross Context Attention Fusion Upsampling to robustly reconstruct deep semantic features back to high-resolution segmentation map. It employs a novel pixel-precise attention model that draws relevant encoder features to aid in effective upsampling. The proposed CNN was evaluated on 3D scans from MosMedData and Jun Ma benchmarked datasets. It achieved state-of-the-art performance with a high dice similarity coefficient of 85.43% and a recall of 88.10%.

Panoramic Radiograph and CBCT in Unilateral Mandibular Condylar Hyperplasia—A Case Report

Condylar hyperplasia (CH) of the mandible is a rare developmental disorder that results in enlargement of the condyle. It is predominant in females, mostly unilateral and self-limiting. This report presents a case of a 35-year-old female patient with unilateral condylar hyperplasia with an increase in vertical height of mandibular ramus of the affected side. After initial radiographic evaluation, the asymmetry index using a panoramic radiograph was determined and also analyzed using cone-beam computed tomography (CBCT). Condylar dimensions in all the planes were measured for both sides using CBCT. This case report emphasizes the importance of CBCT and its role in the diagnosis and assessment of mandibular condylar hyperplasia.

COVID-19 restrictions and their influences on ambient air, surface water and plastic waste in a coastal megacity, Chennai, India

Anthropogenic activities experienced a pause due to the nationwide lockdown, imposed to contain the rapid spread of COVID-19 in the third week of March 2020. The impacts of suspension of industrial activities, vehicular transport and other businesses for three months (25 March-30 June) on the environmental settings of Chennai, a coastal megacity was assessed. A significant reduction in the key urban air pollutants [PM_2.5 (66.5%), PM₁₀ (39.5%), NO₂ (94.1%), CO (29%), O₃ (45.3%)] was recorded as an immediate consequence of the reduced anthropogenic activities. Comparison of water quality of an urban river Adyar, between pre-lockdown and lockdown, showed a substantial drop in the dissolved inorganic N (47%) and suspended particulate matter (41%) during the latter period. During the pandemic, biomedical wastes in India showed an overall surge of 17%, which were predominantly plastic. FTIR-ATR analysis confirmed the polymers such as polypropylene (25.4%) and polyester (15.4%) in the personal protective equipment.

Assessment of groundwater from an industrial coastal area of south India for human health risk from consumption and irrigation suitability

We assessed the groundwater quality in an industrial area (Tiruchendur Taluk) of Tamil Nadu state in coastal south India for human health risk from drinking as well as irrigation suitability by using the drinking water quality index (DWQI), irrigation factors (sodium adsorption ratio, sodium percentage, residual sodium carbonate and permeability index) and health hazard valuation (THQI- by consuming NO₃^- and F^-). About 57% of the samples represented Ca²⁺-Mg²⁺-Cl^--SO₄^2- facies and the anthropological unhygienic inputs elevated the salinity. Our results indicated that all the samples are unsuitable for drinking (DWQI up to 1063) and almost half of them are also unsuitable for irrigation due to sodium risk. Total hazard quotient index (THQI; HQ _nitrate and HQ _fluoride) suggested the order of health risk as children > women > men with about 64%, 70% and 79% of the samples posing non-carcinogenic risks for men, women and children, respectively. Different mitigation measures and sustainable development should be enforced to minimize the health issues from contamination caused by industries, fertilizers in agro-fields and natural processes and reduce the sodium dominance in groundwater. The spatial distribution maps of this study could also be helpful in organization of proper treatment plans to provide safe and hygienic groundwater to the community.

Spatiotemporal variations in anthropogenic marine litter pollution along the northeast beaches of India

Marine litter is widely distributed in marine environments and has been a severe concern worldwide, due to the disposal of waste from diverse sources. The severity of this threat has garnered increasing attention in India over the last decade, but the full consequences of this pollution are yet to be quantified. To estimate the spatiotemporal distribution, composition and beach quality of marine litter pollution, 17 beaches along the Hooghly estuary, a part of the Gangetic delta was studied. Marine litter was collected from 100 m long transects during two seasons (monsoon and post-monsoon). The OSPAR monitoring standard was applied to the 16,597 litter items collected, then grouped under 6 types and 44 categories. In terms of number, litter abundance was higher during monsoon (1.10 ± 0.39 items/m²) than that of post-monsoon (0.86 ± 0.32 items/m²). Most of the beaches were categorized as low cleanliness as computed by the general index and clean coast index and the good for the pellet pollution index. Hazardous litter constituted 6.5% of the total collected litter items. The model prediction revealed that the influence of high discharge from Hooghly, Rasulpur and Subarnarekha River carried enormous anthropogenic litter to the northeast beaches. The litter flux decreases with an increase in distance from the shore, and act as a sink to the sea-floor. The results denote that the distribution and typology of marine litter were representatives of household, tourism and fishing, which in turn highlights the need for better regional litter management measures. Suggested management practices include source reduction, mitigation, management of beach environment and change in littering behaviour through environmental education.

Ischemic Lesion Segmentation using Ensemble of Multi-Scale Region Aligned CNN

The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion from radiological images for effective treatment planning. Manual delineation of the lesion by radiological experts is generally laborious and time-consuming. Sometimes, it is prone to intra-observer and inter-observer variability. State of the art deep architectures based on Fully Convolutional Networks (FCN) and cascaded CNNs have shown good results in automated lesion segmentation. This work proposes a series of enhancements over the learning paradigm in the existing methods, by focusing on learning meticulous feature representations through the CNN layers for accurate ischemic lesion segmentation from multimodal MRI. Multiple levels of losses, integration of features from multiple scales, an ensemble of prediction maps from sub-networks are employed to enable the CNN to correlate between features seen from different receptive fields. To allow for progressive refinement of features from block to block, a custom dropout module has been proposed that suppresses noisy features. Multi-branch residual connections and attention mechanisms were also included in the CNN blocks to enable the integration of information from multiple receptive fields and selectively weigh significant features. Also, to tackle data imbalance both at voxel and sample level, patch-based modeling and separation of concerns into classification & segmentation functional branches are proposed. By incorporating the above mentioned architectural enhancements, the proposed deep architecture was able to achieve better segmentation performance against the existing models. The proposed approach was evaluated on the ISLES 2015 SISS dataset, and it achieved a mean dice coefficient of 0.775. By combining sample classification and lesion segmentation into a fully automated framework, the proposed approach has yielded better results compared to most of the existing works.

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN

COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects

BACKGROUND AND OBJECTIVE

In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The medical field also greatly benefits from the use of improving deep learning models which save time and produce accurate results. This research aims to emphasize the impact of deep learning models in brain stroke detection and lesion segmentation. This is achieved by discussing the state of the art approaches proposed by the recent works in this field.

METHODS

In this study, the advancements in stroke lesion detection and segmentation were focused. The survey analyses 113 research papers published in different academic research databases. The research articles have been filtered out based on specific criteria to obtain the most prominent insights related to stroke lesion detection and segmentation.

RESULTS

The features of the stroke lesion vary based on the type of imaging modality. To develop an effective method for stroke lesion detection, the features need to be carefully extracted from the input images. This review takes an attempt to categorize and discuss the different deep architectures employed for stroke lesion detection and segmentation, based on the underlying imaging modality. This further assists in understanding the relevance of the two-deep neural network components in medical image analysis namely Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN). It hints at other possible deep architectures that can be proposed for better results towards stroke lesion detection. Also, the emerging trends and breakthroughs in stroke detection have been detailed in this evaluation.

CONCLUSION

This work concludes by examining the technical and non-technical challenges faced by researchers and indicate the future implications in stroke detection. It could support the bio-medical researchers to propose better solutions for stroke lesion detection.

Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network

Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due to the subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizing the properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineered features to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quite complex, as the degree of differentiation varies according to each modality. This can be addressed well by Convolutional Neural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from images effectively for image classification. But it loses the context of local information among the pixels that need to be retained for segmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The major contribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segment ischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region by suppressing the details of other regions. Hence the proposed FCN with attention mechanism was able to segment ischemic lesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried out on ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is an improvement of 5% compared to the existing works.

Antileishmanial assessment of isoxazole derivatives against L. donovani

A chemical library comprising substituted 3-nitroisoxazoles and 3-aminoisoxazoles was prepared and screened for their antileishmanial activity against L. donovani. As compared to Miltefosine, the standard drug used in bioassays, several compounds displayed remarkably better inhibition of the promastigote and amastigote stages of parasites. The in vivo evaluation of a few compounds in a golden hamster model showed significant reduction of the parasite load post treatment via the intraperitoneal route by several compounds. The preliminary pharmacokinetic evaluation of a representative compound 4mf via the oral route, however, indicated high systemic clearance from the body.

Feasibility of Endoscopically Assisted Repair of Zygomatico-Maxillary Complex Orbital Fractures: Case Series

Aim

To assess the feasibility and the outcome of endoscopically assisted repair of zygomatico-maxillary complex orbital fractures in a tertiary care hospital.

Materials and methods

A descriptive study was carried out over a period of 2 years (01. 11. 2014-31. 10. 2016). 0°-4 mm endoscopes were used for intra-operative endoscopic exploration. Regular armamentarium for management of maxillofacial trauma along with standard plating systems was used. Commercially available medical grade titanium meshes were utilised for orbital floor reconstruction.

Results

Fifty-seven of hundred and twelve surgically managed zygomatico-maxillary complex fractures required orbital exploration. Twenty-one of these had reconstruction of the orbital floor. Endoscopically assisted exploration was performed in 10 of the 21; trans-nasally in three cases, the Caldwell-Luc approach was used in three cases, and in another three, the existing fracture in the anterior wall of the maxilla was utilised. In addition, an endoscopic trans-nasal-sphenoidal approach was opted for to access the orbital apex to manage fractured bone fragments that were impinging on the optic nerve in one of the patients.

Conclusion

Endoscopes serve as the primary tool in minimally invasive procedures. They are yet to evolve as a major role player in the maxillofacial arena. The authors conclude that endoscopes are truly beneficial as an adjunct to existing techniques. They hold much promise for the future.

Reentrant spin-glass behaviour in highly frustrated Mn-rich spinel zinc manganate

The present work offers an insight into the magnetic properties of Mn-rich spinel zinc manganate. Rietveld refinement reveals the formation of Zn_0.67Mn_2.33O_4, where Zn²⁺ and Mn²⁺ ions are randomly distributed in the tetrahedral sublattice. DC and AC susceptibility measurement of Zn_0.67Mn_2.33O₄ infers the occurrence of two kinds of transition below 11 K. Paramagnetic to ferrimagnetic transition occur at 10.7 K and ferrimagnetic to spin glass-like transition occurs at 5.8 K. The long-range canted ferrimagnetic ordering is corroborated using modified Lotgering model and calculated the exchange interaction values (J _AA = 5.2 K,J _AB = 5.3 K, J _BB = 14.8 K). Further, the observed shift in freezing temperature with DC magnetic field obeys Almeida-Thouless behaviour and frequency dependence of AC susceptibility follows Vogel-Fulcher law. However, the complete establishment of the canonical spin glass state is denied since the manganese ions occupied at the tetrahedral site is equal to the percolation threshold (=33%). Subsequently, the observed spin glass-like behaviour (5.8 K) below Curie temperature (10.7 K) evidences the reentrant spin glass nature. Similarly, the strong frustration in Zn_0.67Mn_2.33O₄ lattice is observed through the substantial negative value of Curie-Weiss temperature (∼-599 K) and very high frustration factor (f = 56). Overall, the chosen Zn_0.67Mn_2.33O₄ is a highly frustrated magnetic system revealing re-entrant spin-glass behaviour.

Holistic assessment of microplastics in various coastal environmental matrices, southwest coast of India

Plastics in the marine environment are introduced through multiple pathways, and pose serious threats to aquatic biota. Recently microplastic pollution and its possible consequences in India have been recognized by the scientific community, however the extent of the crisis has not yet been quantified. The present study attempted to ascertain the abundance, distribution and characteristics of microplastics in coastal waters (14 locations), beach sediments (22 locations) and marine fishes (11 locations) from the state of Kerala, southwest coast of India. The results showed that the mean microplastic abundance was 1.25 ± 0.88 particles/m³ in coastal waters and 40.7 ± 33.2 particles/m² in beach sediments with higher concentrations in the southern coast of the state. The abundance of microplastics, mostly contributed by fragments, fibre/line and foam, in both coastal waters and beach sediments, were highly influenced by river runoff and proximity to urban agglomeration. Fourier Transform Infrared Spectroscopy-Attenuated Total Reflection (FTIR-ATR) revealed that polyethylene (PE) and polypropylene (PP) were the dominant polymers in the marine environment. The digestive tracts of 15 out of 70 commercially important fishes studied, contained 22 microplastic particles. Polyethylene (PE; 38.46%) followed by cellulose (CE; 23.08%), rayon (RY; 15.38%), polyester (PL; 15.38%) and polypropylene (PP; 7.69%) were the major contributors in the fish ingested microplastic composition. A broad range of heavy metals, metalloids and other elements that are potentially indicative of hazardous chemicals were present in microplastics collected from the beaches of Kerala. These results enhance our understanding on the sources, transport pathways and the associated environmental risks of microplastics to marine ecosystems.

Intracranial variant of encephalotrigeminal angiomatosis - A case report

Encephalotrigeminal Angiomatosis is a rare developmental phakomatoses characterized by the occurrence of nevus flammeus (port-wine stain) along the distribution of branches of trigeminal nerve, vascular angiomas in the eye, and leptomeningeal angiomas affecting 1 in 1,00,000 South Asian population. Herewith, such a rare case of such encephalotrigeminal angiomatosis in a 24-year-old male is described.

Microplastics along the beaches of southeast coast of India

Occurrence of microplastics (plastic debris <5 mm) along the coast is a growing concern worldwide, due to increased input of discarded wastes from various sources. In order to evaluate the extent of microplastic pollution on the sandy beaches (25 locations) along Tamil Nadu coast (1076 km), India, microplastic debris were quantified and categorized into four different size classes. The beaches were classified according to potential sources of pollution i.e. riverine, tourism and fisheries. Beach samples collected from the high tide line contained significantly higher abundance of microplastic than at the low tide line. Beaches adjacent to rivers exhibited relatively higher microplastic abundance compared to those influenced by tourism and fishing activities. Out of the total detected debris, plastic fragments were the maximum (47-50%), followed by line/fibres (24-27%) and foam (10-19%) materials. Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed that polyethylene, polypropylene, and polystyrene were the main types of microplastics present in these beaches. Gut content analysis of commercially important fishes, collected from the coastal waters, revealed microplastics ingestion in 10.1% of fishes. The results indicate that microplastics accumulation in the coastal environment, especially close to the river mouths, may be a serious concern, due to its ability to enter into the marine food web and highlights the necessity of microplastics screening from estuarine, coastal waters and other potential sources.

Simple sonochemical synthesis of novel grass-like vanadium disulfide: A viable non-enzymatic electrochemical sensor for the detection of hydrogen peroxide

Design and fabrication of novel inorganic nanomaterials for the low-level detection of food preservative chemicals significant is of interest to the researchers. In the present work, we have developed a novel grass-like vanadium disulfide (GL-VS₂) through a simple sonochemical method without surfactants or templates. As-prepared VS₂ was used as an electrocatalyst for reduction of hydrogen peroxide (H₂O₂). The crystalline nature, surface morphology, elemental compositions and binding energy of the as-prepared VS₂ were analyzed by X-ray diffraction, Raman spectroscopy, field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The electrochemical studies show that the GL-VS₂ modified glassy carbon electrode (GL-VS₂/GCE) has a superior electrocatalytic activity and lower-reduction potential than the response observed for unmodified GCE. Furthermore, the GL-VS₂/GCE displayed a wide linear response range (0.1-260 μM), high sensitivity (0.23 μA μM^-1 cm^-2), lower detection limit (26 nM) and excellent selectivity for detection of H₂O₂. The fabricated GL-VS₂/GCE showed excellent practical ability for detection of H₂O₂ in milk and urine samples, revealing the real-time practical applicability of the sensor in food contaminants.

Open reduction and internal fixation of palatal fractures using three-dimensional plates

The maxilla is arguably the most anatomically intricate structure of the craniofacial skeleton, and the hard palate is an important bone that regulates the width and architecture of the face. The management of palatal fractures has long been a matter of debate, and varies with anatomical pattern and other injuries to the craniofacial skeleton. We have studied 18 palatal fractures during a five-year period that were treated using 3-dimensional rectangular plates placed across the palatal vault together with fixation of other fractures of the facial bones. Healing was satisfactory in all patients by 12weeks, with no complications. We think that open reduction and internal fixation of palatal fractures with 3-dimensional plates offers adequate stability with minimal complications.

Prevalence of Oral Mucosal Lesions among Dental Patients with Mixed Habits in Salem District - A Study

BACKGROUND

The practice of betel nut chewing with or without tobacco is still practiced in south india, salem inspite of its harmful effects.

METHODOLOGY

200 Patients visiting the outpatient department, Oral medicine and radiology from Aug 2015 to Aug 2016.

RESULT AND CONCLUSION

In our study, 3 women were exclusively churut smokers. Thirty-eight percent of the dental patients were beedi smoker, 32% were tobacco chewers, 12% were both betel nut and tobacco chewers, 8% were exclusively betel nut chewers, 1% of the dental population were exclusively churut smokers. Mean age group of the study population is 50.2 (14.4). There are 28 females and 172 males in the study group. Chi-square test revealed a statistically significant difference (P = 0.001) between males and females based on soft-tissue findings and no statistically significant difference (P = 0.572) between males and females based on distribution of hard-tissue findings.

Comparative study on removal of enteric pathogens from domestic wastewater using Typha latifolia and Cyperus rotundus along with different substrates

A comparative study was carried out to evaluate the efficiency of different substrate materials along with macrophytes Typha latifolia and Cyperus rotundus in treating domestic wastewater intended for reuse in agriculture. The study was conducted over a period of 6 months with different retention times, and observations were taken twice per month. One-way analysis of variance and Tukey's Honest Significant Difference (HSD) tests were used to determine statistical significant differences between experimental columns. Treatment with T. latifolia planted in sand and mix substrate with 4-day retention time remarkably reduced the concentration of all bacterial pathogens. Log reductions observed were approximately 5.01 and 4.82 for total coliform (TC), 4.46 and 3.93 for Escherichia coli, and 5.52 and 5.48 for Shigella, respectively. Moreover, these treatments were also efficient in completely removing fecal coliform (FC) and Salmonella.Maximum parasites were removed by the treatment having sand alone as a substrate containing C. rotundus, but the difference was not significant from those planted with T. latifolia in the same substrate. The results suggest that T. latifolia aids in bacterial pathogens removal, while C. rotundus aids in parasites removal. Thus, wastewater treatment through constructed wetland having mix plantation of these species along with sand can eliminate some of the major enteric pathogens.

A selective electrochemical sensor for caffeic acid and photocatalyst for metronidazole drug pollutant - A dual role by rod-like SrV2O6

In the present study, well-defined one-dimensional (1D) rod-like strontium vanadate (SrV2O6) was prepared by simple hydrothermal method without using any other surfactants/templates. The successful formation of rod-like SrV2O6 was confirmed by various analytical and spectroscopic techniques. Interestingly, for the first time the dual role of as-prepared rod-like SrV2O6 were employed as an electrochemical sensor for the detection of caffeic acid (CA) as well as visible light active photocatalyst for the degradation of metronidazole (MNZ) antibiotic drug. As an electrochemical sensor, the SrV2O6 modified glassy carbon electrode (GCE) demonstrated a superior electrocatalytic activity for the detection of CA by chronoamperometry and cyclic voltammetry (CVs). In addition, the electrochemical sensor exhibited a good current response for CA with excellent selectivity, wide linear response range, lower detection limit and sensitivity of 0.01-207 µM, 4 nM and 2.064 μA μM-1cm-2, respectively. On the other hand, as-synthesized rod-like SrV2O6 showed highly efficient and versatile photocatalytic performances for the degradation of MNZ, which degrades above 98% of MNZ solution under visible light irradiation within 60 min. The obtained results evidenced that the improvement of rod-like SrV2O6 might be a resourceful electrocatalyst and photocatalyst material in the probable applications of environmental and biomedical applications.

Assessing Prevalence of Temporomandibular Disorders among University Students: A Questionnaire Study

Aim:

The aim of this study is to assess the prevalence of temporomandibular disorders (TMDs) among university students.

Objectives:

The etiology of TMD is of multifactorial, and our study is designed to assess the prevalence of TMD in an institution within a university. Various parameters including trauma, malocclusion, stress assessment - its correlation with the timing, the duration of TMD in meeting the deadlines, and examinations have been assessed.

Materials and Methods:

A standard questionnaire-based study with modified Fonseca anamnestic index is used. The questionnaire study was conducted with a statistically calculated sample size of 402 students within a university. The data were analyzed statistically for significance and correlations. Statistical analysis was performed using SPSS for Windows release 14.0 (SPSS Inc., Chicago, IL, USA). Differences at the 5% level were accepted as being statistically significant.

Results:

TMD and its associated symptoms are frequent among students of health and science studies. Female predominance is seen. There was also an increased prevalence of symptoms in people who described themselves as being tense. The relationship between orthodontic therapy and TMDs were also evaluated, and the duration of therapy is found to be significant. There was a significant association between history of trauma and presence of TMDs.

Conclusions:

TMD and its associated symptoms are frequent among students of health and science studies. A simple anamnestic index is of use in identifying and classifying temporomandibular joint and TMD symptoms in patients according to the severity of the disorder. Females showed a greater prevalence of signs and symptoms of TMD than males. Longitudinal studies are recommended to identify and follow-up the prevalence and health-care needs of patients with TMDs.

Toward Mentoring in Palliative Social Work: A Narrative Review of Mentoring Programs in Social Work

BACKGROUND

Mentoring by an experienced practitioner enhances professional well-being, promotes resilience, and provides a means of addressing poor job satisfaction and high burnout rates among medical social workers. This is a crucial source of support for social workers working in fields with high risk of compassion fatigue and burnout like palliative care. Implementing such a program, however, is hindered by differences in understanding and application of mentoring practice.

AIM

This narrative review of mentoring practice in social work seeks to identify key elements and common approaches within successful mentoring programs in social work that could be adapted to guide the design of new mentoring programs in medical social work. Methodology and Data Sources: A literature search of mentoring programs in social work between January 1, 2000, and December 31, 2015, using Pubmed, CINAHL, OVID, ERIC, Scopus, Cochrane and ScienceDirect databases, involving a senior experienced mentor and undergraduate and/or junior postgraduates, was carried out. A total of 1302 abstracts were retrieved, 22 full-text articles were analyzed, and 8 articles were included.

RESULTS

Thematic analysis of the included articles revealed 7 themes pertaining to the mentoring process, outcomes and barriers, and the characteristics of mentoring relationships, mentors, mentees, and host organizations.

DISCUSSION AND CONCLUSION

Common themes in prevailing mentoring practices help identify key elements for the design of an effective mentoring program in medical social work. We conclude with a discussion of the implications of these findings upon clinical practice in palliative care and on sustaining such a program.

A facile graphene oxide based sensor for electrochemical detection of prostate anti-cancer (anti-testosterone) drug flutamide in biological samples

An electrochemical sensor based on graphene oxide modified glassy carbon electrode for the determination of anti-cancer drug flutamide.

Comparison between microzooplankton population in polluted and moderately polluted coastal waters of Port Blair, South Andaman

We report on the seasonal and spatial variations of microzooplankton between polluted and moderately polluted coastal waters of South Andaman Island, in relation to certain physicochemical and biological parameters. Microzooplankton communities were studied and recorded for 1 year (September 2011 to August 2012) during the northeast monsoon, the intermonsoon, and the southwest monsoon periods. We recorded a total of 75 species belonging to 29 genera, of which polluted waters encompassed 71 species (28 genera). In contrast, only 59 species (22 genera) were recorded in the moderately polluted waters. We discovered that taxonomic composition, biomass, and abundance of microzooplankton not only varied spatially but also altered seasonally. It is noteworthy to state that the northeast monsoon played a significant role in controlling the abundance of microzooplankton in South Andaman coastal waters.

Advancement in contemporary diagnostic and therapeutic approaches for rheumatoid arthritis

This review is intended to provide a summary of the pathogenesis, diagnosis and therapies for rheumatoid arthritis. Rheumatoid arthritis (RA) is a common form of inflammatory autoimmune disease with unknown aetiology. Bone degradation, cartilage and synovial destruction are three major pathways of RA pathology. Sentinel cells includes dendritic cells, macrophages and mast cells bound with the auto antigens and initiate the inflammation of the joints. Those cells further activates the immune cells on synovial membrane by releasing inflammatory cytokines Interleukin 1, 6, 17, etc., Diagnosis of this disease is a combinational approach comprises radiological imaging, blood and serology markers assessment. The treatment of RA still remain inadequate due to the lack of knowledge in disease development. Non-steroidal anti-inflammatory drugs, disease modifying anti rheumatic drugs and corticosteroid are the commercial drugs to reduce pain, swelling and suppressing several disease factors. Arthroscopy will be an useful method while severe degradation of joint tissues. Gene therapy is a major advancement in RA. Suppressor gene locus of inflammatory mediators and matrix degrading enzymes were inserted into the affected area to reduce the disease progression. To overcome the issues aroused from those therapies like side effects and expenses, phytocompounds have been investigated and certain compounds are proved for their anti-arthritic potential. Furthermore certain complementary alternative therapies like yoga, acupuncture, massage therapy and tai chi have also been proved for their capability in RA treatment.