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The widespread administration of COVID-19 vaccines has prompted a need to understand their safety profile. This investigation focuses on the safety of inactivated and mRNA-based COVID-19 vaccines, particularly concerning potential cardiovascular and haematological adverse events. A retrospective cohort study was conducted for 1.3 million individuals residing in Abu Dhabi, United Arab Emirates, who received 1.8 million doses of the inactivated BBIBP CorV (by SinoPharm) and mRNA-based BNT162b2 (Pfizer-BioNTech) vaccines between June 1, 2021, and June 30, 2022. The study's primary outcome was to assess the occurrence of selected cardiovascular and haematological events leading to hospitalization or emergency room visits within 21 days post-vaccination. Results showed no significant increase in the incidence rates of these events compared to the subsequent 22 to 42 days following vaccination. Analysis revealed no elevated risk for adverse outcomes following first (IRR 1·03; 95% CI 0·82-1·31), second (IRR 0·92; 95% CI 0·72-1·16) and third (IRR 0·82; 95% CI 0·66-1·00) doses of either vaccine. This study found no substantial link between receiving either mRNA and inactivated COVID-19 vaccines and a higher likelihood of cardiovascular or haematological events within 21 days after vaccination. Adverse cardiovascular and haematological events following COVID-19 vaccination have been reported, leading to safety concerns. Here, the authors find no evidence for increased incidence of these events within 21 days of BIBP-CorV or BNT162b2 vaccination in electronic health record data from Abu Dhabi. [ABSTRACT FROM AUTHOR]

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan. [ABSTRACT FROM AUTHOR]

Nanostructured lipid carriers (NLCs) are nano-sized colloidal drug delivery system that contains a lipid mixture consisting of both solid and liquid lipids in their core. This lipid-based nanosystem is introduced as a biocompatible, non-toxic, and safe nano-drug delivery system as compared to polymeric or metallic nanoparticles. Due to its safety, stability, and high drug loading capacity compared to other lipid-based nanocarriers, NLC gained the attention of researchers to formulate safe and effective drug carriers. The ability to increase drug solubility and permeability while encapsulating the drug in a lipidic shell makes them an ideal carrier for drug delivery through difficult-to-achieve routes. Surface modification of NLC and the use of various additives result in drug targeting and increased residence time. With such qualities, NLCs can be used to treat a variety of diseases such as cancer, infections, neurodegenerative diseases, hypertension, diabetes, and pain management. This review focuses on the recent developments being made to deliver the drugs and genes through different routes via these nanocarriers. Here, we also discuss about historical background, structure, types of NLC and commonly employed techniques for manufacturing lipid-based nanocarriers. [ABSTRACT FROM AUTHOR]

In designing a new drug, considering the preferred route of administration, various requirements must be fulfilled. Active molecules pharmacokinetics should be reliable with a valuable drug profile as well as well-tolerated. Associated with the old/traditional of method of drug delivery are several limitation ranging from first-pass effect, low tolerance, minimal bioavailability, fluctuation of plasma drug concentration which result to less or no desired effect produced. This call for the demand for a more efficient drug administration technique. Over the past 20 years, nanotechnologies have provided alternative and complementary solutions to those of an exclusively pharmaceutical chemical nature since scientists and clinicians invested in the optimization of materials and methods capable of regulating effective drug delivery at the nanometer scale. Among the many drug delivery carriers, lipid nano vesicular ones successfully support clinical candidates approaching such problems as insolubility, biodegradation, and difficulty in overcoming the skin and biological barriers such as the blood-brain one. In this review, the authors discussed the structure, the biochemical composition, and the drug delivery applications of lipid nanovesicular carriers, namely, niosomes, proniosomes, ethosomes, transferosomes, pharmacosomes, ufasomes, phytosomes, catanionic vesicles, and extracellular vesicles. [ABSTRACT FROM AUTHOR]

In this paper, a local-moment-driven robust reversible data hiding (LM-RRDH) scheme is proposed, which can provide security to hidden messages against unintentional modifications. The proposed LM-RRDH decomposes an image into LSB and MSB planes and then embeds the secret information into the MSB image so that intrusion by unintentional modifications can be avoided. In addition, the proposed scheme utilizes the prevalent correlation among the pixels on the MSB plane for optimal embedding. In the proposed scheme, a cover image is partitioned into sub-blocks at first, and pixel groups in the sub-block are formed according to local moment and moment-of-moment so that similar-intensity pixels can be grouped into the same group. Next, the secret data is embedded into the pixels of each group by selecting a pairwise embedding strategy adaptively which is based on the number of pixels in each group. As a result, the proposed LM-RRDH can limit the distortion while providing a decent embedding capacity. Further, a protection against non-malicious attacks such as Joint Photographic Experts Group (JPEG) compression is also provided. The experimental results show that the proposed scheme provides a superior quality to the previous works while providing a comparable embedding capacity. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to determine the causes of pediatric ocular morbidity in the in-patient department of a tertiary-care hospital in eastern India. Methods: This was a retrospective descriptive study done on admitted patients in the age group of 0-12 years between July 1, 2019, to December 31, 2019, in a tertiary-care hospital in eastern India. Results: During the study period, a total of 5775 patients were admitted to the hospital, out of which 447 were children belonging to 0-12 years, the prevalence being 7.74%. Out of the 447 children, 276 were males (61.74%) and 171 were females (38.26%) with a male:female ratio of 1.6:1. A total of 31.77% patients were reported from urban areas and 23% from rural areas. The mean age of the children was 5.5 years (range: 20 days-12 years). The highest frequency of admission was found in the age group of zero to 4 years (61.74%). Ocular trauma (44.07%) was found to be the most common ocular morbidity, followed by cataract (32.21%) and infectious ocular diseases (7.61%). The average duration of stay in the hospital was 4.9 days ranging from 2 to 56 days. Conclusion: Timely intervention with early diagnosis and regular follow-up are essential in the management of pediatric ocular morbidity as well as childhood blindness. In our study, ocular trauma was found to be most common ocular morbidity leading to poor visual outcome and hospital admission, which can be prevented by safe home practices and parental education. [ABSTRACT FROM AUTHOR]

In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument. [ABSTRACT FROM AUTHOR]

Images captured by cameras are sometimes contaminated either during acquisition or transmission. Therefore, a preprocessing step is required which reduces noise from images. In this paper, a novel and efficient edge preserving universal noise removal algorithm is proposed which exploits both the local and global characteristics of the neighboring non-corrupted pixels. In the proposed algorithm, corrupted pixels are detected by robust outlying ratio (ROR) and replaced with the weighted sum (local characteristics) of the neighboring non-corrupted pixels in 3 × 3 window and these weights are obtained by solving the kernel ridge regression (KRR) which uses the global mean and covariance (global characteristics). Extensive experimental results demonstrate that our algorithm has better noise removal capability in terms of both objective and subjective evaluation as compared to existing denoising algorithms. [ABSTRACT FROM AUTHOR]

A timely replacement of the rather expensive indium-doped tin oxide with aluminum-doped zinc oxide is hindered by the poor uniformity of electronic properties when deposited by magnetron sputtering. Recent results demonstrated the ability to improve the uniformity and to decrease the resistivity of aluminum-doped zinc oxide thin films by decreasing the energy of the oxygen-negative ions assisting in thin film growth by using a tuning electrode. In this context, a comparative study was designed to elucidate if the same phenomenology holds for gallium-doped zinc oxide and indium-doped tin oxide as well. The metal oxide thin films have been deposited in the same setup for similar discharge parameters, and their properties were measured with high spatial resolution and correlated with the erosion track on the target's surface. Furthermore, the films were also subject to post annealing and degradation tests by wet etching. While the tuning electrode was able to reduce the self-bias for all three materials, only the doped zinc oxide films exhibited properties correlating with the erosion track. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of things- (IoT-) based emerging technologies are playing a very pervasive role in healthcare systems where information is required on a real-time basis. These systems may provide real-time alerts with continuous monitoring to the concern in the odd conditions. Thus, these systems help the elderly and ill person with a good quality life and reduce the number of caregiver persons. Based on the above facts, this paper proposes a secure, efficient, and stable humanoid healthcare information processing and supervisory method with an IoT-based sensor network. Here, five different parameters specifically network residual energy, node density, node average energy, number of neighbors surrounded of a node, and distance between the sensor node and sink are considered for effective utilization of the energy of the sensor nodes. This method also reduces the communication distance of the nodes which helps in proficient data collection from the human body. The performance of the proposed and the existing work is considered the stability period, number of alive and dead nodes, total energy consumption, number of packets send to cluster heads, and base station metrics with two different monitoring areas. Thus, the stability period of the proposed method is increased by 144.52% with respect to the existing method protocol. [ABSTRACT FROM AUTHOR]

In today's environment, electronics technology is growing rapidly because of the availability of the numerous and latest devices which can be deployed for monitoring and controlling the various healthcare systems. Due to the limitations of such devices, there is a dire need to optimize the utilization of the devices. In healthcare systems, Internet of things (IoT) based biosensors networking has minimal energy during transmission and collecting data. This paper proposes an optimized artificial intelligence system using IoT biosensors networking for healthcare problems for efficient data collection from the deployed sensor nodes. Here, an optimized tunicate swarm algorithm is used for optimizing the route for data collection and transmission among the patient and doctor. The fitness function of the optimized tunicate swarm algorithm used the distance, proximity, residual, and average energy of nodes parameters. The proposed method is attributed to the optimal CH chosen under TSA operation having a lower energy consumption. The performance of the proposed method is compared to the existing methods in terms of various metrics like stability period, lifetime, throughput, and clusters per round. [ABSTRACT FROM AUTHOR]

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponentially and has become a major challenge in the field of healthcare. Such peoples require determinant care which increases the cost of the treatment by using a large number of resources because of the nonavailability of the resources. The treatment of sepsis is available in the early state, but treatment is not started at the right time, and then it converts to the advanced level of sepsis and increases the fatalities. Thus, an intensive analysis is required to detect and identify sepsis at the early stage. There are some models available that work based on the manual score and based on only the biomark features, but these are not fully automated. Some machine learning-based models are also available, which can reduce the mortality rate, but accuracy is not up to date. This paper proposes a machine learning model for early detecting and predicting sepsis in intensive care unit patients. Various models, random forest (RF), linear regression (LR), support vector machine (SVM), naive Bayes (NB), ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost), are simulated by using the collected data from intensive care unit patient's database that is based on the clinical laboratory values and vital signs. The performance of the models is evaluated by considering the same datasets. The balanced accuracy of RF, LR, SVM, NB, ensemble (of SVM, RF, NB, and LR), XGBoost, and proposed ensemble (of SVM, RF, NB, LR, and XGBoost) is 0.90, 0.73, 0.93, 0.74, 0.94, 0.95, and 0.96, respectively. It is also evident from the experimental results that the proposed ensemble model performs well as compared to the other models. [ABSTRACT FROM AUTHOR]

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as "very poor" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as "not significant," reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions. [ABSTRACT FROM AUTHOR]

Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract‐based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super‐resolution algorithm and DTI images processed with a motion‐tracked slice‐to‐volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor‐to‐tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto‐occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within‐tract changes. We found tract‐specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within‐tract developmental trajectories for the CST, IFOF, and forceps major. [ABSTRACT FROM AUTHOR]

In the era of fast communication, being connected has become a necessity for all of us due to various reasons. Everyone needs high-speed spontaneous connectivity. This led to exponential growth in the field of information technology. A mobile ad hoc network plays a key role in establishing communication on the fly without any fixed infrastructure. The flexibility and dynamism offered by the mobile ad hoc network made this unique. QoS is one of the key issues whenever the performance of the network is measured. Next-Generation Network ensures better QoS with minimum delay or latency. QoS and QoE are the two faces of the same coin. Like the major two issues of the Next-generation Network can be speed and reliability, which needs to be addressed in a next-generation network. The purpose of this paper is to explore the significance of End-to-End Delay or Latency to provide a fast network and improve reliability also, in the context of Next Generation Ad hoc Network on which other parameters are directly or indirectly dependents. It proposed to measure the values of one of the key parameters such as End-to-End Delay in existing RAT and for E-RAT under different scenarios. To carry out critical analysis NS-3 is used. Apart from the other parameters, the emphasis is on the delay. Thus the results obtained would play a significant role to understand the importance of delay and also help to provide better QoE. [ABSTRACT FROM AUTHOR]

The charge transfer complex (CT complex) formed in ethanol with 1,2-dimethylimidazole (DMI, donor) and picric acid (PA, π- acceptor) at room temperature (RT) and spectrophotometric studies have been performed in various solvents. CT complex was characterised using different analysis like – 1H NMR, FTIR spectra and UV-vis spectra which designates that CT interaction is connected to proton relocation to DMI from PA followed by the formation of N+–H···O− type bonding. Spectrophotometric data helps in calculating various physical parameters (such as KCT, εCT, ΔGo, ID, RN, ECT, μEN and f) were also obtained through Benesi–Hildebrand equation. From these parameters it was observed that [(DMIH)+(PA)−] formation dependents on type of solvent used. 1:1 stoichiometry of [(DMIH)+(PA)−] was found by a straight-line method. Antimicrobial activity of the CT complex was demonstrated. Molecular docking study give us the information of binding of [(DMIH)+(PA)−] with B-DNA which has substantial application for drug designing. [ABSTRACT FROM AUTHOR]

The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion‐tracked slice‐to‐volume registration (MT‐SVR) method, we aimed to quantify tract‐specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2–8) diffusion scans of the fetal head (1–2 b = 0 s/mm2 images and 12 diffusion‐sensitized images at b = 500 s/mm2). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT‐SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto‐occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract‐specific, GA‐related microstructural changes of the developing white matter in human fetal brain. [ABSTRACT FROM AUTHOR]

Developing countries such as India, Pakistan and Bangladesh have diverse cultural bases due to different regions, religions and resources. These countries have large numbers of small-scale industries which consist of a vast variety of traditional industries that have flourished and have become even more diverse following British rule in India. The advent of mechanised finished goods and laws and taxes has led to the decline of these industries, which continued even after the independence of India. Urban or rural communities have grown naturally without any planning or development norms. The concept of urban local government came into existence in India in 1992 after the 74th amendment of the constitution. Most of the urban centres had an industrial economic base, generally comprisimg cottage-based industries that existed in clusters inside the city. For example, Bhagalpur has been known for its silk products, Kanpur for its leather products and Moradabad for its brass industry. The rural area also had diverse small-scale traditional industries that used to cater to the unique demands of the community and reflected the craftsmanship and ethos of the community, hence becoming the identity of the place. The absence of planning in these industrial clusters has led to the decline of these industries, which has affected the socio-economic aspects of these communities. These urban/rural clusters of small-scale industries or cottage industries are in a state of decay either because of adverse government policies or because business has dried up as a result of mechanised goods being readily available on the market; the absence of community planning and basic amenities could also be a reason. The skilled artisans who were once employed and were masters of beautiful art are now working as daily labourers. More and more people are leaving their traditional jobs and hence the future of art and craft is at stake. This paper aims at identifying the issues facing these clusters, especially for cottage-based industries, so that further research can be carried out to solve the issues facing these communities. It focuses on the different dynamics that affect these communities and their relationship to better understand the problems. It also aims to understand the reason why most of the plans or programmes introduced by the Government have failed until now. The weaver community in Bhagalpur will be used as a case study in this paper. By considering different aspects, including infrastructure, raw materials, finance, social linkages to understand the issues, the paper can also be used as a reference for other studies. [ABSTRACT FROM AUTHOR]

In this article, we have shown an easy, one-step, protein-directed approach for the synthesis of water-soluble and functional graphene using ovalbumin. The obtained ovalbumin functionalized graphene was characterized by UV–visible spectroscopy, X-ray diffraction, Raman spectroscopy, and Fourier transform infrared spectroscopy. Atomic force microscopy was used to check the attachment/functionalization of ovalbumin on grapheme sheets. It was shown that the functionalization of ovalbumin on grapheme sheets makes the protein more thermally stable compare to free ovalbumin, which has been shown by using sodium dodecyl sulfate polyacrylamide gel electrophoresis. [ABSTRACT FROM AUTHOR]

Purpose: To achieve motion‐robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi‐slice, diffusion‐weighted brain MRI. Methods: Simultaneous multi‐slice (SMS) acquisition enables fast and dense sampling of k‐ and q‐space. We propose to achieve motion‐robust DCI via slice‐level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice‐to‐volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. Results: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT‐SMR) to multi b‐value SMS diffusion‐weighted brain MRI of healthy volunteers and motion‐corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing‐fiber regions show robust reconstruction in the presence of motion. Conclusion: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non‐cooperative patients. [ABSTRACT FROM AUTHOR]

With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3-D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3-D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3-D pose estimation with a wide capture range in real-time (<100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3-D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future. [ABSTRACT FROM AUTHOR]

Purpose: To generate virtual models using different hinge locations and to investigate the advantages of a temporal hinge to a superior in LASIK.Methods: The study design was cross sectional. An image processing software was used to construct virtual LASIK models of 56 slit lamp photos of eyes. For a given hinge location and width, the largest flap diameter and maximum treatable stromal bed area were calculated, comparing temporal hinge to superior.Results: Temporal hinge allowed a greater flap diameter than superior and thus a greater treatment zone area for a given hinge width. A temporal hinge allowed a greater hinge width than a superior hinge for a given treatment zone area.Conclusion: We provide a rational basis for a mechanically stable temporal hinge. Moreover, we provide a method to pre-operative model flap size and hinge location, using a slit lamp camera and an image editing software. [ABSTRACT FROM AUTHOR]

With proper patient education and relative success rates the major part of Indian population (MIG) are opting for Implant as treatment modality. GUIDENT | Your Guide on the path of Dentistry 8 I mplant Feb 2021 Implant FOLLOW THE BONE INTRODUCTION Dental Implants have become treatment of choice in India since a decade. 10 Implants, wherein 4 mandibular and 6 maxillary regular platform MODE Implants from MODE Medikal INDIA were utilized in this case. [Extracted from the article]

The article presents a case study of a 62 years old female, complained of endentulous space with respect to maxillary and mandibular posteriors. The article discusses that Patient was explained about fixed prosthesis using dental Implants; and Gingival collars were placed immediately after the placement and healing was allowed with definite Osseo integration, over the next 4-month patient was in good health with no post-operative symptoms.

The present paper focuses on the study of non-invariant hypersurfaces of nearly Sasakian manifold with semi-symmetric non-metric connection equipped with - structure. Firstly, some properties of this structure are obtained. Further, the second fundamental forms of non-invariant hypersurfaces of nearly Sasakian manifold with semisymmetric non-metric connection has been traced under the condition when is parallel. The necessary and sufficient conditions also have been obtained for totally umbilical noninvariant hypersurfaces of nearly Sasakian manifold with semi-symmetric non-metric connection with structure of nearly Sasakian manifold to be totally geodesic. [ABSTRACT FROM AUTHOR]

We consider a nearly hyperbolic Sasakian manifold equipped with -structure and study non-invariant hypersurface of a nearly hyperbolic Sasakian manifold equipped with -structure. We obtain some properties of nearly hyperbolic Sasakian manifold equipped with -structure. Further, we find the necessary and sufficient conditions for totally umbilical non-invariant hypersurface with -structure of nearly hyperbolic Sasakian manifold to be totally geodesic. We also calculate the second fundamental form of a non-invariant hypersurface of a nearly hyperbolic Sasakian manifold with -structure under the condition when f is parallel. [ABSTRACT FROM AUTHOR]

In recent years, nanomaterials have made their way into hundreds of biomedical, life-sciences and technological applications. One such nanomaterial of extreme importance is nanoalumina (AlO nanoparticles). This nanomaterial is an epitome of diversity with applications exhibited in the fields of catalysis, cosmetics, theranostics, energy generation, biosensors, drug-delivery, tumor-regression, etc. However, problems persist in terms of biocompatibility, cost-effectiveness, reproducibility and mass-production of nanoalumina by the presently existent physical, chemical and biological methodologies. Herein, we for the first time are presenting a top-down biofabrication method by which size reduction of commercial bulk alumina/aluminum oxide (5 µm) into nanoalumina (5-25 nm) is carried out by a thermophilic fungus Humicola sp. within 96 h of reaction at just 50 °C. The so-formed nanoalumina is highly stable, water dispersible, fluorescent and natural protein capped; characterization engaged standard techniques. These nanoparticles exhibit anti-bacterial properties against Gram-positive Bacillus subtilis strain and may serve as broad spectrum bactericidal agents. We believe that our novel top-down approach may be extensively used in the facile, inexpensive, eco-friendly and reliable fabrication of abundant quantities of nanomaterials of different chemical compositions, sizes and shapes with better control and predictability over the properties as derived from their substrates. The mechanistic aspect of said protocol is underway. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) recurrences are often predicted by assessing the status of surgical margins (SM)– positive surgical margins (PSM) increase the chances of biochemical recurrence by 2-4 times which may lead to PCa recurrence. To this end, an electrical impedance acquisition system with a microendoscopic probe was employed in an ex-vivo study of human prostates. This system measures the tissue bioimpedance over a range of frequencies (1 kHz to 1MHz), and computes a number of Composite Impedance Metrics (CIM). A classifier trained using CIM data can be used to classify tissue as benign or cancerous. The system was used to collect the impedance spectra from 14 excised prostates, which were obtained from men undergoing radical prostatectomy, for a total of 23 cancerous and 53 benign measurements. The data revealed statistically significant (p < 0.05) differences in the impedance properties of the benign and tumorous tissues, and among the measurements taken on the apical, base, and lateral surface of the prostate. Further, in the leave-one-patient-out cross validation, a maximum predictive accuracy of 90.79% was achieved by combining high frequency CIM phase data to train a support vector machine classifier with a radial basis function kernel. The observations are consistent with the physiology and morphology of benign and malignant prostate tissue. CIMs were found to be an effective tool in distinguishing benign from cancerous tissues. [ABSTRACT FROM PUBLISHER]

Radially configured microendoscopic electrical impedance probes intended for intraoperative surgical margin assessment during robot-assisted laparoscopic prostatectomy (RALP) were examined through simulation, bench-top experimentation, and ex vivo tissue studies. Three probe designs with 8, 9, and 17 electrodes, respectively, were analyzed through finite element method based simulations. One mm diameter spherical inclusions (\sigma inclusion =1~\rm S/\rm m) are positioned at various locations within a hemispherical background (\sigma background =0.1~\rm S/\rm m) of radius 5 mm. An 8-electrode configuration is not able to localize the inclusion at these positions while 9 and 17-electrode configurations are able to accurately reconstruct the inclusion at maximum depth of 1 mm and 3 mm, respectively. All three probe designs were constructed and evaluated using saline phantoms and ex vivo porcine and human prostate tissues. The 17-electrode probe performed best in saline phantom studies, accurately reconstructing high contrast, 1-mm-diameter metal cylindrical inclusions in a saline bath (\sigma saline =0.1~\rm S/\rm m) with a position and area error of 0.46 mm and 0.84 mm^2, respectively. Additionally, the 17-electrode probe was able to adequately distinguish cancerous from benign tissues in three ex vivo human prostates. Simulations, bench-top saline experiments, and ex vivo tissue sampling suggest that for intraoperative surgical margin assessment during RALP, the 17-electrode probe (as compared to an 8 and 9 electrode probe) will be necessary to provide sufficient accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is used to image the electrical property distribution of a tissue under test. An EIT system comprises complex hardware and software modules, which are typically designed for a specific application. Upgrading these modules is a time-consuming process, and requires rigorous testing to ensure proper functioning of new modules with the existing ones. To this end, we developed a modular and reconfigurable data acquisition (DAQ) system using National Instruments' (NI) hardware and software modules, which offer inherent compatibility over generations of hardware and software revisions. The system can be configured to use up to 32-channels. This EIT system can be used to interchangeably apply current or voltage signal, and measure the tissue response in a semi-parallel fashion. A novel signal averaging algorithm, and 512-point fast Fourier transform (FFT) computation block was implemented on the FPGA. FFT output bins were classified as signal or noise. Signal bins constitute a tissue's response to a pure or mixed tone signal. Signal bins' data can be used for traditional applications, as well as synchronous frequency-difference imaging. Noise bins were used to compute noise power on the FPGA. Noise power represents a metric of signal quality, and can be used to ensure proper tissue-electrode contact. Allocation of these computationally expensive tasks to the FPGA reduced the required bandwidth between PC, and the FPGA for high frame rate EIT. In 16-channel configuration, with a signal-averaging factor of 8, the DAQ frame rate at 100 kHz exceeded 110 frames s ^-1, and signal-to-noise ratio exceeded 90 dB across the spectrum. Reciprocity error was found to be < 1\% for frequencies up to 1 MHz. Static imaging experiments were performed on a high-conductivity inclusion placed in a saline filled tank; the inclusion was clearly localized in the reconstructions obtained for both absolute current and voltage mode data. [ABSTRACT FROM PUBLISHER]

Ankyloglossia or "tongue-tie" is a rare congenital anomaly characterized by an abnormally short, thick, fibrosed lingual frenulum which may cause restriction in function of tongue including limitation in tongue movement. The lingual frenulum may be fibrous or muscular, and the tie may be complete or partial. Most often, ankyloglossia is seen as an isolated condition in an otherwise normal individual. This article reports the surgical management of a 20-year-old patient having ankyloglossia associated with restricted movement of tongue and difficulty in speech. The treatment involved is surgical removal of the lingual frenum followed by tongue training exercise and speech therapy to functionally rehabilitate the tongue. Six months postoperatively, the patient showed uneventful healing and was satisfied with the procedure. [ABSTRACT FROM AUTHOR]

In this paper, we for the first time show the ability of the mesophilic fungus Fusarium oxysporum in the bioleaching of waste material such as Fly-ash for the extracellular production of highly crystalline and highly stable, protein capped, fluorescent and water soluble silica nanoparticles at ambient conditions. When the fungus Fusarium oxysporum is exposed to Fly-ash, it is capable of selectively leaching out silica nanoparticles of quasi-spherical morphology within 24 h of reaction. These silica nanoparticles have been completely characterized by UV-vis spectroscopy, Photoluminescence (PL), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and Energy dispersive analysis of X-rays (EDAX). [ABSTRACT FROM AUTHOR]

Background: Tobacco smoking in any form (cigarette, bidi etc.) has long been established as a risk factor for CAD (coronary artery disease) but a very little research has been done on tobacco chewing, which is restricted to South Asia (India, Pakistan, Nepal, Bangladesh, Sri Lanka) whether it increases risk for cardiovascular morbidity or not. Aims & Objective: To assess the cardiovascular risks in tobacco chewers as compared to normal population of Aligarh a city of Western Uttar Pradesh. Materials and Methods: The study was conducted in Department of Physiology, Department of TB Chest and Department of Medicine JNMCH, AMU Aligarh. The study consisted of 30 tobacco chewers (in any form) and 30 subjects without any history of tobacco consumption in any form. The age group varied from 20-60years. Anthropometric measurement age, weight, height, BMI, Pulse, SBP (systolic BP), DBP (Diastolic BP) and Lipid Profile estimation from blood sample. Results: Anthropometric measurements did not show any significant change when compared in two groups (p > 0.05). Cardiovascular Parameters like, Systolic pressure (117.46 ± 11.07); diastolic pressure (83.37 ± 8.74); mean arterial pressures (93.40 ± 6.33) and pulse rate (84.29 ± 5.70), were significantly increased in chewers as compared to normal population (p < 0.05). Lipid Profile Parameters like, Total cholesterol (237.04 ± 82.6); HDL (45.54 ± 5.46); LDL (161.16 ± 76.7) were increased in chewers as compared to control (p < 0.05). Atherogenic index T Choles./HDL (5.22 ± 1.90); LDL/HDL (3.57±1.35); Non-HDL/HDL (4.3 ± 1.0), were also showed a significant increase (p < 0.05). Conclusion: All these findings are suggestive that tobacco chewers are at increased risk of cardiovascular diseases as compared to non-tobacco consuming population. [ABSTRACT FROM AUTHOR]

A 61 year old healthy female patient came with complain of decayed teeth since many years. Patient has no relevant past medical history. On oral examination, she had failing dentition with root stumps and decayed teeth. Patient was advised for OPG. After discussing the treatment plan, with the patient, extraction and implants were done. Following 4 months of healing period fixed prosthesis were delivered. MODE Implants from Istanbul, Turkey were used; for better results and wide prosthetic solution. [ABSTRACT FROM AUTHOR]