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Byline: Richa. Agarwal, Alka. Tripathi, Mohit. Agarwal A 54-year-old male presented with sudden decrease in vision in the right eye (RE) after intravitreal injection of triamcinolone acetonide (IVTA) for Diabetic [...]

In this paper, an analytical, detailed two-dimensional potential profile model of a newly proposed rectangular core-shell-based double-gate junctionless metal oxide semiconductor (RCS-DGJLMOS) is presented. A detailed numerical solution of the model is presented for conventional n-type double-gate junctionless MOS (DGJLMOS), and the changes made in the model for RCS-DGJLMOS are extensively presented. While solving 2-D Poisson equations, a parabolic potential is assumed along the silicon thickness for both devices. The characteristics of the potential profile are studied on varying doping concentrations, dielectric constants, silicon thickness, and gate oxide thickness in conventional DGJLMOS, whereas the thickness of the core, total silicon thickness, dielectric constants, and core doping are varied in RCS-DGJLMOS. The detailed study on potential profile reveals the huge depletion of RCS-DGJLMOS as compared to conventional DGJLMOS due to the presence of oppositely doped core and, therefore, improves the device performance. Furthermore, the transfer characteristics are also presented for both devices. The analytical results for DGJLMOS and RCS-DGJLMOS have been assessed with Technology Computer Aided Design (TCAD) software. The analytical results obtained from the proposed model are verified with the simulation data.

Urolithiasis, commonly known as kidney stones, is characterized by the formation of hard deposits in the urinary tract. These stones can cause severe pain and discomfort, and their management typically involves a combination of medical interventions and lifestyle modifications. According to the literature, 30% and 50% of urolithiasis cases recur. Between 9 and 12% of persons in industrialised countries are predicted to have urolithiasis at some time. Due to the high frequency of stone formation, recurrent nature, and prevalence in adults, it has a significant impact on society, the person, and the health care system. Adopting the best prophylactic measures is crucial in light of these developments to decrease the impact of urolithiasis on individuals and society. In recent years, there has been growing interest in the potential role of nutraceuticals in the management of urolithiasis. Nutraceuticals, such as herbal extracts, vitamins, minerals, and probiotics, have gained recognition for their potential in promoting urinary health and reducing the risk of urolithiasis. These compounds can aid in various ways, including inhibiting crystal formation, enhancing urine pH balance, reducing urinary calcium excretion, and supporting kidney function. Additionally, nutraceuticals can help alleviate symptoms associated with urolithiasis, such as pain and inflammation. While medical interventions remain crucial, incorporating nutraceuticals into a comprehensive management plan can offer a holistic approach to urolithiasis, improving patient outcomes and quality of life. Therefore, nutraceuticals may be a desirable choice for treating and avoiding recurring urolithiasis for patients and medical professionals. Therefore, the present study has focused on nutraceuticals' role in preventing urolithiasis.

Document clustering involves grouping together documents so that similar documents are grouped together in the same cluster and different documents in the different clusters. Clustering of documents is considered a fundamental problem in the field of text mining. With a high rise in textual content over the Internet in recent years makes this problem more and more challenging. For example, SpringerNature has alone published more than 67,000 articles in the last few years just on the topic of COVID-19. This high volume leads to the challenge of very high dimensionality in analyzing the textual datasets. In this review paper, several text clustering techniques are reviewed and analyzed theoretically as well as experimentally. The reviewed techniques range from traditional non-semantic to some state-of-the-art semantic text clustering techniques. The individual performances of these techniques are experimentally compared and analyzed on several datasets using different performance measures such as purity, Silhouette coefficient, and adjusted rand index. Additionally, significant research gaps are also presented to give the readers a direction for future research.

Maintaining the security and privacy of data is of paramount priority while using the network and communication systems. Cryptography is an art used to solve the sole purpose of data security. Encryption serves as one of the most popular algorithms for cryptography. In this paper, a new encryption strategy is defined and implemented to enhance the security of data. The advanced encryption standard (AES) of 128-bit encryption has been used to protect the data, which can be moved up to 256-bit encryption if needed, along with a modified one-time pad (OTP) encryption technique. A newly devised method for random key generation named a secure key generator (SKG) is also proposed to add more security to the whole system. Hence, the paper carries a detailed study of the AES and OTP encryption techniques as well as compares the results of the modified OTP technique with existing encryption techniques.

Genetic Algorithm (GA) is a meta-heuristics search and optimization approach which we have utilized for fine-tuning the standard deep learning models for reducing the storage space with improvement in inference time. The pre-trained models have been widely acclaimed as the best choice of CNN for various image classification problems in different domains, but they require huge storage space and it causes a problem for deploying these models on mobile or edge devices. As these devices are constrained with limited memory and computational power. In this paper, a novel GA based method has been proposed which compresses and accelerates the CNN models so that these models can be easily deployed on edge devices. Extensive computer simulations have been conducted on widely used models such as AlexNet, VGG16, SqueezeNet, and ResNet50. We used benchmark datasets such as MNIST, CIFAR-10, and CIFAR-100 to determine the performance. Results reveal that storage space of the AlexNet model was reduced by 87.5%, 86.55% and 86.16% on MNIST, CIFAR-10 and CIFAR- 100 datasets respectively, while VGG16, ResNet50, and SqueezeNet have been compressed by nearly 91%, 78%, 38% respectively. From the results, it has been noticed that there is a significant improvement in inference time of around 35% in AlexNet, 9% in SqueezeNet, 73% in ResNet50, and 80% in VGG16. This improvement is noticed mainly because of the fine-tuning of the deep learning models using the GA. Overall, the proposed GA-based method showed outstanding performance and it motivates research and practitioners to explore it further.

Faster and predictable osseointegration is crucial for the success of dental implants, especially in patients with compromised local or systemic conditions. Despite various surface modifications on the commercially available Titanium (Ti) dental implants, the bioactivity of Ti is still low. Thus, to achieve both biological and therapeutic activity on titanium surfaces, surface modification techniques such as titanium nanotubes have been studied as nanotube surfaces can hold therapeutic drugs and molecules. The main aim of the present research work is to study the early osseointegration around the novel Simvastatin drug eluting nanotubular dental implant. In the present research, the titanium nanotubes were fabricated on the screw-shaped dental implant surface and the Simvastatin drug was loaded into the nanotubes using the ultrasonication dip method. In vitro and In vivostudies were carried out on the modified dental implants. In vitrocell culture study reported enhanced osteogenic activity on the drug-loaded nanotube surface implants. The invivoanimal studies were evaluated by micro-CT, histopathology, and reverse torque removal analysis methods. The test results showed faster osseointegration with the strong interface on the Simvastatin drug-loaded implant surface at 4 weeks of healing as compared to the control implants.

The impression of gate dielectric and spacer dielectric on the performance of rectangular core shell double gate junctionless transistor (RCS-DGJLT) using extensive simulations is studied. The RCS-DGJLT brings captivating response in terms of the performance of the device. The effect of gate dielectric and spacer dielectric is studied for the first time in RCS-DGJLT. The performance of the device gets improve on increasing the dielectric constant till core thickness (tcore) = 3 nm for shell thickness (tshell) = 4 nm. However, beyond core thickness 3 nm, the performance degrades much for high kdielectric. Further, the performance is enhanced for tcore > 3 nm and higher kdielectric on reducing the core doping. The device performance is also studied by incorporating the spacers. It has been shown that the device performance is best at tcore = 4 nm with low kgate dielectric however, the device performance degrades when high kspacer dielectric is used on the same device which is an interesting and different outcome as compared to conventional DGJLT. The ON current and ON/OFF current ratio are improved by ∼3 orders of magnitude using highly doped source/drain region(HD-S/D) with high kspacers. The results show that the core is the integral part of the device, any engineering like spacers, gate dielectrics applied to RCS-DGJLT requires the optimization of the core carefully for better device performance.

Traumatic brain injury disrupts the complex anatomy of the afferent and efferent visual pathways. Injury to the afferentpathway can result in vision loss, visual field deficits, and photophobia. Injury to the efferentpathway primarily causes eye movement abnormalities resulting in ocular misalignment and double vision. Injury to both the afferent and efferent systems can result in significant visual disability.

The distinctive molecular and genetic characteristics of sinonasal tumors have become better understood, and radiologists must be aware of these findings to fully participate in multidisciplinary discussions and help guide appropriate diagnosis and therapy.

Background: There are now clinically available automated MRI analysis software programs that compare brain volumes of patients to a normative sample and provide z-score data for various brain regions. These programs have yet to be validated in primary progressive aphasia (PPA).Objective: To address this gap in the literature, we examined Neuroreader™z-scores in PPA, relative to visual MRI assessment. We predicted that Neuroreader™1) would be more sensitive for detecting left?>?right atrophy in the cortical lobar regions in logopenic variant PPA clinical phenotype (lvPPA), and 2) would distinguish lvPPA (n?=?11) from amnestic mild cognitive impairment (aMCI; n?=?12).Methods: lvPPA or aMCI patients who underwent MRI with Neuroreader™were included in this study. Two neuroradiologists rated 10 regions. Neuroreader™lobar z-scores for those 10 regions, as well as a hippocampal asymmetry metric, were included in analyses.Results: Cohen’s Kappa coefficients were significant in 10 of the 28 computations (k?=?0.351 to 0.593, p=0.029). Neuroradiologists agreed 0% of the time that left asymmetry was present across regions. No significant differences emerged between aMCI and lvPPA in Neuroreader™z-scores across left or right frontal, temporal, or parietal regions (ps?>?0.10). There were significantly lower z-scores in the left compared to right for the hippocampus, as well as parietal, occipital, and temporal cortices in lvPPA.Conclusion: Overall, our results indicated moderate to low interrater reliability, and raters never agreed that left asymmetry was present. While lower z-scores in the left hemisphere regions emerged in lvPPA, Neuroreader™failed to differentiate lvPPA from aMCI.

Evaluation of the greater wings of the sphenoid bone is difficult because of the complex anatomy and the wide range of diseases that can affect this area, but a systematic approach to cross-sectional imaging can help in the diagnosis and reporting of these lesions.

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max–min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

This slide presentation reviews relevant anatomic landmarks in the temporal bone, discusses imaging techniques and protocols, and highlights the clinical manifestations and imaging features of common and uncommon nontraumatic temporal bone emergencies.

Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology: Sixmodels, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p< 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

The traditional ‘one-size-fits-all’ approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.

Multiple applications of dual energy computed tomography (DECT) have been described for the evaluation of disorders in the head and neck, especially in oncology. We review the body of evidence suggesting advantages of DECT for the evaluation of the neck compared with conventional single energy computed tomography scans, but the full potential of DECT is still to be realized. There is early evidence suggesting significant advantages of DECT for the extraction of quantitative biomarkers using radiomics and machine learning, representing a new horizon that may enable this technology to reach its full potential.

Background & Objective: Cloud computing emerges out as a new way of computing which enables the users to fulfill their computation need using the underlying computing resources like software, memory, computing nodes or machines without owning them purely on the basis of pay-per-use that too round the clock and from anywhere. People defined this as the extension of the existing technologies like parallel computing, distributed computing or grid computing. Lots of research have been conducted in the field of cloud computing but the task scheduling is considered to be the most fundamental problem which is still in infancy and requires a lot of attention and a proper mechanism for the optimal utilization of the underlying computing resources. Task scheduling in cloud computing environment lies into the category of NP-hard problem and many heuristics and Meta heuristics strategies have been applied to solve the problem. Methods: In this work, Fuzzy Enabled Genetic Algorithm (FEGA) is proposed to solve the problem of task scheduling in cloud computing environment as classical roulette wheel selection method has certain limitations to solve complex optimization problem. Results & Discussion: In this work, an efficient fuzzy enabled genetic algorithm based task scheduling mechanism has been designed, implemented and investigated. The efficiency of the proposed FEGA algorithm is tested using various randomly generated data sets in different situations and compared with the other meta-heuristics. Conclusion: The authors suggest that the proposed Fuzzy Enabled Genetic Algorithm (FEGA) to solve the task scheduling problem helps in minimizing the total execution time or makespan and on comparing with other Meta-heuristic like genetic algorithm and greedy based strategy found that FEGA outperforms the both in different set of experiments.

Hypocomplementemic urticarial vasculitis syndrome (HUVS) is a rare autoimmune disease characterized by multiple organ system involvement, including renal disease, with low complement levels. We report the case of a 31-year-old woman who presented with nonspecific symptoms including fatigue, diarrhea, macular rash and abdominal pain with acute renal failure leading to end-stage kidney disease. Laboratory results showed hematuria, nephrotic range proteinuria, worsening creatinine and low C1q levels. Left kidney biopsy showed proliferative glomerulonephritis with crescent formation. She was treated with 6 months of intravenous cyclophosphamide, followed by 2 doses of intravenous rituximab (1g each), thereafter maintained on mycophenolate mofetil and glucocorticoid-based therapy. She experienced a full recovery of renal function after 12 months of dialysis dependence. Hypocomplementemic urticarial vasculitis syndrome with crescentic glomerulonephritis is a rare disease with only 5 other reported cases in literature. In our case, we document a delayed but excellent renal recovery during a 2-year follow-up.

A variety of osseous, fibro-osseous, and cartilaginous lesions can affect the maxillofacial structures and skull base including benign and malignant tumors, inflammatory processes, and dysplasias. Computed tomography and MRI are complementary in the characterization of craniofacial and skull base lesions and are instrumental for treatment planning, when therapy is indicated. Often the clinical scenario, location, and imaging appearance are adequate to narrow the diagnostic considerations and, in some cases, arrive at a diagnosis.

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) characterized by relapsing-remitting or progressive neurologic symptoms and focal white matter lesions. The hallmark of the disease is the dissemination of CNS lesions in space and time, which is defined by the McDonald criteria. MRI is an essential diagnostic and prognostic biomarker for MS which can evaluate the entire CNS. MS mimics must be excluded before a diagnosis of MS is made.

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

The CNG dispensing unit is an important investment for taking advantage of CNG fuel, though its handling is not as simple as other conventional fuels. The cost can only be realised if it is operated with maximum availability. In present research work, risk analysis of CNG dispensing unit has been exemplified by applying fuzzy digraph matrix and D-S theory. Forty-three failure causes have been identified, analysed and prioritised using the mentioned approach. The display error and regulator function failure for metering skid, internal valve failure and gasket damage for compressor, internal pipe leakage and PLC failure for priority panel, flow sensor, motherboard and valve failure for dispenser unit, plug and tank leakage have been identified as risk for CNG dispensing unit. The results from both the approaches show synergy. The risk identification and prioritisation results have been forwarded to maintenance engineer for critical analysis and proper maintenance planning.

Editors of medical journals with a clear vision and relevant expertise can leverage social media and other digital tools to advance the journal's mission, further their interests, and directly benefit journal authors and readers.

Objective: The current work was attempted to formulate and evaluate the topical sustained release delivery systems called cubosomes containing Tretinoin for the acne treatment. The recent patents on various formulations of tretinoin (EP0408370A2) helped in selecting a new formulation and evaluation. Methods: Tretinoin loaded cubosomes were prepared by bottom-up technique, using varying the concentration of lipid and surfactant and keeping the drug concentration constant, a total of nine formulations of tretinoin was developed. These preparations were evaluated for surface charge, particle size, particle morphology, encapsulation efficiency, in-vivo and in-vitro release studies of gel enriched with cubosome dispersion. Finally, the stability studies of cubosomal gel were performed on optimized formulations. Results: Significant result were obtained with tretinoin formulation as the drug is lipophilic, so it gives more depot effect on the epidermis and good retention property. The data obtained from the formulations showed that formulation TCF-5 was the optimized formulation which exhibited better drug release and entrapment efficiency. Conclusion: It can be concluded that cubosomes offer benefits of quick onset as well as the maximal release of drug with fewer side effects. Thus, cubosomes represent a capable transporter having the property to sustain the release of drug, potential to localize the drug in the skin with a possible clinical application for acne vulgaris treatment due to cubosome depot effect on the epidermis.

Denosumab is a monoclonal antibody directed against the receptor activator of nuclear factor kappa B ligand (RANKL). Denosumab has been shown to reduce the risk of skeletal-related events, including spinal cord compression, pathologic fracture and hypercalcemia of malignancy in patients with bone metastases. Hypocalcemia is a known side effect of denosumab, occurring in an estimated 8-14% of the patients. Here, we present an asymptomatic patient with stage-5 chronic kidney disease and severe hypocalcemia who had received denosumab 1 month earlier.

Background: Elevation in brain levels of aluminium can be neurotoxic and can cause learning and memory deficiencies. In Chinese medicine, Morus alba is used as a neuroprotective herb. The current study was intended to discover the recuperative effect of morusin against aluminium trichloride (AlCl3)-induced memory impairment in rats along with biochemical mechanism of its protective action. Methods: Memory deficiency was produced by AlCl3 (100 mg/kg; p.o.) in experimental animals. Learning and memory activity was measured using Morris water maze (MWM) test model. Central cholinergic activity was evaluated through the measurement of brain acetylcholinesterase (AChE) activity. In addition to the above, oxidative stress was determined through assessment of brain thiobarbituric acid-reactive species (TBARS) and glutathione (GSH) levels. Results: AlCl3 administration prompted significant deficiency of learning and memory in rats, as specified by a noticeable reduction in MWM presentation. AlCl3 administration also produced a significant deterioration in brain AChE action and brain oxidative stress (increase in TBARS and decrease in GSH) levels. Treatment with morusin (5.0 and 10.0 mg/kg, dose orally) significantly overturned AlCl3- induced learning and memory shortages along with diminution of AlCl3-induced rise in brain AChE activity and brain oxidative stress levels. Conclusion: It may be concluded that morusin exerts a memory-preservative outcome in mental discrepancies of rats feasibly through its various activities.

Computed tomography angiography of the head and neck is a powerful tool for imaging and diagnosis of a plethora of disorders of the cervicocerebral vasculature. This article reviews the technique, indications, and interpretation of many of these disorders. A standard report checklist is also presented.

Proteinuria often precedes any detectable decline in renal filtration function and is a strong and independent predictor of increased risk for all-cause and cardiovascular mortality. In this Review, Dr Agrawal and colleagues examine potential pathophysiologic mechanisms to explain simultaneous renal and cardiac disease, as well as therapeutic strategies for amelioration of proteinuria.

Extra-articular temporomandibular bony ankylosis between the coronoid process and the zygoma is quite uncommon. In this paper we present a case of fusion of the left coronoid to the zygomatic bone in a 23-year-old male. This bony ankylosis was the result of five-week-old untreated zygomatic complex fracture. Ankylosis developed secondary to heterotopic bone formation following trauma. Zygomatico-coronoid ankylosis of the mandible is a complication which rarely occurs if fractures of the middle third of the facial skeleton have been adequately treated. The patient was treated by intraoral coronoidectomy. The rationale for clinical and radiographic diagnosis and treatment is reviewed.

Competing Interests: CONFLICTS OF INTEREST Authors have no conflicts of interest to declare.